Journal of Applied Meteorology and Climatology

Northeast China experiences significant rainfall events driven by the Northeast China Cold Vortex (NCCV) during the warm season. However, the microphysical processes associated with this precipitation require further investigation. This study aims to fill this gap by comparing the microphysical characteristics of local stratiform and convective rain under the influence of the NCCV (NCCV precipitation or rain), and by exploring the underlying meteorological mechanisms. The results indicate that both stratiform and convective NCCV precipitation in Northeast China are characterized by sparser raindrops compared to similar rainfall types in other regions of China. In addition, convective NCCV rain consists of much larger raindrops than any other types of rainfall across China, leading to rain rates over 10 times greater than those of stratiform NCCV precipitation. The accelerated generation and growth of raindrops reinforce both stratiform and convective NCCV precipitation, whereas convective NCCV rain exhibits a more complex intensifying mechanism. If the rain rate (R) ≤ 9 mm h ⁻¹ , raindrops are sparse but large. With the increase in precipitation intensity, raindrops become denser but also collide and break up into smaller particles. When R > 16 mm h ⁻¹ , dense and large raindrops prevail, deriving from the melting of numerous large solid hydrometeors formed through deep convection. The convection is stimulated by higher mid-to-low-level temperatures and greater convective available potential energy, causing clouds to ascend to higher altitudes with faster upward movement, thereby fostering the formation of larger raindrops. Additionally, convective NCCV precipitation is usually enhanced by divergence induced by upper-level jet streams.

Cloud seeding of wintertime orographic clouds in the western United States has been attempted to enhance snow production and snowpack. Due to the scarcity of long-term, high-resolution cloud and precipitation observations over complex terrain, few studies have explored variations in orographic snowfall amounts by comparing environmental conditions and cloud characteristics with surface snowfall distribution and quantity. This study analyzes the environmental conditions and cloud characteristics in relation to surface snowfall patterns for the 24 snowfall events observed during the 2017 Seeded and Natural Orographic Wintertime Clouds: The Idaho Experiment (SNOWIE). The investigation aims to understand: 1) What is the influence, if any, of wind, turbulence, and updraft strength on snowfall amounts, rates, and distribution? 2) What is the relationship, if any, of cloud properties and precipitation-forming effectiveness? and 3) Can cloud seeding modify controlling cloud characteristics sufficiently to increase precipitation in otherwise inefficient orographic clouds? The analysis over a 7200-km² observational domain revealed that the accumulated liquid-equivalent snowfall was <0.9 × 10⁷ m³ and snowfall rates were <0.45 mm h⁻¹ for about half of the events. Low snowfall events were characterized by cloud-top temperatures >−20°C, fewer larger droplets, higher liquid water content, and lower ice water content compared to the other events. Cases with minimal background natural snowfall also permitted radar observation of seeding lines. In these cases, cloud seeding was mainly responsible for snowfall. The amount of silver iodide (AgI) released during cloud seeding did not correlate well with snowfall amount and rate. Significance Statement This study illustrates the complexities of estimating snowfall in wintertime orographic clouds, underscoring the frequent inefficiency of these clouds in generating snowfall—a pivotal concern for regions dependent on snowpack for water resources. By analyzing environmental and cloud characteristics against snowfall patterns during the Seeded and Natural Orographic Wintertime Clouds: The Idaho Experiment (SNOWIE), the research provides critical insights into the complexities of precipitation formation. The findings, particularly on the impact of cloud seeding in enhancing snowfall under specific conditions, contribute significantly to our understanding of weather modification techniques. This research not only is vital for advancing scientific knowledge in understanding wintertime mountain cloud systems but also holds profound implications for water resource management, agriculture, and disaster preparedness in snow-dependent regions.

Air quality, a pivotal global concern, has complex relationships with meteorological factors. This study focuses on daily AQI and meteorological data from 11 prefecture-level cities in Hebei Province spanning December 2, 2013, to October 12, 2023. By employing a novel complex network construction approach, this study explores the intricate network relations between single meteorological factors and AQI, relations among meteorological factors, as well as relations between the entire meteorological factors. The findings reveal that: (1) there are significant spatiotemporal and seasonal variations in network topology; (2) the node strength in the network follows a power-law distribution, indicating critical nodes with substantial impacts on air quality and long-term stability in meteorological impacts on AQI; (3) the transition pattern in the network exhibits the characteristics of block-like pattern. This study offers new insights for AQI prediction and environmental governance.

Nine urban areas in China are examined using a peri-urban thermal variability metric. Of the nine, four showed some evidence of a peri-urban climate that is linked to the location of the climate station within the urban area. A series of metrics that exploit the subtle variation of temperature variability experienced day to day are used to identify the climate station urbanization characteristics from 1951 to 2023. Metrics that identified the local environment as rural, urban or peri-urban were used. This peri-urban analysis was employed in this work for the first time using Chinese climate data by examining nine urban areas, Harbin, Shenyang, Shijiazhuang, Jinan, Zhengzhou, Hefei, Wuhan, Nanchang, and Changsha. Harbin, Shenyang, Jinan and Zhengzhou had distinct peri-urban thermal signatures. Harbin, Heilongjiang, a large city in the northeast of China illustrated most clearly the changing thermal variability characteristics as the local climate station experienced the expanding urban reach of the growing city. The location of its climate station at the fringe of the city in 1951 led to a rural classification for many years. This changed dramatically to peri-urban beginning in the 1980s and to urban in the 2010s and into the 2020s, consistent with the expanding urban sprawl. This research provided some initial insight into the interplay of the various metrics used as the site transitioned from rural to peri-urban to urban.

The uncertainties in absolute daytime top-of-the-atmosphere (TOA) net cirrus cloud radiative forcing (CRF) and radiative heating rates are estimated at five Micropulse Lidar Network (MPLNET) sites spanning the tropics to high latitudes. One year of semitransparent cirrus cloud (optical depth < 3.0 and cloud-top temperature < −37°C) measurements are subject to spectrally consistent optical properties for nine different ice crystal habits, thus providing a range of possible forcing values. The annual average absolute daytime TOA net CRF is positive at Barbados, Kanpur, and Singapore (0.59–0.67, 0.61–0.65, and 1.94–2.09 W m⁻², respectively), negative at Fairbanks (from −0.67 to −0.28 W m⁻²), and can regularly become positive or negative at Goddard Space Flight Center (GSFC) (from −0.06 to 0.32 W m⁻²). The TOA CRF depends on ice crystal shape; in particular, plates lead to relatively large absolute values that decrease for bullet rosettes and columns. Uncertainties in daytime cirrus cloud radiative properties are estimated as the standard deviation of all possible outcomes when considering the different particle habits individually. Annually, the average uncertainty of the absolute daytime TOA net CRF ranges from 0.50 to 1.80 W m⁻². In-cloud daytime net radiative heating rates are positive, on average, at all five sites (0.25–3.84 K day⁻¹) and have an estimated uncertainty of less than 0.30 K day⁻¹. The uncertainties in cirrus radiative forcing and heating that are characterized by assumptions regarding the ice crystal optical properties must be considered in downstream applications, including satellite retrievals and numerical weather prediction. Significance Statement Cirrus clouds are the single most common cloud type in Earth’s atmosphere and, since they exist at high altitudes, are comprised of nonspherical ice crystals. Determining the impact of cirrus clouds on the global energy budget is therefore critical for climate studies and weather prediction. This is a difficult task, as ice crystals uniquely scatter and absorb radiant energy according to their size and shape. Here, the uncertainty in daytime semitransparent cirrus cloud radiative properties is estimated, driven by assumptions related to ice crystal shape, at five locations around the globe using lidar measurements. The cirrus cloud radiative impact has a systematic dependency on ice crystal shape, where plates lead to a relatively stronger greenhouse effect and columns lead to a stronger albedo effect.

There is strong evidence that evaluating different parameterization schemes over diverse land surface forcings and surface-layer (SL) conditions will enhance our understanding of the physical processes required to improve the model parameterizations. Furthermore, shortcomings for representing SL heat, moisture, momentum, and turbulence using traditional parameterizations from Monin–Obukhov similarity theory (MOST) and the bulk Richardson approach are becoming well known within the scientific community. Overcoming the parameterizations’ limitations requires evaluating the parameterizations across a range of land-cover types and meteorological conditions because the biosphere–atmosphere coupling is primarily linked to partitioning energy between sensible and latent heat fluxes. Recent studies over semiarid regions suggested that MOST better parameterized heat fluxes than the Richardson parameterizations, whereas the Richardson approach better parameterized kinematic and turbulence quantities. However, questions remain regarding whether the parameterizations’ efficacy over drylands can be explained by physical parameters, such as the observed Bowen ratio (i.e., the ratio of the surface sensible heat flux to the surface latent heat flux). Addressing these questions allows one to more confidently use the parameterizations in land surface models. In this study, we used micrometeorological observations from two semiarid grassland sites, one in southeastern Arizona and a second in northwestern Texas, for a 3-yr period (1 January 2016–31 December 2018). We found that the heat flux, moisture flux, and turbulence parameterizations’ efficacy do not vary with observed Bowen ratio. Furthermore, the MOST turbulence parameterizations sometimes performed better than the Richardson parameterizations, suggesting that caution is warranted particularly when applying the latter to semiarid regions. Significance Statement The proper parameterization of surface-layer processes is a crucial component of weather forecasting models. To improve the current suite of surface-layer parameterizations, they need to be evaluated across a range of conditions, ranging from very moist to very dry. Although this study found that the efficacy of surface-layer parameterizations varies little across these conditions, it represents an important step toward identifying the parameterizations’ limitations in representing land–atmosphere exchanges. Understanding when these parameterizations perform well versus when their performance declines will help identify targeted areas for improvement and enhance their effectiveness in weather forecasting models.

Misreported weather conditions at airports can cause significant and unnecessary flight delays and cancellations, while increasing costs to the airlines. In 2022, updates to the Federal Aviation Administration (FAA) Holdover Time Tables for aircraft ground deicing operations included guidance for snow (SN) mixed with freezing fog (FZFG). Holdover Time Tables provide information on the length of time (i.e., holdover time) anti-icing fluids will protect the aircraft prior to takeoff under various winter weather conditions. The new holdover times for SN mixed with FZFG are significantly shorter than the holdover times for SN or FZFG reported individually. Prior to the introduction of this guidance, pilots would often assess the SN and FZFG conditions individually and use the most conservative holdover time between the two weather conditions. The new guidance has led pilots and ground deicing crews to express concern that FZFG conditions are often reported with SN when FZFG isn’t present. To assess this, one-minute-observation data from select ASOS locations prone to SN and FZFG conditions were analyzed to determine if a FZFG signal could be detected using measurements other than visibility during SN conditions. Additionally, Meteorological Aerodrome Reports (METARs) from two nearly co-located airports (one in the U.S. and one in Canada) were analyzed since Canada relies on human observers to report obscurations, including FZFG. The outcome of both methods indicates a significant number (~85%) of misreported FZFG reports during SN conditions and provides a basis for improving the automated weather-reporting algorithms.

Electromagnetic ducting, a phenomenon where atmospheric conditions create a layer that traps and channels electromagnetic waves, is crucial for radio wave propagation in communication, radar, and navigation systems. This study analyzes dropsonde data from the Atmospheric River Reconnaissance flights and finds that the sector of atmospheric rivers (ARs) plays a significant role in determining the frequency and properties of electromagnetic ducts, particularly elevated ducts. Elevated ducting is two to three times more frequent in the AR warm sector and non-AR warm sector than in the AR core or cold sector?. The strongest and deepest ducts are found in the non-AR warm sector, which is consistent with the typical synoptic setting of ARs. Most ducts are within 500 m of the marine boundary layer height, but some can be significantly above or below. The relationships between duct strength, depth, and maximum trapped wavelength are invariant across different AR sectors, in contrast to other studies in non-AR environments. These results suggest that large changes in airmass density lead to deeper ducts that can trap larger wavelengths of electromagnetic radiation.

The afternoon-to-evening transition is the period when the atmospheric boundary layer transitions from convective to stable conditions. One noticeable feature during this transition period is the rapid increase in water vapor concentration near the surface. However, the mechanism behind the increase in water vapor remains poorly understood. This study investigated the processes contributing to the water vapor increase and the impacts of the land cover and horizontal advection on the water vapor increase using a single-column model for the clear-sky condition. Numerical experiments were conducted on three cases at the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site. Evapotranspiration was found to be the main moisture source to the water vapor increase and that change of the near-surface vertical temperature gradient from negative to positive is the trigger for this increase during the afternoon-to-evening transition. This is because the near-surface turbulence divergence term decreases due to the change in the buoyancy profile caused by vertical temperature gradient change. The impact of horizontal advection on water vapor varies, and it can either lead to an increase or decrease in water vapor, depending on the spatial horizontal water vapor gradient. This study also found that land cover can influence the timing of the water vapor increase since different land covers may have different Bowen ratios. Water vapor increases earlier under conditions with smaller Bowen ratios compared to those with larger values.

We analyze a combination of probabilistic (pMHW; based on comparison with climatology) and fixed threshold-based (DHW; based on accumulated heat) marine heatwave metrics for the Mesoamerican and Caribbean region to characterize projected coral reef system risks. We use 10 Earth System models (ESMs) bias-adjusted using satellite-derived observations and future projections under intermediate (SSP2-4.5) and very high (SSP5-8.5) emissions scenarios. Climate change causes an increase in marine heat across all seasons with events intensifying and increasingly combining into prolonged events. We calculate a time of emergence (ToE) by noting the projected date in which the typical projected year’s marine heat is more extreme than the mean most extreme year in the 1985–2014 baseline. ToE for pMHW events is 2027 for the intermediate and 2028 for the very high scenario, and ToE for maximum DHW conditions is 2037 and 2034, respectively. Many regions exhibit pMHW events commonly lasting the entire year, notably beginning in the 2060s under the very high scenario and in the 2090s under the intermediate scenario. DHW projections indicate that events once considered rare would become nearly perpetual in the summertime and expand beyond current seasonal constraints across a broad region by 2040 in the very high emissions scenario and by 2050 under the intermediate scenario. Characteristics of change for each type of indicator across a range of climate models provide unique insight into the urgency of potential adaptation and risk management strategies as the Mesoamerican Reef enters a new era of marine heat.

Real-time precipitation data is essential for weather forecasting, flood prediction, drought monitoring, irrigation, fire prevention, and hydroelectric management. To optimize these activities, reliable precipitation estimates are crucial. Environment and Climate Change Canada (ECCC) leads the Canadian Precipitation Analysis (CaPA) project, providing near real-time precipitation estimates across North America. However, during winter, CaPA’s 6-hourly accuracy is limited because many automatic surface observations are not assimilated due to wind-induced gauge undercatch. The objective of this study is to evaluate the added value of adjusted hourly precipitation amounts for gauge undercatch due to wind speed in CaPA. A recent ECCC dataset of hourly precipitation measurements from automatic precipitation gauges across Canada is included in CaPA as part of this study. Precipitation amounts are adjusted based on several types of transfer functions, which convert measured precipitation into what high-quality equipment would have measured with reduced undercatch. First, there are no notable differences in CaPA when comparing the performance of the universal transfer function with that of several climate-specific transfer functions based on wind speed and air temperature. However, increasing solid precipitation amounts using a specific type of transfer functions that depend on snowfall intensity rather than near-surface air temperature is more likely to improve CaPA’s precipitation estimates during the winter season. This improvement is more evident when the objective evaluation is performed with direct comparison with the Adjusted Daily Rainfall and Snowfall (AdjDlyRS) dataset.

The atmospheric boundary layer (ABL) is unique in coastal regions because of kinematic and thermodynamic influences from continental and marine environments. Sea breeze (SB) circulations act to equilibrate the land-sea temperature gradient through advecting marine air onshore. The strength of the SB varies in terms of stability, temperature and moisture advection, and influences air quality and weather forecasts. The TRacking Aerosol Convection interactions ExpeRiment (TRACER) collected a wealth of data on coastal boundary layer evolution, including observations from uncrewed aerial systems (UAS). Vertical profiles of temperature, humidity, and winds were collected by the OU CopterSonde UAS from June to September in the coastal region of Houston. These profiles offer 5 m vertical resolution, on average, every 30 min through diurnal transitions, SB events, and nearby deep convection. During the campaign, CopterSonde observations were gathered through 17 SB events, 6 of which led to convection initiation. The UAS data can resolve the thermodynamic evolution and interactions between the SB and the pre-existing convective boundary layer. Results show large variability across observed SBs and their impacts on temperature and moisture. The intensity of thermodynamic changes depends on the time of sea breeze passage and influence from the Galveston Bay Breeze, a secondary marine circulation commonly observed in this region. In quantifying the spectrum of SB impacts, equivalent potential temperature ( θ e ) is used to contextualize its role in convection initiation and evolution. While all SBs tend to increase θ e from moisture advection, the rate and timing of the θ e rise can distinguish convective from non-convective cases.

The urban heat island effect is influenced by radiation from sidewalks and streets, which alters the apparent air temperature near the surface. Therefore, urban dwellers who are close to the ground (children, pets, etc.) should have higher heat exposure, increasing vulnerability. However, it is not well known how heat health risk varies in the near-surface atmosphere given a high surface radiative temperature. To investigate this problem, wet bulb globe temperature (WBGT), air temperature, humidity, and wind speed were measured with a Kestrel 5400 at two levels, 0.5 m and 1.5 m, in two nearby locations over four summer days and within two hours (1300-1400; 1500-1600), and related to forward looking infrared images (FLIR) of the underlying sidewalks in a hot neighborhood in Charleston, SC. WBGT was consistently higher at 0.5 m than at 1.5 m and this difference was larger than differences based on location or time of day. Cumulative distribution functions between 0.5 m and 1.5 m WBGT showed the largest differences at values well above the highest defined heat stress conditions of “black flag”. Air and dew point temperature differences between these heights were not significantly related to differences in WBGT, but wind speeds were. Infrared surface temperature appears to have little contemporaneous relationship with air temperature at 0.5 m and 1.5 m. However, WBGT at both heights was significantly positively related to the maximum (and average) infrared temperature in the sidewalk images. The potential health impacts on vulnerable children and pets should motivate mitigation measures to reduce radiation coming off urban surfaces.

In an attempt to minimize the adverse impacts of rapid climate change, such as forest fires and droughts, the development of cloud seeding technologies has increasingly attracted attention. However, the effects of cloud seeding have not been verified directly. In the present study, chemical analysis of precipitation samples was explored as a method of confirming the case-by-case effects of cloud seeding experiments. Hourly precipitation samples were obtained using automatic precipitation collectors placed in seeded/nonseeded areas, which were calculated in advance by numerical methods. To directly confirm the effects of cloud seeding, analyses of ionic and heavy metal components (nonsea salt Ca²⁺ and silver) of the samples were carried out. Three aviation experiments are presented (CaCl2, NaCl powder and CaCl2, AgI flare seedings). Each result demonstrated a noticeable increase in the main seeding materials at the rain sampling points within 1–3 h after the experiment, as confirmed by a numerical model. Although a small number of cases were considered in this study, our hourly analysis method highlights the potential for direct and rapid verification of cloud seeding experiments.

During the mei-yu season in China, in addition to the continuous systematic precipitation typically triggered by the stationary shear of the mei-yu front, there are also strong precipitation events caused by other types of weather systems, among which is warm shear-type precipitation. Using multisource observational data from the Integrative Monsoon Frontal Rainfall Experiment (IMFRE-II) in 2020, an analysis was conducted of a strong precipitation event occurring in the middle and lower reaches of the Yangtze River from 27 to 29 June 2020. The results indicate that 1) this rainstorm process was a warm shear-type precipitation influenced by the eastward movement of the southwest China vortex under the background of the northeast China cold vortex. The peak of precipitable water vapor (PWV) occurred approximately 1–2 h earlier than the peak of heavy precipitation. 2) Strong southwest low-level jets (exceeding 30 m s⁻¹) intersected with the cold air from North China in the middle reaches of the Yangtze River and generated the warm shear line on the left side of the low-level jet exit. The rainstorm area was located on the south side of the shear line, with a deep upward motion zone present, providing dynamic lifting conditions for heavy precipitation. 3) The South China Sea provided the maximum water vapor supply, and moisture transport formed a significant moisture convergence zone in the middle and lower reaches of the Yangtze River. Convective available potential energy (CAPE) values at the Yichang station reached up to 1806 J kg⁻¹, and pseudoequivalent potential temperatures θse exceeded 355 K on 27 June, indicating vigorous convection development and providing strong thermal conditions for heavy precipitation. It is hoped to deepen the forecasters’ understanding of the formation mechanism of such rainstorm and play a positive role in improving the extreme precipitation forecast level.

Accurate wind energy assessment (WEA) is essential to reduce the risk of wind project investment. In this paper, the overall framework and detailed process of wind resource assessment are presented for the first time, and the effectiveness of this method is validated by combining meteorological data with copula models. The average hourly wind speed and direction in Wenchang, Hainan Province, from 2016 to 2017 are used to study the joint probability distribution function (JPDF). However, the marginal probability density function (MPDF) of wind direction and wind speed is established and compared by parametric and nonparametric methods. The comparison results reveal that nonparametric kernel density estimation (KDE) performs better than parametric distributions in fitting wind speed empirical distribution but worse in wind direction empirical distribution. The copula function is subsequently utilized to construct JPDF. Six individual and comprehensive goodness-of-fit (GoF) metrics are employed to discern the ideal model out of all the potential models. The evaluation results show that the optimal model varies from year to year, and the Gumbel and Ali–Mikhail–Haq (AMH) copula models can best reproduce the 2-yr empirical distribution of wind speed and wind direction, respectively. Compared with the WEA method based on wind speed, the efficacy of the copula method is confirmed. The study simplifies the WEA procedure and realizes the systematic evaluation of available wind resources, which is of great significance for the further study of direction-related WEA in other regions.

A novel method is described for quantifying average urban heat island (UHI) warming since 1895 in contiguous U.S. (CONUS) summer air temperature data. The method quantifies the sensitivity of Global Historical Climatology Network (GHCN) station raw temperature to station-centered population density ( PD ). Specifically, closely spaced station pair differences in monthly raw (non-homogenized) T AVG (the average of daily maximum and minimum temperature) and PD are sorted by station pair average PD into six PD classes, and linear regression estimates of the temperature sensitivity to population density change (d T AVG /d PD ) are made for each class for historical periods ranging from 1 to 21 years in length. Every one of the resulting six sensitivity relationships in each of 22 historical periods from 1880 to 2020 are found to be positive, and their magnitudes allow construction of station-average urban heat island temperature ( T UHI ) curves as a function of population density. When applied to the history of population changes at each CONUS station location (1895–2023) and grouped into four categories of station population density, the resulting T UHI warming trends range from 8% of observed T AVG warming for the most rural category of stations to about 65% of observed warming for suburban and urban categories. Across all stations the UHI warming amounts to 22% of the observed raw GHCN warming trend, (+0.016 versus +0.072 °C decade ⁻¹ ). The method provides an independent way to quantify station-average UHI warming over time.

This study presents the feasibility of forecasting fire emission fluxes within an operational modeling framework. The aim is to propose a methodology that combines the intensity of the fire detected, the air temperature, wind, and precipitation where the fire is located, to estimate the value of its intensity for the following days. Over the summer of 2022 in Europe, these estimates are compared with fire emission finally observed after the forecast. This is done using two different simulations with horizontal resolutions of 15 and 50 km. This enables us to discuss the best approach for minimizing forecast error and its sensitivity to the model resolution.

Climate change significantly influences electricity and energy demand, yet a comprehensive understanding of heating and cooling energy demand in China’s major cities under the impact of global warming remains incomplete. This study utilized monthly electricity consumption and mean air temperature data to accurately determine the base temperatures for Beijing and Shanghai, identified as 18.94° and 19.54°C, respectively. We further established the heating degree-day (HDD) and cooling degree-day (CDD) indices for both cities over the period from 1961 to 2020. The results indicate a declining trend in winter heating demand and an increasing trend in summer cooling energy demand in both Beijing and Shanghai from 1961 to 2020. Notably, the decrease in winter heating demand exceeds the increase in summer cooling demand for each city. The findings indicate a higher sensitivity of electricity demand to temperature in Beijing during summer (+3.5519 TWh °C⁻¹) compared to winter (−1.5706 TWh °C⁻¹). Similarly, in Shanghai, temperature sensitivity is higher in summer (+5.1133 TWh °C⁻¹) than in winter (−1.7133 TWh °C⁻¹). This high sensitivity during the summer months implies an overall increase in future energy demand. While Beijing demonstrates a higher demand for winter heating compared to Shanghai, its demand for summer cooling is comparatively lower. Nevertheless, due to Shanghai’s greater sensitivity of electricity demand to temperature compared to Beijing, it is projected that Shanghai’s total energy demand will exceed that of Beijing in the future. Significance Statement Climate change alters the demand for electricity and energy, and a comprehensive understanding of heating and cooling energy demand in China’s megacities under global warming is yet to be attained. By calculating the heating and cooling degree-day indices in Beijing and Shanghai, but using real base temperatures, these indices provide a comprehensive resource for exploring the changes in heating and cooling energy demand in Beijing and Shanghai against the background of global warming. Our study shows a decreasing trend in winter heating demand and an increasing trend in summer cooling energy demand for Beijing and Shanghai from 1961 to 2020, and the total energy demand in Beijing and Shanghai will still increase in the future.

Particle size distributions (PSDs) are highly important in numerical models of cloud microphysical schemes. However, PSDs have large uncertainties due to complex ice particle habits and formation processes. The PSDs and influential factors were investigated based on aircraft measurements of cold stratiform orographic clouds on August 16 and 29, 2020, over the Qilian Mountains (QM), northeastern Tibetan Plateau. The results revealed that the orographic clouds displayed multiple vertically layered structures, the number concentration of large ice particles was greater in the mature stage, and the supercooled liquid water content (SLWC) was greater in the dissipating stage. The seeder-feeder mechanism was present in both orographic events. The upper cold clouds containing low-concentration and large-sized ice particles acted as seeding clouds to the low-level feeding clouds with higher SLWC and significantly broadened PSDs. The PSDs were mostly unimodal and were well-fitted with gamma distributions. The gamma fitting parameters of the intercept N 0, slope λ , and dispersion μ were clearly related to the temperature for the non-precipitating particles, whereas there was no clear relationship for the precipitating particles. The λ increased with increasing SLWC for the non-precipitating particles and decreased with the increasing ice water content (IWC) and maximum diameter (D max ) for the precipitating particles, indicating the importance of riming and aggregation processes in cold orographic clouds. N 0, μ , and λ were not independent parameters, and their relationships can be well formulated.

Clouds are a key component in climate change through radiative forcing. However, climate models face challenges in capturing clouds owing to the scale and complexity of cloud formation processes. Hence, long-term cloud observational records are imperative for enhancing our understanding of clouds and refining climate models by examining cloud parameterization and intercomparison. The Advanced Very High Resolution Radiometer (AVHRR) has flown on National Oceanic and Atmospheric Administration (NOAA) and European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) polar-orbiting satellites since the late 1970s and is one of the longest global cloud climate data records. In 2018, the last AVHRR-equipped satellite was launched, so the continuation of these records requires transition to a new generation of satellite imagers. This study evaluates the work needed to generate climate-quality consistent cloud products between AVHRR and the next generation of NOAA polar satellite imagers: Visible Infrared Imaging Radiometer Suite (VIIRS). Through the comparison with Geostationary Operational Environmental Satellite-16 (GOES-16) to accommodate the coverage difference, radiometric discrepancies exist between AVHRR and VIIRS due to the differences in spectral response functions, which are particularly evident in 0.86 and 11.0 μm. These differences impact cloud phase performance, even when utilizing a new lookup table optimized to perform similarly to the original lookup table but without 0.86 μm information. The time series of cloud fraction shows that differences are within 2% between global area coverage (GAC), which is one of the AVHRR data formats for global coverage, and VIIRS global area coverage (VGAC), which is similar to GAC for VIIRS. Cloud-top temperature shows differences within 2 K for ice clouds, underscoring the need for further investigation to ensure the continuity of satellite-based climate datasets.

The societal risks posed by heat waves can be reinforced and magnified during drought conditions. To explore this further in the United States, heat waves and drought events were defined at U.S. Climate Reference Network (USCRN) stations using daily maximum air temperature and weekly time series of the U.S. Drought Monitor (USDM). Standardized air temperature and heat index anomalies were evaluated to determine how droughts influenced heat wave duration, intensity, and heat exposure. Overall, compound heat waves had higher levels of heat exposure than noncompound heat waves, particularly during the late afternoon and early evening hours. On average, compound events had 46.9°C more cumulative extreme heat degree hours (EHDHs; degrees Celsius above the 90th percentile) of exposure than noncompound heat waves with a third of USCRN stations having 50°C EHDHs or more. This reduced to 16.4°C EHDHs when heat index was used to define exposure. Increases in compound heat wave exposure were primarily driven by droughts lengthening heat waves (12 h–2 days longer) than by higher air temperature intensity (0.2 standard deviations warmer). Given that 5-cm standardized soil moisture anomalies were found to both vary (wet and dry conditions) within drought events and strongly impact both air temperature and heat index intensity, the muted intensity response to drought may be related to the USDM’s limited response to short-term precipitation events that are sufficient to moisten the soil and impact heat waves, but not ameliorate drought. This highlights the importance of evaluating heat waves at subdaily scales and explicitly accounting for the role of soil moisture in influencing heat wave intensity and exposure. Significance Statement Droughts were found to increase the risk of heat exposure when comparing compound to noncompound heat waves, particularly during the late afternoon to early evening periods. The increased exposure was driven more by droughts lengthening the duration of heat waves rather than increasing their intensity. Soil moisture anomalies provided further clarity on the compounding effect as a more direct measure of deficits important to heat wave evolution than the all-drought-type approach of the U.S. Drought Monitor.

Pielke deprecates both the ICAT database, which he once recommended, and U.S. tropical cyclone (TC) damage estimates from the National Centers for Environmental Information (NCEI). We do not share these views. Willoughby et al. (hereafter WL24) is based upon ICAT damage for 1900–2017, both then-year and normalized for inflation, population, and individual wealth, extended to 2022 with National Hurricane Center (NHC) official figures from NCEI. Pielke represents the data of Weinkle et al. (hereafter WK18) as a superior source. We find troubling anomalies in the WK18 data. The issue is that WK18 find that normalized TC damage is constant, but WL24 find that it is increasing. Here, we replicate the WL24 analysis with WK18 data and find a statistically significant growth of then-year damage relative to the U.S. economy, a statistically significant increase in the occurrence of the most damaging TCs, and a 0.6% per year increase in TC normalized damage. The last of these is not statistically significant because of the large variance due to the modulation of TC impacts by the Atlantic multidecadal oscillation. Thus, the increase in U.S. TC damage is sufficiently robust to survive the shortcomings of both datasets.

A 16-yr (2007–22) climatology of drylines is presented. Constructed using NOAA Weather Prediction Center (WPC) surface analyses, this climatology addresses the limitations of season, time of day, and region of previous dryline studies by using the full surface analysis archive to include drylines throughout the entire day at 3-h increments for the entire year over the contiguous United States. Severe storm reports and NWS-issued severe thunderstorms and tornado warnings are used to associate individual drylines with severe (or potentially severe) convection. March–June are the months with the greatest frequency of drylines and dryline-associated severe thunderstorms. Regardless of season, average dryline longitude over the course of a day mimics the conceptual model of daily dryline evolution with an eastward shift during the day and westward retreat overnight. The overwhelming majority of WPC-analyzed drylines are located in the southern Great Plains, particularly southwest Texas through the Texas Panhandle, and on average are furthest west in summer and furthest east in winter. A significant increase in the number of analyzed drylines and dryline days per year is identified in the WPC surface analysis archive. This increase is consistent across all analysis times and between drylines associated with and without severe convection. However, using a machine learning model to automate dryline detection, no statistically significant trend is found over this same analysis period. Thus, the increase in WPC-analyzed drylines during the 16-yr period is likely to be the result of nonmeteorological factors. Significance Statement Past studies of the dryline, a boundary important to severe convective storms and the hydrology of the central United States, have only focused on the spring months. This study conducts the first year-round climatology of the dryline and their association with severe storms using 16 years of Weather Prediction Center (WPC) surface analyses. There is a significant increase in dryline frequency over the 16-yr analysis period, but this is attributed to nonmeteorological factors by comparison to an automated dryline identification model. Future work will expand this climatology back through the twentieth century and expand the analysis on the variability of drylines and their association with severe storms.

This study analyzes in-situ aircraft microphysical measurements in deep snow-producing clouds during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field programs in winter 2020, 2022 and 2023 to characterize and compare the microphysics in the updraft and downdraft regions. Measurements were acquired from the NASA P-3 aircraft equipped with a full complement of particle probes and instruments for direct measurements of condensed water content and thermodynamic and 3D wind measurements. We identified the P-3 aircraft flight days collected within snowbands, generating cells, and peripheral regions from fourteen flights. These are composited and used for the analysis. Temperatures sampled ranged between −27 and +1°C. The data are partitioned by air vertical velocity, with strong updrafts defined as >0.5 m s ⁻¹ , very strong updrafts as >1 m s ⁻¹ , and strong downdrafts as <−0.5 m s ⁻¹ . This partitioning revealed precipitation mass concentrations that were 2x higher in the strong updrafts and 3x higher in the very strong updrafts than in the downdrafts, a result of particle growth and relative fallout within the updrafts. Total particle concentrations at the concentrations >1 mm were about the same in each region. However, fallout of the larger aggregates through the updrafts at temperatures >−5°C and into the melting layer result in previously unreported shedding and lofting of the shed particles in the updrafts to subfreezing temperatures. This observation is supported by the overflying NASA ER-2 Doppler radar measurements.