Deep Convective Clouds Research Articles

Changes in the dynamical, thermodynamical and hydrometeor characteristics prior to extreme rainfall events along the southwest coast of India in recent decades

Extremes in rainfall have links with atmospheric dynamics, thermodynamics, and deep cloud properties. Employing station-based gridded rainfall data, analysis product of sea surface temperature (SST), reanalysis product of dynamic/thermodynamic parameters and remotely sensed cloud properties, we demonstrate the features and evolution of rainfall extremes over the southwest coast of India (8–14°N, 75–77.5°E) during monsoon season. Analysis of the frequency and total amount of extreme rainfall reveals that the northwest coast (14–20°N) typically serves as the central node for the precipitation extremes. However, linear trends in rainfall extremes are remarkably different across the northwest and southwest coasts of India. Specifically, we noted a marked decrease in extreme rain events between 14 and 16°N along the northwest coast, while at the same time, the southwest coast emerges as a hot spot for extreme rain events. Results suggest that the evolution of rainfall extremes on the southwest coast is linked to the warming of the south-central Arabian sea (AS), enhanced SST gradient between north and south AS, and low pressure over the Bay of Bengal (BoB) four days before the event day. Under the influence of these factors, moisture-laden winds shift from the northwest coast to the southwest coast of India. Three days before the extreme precipitation, the BoB low-pressure centre advances westward and eventually merges with the trough of low along the west coast on the event day and creates a considerable increase in the mid-tropospheric vorticity. Meteorological conditions on extreme rainfall days over the southwest coast are portrayed by a significant reduction of sea level pressure, an intense flow of low-level jets from the Arabian sea and a cyclonic vortex at mid-troposphere. These elements favour a substantial rise in moist static energy (MSE) throughout the troposphere, leading to deep convective clouds with copious ice content and extreme rainfall over the southwest coast.

Role of mean, variability and teleconnection of clouds behind Indian summer monsoon rainfall

AbstractSkilful prediction of the seasonal Indian summer monsoon (ISM) rainfall (ISMR) at least one season in advance has great socio‐economic value. The ISM is a lifeline for about a sixth of the world's population. The ISMR prediction remained a challenging problem with the subcritical skills of the dynamical models due to a limited understanding of the interaction among clouds, convection and circulation. In this study, we have analysed the seasonal mean of high cloud fraction, ice mixing ratio and ice cloud fraction from satellite and reanalysis and demonstrated their importance for ISM. The variability of the mixing ratio of cloud ice in different time scales (3–7 days, 10–20 days and 30–60 days bands) is also examined from reanalysis during ISM. Here, we have shown the teleconnection of different cloud variables over the ISM region with global sea surface temperature. We found that they are tied with slowly varying forcing (e.g., El Niño and Southern Oscillation). Besides, the correlation of cloud ice with different indices (Niño, Pacific Decadal Oscillation, North Atlantic Oscillation and Extratropics) may enhance the potential predictability of ISMR. The representation of deep convective clouds, which involve the ice‐phase processes in a coupled climate model, strongly modulates ISMR variability in association with global predictors. The results from the two sensitivity simulations using coupled global climate model (CGCM) demonstrate the importance of the cloud ice on ISM rainfall predictability. Therefore, this study provides a scientific basis for improving the simulation of the seasonal ISMR by developing the physical processes of the cloud on a subseasonal time scale and motivating further research in this direction.

Convective Impact on the Global Lower Stratospheric Water Vapor Budget

AbstractWater vapor in the stratosphere is primarily controlled by temperatures in the tropical upper troposphere and lower stratosphere. However, the direct impact of deep convection on the global lower stratospheric water vapor budget is still an actively debated issue. Two complementary modeling approaches are used to investigate the convective impact in boreal winter and summer. Convective influence is diagnosed by tracing trajectories through convective cloud top altitude fields derived from global rainfall and brightness temperature data. Backward trajectory model simulations coupled with a detailed treatment of cloud microphysical processes indicate that convection moistens the global lower stratosphere by approximately 0.3 ppmv in boreal winter and summer 2010. The diurnal peak in convection is responsible for about half of the total convective moistening during winter and nearly all of the convective moistening during summer. Deep convective clouds overshooting the tropopause have relatively minor effect on global lower stratospheric water vapor. A forward trajectory model coupled with a simplified cloud module is used to estimate the relative magnitude of the interannual variability of the convective impact. Combining the results from the two models, we find that the convective impact on the global lower stratospheric water vapor during 2006–2016 is approximately 0.3 ppmv with year‐to‐year variations of up to 0.1 ppmv. An important mechanism of convective hydration of the lower stratosphere is via the detrainment of saturated air and ice into the tropical uppermost troposphere and the subsequent upward transport of some of these moist air parcels across relatively warm and subsaturated tropopause.

Characterization of deep convective cells during the Indian summer monsoon using C-band polarimetric Doppler Weather Radar observations over Thumba (8.5o N, 77o E)

The vertical structure and dynamics of deep convective clouds plays an important role during the Indian summer monsoon period. The characteristics of deep convective cells (DCC) and their microphysics remain unclear, especially during the Indian summer monsoon (ISM). Further, the intensity of convection is an important element, which plays a key role in the progression of the monsoon. The present study focuses on characterizing DCC over a coastal location Thumba (8.50 N, 770 E) during the Indian summer monsoon periods of 2017, 2018 and 2019 using C-band polarimetric Doppler Weather Radar (DWR) measurements in terms of their intensity, vertical extent, top heights and their microphysical properties. A method is devised to identify the DCC from the DWR observed reflectivity cross-sections. The results showed that the occurrence of reflectivities in the range of 25–35 dBZ dominates below 6 km and the reflectivities in the range of 10–20 dBZ dominates above 6 km. The diurnal evolution of 40 dBZ depth radar echoes in DCC were analysed for June–September months, which showed peaks at preferential timings. The mean diurnal evolution of DCC also been investigated and it is found that these systems peak at three local time intervals viz., 00–05, 13–16 and 19–23 h during ISM period. Further, a separate analysis has been carried out for land and oceanic region for 40 dBZ depth radar echoes in DCC. The analysis show that the occurrence of 40 dBZ depth radar echoes peak at early-morning and late-night over the ocean and afternoon and late night over the land regions. The present results provide a quantitative assessment of DCC including their diurnal evolution during the Indian summer monsoon season over Thumba using DWR observations for the first time.

A semi-Lagrangian method for detecting and tracking deep convective clouds in geostationary satellite observations

Abstract. Automated methods for the detection and tracking of deep convective clouds in geostationary satellite imagery have a vital role in both the forecasting of severe storms and research into their behaviour. Studying the interactions and feedbacks between multiple deep convective clouds (DCC), however, poses a challenge for existing algorithms due to the necessary compromise between false detection and missed detection errors. We utilise an optical flow method to determine the motion of deep convective clouds in GOES-16 ABI imagery in order to construct a semi-Lagrangian framework for the motion of the cloud field, independently of the detection and tracking of cloud objects. The semi-Lagrangian framework allows severe storms to be simultaneously detected and tracked in both spatial and temporal dimensions. For the purpose of this framework we have developed a novel Lagrangian convolution method and a number of novel implementations of morphological image operations that account for the motion of observed objects. These novel methods allow the accurate extension of computer vision techniques to the temporal domain for moving objects such as DCCs. By combining this framework with existing methods for detecting DCCs (including detection of growing cores through cloud top cooling and detection of anvil clouds using brightness temperature), we show that the novel framework enables reductions in errors due to both false and missed detections compared to any of the individual methods, reducing the need to compromise when compared with existing frameworks. The novel framework enables the continuous tracking of anvil clouds associated with detected deep convection after convective activity has stopped, enabling the study of the entire life cycle of DCCs and their associated anvils. Furthermore, we expect this framework to be applicable to a wide range of cases including the detection and tracking of low-level clouds and other atmospheric phenomena. In addition, this framework may be used to combine observations from multiple sources, including satellite observations, weather radar and reanalysis model data.

Comprehensive Analysis of Typhoon Nangka Based on the Satellite Data from the GPM, CloudSat and Himawari-8

A typhoon or hurricane is one of the most destructive high-impact weather events. In this study, the genesis and development processes of Typhoon Nangka (2015), which occurred over the Western Pacific in 2015, were investigated based on the comprehensive observation data from three satellites, i.e., the Himawari-8 satellite, the CloudSat satellite and the Global Precipitation Measurement mission satellite (GPM), focusing on the characteristics of typhoon structure, precipitation and cloud. The results (Results) show that during the developing stage of Typhoon Nangka, the cloud system was relatively complex and changed significantly, with large raindrops dominating the precipitation around the eyewall in the first quadrant, and the convection in the eyewall and outer rainband burst upward to 17 km. In addition, three features were obvious: stratiform precipitation was dominant in the inner rainband, both the precipitation type (stratiform or convective) and intensity were distributed unevenly in the outer rainband, and large water content was located in the warm layer of clouds. Moreover, the collision growth and breakup of water droplets tended to be stable. The precipitation in the typhoon eyewall, inner rainband and outer rainband was significantly different; stratiform precipitation mainly occurred in the inner rainband, while convective precipitation mainly appeared in the eyewall and outer rainband. The cloud system was distributed asymmetrically, and the upper-layer and lower-layer clouds were closely related, dominated by single-layer clouds. There were deep convective clouds in the eyewall, and cirrus clouds with the broadest range across the eyewall. The coverage range of cirrus clouds was close to the radius of the typhoon. There were stratocumulus, altostratus and cumulus in the low levels.

Tropical Tropopause Layer Cloud Properties from Spaceborne Active Observations

A significant part of clouds in the tropics appears over the tropopause due to intense convections and in situ condensation activity. These tropical tropopause layer (TTL) clouds not only play an important role in the radiation budget over the tropics, but also in water vapor and other chemical material transport from the troposphere to the stratosphere. This study quantifies and analyzes the properties of TTL clouds based on spaceborne active observations, which provide one of the most reliable sources of information on cloud vertical distributions. We use four years (2007–2010) of observations from the joint Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and CloudSat and consider all cloudy pixels with top height above the tropopause as TTL clouds. The occurrence frequency of TTL clouds during the nighttime is found to be almost 13% and can reach ~50–60% in areas with frequent convections. The annual averages of tropical tropopause height, tropopause temperature, and cloud top height are 16.2 km, −80.7 °C, and 16.6 km, respectively, and the average cloud top exceeds tropopause by approximately 500 m. More importantly, the presence of TTL clouds causes tropopause temperature to be ~3–4 °C colder than in the all-sky condition. It also lifts the tropopause heights ~160 m during the nighttime and lowers the heights ~84 m during the daytime. From a cloud type aspect, ~91% and ~4% of the TTL clouds are high clouds and altostratus, and only ~5% of them are associated with convections (i.e., nimbostratus and deep convective clouds). Approximately 30% of the TTL clouds are single-layer clouds, and multi-layer clouds are dominated by those with 2–3 separated layers.

Climatology, trend of aerosol-cloud parameters and their correlation over the Northern Indian Ocean

Aerosols are one of the important atmospheric constituents and exert indirect impact on climate through the modification of microphysical and radiative properties of clouds that in turn perturb the precipitation pattern. Thus, the long term quantification of changes in aerosol and cloud characteristics and their interactions on both temporal as well as spatial scale will provide a crucial information for the better assessment of future climate change. In present study, 18 years (2003–2020) MODerate Resolution Imaging Spectro-radiometer (MODIS) derived aerosol-cloud dataset over the Northern Indian Ocean (NIO) were analysed to assess climatology and trend of aerosol, cloud characteristics and their correlation. We found a strong heterogeneity in spatio-temporal variation of aerosol and cloud parameters over the NIO that are more prominent for the coastal region. The climatological mean of aerosol loading is found high (AOD ≥ 0.5) over the outflow region along the Indian sub-continent and low (AOD ≤ 0.2) over the northern equatorial open ocean. The climatological mean of cloud properties shows dominance of optically thicker deep convective (CTP < 600 hPa and CTT < 260 K) clouds over the southern Bay of Bengal (BoB) and thinner shallow (CTP > 700 hPa and CTT > 273 K) over the northwestern Arabian Sea (AS). Similarly, bigger effective radii (>17 µm) observed along the equatorial open ocean whereas smaller CER (<17 µm) were found over Indian sub-continental coastline and western AS. Further, trend analysis reveals an increasing pattern in AOD (0.002 yr−1), CER (0.051 µm yr−1), LWP (0.033 gm−2 yr−1) and CF (0.002 yr−1) while COD, CTT and CTP show negative trend in order of −0.005 yr−1, −0.094 K yr−1 and −1.160 hPa yr−1, respectively. We also perform similar analysis for seven sub-region of interest (R1 to R7) across the NIO and results show a decreasing pattern in AOD (−0.001 yr−1) at R4 against maximum mean AOD (0.44 ± 0.03). However, coastal sub-regions R1 and R5 illustrate maximum increase in aerosol loading (>0.003 yr−1) suggesting a significant impact of sub-continental outflow over the regions. The spatial correlation of cloud properties with respect to AOD shows a positive slope for CER (0.14) and CF (0.48) and a negative for COD (−0.19), LWP (−0.18), CTT (−0.37), CTP (−0.41). The present study provides in-depth information about the aerosol-cloud characteristics for a long term scale over NIO and could be useful in regional aerosol-cloud interaction induced climate forcing estimation.

Retrieving Vertical Cloud Radar Reflectivity from MODIS Cloud Products with CGAN: An Evaluation for Different Cloud Types and Latitudes

Retrieving cloud vertical structures with satellite remote-sensing measurements is highly desirable and technically challenging. In this paper, the conditional adversarial neural network (CGAN) for retrieving the equivalent cloud radar reflectivity at 94 GHz of the Cloud Profile Radar (CPR) onboard CloudSat is extended and evaluated comprehensively for different cloud types and geographical regions. The CGAN-based retrieval model was extended with additional data samples and improved with a new normalization adjustment. The model was trained with the labeled datasets of the moderate-resolution imaging spectroradiometer (MODIS) cloud top pressure, cloud water path, cloud optical thickness, and effective particle radius data, and the CloudSat/CPR reflectivity from 2010 to 2017 over the global oceans. The test dataset, containing 24,427 cloud samples, was statistically analyzed to assess the performance of the model for eight cloud types and three latitude zones with multiple verification metrics. The results show that the CGAN model possesses good reliability for retrieving clouds with reflectivity &gt; −25 dBZ. The model performed the best for deep convective systems, followed by nimbostratus, altostratus, and cumulus, but presented a very limited ability for stratus, cirrus, and altocumulus. The model performs better in the low and middle latitudes than in the high latitudes. This work demonstrated that the CGAN model can be used to retrieve vertical structures of deep convective clouds and nimbostratus with great confidence in the mid- and lower latitude region, laying the ground for retrieving reliable 3D cloud structures of the deep convective systems including convective storms and hurricanes from MODIS cloud products and used for predicting these storms.

Evaluation of the spectral misalignment on the Earth Clouds, Aerosols and Radiation Explorer/multi-spectral imager cloud product

Abstract. A cloud identification and profiling algorithm is being developed for the multi-spectral imager (MSI), which is one of the four instruments that the Earth Clouds, Aerosols, and Radiation Explorer (EarthCARE) spacecraft will feature. During recent work, we noticed that the MSI response function could shift substantially among some wavelengths (0.67 and 1.65 µm bands) owing to the spectral misalignment (SMILE), in which a shift in the center wavelength appears as a distortion in the spectral image. We evaluated how SMILE affects the cloud retrieval product qualitatively and quantitatively. We chose four detector pixels from bands 1 and 3 with the nadir pixel as the reference to elucidate how the SMILE error affects the cloud optical thickness (τ) and effective cloud droplet radius (re) by simulating the MSI forward radiation with Comprehensive Analysis Program for Cloud Optical Measurement (CAPCOM). We also evaluated the error in simulated scenes from a global cloud system-resolving model and a satellite simulator to measure the effect on actual observation scenes. For typical shallow warm clouds (τ = 8, re = 8 µm), the SMILE error on the cloud retrieval was not significant in most cases (up to 6 % error). For typical deep convective clouds (τ = 8, re = 40 µm), the SMILE error on the cloud retrieval was even less significant in most cases (up to 4 % error). Moreover, our results from two oceanic scenes using the synthetic MSI data agreed well with the forward radiation simulation, indicating that the SMILE error was generally within 10 %. Generally, this negligible impact of the SMILE is true for water surfaces, but it still needs to be investigated further for land surfaces in future works.

Description and Evaluation of a New Deep Convective Cloud Model Considering In‐Cloud Inhomogeneity

AbstractConvections still need to be parameterized in general circulation models (GCMs) in the coming decades. Performances of GCMs are significantly influenced by the convection schemes used. In contrast to most conventional cloud models that ignore in‐cloud inhomogeneities, a new convective cloud model explicitly considering in‐cloud inhomogeneities is developed. The new model adopts a single bulk plume approach, but divides the plume into a series of interacting sub‐plumes in order to mimic the transition from the plume core to its edges. We implemented the new cloud model in NCAR Community Atmosphere Model version 5.0 (CAM5) and evaluated its performance. Single‐column tests show a stronger mass flux profile and low‐level detrainment with the new model. In global simulations, the long‐lasting wet bias in the tropical free troposphere of CAM5 is alleviated. Spatial pattern and intensity distribution of precipitation is improved, but the global hydrological cycle is over‐enhanced. The diurnal cycle of tropical precipitation is also better captured though the precipitation peak still occurs earlier than the observations and the amplitude of the cycle tends to weaken. MJO simulation is slightly improved with the new scheme. In addition, the new cloud model generated more tropical cyclones, in better agreement with observations.

Nitrogen oxides in the free troposphere: implications for tropospheric oxidants and the interpretation of satellite NO2 measurements

Abstract. Satellite-based retrievals of tropospheric NO2 columns are widely used to infer NOx (≡ NO + NO2) emissions. These retrievals rely on model information for the vertical distribution of NO2. The free tropospheric background above 2 km is particularly important because the sensitivity of the retrievals increases with altitude. Free tropospheric NOx also has a strong effect on tropospheric OH and ozone concentrations. Here we use observations from three aircraft campaigns (SEAC4RS, DC3, and ATom) and four atmospheric chemistry models (GEOS-Chem, GMI, TM5, and CAMS) to evaluate the model capabilities for simulating NOx in the free troposphere and attribute it to sources. NO2 measurements during the Studies of Emissions and Atmospheric Composition, Clouds, and Climate Coupling by Regional Surveys (SEAC4RS) and Deep Convective Clouds and Chemistry (DC3) campaigns over the southeastern U.S. in summer show increasing concentrations in the upper troposphere above 10 km, which are not replicated by the GEOS-Chem, although the model is consistent with the NO measurements. Using concurrent NO, NO2, and ozone observations from a DC3 flight in a thunderstorm outflow, we show that the NO2 measurements in the upper troposphere are biased high, plausibly due to interference from thermally labile NO2 reservoirs such as peroxynitric acid (HNO4) and methyl peroxy nitrate (MPN). We find that NO2 concentrations calculated from the NO measurements and NO–NO2 photochemical steady state (PSS) are more reliable to evaluate the vertical profiles of NO2 in models. GEOS-Chem reproduces the shape of the PSS-inferred NO2 profiles throughout the troposphere for SEAC4RS and DC3 but overestimates NO2 concentrations by about a factor of 2. The model underestimates MPN and alkyl nitrate concentrations, suggesting missing organic NOx chemistry. On the other hand, the standard GEOS-Chem model underestimates NO observations from the Atmospheric Tomography Mission (ATom) campaigns over the Pacific and Atlantic oceans, indicating a missing NOx source over the oceans. We find that we can account for this missing source by including in the model the photolysis of particulate nitrate on sea salt aerosols at rates inferred from laboratory studies and field observations of nitrous acid (HONO) over the Atlantic. The median PSS-inferred tropospheric NO2 column density for the ATom campaign is 1.7 ± 0.44 × 1014 molec. cm−2, and the NO2 column density simulated by the four models is in the range of 1.4–2.4 × 1014 molec. cm−2, implying that the uncertainty from using modeled NO2 tropospheric columns over clean areas in the retrievals for stratosphere–troposphere separation is about 1 × 1014 molec. cm−2. We find from GEOS-Chem that lightning is the main primary NOx source in the free troposphere over the tropics and southern midlatitudes, but aircraft emissions dominate at northern midlatitudes in winter and in summer over the oceans. Particulate nitrate photolysis increases ozone concentrations by up to 5 ppbv (parts per billion by volume) in the free troposphere in the northern extratropics in the model, which would largely correct the low model bias relative to ozonesonde observations. Global tropospheric OH concentrations increase by 19 %. The contribution of the free tropospheric background to the tropospheric NO2 columns observed by satellites over the contiguous U.S. increases from 25 ± 11 % in winter to 65 ± 9 % in summer, according to the GEOS-Chem vertical profiles. This needs to be accounted for when deriving NOx emissions from satellite NO2 column measurements.

Liquid water determination by airborne millimeter cloud radar and in-situ size distribution measurements

Liquid water content (LWC) in clouds determines the precipitable water of clouds, which is a crucial factor for aircraft safety and weather modification operations. More importantly, it influences the optical depth of clouds in the visible wavelength range, thus determining their climate cooling effects. Identifying and quantifying the LWC in mixed-phase clouds via remote sensing techniques remains challenging owing to the large variability of hydrometeor sizes in the cloud. In this study, we used in-situ aircraft measured full size distributions and collocated airborne radar reflectivity (Z) to explicitly fractionate the contributions of hydrometeors (cloud liquid droplets, ice, and precipitation particles) at different size ranges from the measured total Z. A linearly decreasing contribution of non-precipitation hydrometeors with increasing total Z was discovered for a range of cloud types, including cumulus, status, and deep convection clouds. The relationship between the mass and Z for each type of hydrometeor derived from in situ measurements was then applied. This approach of apportioning the contribution of cloud liquid droplets from the measured total Z as the first step significantly reduced the scattering of the correlation between the LWC and Z, as presented in previous studies; thus, the LWC was more accurately determined. The derived liquid water path exhibited high agreement with the microwave radiometer measurements. Our method of deriving the LWC from the total Z stemming from the in situ measured size distributions may be applied in other situations to derive the cloud liquid droplets, ice, and precipitation masses for clouds with a given radar Z.

Modeling of BRDF Characteristics of Deep Convective Cloud Based on Himawari-8 Satellite Imager

Modeling of BRDF Characteristics of Deep Convective Cloud Based on Himawari-8 Satellite Imager

Characteristics of Deep Convective Clouds, Precipitation, and Cloud Properties of Rapidly Intensifying Tropical Cyclones in the Western North Pacific

AbstractToward the understanding of rapid intensification (RI) of tropical cyclones (TCs) in the western North Pacific, the TC's deep convective cloud (DCC), precipitation, and cloud properties in terms of cloud effective radius, optical thickness, and top height from satellite observations are investigated. Mean and radial distributions of the variables at different intensity stages and intensification categories are examined. The relationship indicates that the DCC percentage and temperature, especially their radial distributions, could be used to identify an impending RI regardless of TC intensity. Meanwhile, the mean and radial distribution of precipitation may discriminate RI from non‐RI in tropical depression (TD) and tropical storm (TS). The radial distribution of the cloud properties in rapidly intensifying TD and TS also suggest that most of the clouds near the center of the storm has deepened already while those that are far from the center are generally in developing or dissipating stage. Moreover, rapidly intensifying TCs, regardless of their intensities, manifest common DCC, precipitation, and cloud properties characteristics near the TC center. It is to be noted that the different mean and radial distribution characteristics of the variables between initial and continuing stages of RI are inferred to be artifacts of their intensities and RI rates (or radius of maximum wind sizes) rather than whether the TCs are at the onset or 24 hr of RI.

Interaction between cloud–radiation, atmospheric dynamics and thermodynamics based on observational data from GoAmazon 2014/15 and a cloud-resolving model

Abstract. Observational meteorological data from the field experiment GoAmazon 2014/15 and data from numerical simulations with the cloud-resolving model (CRM) called the System for Atmospheric Modeling (SAM) are used to study the interaction between the cloudiness–radiation as well as the atmospheric dynamics and thermodynamics variables for a site located in the central Amazon region (−3.2∘ S, −60.6∘ W) during the wet and dry periods. The main aims are to (a) analyze the temporal series of the integrated cloud fraction, precipitation rate and downward shortwave flux as well as (b) to determine the relationship between the integrated cloud fraction, radiative fluxes and large-scale variable anomalies as a function of the previous day's average. The temporal series of the integrated cloud fraction, precipitation rate and downward shortwave flux from SAM simulations showed physical consistency with the observations from GoAmazon 2014/15. Shallow and deep convection clouds show to have a meaningful impact on radiation fluxes in the Amazon region during wet and dry periods. Anomalies of large-scale variables (relative to the previous day's average) are physically associated with cloud formation, evolution and dissipation. SAM consistently simulated these results, where the cloud fraction vertical profile shows a pattern very close to the observed data (cloud type). Additionally, the integrated cloud fraction and large-scale variable anomalies, as a function of the previous day's average, have a good correlation. These results suggest that the memory of the large-scale dynamics from the previous day can be used to estimate the cloud fraction as well as the water content, which is a variable of the cloud itself. In general, the SAM satisfactorily simulated the interaction between cloud–radiation as well as dynamic and thermodynamic variables of the atmosphere during the periods of this study, being able to obtain atmospheric variables that are impossible to obtain in an observational way.

ХАРАКТЕРИСТИКИ И СТРУКТУРА МЕЗОМАСШТАБНЫХ КОНВЕКТИВНЫХ СИСТЕМ НАД ЗАПАДНОЙ СИБИРЬЮ ПО ДАННЫМ ДИСТАНЦИОННЫХ НАБЛЮДЕНИЙ

Many natural hazards are associated with deep convective clouds. Among them, mesoscale convective systems characterized by a large size and a long duration are the most dangerous. The estimates of their area, cloud top height, moisture content, duration, and intensity of related thunderstorm activity for Western Siberia were obtained using the data of radio and optical (passive and active) satellite sounding (for the summer months in 2010-2019) and World Wide Lightning Location Network (WWLLN, 2016-2020).

Effects of aerosols on the forecasting of Mei-yu frontal storms over the Yangtze–Huai River valley

The evolutions of Mei-yu frontal storms during the Mei-yu season in 2018 were simulated in the Weather Research and Forecasting with Chemistry (WRF/Chem) model to investigate the effects of aerosols on the prediction of clouds and precipitation. Two groups of daily launched forecast experiments were conducted with a duration of 17 days, by turning the chemistry–aerosol module on (WRF/Chem) and off (WRF), and additional cloud droplet number concentration (Nd) experiments were perturbed in WRF. Comparison between the two groups of experiments suggested that activating the chemistry–aerosol module improved the forecast skill for precipitation exceeding 5 mm/h during the Mei-yu season, while had no positive forecast skill on precipitation exceeding 0.1 mm/h in terms of both traditional and neighborhood spatial verifications. In contrast to the uniform Nd distribution adopted in WRF, the inhomogeneous Nd distribution in WRF/Chem agreed better with in-situ aircraft observations collected during the field campaign. As Nd increases in WRF experiments, the warm clouds decreased and deep convective clouds significantly increased with more convective updraft mass flux and net heat budget. Scavenging in Mei-yu frontal storms generally reduced the median Nd in the WRF/Chem experiment. The fewer and larger cloud particles in WRF/Chem promoted the conversion of cloud to rain and the accretion of cloud by rain, thereby leading to invigoration of warm clouds and precipitation efficiency, which explains the improved forecast skill for precipitation exceeding 5 mm/h. Enhanced warm-rain processes caused fewer cloud particles for further ascent and ultimately resulted in suppression of deep convective clouds. The preliminary findings in this study highlights the potential significance of incorporating chemistry–aerosol schemes in the operational forecasting of Mei-yu rainfall.

Analyzing Sensitive Aerosol Regimes and Active Geolocations of Aerosol Effects on Deep Convective Clouds over the Global Oceans by Using Long-Term Operational Satellite Observations

Long-term satellite climate data records of aerosol and cloud along with meteorological reanalysis data have been used to study the aerosol effects on deep convective clouds (DCCs) over the global oceans from a climatology perspective. Our focus is on identifying sensitive aerosol regimes and active geolocations of the aerosol effects on DCCs by using statistical analyses on long-term averaged aerosol and cloud variables. We found the aerosol effect tends to manifest relatively easily on the long-term mean values of observed cloud microphysical variables (e.g., cloud particle size and ice water amount) compared to observed cloud macrophysical variables (e.g., cloud cover and cloud top height). An increase of aerosol loading tends to increase DCC particle size and ice water amount in the tropical convergence zones but decrease them in the subtropical subsidence regions. The aerosol effect on the cloud microphysical variables is also likely to manifest over the northwestern Pacific Ocean and central and eastern subtropical Pacific Ocean. The aerosol effect manifested on the microphysical cloud variables may also propagate to cloud cover but weakly to cloud top height since the latter is more susceptible to the influence of cloud dynamical and thermodynamic processes. Our results, based on the long-term averaged operational satellite observation, are valuable for the evaluation and improvement of aerosol-cloud interactions in global climate models.

Deep Convective Cloud Top Altitudes at High Temporal and Spatial Resolution

AbstractWe describe and validate a method of calculating convective cloud top altitudes and potential temperatures up to 50° latitude at high spatial (0.25°) and temporal (3 hr) resolution. The approach uses the statistics of the CloudSat cloud radar deep convective cloud classification product coupled with nighttime CALIOP lidar measurements to effectively “calibrate” the high frequency, high resolution global rainfall and brightness temperature data that is used to derive convective cloud top altitudes. Thus, our product agrees well with the statistics of the CloudSat/CALIOP convective cloud tops, especially in the tropics and over oceanic regions. Agreement is reasonable, but not as good, for land‐based convection. The estimated uncertainty in cloud top altitudes in our product is 0.5–1.0 km over land areas, with smaller uncertainties over the oceans. The diurnal cycle of the new convection data set is in good agreement with precipitation radar convective climatology.