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Abstract

The accurate prediction of convective cloud development in advance of thunderstorm formation (so‐called “convective initiation,” CI) is a challenging forecast problem, one in which the processing of 5–15 min interval imagery from geostationary satellites (e.g., Meteosat Second Generation) offers considerable promise. A present drawback to using sequences of visible or infrared (IR) satellite images to monitor growing cumulus clouds is that higher altitude cirrus clouds often obscure the view of the low‐level cumulus in the pre‐convective environment. In particular, cirrus anvils from pre‐existing convection, and cirrus caused by deep layer quasi‐geostrophic ascent, are very common in pre‐CI environments. Cloud derived parameters from GOES are used here to demonstrate how quantities like visible optical depth (τ), emittance, liquid water path, and effective particle size can be used to quantify cumulus cloud growth in advance of CI. Time rates of change of these derived quantities, as well as IR interest fields that describe cumulus cloud development rates beneath cirrus, are analyzed as τ of the cirrus are binned from 1 to > 50. Results confirm that if cirrus possess τ < 20, up to > 90% of the “signal” in the IR interest field remains, compared to clear‐sky values, and it is proposed that CI can still be adequately nowcasted using IR channel data similar to what is done in the absence of cirrus. Similarly, cloud derived parameters become valuable as their time rates of change measure cumulus cloud growth beneath the higher clouds. In contrast, once τ values increase beyond ∼ 20, cumulus cloud growth signals decrease significantly through cirrus, and as τ becomes > 40, little information from the cumulus remains.

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... Operational radars such as the WSR-88D are not sensitive to the small cloud hydrometeors associated with nonprecipitating clouds and thus produce little in the way of useful data that can be assimilated into an NWP model under these conditions. Forecasting the time and location of convection initiation has proven to be a significant challenge (Kain et al. 2013), and determining a way to assimilate information relating to convection initiation is receiving greater interest in the research community (e.g., Mecikalski et al. 2013). ...
Article
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Assimilating high-resolution radar reflectivity and radial velocity into convection-permitting numerical weather prediction models has proven to be an important tool for improving forecast skill of convection. The use of satellite data for the application is much less well understood, only recently receiving significant attention. Since both radar and satellite data provide independent information, combing these two sources of data in a robust manner potentially represents the future of high-resolution data assimilation. This research combines Geostationary Operational Environmental Satellite 13 (GOES-13) cloud water path (CWP) retrievals with Weather Surveillance Radar-1988 Doppler (WSR-88D) reflectivity and radial velocity to examine the impacts of assimilating each for a severe weather event occurring in Oklahoma on 24 May 2011. Data are assimilated into a 3-km model using an ensemble adjustment Kalman filter approach with 36 members over a 2-h assimilation window between 1800 and 2000 UTC. Forecasts are then generated for 90 min at 5-min intervals starting at 1930 and 2000 UTC. Results show that both satellite and radar data are able to initiate convection, but that assimilating both spins up a storm much faster. Assimilating CWP also performs well at suppressing spurious precipitation and cloud cover in the model as well as capturing the anvil characteristics of developed storms. Radar data are most effective at resolving the 3D characteristics of the core convection. Assimilating both satellite and radar data generally resulted in the best model analysis and most skillful forecast for this event.
... Downwelling irradiance measurements from the pyranometer ranged from predominantly collimated direct illumination (SUN: 825.4 W/m 2 total irradiance of which 76% was direct), to predominantly diffuse skylight and very low direct irradiance (DIFF: 214.6 and 2.5 W/ m 2 , respectively). Cloud Optical Depth (τ) and Cloud Particle Size (CPS) (GOES-R Algorithm Working Group and GOES-R Program Office, 2018; Mecikalski et al., 2013;Walther et al., 2013) information from GOES-17 for the duration of the HSI acquisition corroborate, as expected, that for SUN τ was low (1.5-1.8) indicating optically thin clouds (Supplementary Video 4) with a CPS of 36.6-39.7 μm. ...
Article
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The recent development of small form-factor (<6 kg), full range (400–2500 nm) pushbroom hyperspectral imaging systems (HSI) for unmanned aerial vehicles (UAV) poses a new range of opportunities for passive remote sensing applications. The flexible deployment of these UAV-HSI systems have the potential to expand the data acquisition window to acceptable (though non-ideal) atmospheric conditions. This is an important consideration for time-sensitive applications (e.g. phenology) in areas with persistent cloud cover. Since the majority of UAV studies have focused on applications with ideal illumination conditions (e.g. minimal or non-cloud cover), little is known to what extent UAV-HSI data are affected by changes in illumination conditions due to variable cloud cover. In this study, we acquired UAV pushbroom HSI (400–2500 nm) over three consecutive days with various illumination conditions (i.e. cloud cover), which were complemented with downwelling irradiance data to characterize illumination conditions and in-situ and laboratory reference panel measurements across a range of reflectivity (i.e. 2%, 10%, 18% and 50%) used to evaluate reflectance products. Using these data we address four fundamental aspects for UAV-HSI acquired under various conditions ranging from high (624.6 ± 16.63 W·m²) to low (2.5 ± 0.9 W·m²) direct irradiance: atmospheric compensation, signal-to-noise ratio (SNR), spectral vegetation indices and endmembers extraction. For instance, two atmospheric compensation methods were applied, a radiative transfer model suitable for high direct irradiance, and an Empirical Line Model (ELM) for diffuse irradiance conditions. SNR results for two distinctive vegetation classes (i.e. tree canopy vs herbaceous vegetation) reveal wavelength dependent attenuation by cloud cover, with higher SNR under high direct irradiance for canopy vegetation. Spectral vegetation index (SVIs) results revealed high variability and index dependent effects. For example, NDVI had significant differences (p < 0.05) across illumination conditions, while NDWI appeared insensitive at the canopy level. Finally, often neglected diffuse illumination conditions may be beneficial for revealing spectral features in vegetation that are obscured by the predominantly non-Lambertian reflectance encountered under high direct illumination. To our knowledge, our study is the first to use a full range pushbroom UAV sensor (400–2500 nm) for assessing illumination effects on the aforementioned variables. Our findings pave the way for understanding the advantages and limitations of ultra-high spatial resolution full range high fidelity UAV-HSI for ecological and other applications.
... Clouds must be adequately analysed, since they affect the model's energy balance and indicate locations of possible convective initiation (Mecikalski et al., 2013). By applying forward operators to model state, clouds can be easily examined based on the comparison to observations. ...
Preprint
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Satellite observations provide a wealth of information on atmospheric clouds and cover almost every region of the globe with high spatial resolution. The measured radiances constitute a valuable data set for evaluating and improving clouds and radiation representation in climate and numerical weather prediction (NWP) models. An accurate, bias-free representation of clouds and radiation is crucial for data assimilation and the increasingly important solar photovoltaic (PV) power production prediction. The present study demonstrates that visible (VIS) and infrared (IR) Meteosat SEVIRI observations contain valuable and complementary cloud information for these purposes. We analyse systematic deviations between satellite observations and convection-permitting, semi-free ICON-D2 hindcast simulations for a 30-day period with strong convection. Both visible and infrared satellite observations reveal significant deviations between the observations and model equivalents. The combination of infrared brightness temperature and visible solar reflectance allowed to attribute individual deviations to specific model shortcomings. Furthermore, we investigate the sensitivity of model-derived VIS and IR observation equivalents to modified model and visible forward operator settings to identify dominant error sources. The results reveal that model assumptions on subgrid-scale water clouds are the primary source of systematic deviations in the visible spectrum. Visible observations are, therefore, well-suited to advance this essential model assumption. The visible forward operator uncertainty is lower than uncertainties introduced by model parameter assumptions by one order of magnitude. In contrast, infrared satellite observations are very sensitive to ice cloud model assumptions. Finally, we show a strong negative correlation between VIS solar reflectance and global horizontal irradiance. This implies that improvements in VIS satellite reflectance prediction will coincide with improvements in the prediction of surface irradiation and PV power production.
... By 2012 the GOES-R CI algorithm routinely used 15 National Oceanic and Atmospheric Administration (NOAA) Rapid Update model and 10 GOES satellite fields in a logistic regression model, forming a 0-100% probability of CI per each convective cloud object [Walker et al., 2012;Mecikalski et al., 2015], and is therefore a day-night multisensor approach. Presently, the algorithm uses Algorithm Working Group Cloud Height Algorithm satellite-derived cloud parameters [Heidinger and Pavolonis, 2009;Walther et al., 2011;Walther and Heidinger, 2012] to detect CI beneath cirrus [Mecikalski et al., 2013] and enhance the nighttime detection of growing cumulus clouds [Mecikalski et al., 2011]. ...
Article
A study was conducted to gain insights into the use of geostationary satellite-based indicators for characterizing and identifying growing cumulus clouds that evolve into severe weather producing convective storms. Eleven convective initiation (CI), 41 cloud top temperature–effective radius (T-re), and 9 additional fields were formed for 340 growing cumulus clouds that were manually tracked for 2 h and checked for association with severe weather to 2–3 h into the future. The geostationary satellite data were at 5 min resolution from Meteosat-8 on six convectively active days in 2010, 2012, and 2013. The study's goals were to determine which satellite fields are useful to forecasting severe storms and to form a simple model for predicting future storm intensity. The CI fields were applied on 3 × 3 pixel regions, and the T-re fields were analyzed on 9 × 9 and 51 × 51 pixel domains (needed when forming T-re vertical profiles). Of the 340 growing cumulus clouds examined, 34 were later associated with severe weather (using European Severe Weather Database reports), with the remaining being nonsevere storms. Using a multivariate analysis, transforming predictors into their empirical posterior probability, and maximizing the Peirce skill score, the best predictors were T1451 (51 × 51 pixel T, where re exceeds 14 µm), TG9 (9 × 9 pixel glaciation T surrounding a growing cloud), and ReBRTG51 (51 × 51 pixel re at the breakpoint T in the T-re profile). Rapid cloud growth prior to severe storm formation leads to delayed particle growth, colder temperatures of the first 14 µm particles, and lower TG values.
... One important disadvantage of radar data assimilation is that it does not capture the nonprecipitation phase of cloud development during convective initiation nor does it provide much information from the nearstorm environment. Forecasting convection initiation has proven to be challenging (Kain et al. 2013) and determining a way to assimilate information relating to convection initiation is receiving greater interest in the research community (e.g., Mecikalski et al. 2013). As a result, assimilating high-resolution satellite observations has also recently received a high degree of attention (Vukicevic et al. 2004(Vukicevic et al. , 2006Otkin 2010;Polkinghorne et al. 2010;Pincus et al. 2011;Polkinghorne and Vukicevic 2011;Zupanski et al. 2011;Jones et al. 2013bJones et al. , 2015Zhang et al. 2013;Kerr et al. 2015). ...
Article
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This research represents the second part of a two-part series describing the development of a prototype ensemble data assimilation system for the Warn-on-Forecast (WoF) project known as the NSSL Experimental WoF System for ensembles (NEWS-e). Part I describes the NEWS-e design and results from radar reflectivity and radial velocity data assimilation for six severe weather events occurring during 2013 and 2014. Part II describes the impact of assimilating satellite liquid and ice water path (LWP and IWP, respectively) retrievals from the GOES Imager along with the radar observations. Assimilating LWP and IWP observations may improve thermodynamic conditions at the surface over the storm-scale domain through better analysis of cloud coverage in the model compared to radar-only experiments. These improvements sometimes corresponded to an improved analysis of supercell storms leading to better forecasts of low-level vorticity. This positive impact was most evident for events where convection is not ongoing at the beginning of the radar and satellite data assimilation period. For more complex cases containing significant amounts of ongoing convection, only assimilating clear-sky satellite retrievals in place of clear-air reflectivity resulted in spurious regions of light precipitation compared to the radar-only experiments. The analyzed tornadic storms in these experiments are sometimes too weak and quickly diminished in intensity in the forecasts. The lessons learned as part of these experiments should lead to improved iterations of the NEWS-e system, building on the modestly successful results described here.
... Further investigations based on the type of the clouds might be needed to fine tune the relative humidity mask (Molders et al. 1995;Key et al. 2004). Figure 8 shows a comparison chart of different land/ cloud cover classes, where the proposed algorithm (y-axis) clearly outperforms the selected reference independently of whether they were represented in the training set or not; and the proposed algorithm mainly provides validation values over 0.8 while the reference algorithm provides validation values between 0 and 1 (Gallaudet and Simpson 1991;Mecikalski et al. 2013). ...
Article
Remote sensing applications in water resources management are becoming an essential asset in all different levels of integrated water rational use. Due to remote sensing data availability and different acquisition sensors of satellite images, a wide variability of benchmarks could be conducted under the same theme. Rainwater harvesting is the branch of science where the rainwater is the main target to improve groundwater recharge, stratocumulus clouds are the main source of rain in arid regions. Cloud detection using remote sensing techniques proved to be efficient recently but the general uses of different cloud detection techniques are to precisely omit clouds from satellite images. The use of cloud detection scheme described herein is designed for the MERIS Level1B data; therefore, total set of 60 MERIS images was collected on monthly basis for 5 years started from January 2008. The use of the cloud detection algorithm is to find proper land cover suitable for rainwater harvesting mostly covered with cloud all over the year. Evaluation of land use for rainwater harvesting in terms of groundwater recharge is considered, several factors were taken into consideration and NDWI is one of the most important factors involved. Results pointed out that some regions in southern Saudi Arabia are qualified enough to be considered as potential sites for better rainwater harvesting.
... Further investigations based on the type of the clouds might be needed to fine tune the relative humidity mask (Molders et al. 1995;Key et al. 2004). Figure 8 shows a comparison chart of different land/ cloud cover classes, where the proposed algorithm (y-axis) clearly outperforms the selected reference independently of whether they were represented in the training set or not; and the proposed algorithm mainly provides validation values over 0.8 while the reference algorithm provides validation values between 0 and 1 (Gallaudet and Simpson 1991;Mecikalski et al. 2013). ...
Article
Remote sensing technology have showed robust capacities in meeting challenges of water resource management, in the countries like kingdom of Saudi Arabia where rapid population growth is imposing stress on scarce water resources. In addition, continual Earth observations from space are important to manage water resources for the benefit of humankind and the environment, as well provide important forecasting services to prevent water-related disasters such as floods and droughts. Remote sensing approaches to assess and manage of water resources are important especially in the region of Saudi Arabian because no satisfactory hydrological networks exist. Cloud detection is important issue in extracting information of geophysical, geomorphological and meteorological interest from remotely sensed images. Present work aimed at imposing a new method for cloud detecting and producing cloud probability mapping of multispectral images acquired using MERIS images. The algorithm was implemented on 59 satellite imageries collected from January 2006 to October 2011.
... Also, some surface types like snow and ice have spectral properties that are similar to some of the cloud properties. Therefore simple thresholding algorithms often fail, and existing cloud detection schemes use several different cascaded threshold based tests to account for the complexity [33][34][35]. ...
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The amount of water on earth is the same and only the distribution and the reallocation of water forms are altered in both time and space. To improve the rainwater harvesting a better understanding of the hydrological cycle is mandatory. Clouds are major component of the hydrological cycle; therefore, clouds distribution is the keystone of better rainwater harvesting. Remote sensing technology has shown robust capabilities in resolving challenges of water resource management in arid environments. Soil moisture content and cloud average distribution are essential remote sensing applications in extracting information of geophysical, geomorphological, and meteorological interest from satellite images. Current research study aimed to map the soil moisture content using recent Landsat 8 images and to map cloud average distribution of the corresponding area using 59 MERIS satellite imageries collected from January 2006 to October 2011. Cloud average distribution map shows specific location in the study area where it is always cloudy all the year and the site corresponding soil moisture content map came in agreement with cloud distribution. The overlay of the two previously mentioned maps over the geological map of the study area shows potential locations for better rainwater harvesting.
... One disadvantage of radar data assimilation is that precipitation radars are not very sensitive to nonprecipitating clouds. Yet it is important that nonprecipitating clouds be properly analyzed, since they can affect energy balances within the model and also indicate the locations of possible convective initiation (e.g., Mecikalski et al. 2013). Remote sensing observations from satellites provide information on cloud properties on similar horizontal and temporal scales as radar data, but with greater sensitivity to the non-or preconvective clouds that may be present. ...
Article
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Assimilating satellite-retrieved cloud properties into storm-scale models has received limited attention despite its potential to provide a wide array of information to a model analysis. Available retrievals include cloud water path (CWP), which represents the amount of cloud water and cloud ice present in an integrated column, and cloud-top and cloud-base pressures, which represent the top and bottom pressure levels of the cloud layers, respectively. These interrelated data are assimilated into an Advanced Research Weather Research and Forecasting Model (ARW-WRF) 40-member ensemble with 3-km grid spacing using the Data Assimilation Research Testbed (DART) ensemble Kalman filter. A new CWP forward operator combines the satellite-derived cloud information with similar variables generated by WRF. This approach is tested using a severe weather event on 10 May 2010. One experiment only assimilates conventional (CONV) observations, while the second assimilates the identical conventional observations and the satellite-derived CWP (PATH). Comparison of the CWP observations at 2045 UTC to CONV and PATH analyses shows that PATH has an improved representation of both the magnitude and spatial orientation of CWP compared to CONV. Assimilating CWP acts both to suppress convection in the model where none is present in satellite data and to encourage convection where it is observed. Oklahoma Mesonet observations of downward shortwave flux at 2100 UTC indicate that PATH reduces the root-mean-square difference errors in downward shortwave flux by 75 W m−2 compared to CONV. Reduction in model error is generally maximized during the initial 30-min forecast period with the impact of CWP observations decreasing for longer forecast times.
... Further investigations based on the type of the clouds might be needed to fine tune the relative humidity mask (Molders et al. 1995;Key et al. 2004). Figure 8 shows a comparison chart of different land/ cloud cover classes, where the proposed algorithm (y-axis) clearly outperforms the selected reference independently of whether they were represented in the training set or not; and the proposed algorithm mainly provides validation values over 0.8 while the reference algorithm provides validation values between 0 and 1 (Gallaudet and Simpson 1991;Mecikalski et al. 2013). ...
Conference Paper
Full-text available
Remote sensing applications in water resources management are becoming an essential asset in all different levels of integrated water rational use. Due to remote sensing data availability and different acquisition sensors of satellite images, a wide variability of benchmarks could be conducted under the same theme. Rainwater harvesting is the branch of science where the rainwater is the main target to improve groundwater recharge, stratocumulus clouds are the main source of rain in arid regions. Cloud detection using remote sensing techniques proved to be efficient recently but the general uses of different cloud detection techniques are to precisely omit clouds from satellite images. The use of cloud detection scheme described herein is designed for the MERIS Level1B data, therefore total set of 60 MERIS images were collected on monthly basis for five years started from January 2008. The use of the cloud detection algorithm is to find proper land cover suitable for rainwater harvesting mostly covered with cloud all over the year. Evaluation of land use for rainwater harvesting in term of groundwater recharge is considered, several factors were taken into consideration and NDWI is one of the most important factors involved. Results pointed out that some regions in southern Saudi Arabia are qualified enough to be considered as potential sites for better rainwater harvesting.
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Remote sensing technology have showed robust capacities in meeting challenges of water resource management, in the countries like kingdom of Saudi Arabia where rapid population growth is imposing stress on scarce water resources. Also, continual Earth observations from space are important to manage water resources for the benefit of humankind and the environment, as well provide important forecasting services to prevent water related disasters such as floods and droughts. Remote sensing approaches to assess and manage of water resources are important especially in the region of Saudi Arabian because no satisfactory hydrological networks exist. Cloud detection is important issue in extracting information of geophysical, geomorphological and meteorological interest from remotely sensed images. Present work aimed at imposing a new method for cloud detecting and producing cloud probability mapping of multispectral images acquired using MERIS images. The algorithm was implemented on 59 satellite imageries collected from January 2006 to October 2011.
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Cloud properties were retrieved by applying the Clouds and Earth's Radiant Energy System (CERES) project Edition-2 algorithms to 3.5 years of Tropical Rainfall Measuring Mission Visible and Infrared Scanner data and 5.5 and 8 years of MODerate Resolution Imaging Spectroradiometer (MODIS) data from Aqua and Terra, respectively. The cloud products are consistent quantitatively from all three imagers; the greatest dis- crepancies occur over ice-covered surfaces. The retrieved cloud cover (∼59%) is divided equally between liquid and ice clouds. Global mean cloud effective heights, optical depth, effective parti- cle sizes, and water paths are 2.5 km, 9.9, 12.9 μm, and 80 g · m −2 , respectively, for liquid clouds and 8.3 km, 12.7, 52.2 μm, and 230 g · m −2 for ice clouds. Cloud droplet effective radius is greater over ocean than land and has a pronounced seasonal cycle over southern oceans. Comparisons with independent measurements from surface sites, the Ice Cloud and Land Elevation Satellite, and the Aqua Advanced Microwave Scanning Radiometer-Earth Observing System are used to evaluate the results. The mean CERES and MODIS Atmosphere Science Team cloud properties have many similarities but exhibit large discrepancies in certain parameters due to differences in the algorithms and the number of unretrieved cloud pixels. Problem areas in the CERES algorithms are identified and discussed. Index Terms—Climate, cloud, cloud remote sensing, Clouds and the Earth's Radiant Energy System (CERES), Moderate Resolu- tion Imaging Spectroradiometer (MODIS), Visible and Infrared Scanner (VIRS).
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High quality weather forecasts are essential to minimizing delays in the National Airspace System. The goal of the Collaborative Storm Prediction System for Aviation (CoSPA) is to provide high resolution, rapidly-updating storm forecasts for air traffic management out to 8 hours. To achieve this goal, CoSPA will optimally blend heuristics and numerical weather prediction models into a unified set of aviation-specific storm forecast products with the best overall performance possible. Convective initiation remains a significant forecasting challenge in CoSPA. Satellite data can provide valuable information to aid forecasting of storm formation, particularly in the early portions of the forecast. This paper will present two techniques that address the use of satellite data to initiate convection in CoSPA. The first technique uses visible satellite and radar data to initiate convection in convective lines. The second technique uses infrared convective initiation interest fields from the SATellite Convection AnalySis and Tracking system to initiate convection in situations with little or no pre-existing radar precipitation.
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This study is a companion research effort to "Part I," which emphasized use of infrared data for understanding various aspects of growing convective clouds in the Meteosat Second Generation (MSG) satellite's Spinning Enhanced Visible and Infrared Imager (SEVIRI) imagery. Reflectance and derived brightness variability (BV) fields from MSG SEVIRI are used here to understand relationships between cloud-top signatures and physical processes for growing cumulus clouds prior to known convective initiation (CI) events, or the first occurrence of a ≥35-dBZ echo from a new convective cloud. This study uses daytime SEVIRI visible (VIS) and near-infrared (NIR) reflectances from 0.6 to 3.9 μm (3-km sampling distance), as well as high-resolution visible (1-km sampling distance) fields. Data from 123 CI events observed during the 2007 Convection and Orographically Induced Precipitation Study (COPS) field experiment conducted over southern Germany and northeastern France are processed, per convective cell, so to meet this study's objectives. These data are those used in Part I. A total of 27 VIS-NIR and BV "interest fields" are initially assessed for growing cumulus clouds, with correlation and principal component analyses used to highlight the fields that contain the most unique information for describing principally cloud-top glaciation, as well as the presence of vigorous updrafts. Time changes in 1.6- and 3.9-μm reflectances, as well as BV in advance of CI, are shown to contain the most unique information related to the formation and increase in size of ice hydrometeors. Several methods are proposed on how results from this analysis may be used to monitor growing convective clouds per MSG pixel or per cumulus cloud "object" over 1-h time frames.
Article
Global analyses of satellite spectral observations indicate the existence of negative brightness temperature differences between 11 and 6.7 µm (BT11 BT6.7) when cold scenes are viewed. Differences are typically greater than 5 K for the Tropics and midlatitudes but can be smaller than 15 K over high-altitude polar regions during winter. In July, more than 60% of the observations over the Antarctic Plateau had BT11 BT6.7 < 5 K. In January, over Greenland, the frequency of occurrence is approximately 20%.Three factors are investigated that may contribute to these observed negative brightness temperature differences: 1) calibration errors, 2) nonuniform scenes within the field of view, and 3) physical properties of the observed phenomena. Calibration errors and nonuniform scenes may generate values of BT11 BT6.7 that are less than zero; however, these differences are on the order of 2 K and, therefore, cannot fully explain the observations.A doubling and adding radiative transfer model is used to investigate the physical explanations of the negative differences. Simulations of satellite spectral observations for thick clouds produce negative differences that are comparable to those observed in the Tropics and midlatitudes. The magnitude of the differences is a function of cloud microphysics, cloud-top pressure, view angle, and the cloud optical thickness. The model simulations are also capable of producing large negative differences over high-altitude polar regions.Distinguishing clear and cloudy regions from satellite infrared radiances is a challenging problem in polar winter conditions. Brightness temperature differences between 11 and 6.7 µm provide a technique to separate cold, optically thick clouds from clear-sky conditions when strong radiation inversions exist at the surface. The presence of a cloud inhibits the development of this inversion and shields its detection using satellite radiance measurements. While physically reasonable, and in agreement with radiative transfer calculations, this technique has not been verified with ground nor with aircraft observations. Further evidence that the large negative values of BT11 BT6.7 are associated with surface inversions is presented by comparing the satellite observations with surface temperature measurements from an Antarctica automated weather station.
Article
This study analyzes two of the many convergent windshift lines that occurred during the Convection Initiation Project in eastern Colorado during the summer of 1984. The coincidence of these boundaries with the initiation of convection is examined by means of Doppler radar observations, surface mesonet data, and chase team verifications and measurements. The surface mesonet data and chase team measurements in both cases presented verify that a sensitive Doppler radar can effectively detect windshift lines. It is also shown that in both cases the convergent windshift lines were directly associated with thunderstorm development and were likely a primary forcing mechanism. -Author
Article
We describe a family of inversion methods to infer the optical depth, τ, of warm clouds from surface measurements of spectral irradiance. Our most complex retrieval also uses the total liquid water path measured by a microwave radiometer to obtain the effective radius, re, of the cloud droplets. We apply these retrievals to data from the Atmospheric Radiation Measurement (ARM) Program, and compare our results to those produced by the GOES satellite for episodes where total overcast was observed. Our surface-based estimates of τ agree with those from GOES when the optical depths are
Article
where L is the number of sferics observed per minute, r the distance of the storm (km), and A the area (km2) of the storm region as specified above. This study supports the findings of Larsen and Stansbury for an earlier day (J. Atmos. Terr. Phys., 1974, 36, 1547-1553) and adds the algebraic relation.
Article
A new technique for simultaneously retrieving cloud optical depth and effective radius has been proposed. This approach is based on the angular distribution of scattered light in the forward scattering lobe of cloud drops. The angular distributions can be observed by multiple shadowband scans. Radiative transfer modeling simulations demonstrate that accuracies for cloud optical depth, effective radius, and liquid water path are 2%, 10%, and 2 gm-2, respectively, for given possible instrument noise and uncertainties. Further, we have tested different measurement strategies and achieved consistent accuracies. This technique will provide an approach to deal with the issue of ``CLOWD (cloud with low optical (water) depth).''
Article
The origin of a severe local storm is traced back to a cluster of three shower cells, each of which produced a fist radar echo close to the 30C level. This level is much higher than that associated with the majority of convective clouds studied by other workers. The great height of the first echoes is attributed to the presence of strong updrafts which carry the cloud particles to high levels in the time taken for them to grow to radar detectable sizes.Because of their low temperatures, the first echoes were probably due to ice particles. Echo intensification in each cell was fairly rapid during the minute or so after first detection and corresponded to the growth of these particles by gravitational accretion in the presence of a liquid water concentration equal to about half the adiabatic value.In each cell the reflectivity core due to the growing particles was balanced by the updraft at a constant level for a 5-min period following first detection, after which it descended to the ground with very little further growth. The low reflectivity of the echo core in relation to its rate of descent is interpreted as being due mainly to a smaller-than-usual particle concentration.
Article
This study develops an understanding on how retrieved cloud parameter fields from the Optimal Cloud Analysis (OCA) algorithm, operating on Meteosat Second Generation (MSG), Spinning Enhanced Visible and Infrared Imager (SEVIRI) data, behave at 5-min time resolutions for growing cumulus clouds. Fields retrieved by the OCA algorithm include cloud optical thickness (τ), cloud-top particle effective radius (re), cloud-top pressure (pc), and cloud-top phase. OCA is based on a one-dimensional optimal estimation methodology, and a measure of radiance fit, the cost function (Jm), is a quantity developed as part of the retrieval process and is shown to be useful in delineating mixed phase clouds; it too is evaluated (at 5-min intervals) for the information it provides.
Article
This study surveys the optical and microphysical properties of high (ice) clouds over the Tropics (30°S– 30°N) over a 3-yr period from September 2002 through August 2005. The analyses are based on the gridded level-3 cloud products derived from the measurements acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments aboard both the NASA Earth Observing System Terra and Aqua platforms. The present analysis is based on the MODIS collection-4 data products. The cloud products provide daily, weekly, and monthly mean cloud fraction, cloud optical thickness, cloud effective radius, cloud-top temperature, cloud-top pressure, and cloud effective emissivity, which is defined as the product of cloud emittance and cloud fraction. This study is focused on high-level ice clouds. The MODIS-derived high clouds are classified as cirriform and deep convective clouds using the International Satellite Cloud Climatology Project (ISCCP) classification scheme. Cirriform clouds make up more than 80% of the total high clouds, whereas deep convective clouds account for less than 20% of the total high clouds. High clouds are prevalent over the intertropical convergence zone (ITCZ), the South Pacific convergence zone (SPCZ), tropical Africa, the Indian Ocean, tropical America, and South America. Moreover, land–ocean, morning– afternoon, and summer–winter variations of high cloud properties are also observed.
Article
We use a Lagrangian microphysical aerosol-cloud model to simulate cirrus clouds along trajectories at northern hemisphere midlatitudes. The model is constrained by recent in situ observations in terms of aerosol size distributions, freezing relative humidities, cooling rates, and cirrus particle sedimentation rates. Key features include competition between insoluble and volatile aerosol particles and temperature perturbations induced by high-frequency gravity waves. Recent analyses of field measurements have revealed the crucial roles both factors play in cirrus formation. We show that most cirrus form in synoptic cold pools, but with microphysical properties determined by mesoscale variability in vertical velocities. Heterogeneous ice nuclei (IN) present in concentrations probably typical for northern midlatitude background conditions (
Article
Simultaneous observations of deep convective clouds in the infrared window (IR: 10.5 – 12.5 μm) and the water vapour absorption band (WV: 5.7 – 7.1 μm) from METEOSAT reveal that the equivalent brightness temperature in the WV channel can be larger than in the IR channel by as much as 6 – 8 K. Calibration errors, cloud microphysics and the effect of the Planck function over horizontally inhomogeneous areas cannot explain the observation. Simulations with a line-by-line radiative transfer model show that the larger brightness temperatures in the WV absorption band are due to stratospheric water vapour, which absorbs radiation from the cold cloud top and emits radiation at higher stratospheric temperatures. The brightness temperature difference depends on the amount of water vapour and on the temperature lapse rate in the stratosphere. The temperature difference is largest when the cloud top is at the tropopause temperature inversion. The tropopause temperature in regions of deep convective clouds can be estimated from the brightness temperature differences observed in consecutive images from geostationary satellites. The method also has the potential for monitoring areas of troposphere-stratosphere exchange.
Article
The Clouds and the Earth's Radiant Energy System (CERES) is part of NASA's Earth Observing System (EOS), CERES objectives include the following. (1) For climate change analysis, provide a continuation of the Earth Radiation Budget Experiment (ERBE) record of radiative fluxes at the top-of-the-atmosphere (TOA), analyzed using the same techniques as the existing ERBE data. (2) Double the accuracy of estimates of radiative fluxes at TOA and the Earth's surface. (3) Provide the first long-term global estimates of the radiative fluxes within the Earth's atmosphere. (4) Provide cloud property estimates collocated in space and time that are consistent with the radiative fluxes from surface to TOA. In order to accomplish these goals, CERES uses data from a combination of spaceborne instruments: CERES scanners, which are an improved version of the ERBE broadband radiometers, and collocated cloud spectral imager data on the same spacecraft. The CERES cloud and radiative flux data products should prove extremely useful in advancing the understanding of cloud-radiation interactions, particularly cloud feedback effects on the Earth's radiation balance. For this reason, the CERES data should be fundamental to the ability to understand, detect, and predict global climate change. CERES results should also be very useful for studying regional climate changes associated with deforestation, desertification, anthropogenic aerosols, and ENSO events. This overview summarizes the Release 3 version of the planned CERES data products and data analysis algorithms. These algorithms are a prototype for the system that will produce the scientific data required for studying the role of clouds and radiation in the Earth's climate system
Article
Vertically pointing Doppler radar has been used to study the evolution of ice particles as they sediment through a cirrus cloud. The measured Doppler fall speeds, together with radar-derived estimates for the altitude of cloud top, are used to estimate a characteristic fall time tc for the `average' ice particle. The change in radar reflectivity Z is studied as a function of tc, and is found to increase exponentially with fall time. We use the idea of dynamically scaling particle size distributions to show that this behaviour implies exponential growth of the average particle size, and argue that this exponential growth is a signature of ice crystal aggregation.
Convective cloud detection in satellite imagery using standard deviation limited adaptive clustering Initiation of precipitation in vigorous convective clouds
  • T A Berendes
  • J R Mecikalski
  • W M Mackenzie
  • K M Bedka
  • U S Nair
Berendes, T.A., Mecikalski, J.R., Mackenzie, W.M., Bedka, K.M., Nair, U.S., 2008. Convective cloud detection in satellite imagery using standard deviation limited adaptive clustering. J. Geophys. Res. 113, 20207. http:// dx.doi.org/10.1029/2008JD010287. Browning, K.A., Atlas, D., 1965. Initiation of precipitation in vigorous convective clouds. J. Atmos. Sci. 22, 678–683.
  • J R Mecikalski
J.R. Mecikalski et al. / Atmospheric Research 120–121 (2013) 192–201
Cloud-top properties of growing cumulus prior to convective initiation as measured by Meteosat Second Generation. Part 1. Use of visible reflectance
  • J R Mecikalski
  • W M Mackenzie
  • M König
  • S Muller
Mecikalski, J.R., MacKenzie, W.M., König, M., Muller, S., 2010. Cloud-top properties of growing cumulus prior to convective initiation as measured by Meteosat Second Generation. Part 1. Use of visible reflectance. J. Appl. Meteorol. Climatol. 49, 2544–2558.