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A Confirmatory Snowfall Enhancement Project in the Snowy Mountains of Australia. Part II: Primary and Associated Analyses
Journal of Applied Meteorology and Climatology
Abstract
The Snowy Precipitation Enhancement Research Project (SPERP) was undertaken in winters from May 2005 to June 2009 in the Snowy Mountains region of southeastern Australia. Part I of this paper describes the design and implementation of the project, as well as the characteristics of the key datasets collected during the field phase. The primary analysis in this paper (Part II) shows an unequivocal impact on the targeting of seeding material, with the maximum level of silver in snow samples collected from the primary target area found to be significantly greater in seeded than unseeded experimental units (EUs). A positive but not statistically significant impact on precipitation was found. Further analysis shows that a substantial source of uncertainty in the estimation of the impacts of seeding on precipitation is associated with EUs where the seeding generators operated for relatively few hours. When the analysis is repeated using only EUs with more than 45 generator hours, the increase in precipitation in the primary target area is 14% at the 8% significance level. When applying that analysis to the overall target area, the precipitation increase is 14% at the 3% significance level. A secondary analysis of the ratio of silver to indium in snow supports the hypothesis that seeding material affected the cloud microphysics. Other secondary analyses reveal that seeding had an impact on virtually all of the physical variables examined in a manner consistent with the seeding hypothesis.
... Villarini et al. (2008) confirm that sampling errors for precipitation increase as the temporal integration time decreases. The specified periods when seeding may occur in a cloud seeding project are known as experimental units (EUs), and they can currently be as short as a few hours (Manton et al. 2011;Breed et al. 2014 Scanning radars can be used to estimate precipitation over a large area, especially where the terrain is steep and rough. While progress continues to be made in reducing the uncertainties from radar reflectivity (e.g., Hasan et al. 2016), the application of dual-polarisation radar to precipitation estimation has been a major development (Brandes et al. 2002), so that information on hydrometeor phase and shape can be used when calibrating radars against local disdrometers. ...
... The development of a cloud system, especially for winter orographic clouds, is substantially controlled by the synoptic environment, which generally determines when clouds are suitable for seeding. Routine operational analysis and prediction systems provide essential information on these conditions, but they should be supplemented by dedicated upper-air soundings during cloud seeding projects (Manton et al. 2011;Breed et al. 2014). These data can be supplemented by microwave radiometers and wind profilers to yield additional information on local temperature, humidity and wind profiles. ...
... Once it is clear that clouds suitable for seeding occur in the region of interest, seeding simulations using historical climate data should be carried out to test whether the impact of seeding is likely to be detected within a few years (e.g., Manton et al. 2011). The probability of detection increases with the number of seedable events and the expected seeding impact. ...
This paper provides a summary of the assessment report of the World Meteorological Organization (WMO) Expert Team on Weather Modification that discusses recent progress on precipitation enhancement research. The progress has been underpinned by advances in our understanding of cloud processes and interactions between clouds and their environment, which, in turn, have been enabled by substantial developments in technical capabilities to both observe and simulate clouds from the microphysical to the mesoscale. We focus on the two cloud types most commonly seeded in the past: winter orographic cloud systems and convective cloud systems. A key issue for cloud seeding is the extension from cloud-scale research to water catchment–scale impacts on precipitation on the ground. Consequently, the requirements for the design, implementation, and evaluation of a catchment-scale precipitation enhancement campaign are discussed. The paper concludes by indicating the most important gaps in our knowledge. Some recommendations regarding the most urgent research topics are given to stimulate further research.
... This project, however, did not conduct a randomized experiment and statistical evaluation. The Snowy Mountain randomized cloudseeding program in Australia has provided recent evidence of an increase in precipitation because of AgI seeding of winter orographic clouds based on a 5-yr statistical program (Manton and Warren 2011). Only limited physical evidence was collected as part of this program. ...
... Therefore, for the RSE, a seeding period of 4 h was decided upon based on the past studies and the early observations collected in the WWMPP. Other randomized programs have used similar time periods (6 h) for their statistical analysis (Super and Heimbach 2009;Manton and Warren 2011). ...
... However, results from only select cases seeded during randomized experiments, which occurred under conditions affecting only a fraction of the seasonal snowfall, have at times shown substantially larger increases. Recently, after the WWMPP design was established, results of the Snowy Mountain experiment in Australia were published that showed an increase of 14% when tested on the covariate of seeding generator hours greater than 45, signifying well-seeded cases (Manton and Warren 2011). Therefore, for the sake of estimating sample sizes that would be required to reach a definite conclusion, reasonable precipitation increases because of seeding are estimated to range between 10% and 15%. ...
An overview of the Wyoming Weather Modification Pilot Project (WWMPP) is presented. This project, funded by the State of Wyoming, is designed to evaluate the effectiveness of cloud seeding with silver iodide in the Medicine Bow and Sierra Madre Ranges of south-central Wyoming. The statistical evaluation is based on a randomized crossover design for the two barriers. The description of the experimental design includes the rationale behind the design choice, the criteria for case selection, facilities for operations and evaluation, and the statistical analysis approach. Initial estimates of the number of cases needed for statistical significance used historical Snow Telemetry (SNOTEL) data (1987-2006), prior to the beginning of the randomized seeding experiment. Refined estimates were calculated using high-resolution precipitation data collected during the initial seasons of the project (2007-10). Comparing the sample size estimates from these two data sources, the initial estimates are reduced to 236 (110) for detecting a 10% (15%) change. The sample size estimates are highly dependent on the assumed effect of seeding, on the correlations between the two target barriers and between the target and control sites, and on the variance of the response variable, namely precipitation. In addition to the statistical experiment, a wide range of physical studies and ancillary analyses are being planned and conducted.
... In general, a fixed target-control design (Dennis 1980) and the randomized-seeding technique are used in modern confirmatory cloud-seeding experiments, especially wintertime orographic glaciogenic cloud-seeding programs. The Snowy Precipitation Enhancement Research Project (SPERP) undertaken in winters from 2005 to 2009 in southeastern Australia is one such experiment (Manton et al. 2011;Manton and Warren 2011). A positive, but not statistically significant, impact on precipitation by ground-based AgI seeding was found. ...
... In general, a fixed target-control design (Dennis 1980) and the randomized-seeding technique are used in modern confirmatory cloud-seeding experiments, especially wintertime orographic glaciogenic cloud-seeding programs. The Snowy Precipitation Enhancement Research Project (SPERP) undertaken in winters from 2005 to 2009 in southeastern Australia is one such experiment (Manton et al. 2011;Manton and Warren 2011). A positive, but not statistically significant, impact on precipitation by ground-based AgI seeding was found. ...
... Table 2 lists the total precipitation (mm), snow ratio defined as snow amount to total precipitation (%), precipitation difference between the seeding and the control case (mm), relative change of precipitation between the seeding and the control case (%), and spillover ratio, defined as leeward accumulated precipitation to total precipitation (%), for the BASE experiments. The ground-based seeding cases increased precipitation by 10%, which is in agreement with previous studies (Givati and Rosenfeld 2005;Manton and Warren 2011). Ground seeding did not modify the snow ratio much. ...
A silver iodide (AgI) cloud-seeding parameterization has been implemented into the Thompson microphysics scheme of the Weather Research and Forecasting model to investigate glaciogenic cloud-seeding effects. The sensitivity of the parameterization to meteorological conditions, cloud properties, and seeding rates was examined by simulating two-dimensional idealized moist flow over a bell-shaped mountain. The results verified that this parameterization can reasonably simulate the physical processes of cloud seeding with the limitations of the constant cloud droplet concentration assumed in the scheme and the two-dimensional model setup. The results showed the following: 1) Deposition was the dominant nucleation mode of AgI from simulated aircraft seeding, whereas immersion freezing was the most active mode for ground-based seeding. Deposition and condensation freezing were also important for ground-based seeding. Contact freezing was the weakest nucleation mode for both ground-based and airborne seeding. 2) Diffusion and riming on AgI-nucleated ice crystals depleted vapor and liquid water, resulting in more ice-phase precipitation on the ground for all of the seeding cases relative to the control cases. Most of the enhancement came from vapor depletion. The relative enhancement by seeding ranged from 0.3% to 429% under various conditions. 3) The maximum local AgI activation ratio was 60% under optimum conditions. Under most seeding conditions, however, this ratio was between 0.02% and 2% in orographic clouds. 4) The seeding effect was inversely related to the natural precipitation efficiency but was positively related to seeding rates. 5) Ground-based seeding enhanced precipitation on the lee side of the mountain, whereas airborne seeding from lower flight tracks enhanced precipitation on the windward side of the mountain.
... As such INPs are of specific interest in weather modification to enhance precipitation (Rauber et al., 2019) or suppress hail (Dessens et al., 2016) by promoting ice production under appropriate meteorological and cloud water conditions. Ambiguous conclusions on the efficiency of previous cloud-seeding projects (French et al., 2018;Friedrich et al., 2020;Kerr, 1982;Manton & Warren, 2011;Pokharel et al., 2017;Rauber et al., 2019;Silverman, 2010) indicate that large uncertainties still exist in how INPs work under different meteorological conditions to influence cloud microphysics. For glaciogenic cloud seeding to work, the first step is to identify the ice nucleation ability (INA) of commercially available cloud-seeding aerosols. ...
... Silver iodide (AgI) containing particles are widely used in cloud-seeding programs (French et al., 2018;Friedrich et al., 2020;Kerr, 1982;Manton & Warren, 2011;Pokharel & Geerts, 2016;Pokharel et al., 2017;Rauber et al., 2019;Silverman, 2010) due to its strong INA in the heterogeneous freezing regime (DeMott, 1995;Marcolli et al., 2016;Nagare et al., 2016;Vonnegut, 1947;Vonnegut, 1949). Laboratory experiments have been conducted to investigate the ice nucleation mechanism of pure AgI particles. ...
Plain Language Summary
Ice‐forming aerosol is commonly added to clouds, expecting precipitation enhancement via promotion of ice production. In this work, silver iodide (AgI) containing aerosol was generated from commercial cloud‐seeding products under different wind speed conditions. Its ice‐forming ability was studied at mixed‐phase cloud temperatures. The lower size limit for effective ice‐forming ability of the cloud‐seeding particles (90 nm) is higher than that of pure AgI particles (40 nm). The non‐AgI components produced by cloud‐seeding products are hypothesized to decrease the ice‐forming ability of smaller particles, as the mass fraction of ice‐nucleating AgI decreases. To estimate the minimum mass of AgI in a particle required for efficient ice nucleation under cloud‐seeding relevant conditions, we derived the critical ice‐activated mass fraction of the generated aerosols. These findings provide valuable insights into the optimization of cloud‐seeding practices for enhanced precipitation.
... The methods used to evaluate the precipitation effects of APE include physical tests, numerical simulations, and statistical tests (Sin Kevich et al., 2013;Breed et al., 2014;Spiridonov et al., 2015). Ground-based, aircraft-based and satellite-based technologies play a major role in understanding the physical processes of APE operation (Manton et al., 2011;Wang et al., 2012;Baumgardner et al., 2017), however, these processes are affected by multiple factors with a high natural variability. Numerical simulations require high data quality and spatiotemporal resolution, ideal assumptions, and the simplification of the precipitation process (Wu et al., 2018). ...
... During the APE operation period, the precipitation increased 14% and the runoff increased by 539 million m 3 in 2016; the precipitation increased 22% and the runoff increased by 699 million m 3 in 2017. APE has achieved an impressive effect on precipitation when compared to previous studies such as: Western America (18%) (Mielke et al., 1981), Southeastern Australia (14%) (Manton et al., 2011) and Jilin in China (12%) (Wu et al., 2015). As the study of the runoff effect of APE is scarce, this study tried to evaluate the runoff effect and the result showed that runoff increment is considerable. ...
The Hequ region of the upper Yellow River, eastern Tibetan Plateau, was selected as a case study for evaluating the artificial precipitation enhancement (APE) performance and for optimizing site selection. The distinctive precipitation-runoff process at large scale and in complex land surfaces and soil conditions was relatively considered in high-altitude cold regions. A comprehensive approach, combining precipitation regression analysis and a Soil and Water Assessment Tool (SWAT) semidistributed hydrological model, was proposed to improve artificial precipitation enhancement. First, a historical precipitation regression formula was established for the Hequ region and the Banma-Aba region during 1969 e1996 and then applied to quantify the precipitation effect of APE during 2016e2017 in the Hequ region. The artificial and natural precipitation datasets were used to drive the SWAT model, and the model results were compared to evaluate the runoff effect of APE. Finally, the spatial distribution of the runoff coefficient was used to determine the optimal site selection for APE. From late May to September, the natural precipitation was 381.7 mm, and the increased precipitation of 52.9 mm led to the runoff increase of 539 million m 3 over the operation area of 20,565 km 2 in 2016; from late May to August, the natural precipitation was 377.7 mm, and the increased precipitation of 81.7 mm led to the runoff increase of 699 million m 3 over the operation area of 13,482 km 2 in 2017. Areas with high runoff coefficients were distributed in the southwestern Hequ region. The optimal sites of APE are concentrated in the Tangke region and Mentang surrounding area; the high-altitude areas (3753e5318 m) and high-water-yield land cover regions should be given priority. This study provides an objective evaluation method of APE precipitation and runoff effects and a practical suggestion for improving APE by optimizing site selection in high-altitude cold regions.
... Most storms were relatively shallow (topping out at 3-4 km above the terrain), with a cloud-top temperature warmer than 2208C in all but one case (11 February). Clouds with tops colder than 2258C are deemed unsuitable for glaciogenic seeding because of natural seeding from aloft (Grant and Elliott 1974;Manton and Warren 2011). In general, considerable variation in all parameters was observed. ...
... Natural seeding mechanisms, such as the seeder-feeder mechanism (e.g., Rutledge and Hobbs 1983) or the supply of ice crystals from the ground up (Geerts et al. 2011) can thus significantly affect natural snow growth, and suppress seeding efficacy. The seeder-feeder mechanism is common in deep stratiform clouds and becomes very likely when the cloud-top temperature falls below 2258C (Grant and Elliott 1974;Manton and Warren 2011). ...
The impact of ground-based glaciogenic seeding on wintertime orographic, mostly stratiform clouds is analyzed by means of data from an X-band dual-polarization radar, the Doppler-on-Wheels (DOW) radar, positioned on a mountain pass. This study focuses on six intensive observation periods (IOPs) during the 2012 AgI Seeding Cloud Impact Investigation (ASCII) project in Wyoming. In all six storms, the bulk upstream Froude number below mountaintop exceeded 1 (suggesting unblocked flow), the clouds were relatively shallow (with bases below freezing), some liquid water was present, and orographic flow conditions were mostly steady. To examine the silver iodide (AgI) seeding effect, three study areas are defined (a control area, a target area upwind of the crest, and a lee target area), and comparisons are made between measurements from a treated period and those from an untreated period. Changes in reflectivity and differential reflectivity observed by the DOW at low levels during seeding are consistent with enhanced snow growth, by vapor diffusion and/or aggregation, for a case study and for the composite analysis of all six IOPs, especially at close range upwind of the mountain crest. These low-level changes may have been affected by natural changes aloft, however, as evident from differences in the evolution of the echo-top height in the control and target areas. Even though precipitation in the target region is strongly correlated with that in the control region, the authors cannot definitively attribute the change to seeding because there is a lack of knowledge about natural variability, nor can the outcome be generalized, because the sample size is small.
... Previous evaluations of the cloud seeding impact in the Snowy Mountains have demonstrated an overall positive and statistically significant seeding impact based on traditional statistical analysis using precipitation observations from a network of precipitation gauges and multi-year randomised seeding statistics (Manton and Warren, 2011;Manton et al., 2017;Smith 440 et al., 1963;Smith, 1967). This numerical study supports findings from previous statistical analysis which also show significant variability in seeding impacts, for example, Smith et al. (1963) shows that the seeding impacts over Snowy Mountains varied significantly from year to year. ...
Winter precipitation over Australia's Snowy Mountains provide crucial water resource in the region. Cloud seeding has been operational to enhance snowfall and water storage. This study presents an ensemble simulations to assess cloud seeding impacts across diverse meteorological conditions and evaluate associated model uncertainties. Nine seeding cases from 2016 to 2019 were simulated, with 18 ensemble members varying initialization datasets and model configurations. Two main storm categories were studied (convective vs stratiform). Results demonstrate that simulated seeding efficacy highly depends on meteorological conditions. Stratiform cases exhibited consistent precipitation enhancement, while convective cases showed reductions and downwind shifts of precipitation. Significantly inter-member variability was also observed. Notably, BARRA-driven simulations show better representation in supercooled liquid water. Aerosol and PBL scheme variations also contributed to ensemble spread. The findings demonstrate the value of ensemble modeling for reliable cloud seeding assessment. Key areas are also identified for future investigations in winter cloud seeding.
... To alleviate supply and demand conflict in water-scarce regions and promote sustainable and stable economic and social development, many countries globally have initiated inter-basin water diversion projects. Examples include, the South-to-North water diversion project (SNWDP) in China (Liu et al., 2015;Xu et al., 2023;Yu et al., 2018;Zhang et al., 2020), the Snowy Mountains scheme in Australia, (Manton & Warren, 2011;Reinfelds et al., 2014) and the California state water project in the USA (Hanak, 2011;Loomis, 1994). Approximately 14% of global water withdrawals are derived from inter-basin diversion projects, and this is projected to reach 25% by 2025 (Gohari et al., 2013). ...
Climate impacts of the South‐to‐North water diversion project in China on water‐receiving areas (WRA) is simulated by the Weather Research and Forecasting (WRF) model. The results show that during the 2015–2022 water diversion period, the WRA experiences increased precipitation and decreased temperature. Annual precipitation increased by 2.8 mm, mainly dominated by non‐convective precipitation (1.92 mm). Although the upwind region receives more water, the increase in water vapor flux is more dramatic in the downwind region due to the spring northwest monsoon; The decreased temperature effect is most pronounced in spring (over 0.15°C), and over 10 mm of evaporation increase in the downwind region. The sensible heat flux decrease is less pronounced than the latent heat flux increase, mainly because of the insulating effect, which prevented evaporative cooling reduction. This study advances our understanding of the mechanisms by which large‐scale water diversion affects WRA climates.
... The efficiency of cloud seeding depends on the cloud conditions. Previous studies suggest that for optimal seeding conditions, supercooled liquid water must be present in the cloud (Geerts and Rauber, 2022); the cloud depth above -5 °C should be deeper than 400 m for ice growth (Manton et al., 2011); the seeding temperature should be lower 75 than -8 °C (Breed et al., 2014), and the concentrations of natural ice crystals should be low (Jing et al., 2016). However, the thermodynamic and microphysical conditions vary significantly in a cloud and differ substantially among different clouds. ...
Detecting an unambiguous radar reflectivity signature is vital to investigate cloud seeding impacts. Radar reflectivity change attributed to seeding depends on both the cloud conditions and on the concentration of silver iodide (AgI) particles. In this study, the reflectivity change induced by glaciogenic seeding using different AgI particle concentrations is investigated under various cloud conditions, using a 1D ice growth model coupled with an AgI nucleation parameterization. In addition, an algorithm is developed to estimate the minimum AgI particle concentration needed for a measurable glaciogenic cloud seeding signature. The results show that the 1D model captures the ice growth habit compared to available observations, and yields an unambiguous reflectivity change that is consistent with 3D model simulations and previous observational studies. Simulations indicate that seeding at a temperature of about -15 °C has the highest probability of detecting the radar seeding signature. This finding is consistent with the fact that the seeding temperature was about -15 °C or slightly warmer in most documented unambiguous seeding signature cases. Using the 1D model, 1000 numerical experiments are conducted, and the outputs are used to develop a parameterization to estimate the AgI particle concentration that is needed to detect an unambiguous seeding signature. Application of this parameterization to a real case suggests that seeding between -21 °C and -11 °C can possibly produce unambiguous seeding signatures, and seeding at about -15 °C requires the least AgI particle concentration. Seeding at warmer temperatures in precipitating clouds requires an extremely high AgI amount and supercooled liquid water content. The results shown in this study deepen our understanding of the relationship between the AgI particle concentration and radar seeding signature under different cloud conditions. The parameterization can be used in operational seeding decision making of the optimal amount of AgI dispersed.
... AgI serves as a particularly good INP, as it has high ice activity at temperatures up to −5°C due to its lattice structure, which closely resembles that of ice (DeMott, 1995;Marcolli et al., 2016). This has led to worldwide programs that have actively pursued increasing precipitation over land to mitigate water scarcity (e.g., Woodley et al., 2003;Griffith et al., 2009;Geerts et al., 2010;Manton and Warren, 2011;Sin'kevich et al., 2018;Yang et al., 2018;Kulkarni et al., 2019;Wang et al., 2019;Al Hosari et al., 2021;Benjamini et al., 2023). Often, wintertime orographic clouds are targeted for glaciogenic cloud-seeding experiments, as the lifting of air along mountain slopes induces a high supercooled liquid water content, which serves as a water source for the ice crystals to grow (French et al., 2018;Tessendorf et al., 2019). ...
The ice phase in clouds is essential for precipitation formation over continents. The underlying processes for ice growth are still poorly understood, leading to large uncertainties in precipitation forecasts and climate simulations. One crucial aspect is the Wegener–Bergeron–Findeisen (WBF) process, which describes the growth of ice crystals at the expense of cloud droplets, leading to a partial or full glaciation of the cloud. In the CLOUDLAB project, we employ glaciogenic cloud seeding to initiate the ice phase in supercooled low-level clouds in Switzerland using uncrewed aerial vehicles with the goal of investigating the WBF process. An extensive setup of ground-based remote-sensing and balloon-borne in situ instrumentation allows us to observe the formation and subsequent growth of ice crystals in great detail. In this study, we compare the seeding signals observed in the field to those simulated using a numerical weather model in large-eddy mode (ICON-LEM). We first demonstrate the capability of the model to accurately simulate and reproduce the seeding experiments across different environmental conditions. Second, we investigate the WBF process in the model by comparing the simulated cloud droplet and ice crystal number concentration changes to in situ measurements. In the field experiments, simultaneous reductions in cloud droplet number concentrations with increased ice crystal number concentrations were observed, with periods showing a full depletion of cloud droplets. The model can reproduce the observed ice crystal number concentrations most of the time; however, it cannot reproduce the observed fast reductions in cloud droplet number concentrations. Our detailed analysis shows that the WBF process appears to be less efficient in the model than in the field. In the model, exaggerated ice crystal number concentrations are required to produce comparable changes in cloud droplet number concentrations, highlighting the inefficiency of the WBF process in the numerical weather model ICON.
... In future field campaigns, efforts should therefore be made to include alternate methodologies to determine the efficacy of orographic cloud seeding when heavy background precipitation is present. Examples include analysis of anomalous concentrations of silver in snow (e.g., Fisher et al. 2016Fisher et al. , 2018 or the ratio of silver to a nonnucleating, naturally covarying aerosol tracer such as indium oxide (Warburton et al. 1995;Manton and Warren 2011), as well as airborne observations of the microphysical chain of events associated with seeding from initiation to the growth of ice crystals to precipitation. Models also provide yet another approach to evaluate the physical chain of events associated with seeding and the impact on orographic precipitation (e.g., Xue et al. 2022). ...
Recent studies from the Seeded and Natural Orographic Wintertime Clouds: The Idaho Experiment (SNOWIE) demonstrated definitive radar evidence of seeding signatures in winter orographic clouds during three intensive operation periods (IOPs) where the background signal from natural precipitation was weak and a radar signal attributable to seeding could be identified as traceable seeding lines. Except for the three IOPs where seeding was detected, background natural snowfall was present during seeding operations and no clear seeding signatures were detected. This paper provides a quantitative analysis to assess if orographic cloud seeding effects are detectable using radar when background precipitation is present. We show that a 5-dB change in equivalent reflectivity factor Z e is required to stand out against background natural Z e variability. This analysis considers four radar wavelengths, a range of background ice water contents (IWC) from 0.012 to 1.214 g m ⁻³ , and additional IWC introduced by seeding ranging from 0.012 to 0.486 g m ⁻³ . The upper-limit values of seeded IWC are based on measurements of IWC from the Nevzorov probe employed on the University of Wyoming King Air aircraft during SNOWIE. This analysis implies that seeding effects will be undetectable using radar within background snowfall unless the background IWC is small, and the seeding effects are large. It therefore remains uncertain whether seeding had no effect on cloud microstructure, and therefore produced no signature on radar, or whether seeding did have an effect, but that effect was undetectable against the background reflectivity associated with naturally produced precipitation.
Significance Statement
Operational glaciogenic seeding programs targeting wintertime orographic clouds are funded by a range of stakeholders to increase snowpack. Glaciogenic seeding signatures have been observed by radar when natural background snowfall is weak but never when heavy background precipitation was present. This analysis quantitatively shows that seeding effects will be undetectable using radar reflectivity under conditions of background snowfall unless the background snowfall is weak, and the seeding effects are large. It therefore remains uncertain whether seeding had no effect on cloud microstructure, and therefore produced no signature on radar, or whether seeding did have an effect, but that effect was undetectable against the background reflectivity associated with naturally produced precipitation. Alternative assessment methods such as trace element analysis in snow, aircraft measurements, precipitation measurements, and modeling should be used to determine the efficacy of orographic cloud seeding when heavy background precipitation is present.
... This technology has been developed since the middle of the last century, and in a number of successful experiments, a statistically significant increase in precipitation from clouds by 5-20% was obtained compared to the amount produced by untreated naturally developing clouds [8,9,14,15,[19][20][21][22]. Analysis of the extra area effect of CS on precipitation [23] showed positive (5-15%) increases on the downwind side of the experimental area, for both winter orographic cloud seeding and summer convective cloud seeding projects which may extend to a couple of hundred kilometers. ...
Water scarcity due to rainfall variability, and exacerbated by climate change, is prevalent in many regions of the world. Lack of precipitation and excessive water extraction contribute to the intensification of the problem. Among different mitigation measures, rain enhancement through cloud seeding could be a tool as part of a water management strategy to replenish ground water sources. However, implementation of this technology requires proper preliminary analysis of the available cloud data and specific meteorological conditions under which rainfall forms. The aim of this paper is to assess the potential of for rain enhancement in Minas Gerais State in Brazil. The paper focuses on analysis of multiyear climate reanalysis ERA-5, upper air sounding, weather radar and ground stations data. Analysis showed that, between 2000 and 2019, precipitation declined on average by 212 mm per annum or 21% compared to the long term climatological mean. The natural precipitation, however, remains sufficiently high to implement weather modification technology. Assuming an increase of 15–20% could be achieved on a catchment area basis, the increases would be significant and could offset the recently observed decline in natural precipitation. The methodology proposed in this study can be used as a baseline for similar analysis in other vulnerable regions of the world experiencing freshwater shortages or declines. Its shortcomings and uncertainties are also discussed.
... In China, the United States, Israel, and Japan, cloud seeding has been studied to mitigate drought and suppress hail. Hygroscopic and glaciogenic materials are spread into clouds by using aircraft, rockets, and ground-based cloud seeding generators [18][19][20][21][22][23][24][25]. In most experiments, dry ice, calcium chloride (CaCl 2 ), and silver iodide (AgI) are used to enhance precipitation. ...
In this study, a method for verifying the effect of cloud seeding in the case of a mixture of natural and artificial rainfall bands was proposed, and its applicability to each experimental case was evaluated. Water resources that could be secured through cloud seeding were also quantified for the experiments on forest fire prevention, drought mitigation, and dust reduction in 2020. Data on numerical simulations, radar-derived rainfall, rain gauge-derived rainfall, and weather conditions were applied. Areas with seeding and nonseeding effects were classified according to the numerical simulation results and wind system, and enhanced rainfall was determined by comparing the changes in rainfall between the two areas. The amount of water resources was determined by considering the area of the seeding effect and rainfall density. As a result, 1.74 mm (4.75 million tons) of rainfall increased from the experiment on forest fire prevention, 0.84 mm (1.30 million tons) on drought mitigation, and 2.78 mm (24.44 million tons) on dust reduction. Thus, an average rainfall of 1.0 mm could be achieved through the experiment. These results helped verify the pure seeding effect and achieve the experimental purpose.
... Mardani et al. [52] surveyed both its theory and applications with the recent fuzzy developments. For further study on various methodologies, one may refer to [53][54][55][56][57][58][59][60][61][62][63][64]. Table 1 summarizes contributions related to the MCDM techniques under various fuzzy models. ...
The use of Pythagorean fuzzy N-soft sets (PFNSs) enables the examination of belongingness and non-belongingness of membership degrees, as well as their combinations with N-grading, in the unpredictable nature of individuals. This research aims to enhance our understanding of a popular multi-criteria group decision making (MCGDM) technique, Preference Ranking Organization Method for Enrichment of Evaluations, under the PFNS environment, aiding in making effective decisions for real-life problems, as fuzzy set theory is directly relevant to real-life applications. The PROMETHEE technique's main principle is to calculate the inflow and outflow streams of alternatives based on the deviation of their score degrees, ultimately providing partial and complete rankings of the given options. To capture the uncertainty of human nature, which demands both the association and disassociation of the considered criteria and provision of N-grading, the PFNS PROMETHEE technique is introduced in this research article. First, an Analytic Hierarchy Process AHP is used to check the feasibility of the standard weights of the criteria. The article then explains the detailed method of the fuzzy N-soft PROMETHEE technique to rank alternatives, with all the steps presented in an extensive flowchart for better understanding of the methodology. Furthermore, the practicality and viability of the proposed technique are demonstrated through an example of selecting the best chemical element in cloud seeding, where the most suitable choice is identified using an outranking directed graph. The credibility of the PFNS PROMETHEE technique is assessed by comparison with an existing method. Finally, the proposed technique's strengths and weaknesses are discussed to demonstrate its efficiency and drawbacks.
... An acetone-silver iodide solution, usually containing 1-2% AgI and solubilizing agents, can produce more than 10 15 artificial ice nuclei depending on atmospheric conditions (Garvey, 1975). Recent estimates suggest the addition of artificial ice nuclei from AgI enhances precipitation by 3 -15% (Manton and Warren, 2011;WWMPP, 2014). ...
Glaciogenic cloud seeding with silver iodide (AgI) has been used to enhance precipitation for over 60 years. Assessments of AgI impact and dispersion are often quantified using atmospheric processes models with impact assessed by comparing models with and without inclusion of cloud seeding modules. However, there is inherent uncertainty in these aerosol models and physical validation of AgI distribution is of value to both validate and improve model performance. The purpose of this study is to demonstrate the capacity to physically validate the dispersion of AgI by measuring silver enrichments in snow.Field and laboratory methods were developed to detect trace seeding signatures in snowpack. Unique laboratory layout and protocols were developed to reduce contamination potential within a traditional ICP-MS laboratory setting (not housed in a Class 100 Clean Room). Using these methods, we sampled a series of snow profiles within the target area of active cloud seeding in the central mountains of Idaho. Our results demonstrate the ability the ability to reproduce distinct evidence of elevated Ag at concentrations at a hillslope (0.25 km2) and at the basin (2,400 km2) scale. The construction of 8 snow pits at one site (hillslope scale) and 6 sites along a 65 km transect (basin scale) reliably identified both of the seeded storm layers sampled. The location of the peaks in Ag concentration within the snow profiles generally corresponds in timing to known cloud seeding events. Distinct seeded storm layers were reliably identified seeding signatures more than 60 km from the AgI sources, where silver concentrations were only enhanced 1-3 parts per trillion. Ag enriched snow in these chemical profiles generally correspond to downwind target locations and AgI seeding times.
... For example, the Wyoming Weather Modification Pilot Project ), a 6-year study, estimated a small positive seeding effect, but far from strong enough to discredit the null hypothesis that seeding has no effect (Rasmussen et al. 2018). The Snowy Precipitation Enhancement Research Program in Australia reported similar results (Manton and Warren 2011). The review by Rauber et al. (2019) described additional experiments, with little support for rainfall enhancement by seeding. ...
After 38 years of operational cloud seeding for rain enhancement in northern Israel, the Israel 4 experiment was conducted to reassess its effect on rainfall and provide a basis to evaluate its utility. Operational seeding started after two randomized experiments, the second ending in 1976, found a large and statistically significant effect of cloud seeding on rainfall. Observational studies in later years raised doubts as to the magnitude of the effect, possibly because of changing climatological conditions. A carefully designed randomized experiment was conducted from 2013 to 2020. A unique feature of the design was the use of forecast rainfall on target, rather than rainfall in an unaffected area, as a control variate to attenuate variability. The Israel 4 experiment was stopped a year earlier than planned, because the result was disappointing: a 1.8% increase, p value = 0.4, and 95% confidence interval of (−11%, 16%). These results led to a decision by the Israel Water Authority to stop operational seeding.
Significance Statement
The recent cloud seeding experiment in northern Israel did not show a significant rainfall increase—unlike the sequence of seeding experiments conducted in Israel in the previous century.
... The following two factors were considered to determine the index of selected comparison area. First, the significance of cloud precipitation represented by physical cloud parameters themselves, and the test effect of cloud physical parameters in the test process of artificial precipitation enhancement; the second is the application of macro and micro parameters of cloud physics in the test of precipitance enhancement effect [9,46,47]. Cloud top temperature, cloud effective particle radius, cloud optical thickness, liquid water path, combined reflectivity, ≥30 dBZ echo area, and vertical cumulative liquid water content were used to select the contrast area. ...
The precipitation enhancement operation data of aircraft from 2014 to 2019 and the global data assimilation system (NCEP GDAS) were used in this study. The transport process of the transmission of artificial precipitation enhancement seeding agents for aircraft was successfully simulated by the HYSPLIT model. The purpose of the study was to explore the applicability of the model in determining the artificial precipitation enhancement influence area and provide a technical method for evaluating the effect of artificial precipitation enhancement. The results show that (1) the HYSPLIT model can be used to track the transmission of aircraft precipitation enhancement seeding agents hourly. Suppose the seeding route satisfies the condition that the route and its interval area are the effective seeding area within 3 h after the end of the seeding agent. In that case, the seeding area’s boundary points can be used as dynamic change markers in the influence area. (2) The HYSPLIT model was used to simulate 24 aircraft precipitation enhancement seeding agent transmission processes. The transmission path for the seeding agent influence altitude layer was mostly southwest or west; the angle ranged from 225° to 268°; the horizontal transport distance of the seeding agent for three hours was 100–200 km; the vertical transmission direction was mostly upward; the range was 0–1200 m; the influence area decreased at the third h of seeding agent transport for 71% of the precipitation enhancement operations. (3) Based on the dynamic variations of 24 aircraft precipitation affected areas determined by the HYSPLIT model, and the contrast area selected by the similarity measurement method, 15 (63%) aircraft precipitation operations contributed to the increase in precipitation.
... Moreover, increased total water demand in Utah due to rapid population and economic growths and the direct and indirect impact of climate variability has resulted in increased pressure on scarce water resources (Khatri et al., 2018). Within this context, cloud seeding has been one of the viable options for enhancing water supplies in the western United States and other parts of the world (Flossmann et al., 2019;Manton and Warren, 2011;Bruintjes, 1999;Breed et al., 2014;Griffith et al., 2009;Silverman, 2010). ...
Cloud seeding operations to enhance winter precipitation and augment water supply sources have been regular annual programs in Utah, Colorado, Wyoming, Nevada, Idaho, and California in the Western United States. The winter cloud seeding program generally runs from November to April and is continued until the snow water equivalent (SWE) reaches a specified monthly average SWE level. Cloud seeding operations are suspended if snowfall exceeds a given threshold. For example, cloud seeding operation in Utah is suspended when SWE reaches 200%, 180%, 160%, and 150% of the average SWE values on January 1, February 1, March 1, and April 1, respectively. Local hydrological factors such as reservoir levels, soil moisture content, and possible risks of flash flood are often evaluated while suspending the cloud seeding operations. The current SWE-based criteria have been used since the 1980s, however there is no supporting document or scientific study that describes how these indices were derived. Additionally, the same indices have been adopted for the entire state without considering the hydrological and metrological variability among the watersheds. This study presents an objective method for deriving SWE-based indices for cloud seeding suspension criteria. The revised suspension criterion were calculated based on statistical analyses that establish a relationship between the SWE and seasonal natural streamflow for different watersheds in Utah. The updated indices were derived by inverting the regression equation to determine the SWE value associated with the 95th percentile of the historical seasonal cumulative streamflow, and these values have been used in Utah since 2019. The revised suspension criterion are 17–30% higher in magnitude than those previously used, and the difference is even larger if the results are compared for particular river basins. The revised suspension criterion capture the spatial and temporal variability of hydro-meteorological conditions within and between the watersheds. The developed method is easy to replicate in other states and watersheds.
Keywords
Cloud seedingSWESuspension criteriaStatistical analysisStreamflowDecision-making
... (Kraus and Squires, 1947;Reynolds, 1989;Bruintjes, 1999;Javanmard et al., 2007;Manton and Warren, 2011;Breed et al., 2014;Pokharel et al., 2014). 이들 연구에서는 강수 시스템이 강화된 것을 확인하였다. ...
Weather modification research has been actively performed worldwide, but a technology that can more quantitatively prove the research effects are needed. In this study, the seeding effect, the efficiency of precipitation enhancement in weather modification experiment, was verified using the radar data. Also, the effects of seeding material on hydrometeor change was analyzed. For this, radar data, weather conditions, and numerical simulation data for diffusion were applied. First, a method to analyze the seeding effect in three steps was proposed: before seeding, during seeding, and after seeding. The proposed method was applied to three cases of weather modification experiments conducted in Gangwon-do and the West Sea regions. As a result, when there is no natural precipitation, the radar reflectivity detected in the area where precipitation change is expected was determined as the seeding effect. When natural precipitation occurs, the seeding effect was determined by excluding the effect of natural precipitation from the maximum reflectivity detected. For the application results, it was found that the precipitation intensity increased by 0.1 mm/h through the seeding effect. In addition, it was confirmed that ice crystals, supercooled water droplets, and mixed-phase precipitation were distributed in the seeding cloud. The results of these weather modification research can be used to secure water resources as well as for future study of cloud physics.
... The transportation and dispersion of artificial ice nucleating particles (INP) determines where and when these seeding materials interact with SLW and is an important process to understand and quantify in order to assess the seeding impacts. Direct observations of the AgI dispersion features have been tried with airborne detection of coreleased trace gases (Bruintjes et al. 1995), airborne detection of AgI particles in clear conditions , and tracer chemistry analysis of snow samples (Warburton et al. 1995;Manton and Warren 2011;Fisher et al. 2016Fisher et al. , 2018. Based on these observations limited in both space and time, only general dispersion features such as the height above mountain peak and the horizontal dispersion speed were estimated. ...
The University of Pecs and NCAR Bin (UPNB) microphysical scheme was implemented into the Weather Research and Forecast (WRF) mesoscale model which was used to study the impact of AgI seeding on the precipitation formation in the winter orographic clouds. Four different experimental units were chosen from the Wyoming Weather Modification Pilot Project to simulate the seeding effect.
The results of the numerical experiments show that: (i) Comparisons with the soundings, snow gauges and micro-wave radiometer (MWR) data indicate that the three dimensional simulations with detailed microphysics reasonably represent both the dynamics and the microphysics of the real clouds. (ii) The dispersion of the AgI particles from the simulated ground-based seeding was effective due to the turbulent mixing. (iii) In the investigated cases (surface temperature is less than 0°C) the surface precipitation and the precipitation efficiency show low susceptibility to the concentrations of Cloud Condensation Nuclei (CCN) and natural Ice Nucleating Particles (INP). (iv) If the available liquid water content promotes the enhancement of the amount of snowflakes by diffusional growth, the surface precipitation can be increased by more than 5%. A novel parameter relevant to orographic clouds, the horizontally integrated liquid water path, was evaluated to find the relation between the seeding efficiency and the liquid water content. The impact of seeding is negligible if the horizontal LWP is less than 0.1 mm, while it is apparent if the horizontal LWP is larger than 1 mm based on the cases investigated in this study.
... The United States, China, Thailand, and Australia have conducted long-term precipitation enhancement experiments using aircraft. Researchers from these countries have reported statistical annual increases in precipitation in these experiments [15][16][17][18][19]. In South Korea, precipitation enhancement experiments have promised high economic savings, including an annual KRW 22,500 million from forest fire prevention, KRW 28,500 million from a reduction in drought damage, and KRW 350 million from securing water resources in dam watershed areas [20]. ...
Our study analyzed the occurrence frequency and distribution of seedable clouds around the Korean Peninsula in order to better secure water resources. Cloud products from the Communication, Ocean, and Meteorological Satellite (COMS), including cloud fraction, cloud top height, cloud top temperature, cloud phase, cloud top pressure, cloud optical thickness, and rainfall intensity, were used. Daytime hourly data between 0900 and 1800 local standard time (LST) observed from December 2016 to November 2019 was used. Seedable clouds occurring within this period were evaluated based on seasonal cloud phases, occurrence frequency, and cloud characteristics according to land, sea, and cloud type. These clouds exhibited varying average occurrence frequencies in different seasons. Sc (stratocumulus) clouds exhibited the highest occurrence frequency for all seasons, with an average of 63%, followed by Cu (cumulus) at 15%, As (altostratus) at 13%, and Ac (altocumulus) at 6%. We determined that low-level clouds primarily occurred around the Korean Peninsula, and the occurrence frequency of stratiform clouds was highest for water phase seedable clouds, while the occurrence frequency of cumuliform clouds was highest for ice phase seedable clouds. We believe that precipitation enhancement experiments could be suitable for western and eastern seas around the Korean Peninsula as well as for mountainous regions on land.
... Another indication could be deep clouds with cold cloud top temperatures; e.g., ≤ -25 o C (Grant and Elliott, 1974). A statement from an analysis of a more recent research program conducted in the Snowy Mountains of southeastern Australia (Manton and Warren, 2011) provides support to the above; "An analysis of cloud top temperatures indicated that natural precipitation tended to increase as cloud top temperatures decrease, in other words deeper clouds are more efficient in producing precipitation." However, if icing is being measured in colder situations it is believed that seeding would help nucleate ice particles and therefore increase precipitation efficiency. ...
North American Weather Consultants has operated a cloud seeding program to impact select target areas in the Upper Gunnison River Basin of Colorado each winter since the 2003-2004 winter season. Since the presence of supercooled liquid water (SLW) is the key ingredient that renders portions of some winter storms " seedable " , provisions were made to acquire and install a ground based icing meter, which measures the occurrence of SLW, along with some supporting meteorological instruments at an exposed location on Mt. Crested Butte located north of Gunnison, Colorado. This system was installed in the fall of 2014 and then operated throughout the 2014-2015 winter season. Various analyses were performed based upon the observance of SLW at this location. Analyses of meteorological features during icing events included: temperature, precipitation, wind direction and speed, synoptic pattern, and low-level stability. Since ground based generators are used on this program and the Gunnison Basin is known for cold temperatures caused by trapping of cold air and the presence of atmospheric inversions, special attention was given to determine if such inversions might occur when " seedable " conditions were present. In other words, do winter storms typically scour out any pre-existing low-level atmospheric inversions that may form during clear weather conditions? To provide additional information on this question, the NOAA HYSPLIT model was run for each seeded storm that occurred during the 2014-2015 winter season. These model runs resulted in predicted plume trajectories from each ground based generator. It needs to be emphasized in the following conclusions and key findings that all of these are based upon only one winter season of data. Additional seasons of data would lead to more climatologically representative conditions associated with icing at the Mt. Crested Butte site. • A large proportion of barrier summit height icing activity was observed in the-5 to-15 o C temperature range (the range generally used as a primary seeding criterion). In general, it appears that about 75% of icing activity occurs within this range, with most of the remainder observed at warmer temperatures (16%). A smaller portion (~9%) was observed at temperatures colder than-15 o C. • Of five synoptic categorizations (pre-frontal, post-frontal/pre-500-mb trough, post-trough, closed low, and undefined), the pre-frontal situation was associated with the greatest amount of icing at the Mt. Crested Butte site during one season of data with 46% of icing events. As a consequence, most icing events were associated with south-westerly winds aloft. • The ice detector data paired with surface data and modeling indications suggests that in ~63-70% of icing periods, atmospheric stability would not significantly inhibit orographic lofting of lower elevation ground generator seeding plumes into the SLW zones in winter clouds associated with storms passing over the target area. ~ SCIENTIFIC PAPERS ~ VOLUME 48 GRIFFITH ET AL. APRIL 2016 9 ~ SCIENTIFIC PAPERS ~ • HYSPLIT computer model runs as well as plots indicating the amount of stability dur
... Inspired by the very original cloud seeding concept based on early laboratory findings of ice nucleation ability of dry ice and silver iodide (AgI ;Schaefer 1946;Vonnegut 1947), many attempts all around the world have been tried over the last several decades to validate the effect of wintertime orographic cloud seeding through all kinds of field programs, ranging from physical-evidence-oriented campaigns (e.g. , Hobbs 1975;Marwitz and Stewart 1981;Prasad et al. 1989;Deshler et al. 1990;Deshler and Reynolds 1990;Super and Heimbach 1988;Super and Boe 1988;Warburton et al. 1995;Huggins 2007;Geerts et al. 2010) to statistical-evidence-oriented randomized experiments (e.g., Mielke et al. 1970Mielke et al. , 1971Mielke et al. , 1981Chappell et al. 1971;Elliott et al. 1978;Vardiman and Moore 1978;Rottner et al. 1980;Super and Heimbach 1983;Mielke 1995;Gabriel 1995;Morrison et al. 2009;Manton and Warren 2011). Despite the large-scale efforts of validations, the effect of glaciogenic seeding on precipitation is still inconclusive mainly because of the inability to repeat experiments in a controlled environment, the scale mismatch between seeding operations and their precipitation response (including the diversity of seeding responses under different cloud conditions), the signal detection difficulty of seeding effect, and the high cost of conducting randomized seeding programs, as pointed out in Xue et al. (2013b). ...
... But both actual seeding impact and suitable conditions remain poorly understood (National Research Council, 2003;Garstang et al., 2005). Much research has been conducted into the impact of glaciogenic cloud seeding of cold-season clouds over the mountains in the western United States and elsewhere, mostly using statistical techniques (e.g., Elliott et al., 1978;Mielke et al., 1981;Gabriel, 1995;Manton and Warren, 2011). Several case studies have reported a change in surface precipitation and/or in radar reflectivity following the injection of AgI nuclei (Hobbs et al., 1981;Super and Heimbach, 1988;Super and Boe, 1988;Deshler and Reynolds, 1990;Holroyd et al., 1995;Super, 1999;Huggins, 2007), although attribution is uncertain. ...
The ice phase in clouds is essential for precipitation formation over continents. The underlying processes for ice growth are still poorly understood, leading to large uncertainties in precipitation forecasts and climate simulations. One crucial aspect is the Wegener-Bergeron-Findeisen (WBF) process, which describes the growth of ice crystals at the expense of cloud droplets leading to a partial or full glaciation of the cloud. In the CLOUDLAB project, we employ glaciogenic cloud seeding to initiate the ice phase in supercooled low-level clouds in Switzerland using uncrewed aerial vehicles with the goal to investigate the WBF process. An extensive set-up of ground-based remote sensing and balloon-borne in situ instrumentation allows us to observe the formation and subsequent growth of ice crystals in great detail. In this study, we compare the seeding signals observed in the field to those simulated using a numerical weather model in large-eddy mode (ICON-LEM). We first demonstrate the capability of the model to accurately simulate and reproduce the seeding experiments across different environmental conditions. Second, we investigate the WBF process in the model by comparing the simulated cloud droplet and ice crystal number concentration changes to in situ measurements. In the field experiments, simultaneous reductions in cloud droplet number concentrations with increased ice crystal number concentrations were observed with periods showing a full depletion of cloud droplets. The model can reproduce the observed ice crystal number concentrations most of the time, but not the observed fast reductions in cloud droplet number concentrations. Our detailed analysis shows that the WBF process appears to be less efficient in the model than in the field. In the model, exaggerated ice crystal number concentrations are required to produce comparable changes in cloud droplet number concentrations, highlighting the inefficiency of the WBF process in ICON.
Cloud seeding has been actively carried out across the globe in the past several decades due to the uneven spatial-temporal distribution of precipitation. The catalytic effects of artificial precipitation enhancement experiments and operations caused widely academic debates in atmospheric physics. Both atmospheric physicists and statisticians have made much effort for exploratory and confirmatory studies on scientific evaluation of cloud seeding. Recent progresses and critical problems on how to evaluate cloud seeding and acquire statistical evidence for the enhancement of precipitation, including the design of statistical tests, the selection of target indicators and covariates, the evaluation of statistical methods, and the outlook of cloud seeding evaluation, are reviewed. We describe important issues, aiming to reduce systematic errors and uncertainties in statistical tests as well as increase the level of quantitative sounding and evaluation. First, a regularized set of design and operation in the steps should be established to optimize techniques of cloud seeding. Second, the latest achievements in atmospheric physics, physical sounding, and statistics need to be introduced to help improve the correctness and scientificity. Third, middle-and long-term special research projects are expected to investigate the influence of ideal hypotheses of seeding schemes, statistical test plans, and statistical methods. These demands can update our knowledge and technology of weather modification and increase the cooperation of multidiscipline, such as logical integration of statistical tests, physical analysis, and numerical modeling.
This study presents the first numerical simulations of seeded clouds over the Snowy Mountains of Australia. WRF-WxMod®, a novel glaciogenic cloud seeding model, was utilized to simulate the cloud response to winter orographic seeding under various meteorological conditions. Three cases during the 2018 seeding periods were selected for model evaluation, coinciding with an intensive ground-based measurement campaign. The campaign data were used for model validation and evaluation.
Comparisons between simulations and observations demonstrate that the model realistically represents cloud structures, liquid water path, and precipitation. Sensitivity tests were performed to pinpoint key uncertainties in simulating natural and seeded clouds and precipitation processes. They also shed light on the complex interplay between various physical parameters/processes and their interaction with large-scale meteorology.
Our study found that in unseeded scenarios, the warm and cold biases in different initialization datasets can heavily influence the intensity and phase of natural precipitation. Secondary ice production via Hallett-Mossop processes exerts a secondary influence. On the other hand, the seeding impacts are primarily sensitive to aerosol conditions and the natural ice nucleation process. Both factors alter the supercooled liquid water availability and the precipitation phase, consequently impacting the AgI nucleation rate. Furthermore, model sensitivities were inconsistent across cases, indicating no single model configuration optimally represents all three cases. This highlights the necessity of employing an ensemble approach for a more comprehensive and accurate assessment of the seeding impact.
The concept of “winter cloud seeding windows” is a familiar theme found in a number of earlier publications. More recent feasibility studies, physical observations and analyses of existing cloud seeding programs have indicated some of this earlier thinking has considerable merit. The concept that deep winter storm systems with cold cloud tops often appear to be naturally efficient with little or no supercooled liquid water content is especially important. It appears from a variety of earlier sources of information and more recent observations that shallow, orographically induced clouds often contain supercooled liquid water and therefore offer good cloud seeding potential. Several studies and observations suggest that shallow orographic clouds that contain supercooled liquid water frequently occur after the passage of a surface cold front and even after the passage of an upper level trough. If the occurrence of such clouds is viewed in the context of the orientation of the targeted mountain barriers, the question can be asked if mountain barrier orientations have any impact on the development of these types of “seedable” clouds? This is basically a question of the amount of up barrier flow that accompanies these shallow orographic clouds. North American Weather Consultants has developed a conceptual model that the barrier orientation that provides the best conditions for the formation of these kinds of clouds in the western United States (and perhaps elsewhere) are barriers with a north-south orientation since post-frontal or post upper trough passage conditions will produce considerable up barrier flow over these barriers. Fortunately, most mountain barriers in the western United States have such an orientation. North American Weather Consultants believes that recognition and verification of the above will be important in the conduct of future winter orographic cloud seeding programs. Placing “seedabilty” in the synoptic setting and relating “seedability” to barrier orientation will be important in estimating potential cloud seeding effects in different project areas in the future.
This essay is intended to provide stakeholders and news outlets with a plain-language summary of orographic cloud seeding research, new capabilities, and prospects. Specifically, we address the question of whether a widely-practiced type of weather modification, glaciogenic seeding of orographic clouds throughout the cold season, can produce an economically-useful increase in precipitation over a catchment-scale area. Our objective is to clarify current scientific understanding of how cloud seeding may affect precipitation, in terms that are more accessible than in the peer-reviewed literature. Public confidence that cloud seeding “works” is generally high in regions with operational seeding, notwithstanding decades of scientific reports indicating that the changes in precipitation are uncertain. Randomized seeding experiments have a solid statistical foundation and focus on the outcome, but, in light of the small seeding signal and the naturally noisy nature of precipitation, they generally require too many cases to be affordable, and therefore are discouraged. A complementary method, physical evaluation, examines changes in cloud and precipitation processes when seeding material is injected, and yields insights into the most suitable ambient conditions. Recent physical evaluations have established a robust, well-documented scientific basis for glaciogenic seeding of cold-season orographic clouds to enhance precipitation. The challenge of seeding impact assessment remains, but evidence is provided that, thanks to recent significant progress in observational and computational capabilities, the research community is finally on track to be able to provide stakeholders with guidance on the likely quantitative precipitation impact of cloud seeding in their region. We recommend further process-level evaluations combined with highly-resolved, well-constrained numerical simulations of seasonal cloud seeding.
Assessment of how the wintertime cloud seeding conditions over Utah's mountains may change provides essential information for the state to work on increasing its snowpack in the warmer climate. In this study, data from the National Center for Atmospheric Research's high-resolution, convection-permitting Weather Research and Forecasting model are used to evaluate suitable conditions for wintertime cloud seeding over Utah's mountains, and to estimate future changes to these conditions under a pseudo-global warming scenario. While previous studies have examined historical cloud seeding conditions, this is the first study that explores the impact of future climate change on these conditions. Based on two atmospheric variables commonly used in cloud-seeding operations, i.e. average temperature from the surface to 1 km above ground level (−20 °C≤T≤−6 °C) and vertically integrated supercooled liquid water (SLW > 0.01 mm), Utah's mountains are suitable for seeding more than 20% of the time during winter. A greater rate of suitability exists in the northern and Uinta Mountains. In the warmer climate under a high-emissions scenario, these seeding conditions may wane as the percentage of precipitating clouds suitable for seeding, about 60% under current conditions, would decline to about 40% across the state with significance. This projected decrease is due to rising temperatures and a decreased frequency of precipitation events. These findings imply that climate warming will narrow the window of opportunity for winter cloud seeding operation in Utah.
This paper reviews research conducted over the last six decades to understand and quantify the efficacy of wintertime orographic cloud seeding to increase winter snowpack and water supplies within a mountain basin. The fundamental hypothesis underlying cloud seeding as a method to enhance precipitation from wintertime orographic cloud systems is that a cloud’s natural precipitation efficiency can be enhanced by converting supercooled water to ice upstream and over a mountain range in such a manner that newly created ice particles can grow and fall to the ground as additional snow on a specified target area. The review summarizes the results of physical, statistical, and modeling studies aimed at evaluating this underlying hypothesis, with a focus on results from more recent experiments that take advantage of modern instrumentation and advanced computation capabilities. Recent advances in assessment and operations are also reviewed, and recommendations for future experiments, based on the successes and failures of experiments of the past, are given.
The increasing anthropogenic pollution and its interaction with precipitation received much attention from the research community and have been explored extensively for understanding the aerosol-cloud interactions. The impacts of orography and aerosols on the precipitation processes have unveiled the Aerosol-Orography-Precipitation (AOP) interaction as an essential research area. The understanding of AOP interaction is critical for improving the extreme rainfall events prediction over mountainous regions. The phase of clouds (warm or mixed) along with orography has emerged as a significant factor for influencing the AOP relations. The present work reviews the modelling and observational based studies dealing with the relationship between orography and aerosols on the precipitation. The study reveals the principal role of aerosols in shifting the precipitation pattern for orographic regions. The environmental factors, especially ambient temperature, humidity and flow patterns are also identified to affect the orographic precipitation. The review also discovers that AOP studies exist only to limited areas of the world due to limited observations, and mostly with idealised cases in the modelling framework.
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Operational cloud seeding program was conducted in the Karnataka State, India during 21 August to 7 November 2017 with robust, pre-established experimental design plan following the guidelines developed under Indian national CAIPEEX program. Hygroscopic and glaciogenic seeding were carried out using areal method. A very high resolution raingauge network (spatial resolution: 5 km, temporal resolution: 15 min) was used to measure the surface rainfall. Two approaches have been used for estimating the enhancement: (i) Rainfalls within 4-h after seeding was compared at station levels with the rainfalls prior to seeding. The analysis of 618 cases showed average enhancement of 27.9% above the natural rainfall. (ii) The floating control-target area rainfall analysis was used to estimate the natural rainfall that would have occurred without seeding. For understanding maximum absolute increase in the rainfall, 7 cases in the top 5% which were well distributed within seeding period (with 4 hygroscopic and 3 glaciogenic cases) have been analyzed. The maximum increase in rainfalls in hygroscopic/glaciogenic cases were 20.8/28.1 mm which are ~5/7 times higher than the mean daily rainfall (4.3 mm) during September–October. Additional water made available due to seeding has been estimated to be 2.1 TMC. The study showed that seeding became effective in increasing rainfall under the particular dynamic and thermodynamical conditions. This is a unique study to estimate the effect of operational cloud seeding in (i) enhancement of precipitation and (ii) availability of additional water. The results of the study may find basis for the operational programs in the world.
Cloud seeding has been actively carried out across the globe in the past several decades due to the uneven spatial-temporal distribution of precipitation. The catalytic effects of artificial precipitation enhancement experiments and operations caused widely academic debates in atmospheric physics. Both atmospheric physicists and statisticians have made much effort for exploratory and confirmatory studies on scientific evaluation of cloud seeding. Recent progresses and critical problems on how to evaluate cloud seeding and acquire statistical evidence for the enhancement of precipitation, including the design of statistical tests, the selection of target indicators and covariates, the evaluation of statistical methods, and the outlook of cloud seeding evaluation, are reviewed. We describe important issues, aiming to reduce systematic errors and uncertainties in statistical tests as well as increase the level of quantitative sounding and evaluation. First, a regularized set of design and operation in the steps should be established to optimize techniques of cloud seeding. Second, the latest achievements in atmospheric physics, physical sounding, and statistics need to be introduced to help improve the correctness and scientificity. Third, middle- and long-term special research projects are expected to investigate the influence of ideal hypotheses of seeding schemes, statistical test plans, and statistical methods. These demands can update our knowledge and technology of weather modification and increase the cooperation of multidiscipline, such as logical integration of statistical tests, physical analysis, and numerical modeling.
The field of atmospheric science has been enhanced by its long-standing collaboration with entities with specific needs. This chapter and the two subsequent ones describe how applications have worked to advance the science at the same time that the science has served the needs of society. This chapter briefly reviews the synergy between the applications and advancing the science. It specifically describes progress in weather modification, aviation weather, and applications for security. Each of these applications has resulted in enhanced understanding of the physics and dynamics of the atmosphere, new and improved observing equipment, better models, and a push for greater computing power.
The first phase of the Snowy Precipitation Enhancement Research Project (SPERP-1) was a confirmatory experiment on winter orographic cloud seeding (Manton et al., 2011). Analysis of the data (Manton andWarren, 2011) found that a statistically significant impact of seeding could be obtained by removing any 5-hour experimental units (EUs) for which the amount of released seeding material was below a specified minimum. Analysis of the SPERP-1 data is extended in the present work by first considering the uncertainties in the measurement of precipitation and in the methodology. It is found that the estimation of the natural precipitation in the target area, based solely on the precipitation in the designated control area, is a significant source of uncertainty. A systematic search for optimal predictors shows that both the Froude number of the low-level flow across the mountains and the control precipitation should be used to estimate the natural precipitation. Applying the optimal predictors for the natural precipitation, statistically significant impacts are found using all EUs. This approach also supports a novel analysis of the sensitivity of seeding impacts to environmental variables, such as wind speed and cloud top temperature. The spatial distribution of seeding impact across the target is investigated. Building on the results of SPERP-1, phase 2 of the experiment (SPERP-2) ran from 2010 to 2013 with the target area extended to the north along the mountain ridges. Using the revised methodology, the seeding impacts in SPERP-2 are found to be consistent with those in SPERP-1, provided that the natural precipitation is estimated accurately.
The AgI Seeding Cloud Impact Investigation (ASCII) campaign, conducted in early 2012 and 2013 over two mountain ranges in southern Wyoming, was designed to examine the impact of ground-based glaciogenic seeding on snow growth in winter orographic clouds. Part I of this study (Pokharel and Geerts, 2016) describes the project design, instrumentation, as well as the ambient atmospheric conditions and macrophysical and microphysical properties of the clouds sampled in ASCII. This paper (Part II) explores how the silver iodide (AgI) seeding affects snow growth in these orographic clouds in up to 27 intensive operation periods (IOPs), depending on the instrument used.
In most cases, 2 h without seeding (NOSEED) were followed by 2 h of seeding (SEED). In situ data at flight level (2D-probes) indicate higher concentrations of small snow particles during SEED in convective clouds. The double difference of radar reflectivity Z (SEED − NOSEED in the target region, compared to the same trend in the control region) indicates an increase in Z for the composite of ASCII cases, over either mountain range, and for any of the three radar systems (WCR, MRR, and DOW), each with their own control and target regions, and for an array of snow gauges. But this double difference varies significantly from case to case, which is attributed to uncertainties related to sampling representativeness and to differences in natural trends between control and target regions. We conclude that a sample much larger than ASCII's sample is needed for clear observational evidence regarding the sensitivity of seeding efficacy to atmospheric and cloud conditions.
Recent confirmatory results in cloud seeding trials in the Australian Snowy Mountains have generated interest in performing similar experiments elsewhere. The Brindabella Ranges, which form the western border and catchment watershed of the Australian Capital Territory, share similarities in both climate and topography to the Snowy Mountains, so there is some prospect of conducting cloud seeding operations there. This paper presents an analysis of observations and high-resolution weather research and forecasting (WRF) model simulations of two wintertime storms from 2008 with the purpose of (1) evaluating the performance of the WRF model in simulating wintertime storms in the Australian alpine environment, and (2) investigating the nature of these storms from the perspective of cloud seeding research. The WRF model results compare favourably with much of the meteorological data used. There was a tendency to simulate too much moisture in the lower levels, and as a result the simulated low-level cloud coverage was somewhat more extensive than observed. Precipitation amounts were generally well represented, but the paucity of the observational network in the Brindabella Ranges made a comprehensive evaluation impossible. Cloud liquid water path (LWP), observed with a mountain top dual-channel microwave radiometer, was surprisingly well represented by the WRF model, especially in the post-frontal conditions. Radar reflectivity in the analysis region showed significant differences upwind and downwind of the Brindabella Ranges, suggesting that the mountains played an important role in the modulation of precipitation structures. Both of the case study storms were characterised by extended periods with appreciable quantities of supercooled liquid water, which is central to the glaciogenic cloud seeding hypothesis. However, further research would need to be conducted to determine whether such conditions occur frequently enough to permit cloud seeding operations, and whether it would be feasible to target the catchment regions with seeding material.
A "climatology" of supercooled cloud tops is presented for southeastern Australia and the western United States, where historic glaciogenic cloud-seeding trials have been located. The climatology finds that supercooled cloud tops are common over the mountainous region of southeastern Australia and Tasmania (SEAT). Regions where cloud-seeding trials reported positive results coincide with a higher likelihood of observing supercooled cloud tops. Maximum absolute frequencies (AFs) occur ;40% of the time during winter. There is a relationship between the underlying orography and the likelihood of observing supercooled liquid water (SLW)-topped clouds. Regions of the United States that have been the subject of cloud-seeding trials show lower AFs of SLW-topped clouds. The maximum is located over the Sierra Nevada and occurs ;20% of the time during winter (Sierra Cooperative Pilot Project). These sites are on mountains with peaks higher than any found in SEAT (.3000 m). For the Sierra Nevada, the AF of SLW-topped clouds decreases as the elevation increases, with glaciation occurring at the higher elevations. The remote sensing of supercooled cloud tops is not proof of a region's amenability for glaciogenic cloud seeding. This study simply highlights the significant environmental differences between historical cloud-seeding regions in the United States and Australia, suggesting that it is not reasonable to extrapolate results from one region to another. Without in situ cloud microphysical measurements, in-depth knowledge of the timing and duration of potentially seedable events, or knowledge of the synoptic forcing of such events, it is not possible to categorize a region's potential for precipitation augmentation operations.
Data from in situ probes and a vertically-pointing mm-wave Doppler radar aboard a research aircraft are used to study the cloud microphysical effect of glaciogenic seeding of cold-season orographic clouds. A previous study (Geerts et al., 2010) has shown that radar reflectivity tends to be higher during seeding periods in a shallow layer above the ground downwind of ground-based silver iodide (AgI) nuclei generators. This finding is based on seven flights, conducted over a mountain in Wyoming (the Unites States), each with a no-seeding period followed by a seeding period. In order to assess this impact, geographically fixed flight tracks were flown over a target mountain, both upwind and downwind of the AgI generators.
This paper examines data from the same flights for further evidence of the cloud seeding impact. Composite radar data show that the low-level reflectivity increase is best defined upwind of the mountain crest and downwind of the point where the cloud base intersects the terrain. The main argument that this increase can be attributed to AgI seeding is that it is confined to a shallow layer near the ground where the flow is turbulent. Yet during two flights when clouds were cumuliform and coherent updrafts to flight level were recorded by the radar, the seeding impact was evident in the flight-level updrafts (about 610 m above the mountain peak) as a significant increase in the ice crystal concentration in all size bins. The seeding effect appears short-lived as it is not apparent just downwind of the crest.
The Snowy Precipitation Enhancement Research Project (SPERP) was undertaken from May 2005 to June 2009 in the Snowy Mountains of southeastern Australia with the aim of enhancing snowfall in westerly flows associated with winter cold fronts. Building on earlier field studies in the region, SPERP was developed as a confirmatory experiment of glaciogenic static seeding using a silver-chloroiodide material dispersed from ground-based generators. Seeding of 5-h experimental units (EUs) was randomized with a seeding ratio of 2:1. A total of 107 EUs were undertaken at suitable times, based on surface and upper-air observations. Indium (III) oxide was released during all EUs for comparison of indium and silver concentrations in snow in seeded and unseeded EUs to test the targeting of seeding material. A network of gauges was deployed at 44 sites across the region to detect whether precipitation was enhanced in a fixed target area of 832 km2, using observations from a fixed control area to estimate the natural precipitation in the target. Additional measurements included integrated supercooled liquid water at a site in the target area and upper-air data from a site upwind of the target.
A gradual reduction in water from snow-melt over the past century has motivated Snowy Hydro Ltd. to pursue a wintertime cloud seeding project in the Snowy Mountains of south-eastern Australia. The Snowy Precipitation Enhancement Research Project is one of only a few cloud seeding experiments in the last two decades to employ a randomized design, and the first such randomized experiment to incorporate dual-trace chemistry analysis of snowfall as part of the pro-ject evaluation. The project design, seeding criteria, ground-seeding network, and measurement infrastructure are described, as are the general components of the statistical evaluation plan. Some initial results from analysis of physical and trace chemical measurements are presented for an extended storm period in 2006 that included five randomized experimental units. The trace chemistry results were found to validate several of the components of the seeding conceptual model, and a unique time series of tracer element concentrations appears to indicate when seed-ing and tracer materials were released. Progress during the first four seasons of the project is de-scribed, as are various findings that could affect the outcome of the project.
A rotated principal component analysis of Australian winter
(June-August) rainfall revealed two large-scale patterns of variation
which together accounted for more than half of the total rainfall
variance. The first pattern was a broadband stretching from the
northwest to the southeast corners of the country. The second was
centered in the eastern third of the continent. The two patterns were
correlated to sea surface temperatures in the Indian and Pacific oceans.
The first rainfall pattern was best related to the difference in sea
temperatures between the Indonesian region and the central Indian Ocean.
The second rainfall pattern was related to equatorial Pacific sea
surface temperatures. This relationship reflects the influence of the
Southern Oscillation on both sea surface temperatures and Australian
rainfall but the relationship between the first rainfall pattern and the
difference between Indonesian and central Indian Ocean sea surface
temperatures is largely independent of the Southern Oscillation. This
sea surface temperature difference may be another factor influencing
Australian rainfall, some-what separate from the well-known effect of
the Southern Oscillation.
Kolmogorov's goodness-of-fit measure, D_n , for a sample CDF has consistently been set aside for methods such as the D^+_n or D^-_n of Smirnov, primarily, it seems, because of the difficulty of computing the distribution of D_n . As far as we know, no easy way to compute that distribution has ever been provided in the 70+ years since Kolmogorov's fundamental paper. We provide one here, a C procedure that provides Pr(D_n < d) with 13-15 digit accuracy for n ranging from 2 to at least 16000. We assess the (rather slow) approach to limiting form, and because computing time can become excessive for probabilities>.999 with n's of several thousand, we provide a quick approximation that gives accuracy to the 7th digit for such cases.
Systematic procedures for constructing confidence bounds and point estimates based on rank statistics are given for the two sample location parameter, two sample scale parameter and one sample location parameter problems.
Le but principal de l'Organisation des Nations Unies pour l'Alimentation et l'Agriculture est de produire suffisamment de nourriture pour tous les habitants de la terre. Elle pourrait donc potentiellement tirer profit important de la modification du temps par ensemencement des nuages dans un grand nombre des programmes sur les ressources naturelles qu'elle dirige dans les pays en voie de développement. Jusqu'ici nous ne sommes pas servi de cet expédient car ses résultats sont encore trop imprévisibles. L'ensemencement des nuages dans un but particulier peut avoir de nombreux effects secondaires imprévus et indésirables. On ne devrait pas entreprendre un opération d'ensemencement de nuages sans avoir la certitude raisonnable qu'elle va être: 1) Techniquement practicable; 2) Rentable; 3) Socialement acceptable; 4) Sans danger du point de vue écologique. Les institutions techniques des Nations Unies sont de plus en plus sensibles à la nécessité d'une approche intégrée à l'égard des problèmes de l'influence de l'homme sur le milieu physique qui l'entoure. Les Nations Unies nous offrent un cadre approprié pour l'exécution de ces responsabilités globales et le moment est venu de préparer un protocol pour l'ensemencement des nuages.
Further exploratory analysis of the Bridger Range Experiment was carried out with 6 h data blocks partitioned from the original 24 h experimental units. The analysis was limited to 6 h periods having a rawinsonde observation, Main Ridge temperature 9°C and westerly flow. The results suggest that silver iodide seeding was particularly effective in increasing precipitation in a small fraction of the cases, but had little or no effect most of the time. Seeding appeared to be especially effective when cloud top temperatures were warmer than about 25°C and the wind had a Wong cross-barrier component. Marked decreases in precipitation were not apparent during seeded periods.
The Snowy Precipitation Enhancement Research Project (SPERP) was undertaken from May 2005 to June 2009 in the Snowy Mountains of southeastern Australia with the aim of enhancing snowfall in westerly flows associated with winter cold fronts. Building on earlier field studies in the region, SPERP was developed as a confirmatory experiment of glaciogenic static seeding using a silver-chloroiodide material dispersed from ground-based generators. Seeding of 5-h experimental units (EUs) was randomized with a seeding ratio of 2:1. A total of 107 EUs were undertaken at suitable times, based on surface and upper-air observations. Indium (III) oxide was released during all EUs for comparison of indium and silver concentrations in snow in seeded and unseeded EUs to test the targeting of seeding material. A network of gauges was deployed at 44 sites across the region to detect whether precipitation was enhanced in a fixed target area of 832 km2, using observations from a fixed control area to estimate the natural precipitation in the target. Additional measurements included integrated supercooled liquid water at a site in the target area and upper-air data from a site upwind of the target.
Cloud seeding to increase Winter snowpacks over mountainous regions of the western United States have been in existence for almost 40 years. However, our understanding of the physical processes taking place in the clouds in response to this seeding and the expected precipitation increases are still subjects of great scientific interest and investigation. Recent field observations that have emphasized direct physical observations of winter clouds, their structure and liquid water content, as well as their response to the injection of glaciogenic seeding agents have aided to our knowledge. These physical observations are helping to provide some insight into the mechanisms of precipitation increases, inferred from statistical analyses, that have been reported in certain winter orographic cloud seeding programs. This paper attempts to compare physical and statistical results, to show consistency, and to help provide limits to what one might expect when winter snowpack augmentation is applied within suitable cloud systems.
In a 1984 85 winter cloud-seeding program at Lake Almanor, California, indium sesquioxide (In2O3) aerosol particle generators were collocated with silver iodide (AgI) aerosol particle generators as a source of inert tracer aerosol. The In2O3 aerosol served as an indicator of the amount of AgI aerosol scavenged. Based on the aerosol emission rates, if AgI aerosol was only captured by scavenging processes, and played no part in forming ice crystals and snowfall, the silver to indium ratio (Ag:In) in the analyzed snow would be 0.8.Analysis of snow samples from the target area produced frequent Ag[ratio]In ratio values in excess of 1.1. In the snowfall at the closest sampling sites to the aerosol generator the high ratios of Ag[ratio]In cannot be explained by the contact-freezing ice formation mechanism. A mechanism with a much faster rate than possible by contact freezing is necessary to produce the high Ag[ratio]In ratios that were observed. Part of the AgI seeding aerosol functioned rapidly to produce ice crystals by a forced condensation-freezing mechanism immediately after generation, and those ice crystals contributed to the snowfall at those sites closest to the generator.
From 1947 to 1994 a number of cloud-seeding experiments were done in
Australia based on the static cloud-seeding hypothesis. A critical
analysis of these successive cloud-seeding experiments, coupled with
microphysical observations of the clouds, showed that the static
cloud-seeding hypothesis is not effective in enhancing winter rainfall
in the plains area of Australia. However, there is evidence to suggest
that cloud seeding is effective for limited meteorological conditions in
stratiform clouds undergoing orographic uplift. In particular, two
successive experiments in Tasmania show strong statistical evidence for
rainfall enhancement when cloud-top temperatures are between -10°
and -12°C in a southwesterly stream. The evidence for similar
effects on the Australian mainland is more controversial. In the summer
rainfall regions of northern Australia, the extreme rainfall variability
makes it impossible to design a statistical experiment that can to be
evaluated in a reasonable time using currently available techniques.
Rainfall enhancement in these regions remains inconclusive.
Systematic procedures for constructing confidence bounds and point estimates based on rank statistics are given for the two sample location parameter, two sample scale parameter and one sample location parameter problems.
This book is intended as a guide to data analysis with the R system for statistical computing. R is an environment incorporating an implementation of the S programming language, which is powerful and flexible and has excellent graphical facilities (R Development Core Team, 2009). In the Handbook we aim to give relatively brief and straightforward descriptions of how to conduct a range of statistical analyses using R. Each chapter deals with the analysis appropriate for one or several data sets. A brief account of the relevant statistical background is included in each chapter along with appropriate references, but our prime focus is on how to use R and how to interpret results. We hope the book will provide students and researchers in many disciplines with a self-contained means of using R to analyse their data. R is an open-source project developed by dozens of volunteers for more than ten years now and is available from the Internet under the General Public Licence. R has become the lingua franca of statistical computing. Increasingly, implementations of new statistical methodology first appear as R add-on packages. In some communities
A cloud-seeding experiment was conducted in the Snowy Mountains of
Australia from 1955-1959 inclusive. The objective was to determine if
silver-iodide smoke released from an aircraft into clouds could increase
the precipitation over a selected area. The method involved a comparison
of the precipitation in a target area and that in a control area during
randomized periods of seeding and no seeding. Over the five years, the
ratio of the precipitation in the target to that in the control area was
higher in seeded than in unseeded periods. Three statistical tests are
presented which show that the seeded periods are different from the
unseeded periods at significance levels of 0.03, 0.09 and 0.03 (one
sided). This supports a positive seeding effect. Other analyses both
detract from and support this contention. The net result is that the
experiment in inconclusive. Further, improved experiments are proposed.
Some results of the first (1988) Australian Winter Storms Experiment are
described. The results shed light on precipitation-enhancement
opportunities in winter cyclonic storms interacting with the Great
Dividing Range of southeast Australia. The results come from analysis of
supercooled liquid water amounts provided by a dual-wavelength microwave
radiometer, atmospheric structure from Omegasondes, and precipitation
amounts from a large number of tipping-bucket gauges. With these data it
is possible to calculate and compare two of the terms in a
condensed-phase water budget over a cloud-seeding target area in the
Great Dividing Range. The two terms are the horizontal flux of
supercooled liquid cloud water entering the budget volume and the
vertical precipitation flux at ground level out of the volume. The
budget terms have implications for the amount of extra precipitation
that may result from seeding. It is found that the amount depends on the
frontal or postfrontal stage of activity in the target area and on the
wind direction with respect to the mountainous terrain.
A research program was initiated in 1988 to investigate the potential for winter snowpack enhancement in the Snowy Mountains of Australia. Field studies were conducted during the austral winters of 1988 and 1989 in order to characterize the water and ice composition of clouds passing over these mountains. We utilized a dual-channel microwave radiometer and a Ka-band (8.6 mm wavelength) radar in a coordinated observational program to document the distribution of supercooled liquid water, ice phase precipitation and water vapor in the winter storm systems. The vertical temperature, humidity and wind profiles were obtained at high temporal resolution from local rawinsonde launches. Ground-based sampling of the snow crystal types and the stable isotopic composition of the snow provided information on the processes of crystal formation and riming during these storms. A precipitation trajectory model was used to simulate ice crystal growth for snow which precipitates within the study region. Analysis of these data indicate buildup of supercooled liquid water during certain storm periods, while during other periods the ice crystal growth processes efficiently remove cloud liquid. Several aspects of the cloud seeding potential are discussed in this paper and the overall conclusion is that winter precipitation enhancement is scientifically feasible, based upon the World Meteorological Organization criterion of supercooled liquid water availability.
Evaluation plan for a snow enhancement experiment in Australia
- M J Manton
- J Dai
- L Warren
Manton, M. J., J. Dai, and L. Warren, 2009: Evaluation plan for
a snow enhancement experiment in Australia. J. Wea. Modif.,
41, 59-74.