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This study is the first to reach a global view of the precipitation process partitioning, using a combination of satellite and global climate modeling data. The pathways investigated are 1) precipitating ice (ice/snow/graupel) that forms above the freezing level and melts to produce rain (S) followed by additional condensation and collection as the melted precipitating ice falls to the surface (R); 2) growth completely through condensation and collection (coalescence), warm rain (W); and 3) precipitating ice (primarily snow) that falls to the surface (SS). To quantify the amounts, data from satellite-based radar measurements—CloudSat, GPM, and TRMM—are used, as well as climate model simulations from the Community Atmosphere Model (CAM) and the Met Office Unified Model (UM). Total precipitation amounts and the fraction of the total precipitation amount for each of the pathways is examined latitudinally, regionally, and globally. Carefully examining the contributions from the satellite-based products leads to the conclusion that about 57% of Earth’s precipitation follows pathway S, 15% R, 23% W, and 5% SS, each with an uncertainty of ±5%. The percentages differ significantly from the global climate model results, with the UM indicating smaller fractional S, more R, and more SS; and CAM showing appreciably greater S, negative R (indicating net evaporation below the melting layer), a much larger percentage of W and much less SS. Possible reasons for the wide differences between the satellite- and model-based results are discussed.

A 16-yr Tropical Rainfall Measuring Mission (TRMM) convective feature (CF) dataset and ERA-Interim data are used to understand the favorable thermodynamic and kinematic environments for high-flash-rate thunderstorms globally as well as regionally. We find that intense thunderstorms, defined as having more than 50 lightning flashes within a CF during the ~90-s TRMM overpassing time share a few common thermodynamic features over various regions. These include large convective available potential energy (>1000 J kg−1), small to moderate convection inhibition (CIN), and abundant moisture convergence associated with low-level warm advection. However, each region has its own specific features. Generally, thunderstorms with high lightning flash rates have greater CAPE and wind shear than those with low flash rates, but the differences are much smaller in tropical regions than in subtropical regions. The magnitude of the low- to midtropospheric wind shear is greater over the subtropical regions, including the south-central United States, Argentina, and southwest of the Himalayas, than tropical regions, including central Africa, Colombia, and northwest Mexico, with the exception of Sahel region. Relatively, favorable environments of high-flash-rate thunderstorms in the tropical regions are characterized by higher CAPE, lower CIN, and weaker wind shear compared to the high-flash-rate thunderstorms in the subtropical regions, which have a moderate CAPE and CIN, and stronger low to midtropospheric wind shear.

The uncertainties in the version 5 Global Precipitation Measurement (GPM) Microwave Imager (GMI) precipitation retrievals are evaluated via comparison with the radar–radiometer (so-called “Combined”) retrievals between 40°S and 40°N. Results show the precipitation estimates are close (~7% GMI overestimation) globally. However, some specific regions, such as central Africa, the Amazon, the Himalayan region, and the tropical eastern Pacific, show a large overestimation (up to 50%) in GMI retrievals when compared to Combined retrievals. The uncertainties are further evaluated based on precipitation system properties, such as size and intensity of the system. GMI tends to underestimate precipitation volume when the system is relatively warm (>250 K) and small (2000 km2), GMI-derived precipitation is typically higher than Combined over all surfaces. Based on the system properties, a simple bias correction methodology is proposed to implement in the Goddard Profiling Algorithm (GPROF) to reduce GMI biases. GMI precipitation volume is adjusted in each precipitation system based on the size and minimum 89 GHz polarization-corrected temperature (PCT) over land and ocean separately. The overall GMI bias is reduced to 3%, with significant improvement over land. The GMI biases (up to 50%) over the previously mentioned regions are significantly or partially removed, becoming less than 20%. This method also shows effectiveness in removing zonal and seasonal biases from GMI estimates. These results suggest the importance of utilizing the information of whole precipitation systems instead of individual pixels in the precipitation retrieval.

A 16-yr (1998–2013) Tropical Rainfall Measuring Mission (TRMM) Precipitation Feature (PF) database is used to examine the impacts of El Niño–Southern Oscillation (ENSO) on the characteristics of precipitation systems in the tropics and subtropics. Noticeable differences in the fractions of deep systems (20-dBZ radar echo tops greater than 10 km) and mesoscale convective systems (MCSs) (an area greater than 2000 km2) between different phases of ENSO are found over specific regions, including the central Pacific (CPACI), the western Maritime Continent (WMC), the eastern Maritime Continent (EMC), Gulf of Mexico (GM), Argentina (ARGEN), and Australia (AUS). The coefficients of determination R2 between the multivariate ENSO index (MEI) and the population fractions of deep convection and MCSs are analyzed seasonally over these regions. The responses from these precipitation systems to ENSO are found to be more pronounced in the winter half-year than in the summer half-year. An increase of rainfall during El Niño periods over the CPACI, GM, and ARGEN is found to be associated with more precipitation events and a higher fraction of intense, deep, and large precipitation systems. AUS has fewer precipitation events and a higher fraction of shallow and small precipitation systems during El Niño conditions. Both EMC and WMC have a higher fraction of MCSs during La Niña than El Niño conditions. The EMC observes a higher fraction of deep convection during La Niña conditions. However, the WMC has a higher fraction of deep convection during El Niño conditions, possibly related to the effect of the Indian Ocean dipole.

Changes in precipitation amount, intensity and frequency in response to global warming are examined using global high-resolution (16 km) climate model simulations based on the European Centre for Medium-range Weather Forecasts (ECMWF) Integrated Forecast System (IFS) conducted under Project Athena. Our study shows the increases of zonal-mean total precipitation in all latitudes except the northern subtropics (15°-30°N) and southern subtropics-to-midlatitudes (30°-40°S). The probability distribution function (PDF) changes in different latitudes suggest a higher occurrence of light precipitation (LP; ≤1 mm/day) and heavy precipitation (HP; ≥30 mm/day) at the expense of moderate precipitation reduction (MP; 1-30 mm/day) from Tropics to midlatitudes, but an increase in all categories of precipitation in polar regions. On the other hand, the PDF change with global warming in different precipitation climatological zones presents another image. For all regions and seasons examined, there is an HP increase at the cost of MP, but LP varies. The reduced MP in richer precipitation zones resides in the PDF peak intensities, which linearly increase with the precipitation climatology zones. In particular in the Tropics (20°S to 20°N), the precipitation PDF has a flatter distribution (i.e. HP and LP increases with MP reduction) except for the Sahara Desert. In the primary precipitation zones in the subtropics (20°-40°) of both hemispheres, precipitation over land switches toward higher intensity (HP increases, but MP and LP decrease) in both winter and summer, while precipitation over ocean in both seasons shows a flattening trend in the intensity distribution. For the major precipitation zones of the mid-to-high latitude belt (40°-70°), PDF of precipitation tends to be flatter over ocean in summer, but switches toward higher intensities over land in both summer and winter, as well as over ocean in winter.

The Lightning Mapping Imager (LMI) onboard Fengyun‐4A (FY‐4A) is the first Chinese lightning detection sensor in geostationary orbit. This study presents the lightning characteristics observed by the FY‐4A LMI in its first year, and then observations from the Lightning Imaging Sensor (LIS) onboard the International Space Station (ISS) are used to validate the performance of the LMI sensor. LMI lightning events are defined by the pixel's luminosity exceeding the threshold radiance. During the first year of operation, LMI events revealed a reasonable geographical distribution. The diurnal cycles of LMI event/background/threshold radiance are in general agreement with the later afternoon peaks in previous findings. However, the LMI events that occurred around the LIS flash time and in the LMI observation field‐of‐view show a slightly delayed peak at around 1900 LST. This peak can be shifted closer to LIS's results after removing LMI events with event radiance lower than 300 μJsr−1m−2μm−1, suggesting that a larger threshold radiance value is needed to qualify some of these LMI events. Finally, LIS lightning groups were matched with LMI lightning events using different temporal and spatial collocation criteria. The detection efficiency is defined by the fraction of LIS groups detected by LMI to assess LMI's performance. The LMI detection efficiency relative to LIS was found to be regionally dependent and lower in the daytime than at nighttime. These results contribute to applications of LMI lightning observations in storm research and provide suggestions for further algorithm improvement.

Tropical gravity waves that are generated by convection are generally too small in scale and too high in frequency to be resolved in global climate models, yet their drag forces drive the important...

Since early March 2014, the NASA/JAXA Global Precipitation Measurement Core- Observatory (GPM-CO) satellite has allowed analysis of precipitation systems around the globe, thanks to the capabilities of the GPM Microwave Imager (GMI) and Dual-Frequency Precipitation Radar (DPR). In this work, we demonstrate how GPM-CO measurements obtained from 4 years of observations over the Mediterranean area can be used as an extremely effective tool to study the main climatological characteristics of the most intense Mediterranean storm structures. DPR and GMI-based Precipitation Features (PFs) parameters are used as proxies of the vertical structure and microphysical properties of these events, and their statistical distribution is analyzed to identify extremes. The analysis of annual, seasonal and geographical distribution of the identified deep convective systems highlights substantial differences in their diurnal cycle and in the distribution between land-sea and summer-winter. There is a general shift of the convective systems from the south (mostly over the sea) in the cold season, to the north (mostly over land) in the warm season. The analysis shows also that the inferred convective intensity is not always related to heavy precipitation. Known DPR and GMI-based criteria were adopted to identify overshooting top events and potential hailstorms, identify extreme deep convection signatures, like those observed for tropical and subtropical systems, and the most intense occur mostly over the sea. Although the analysis is limited to four years, the results show that the GPM-CO offers unprecedented measurements to identify and characterize extreme weather events in the Mediterranean region, with unique potentials for future long-term climatology and interannual variability analysis.

Satellite-based instruments are essential to the observation of precipitation at a global scale, especially over remote regions. Each instrument has its own strengths and limitations in accurately determining the rate of precipitation at the surface. By using the complementary strengths of two instruments, a more complete analysis of global precipitation can be performed. The Global Precipitation Measurement (GPM) Core Observatory’s Dual-Frequency Precipitation Radar (DPR) is capable of measuring precipitation at high and medium precipitation rates by using Ku-band (13.6 GHz) radiation. The CloudSat satellite’s Cloud Profiling Radar (CPR) uses higher-frequency W-band (94 GHz) radiation and is therefore capable of measuring precipitation at low rates not detected by the GPM DPR. CloudSat observations from January 2007 to December 2016 and DPR observations from March 2014 to February 2018 are combined and the results examined. Since these datasets are not completely coincident, this study is conducted as a multiyear analysis. Observed precipitation from CloudSat is used starting at the lowest precipitation rates and increasing rates until the occurrence observed by GPM surpasses that of CloudSat, at which point data from GPM are used. The precipitation rate at which this change occurs contains important information on the amount of precipitation missed by each instrument and implications as to the size of the hydrometeors present. Liquid precipitation retrieval from CloudSat is not performed over land; analysis over land is produced here using the information available. By combining the two datasets, a more complete picture of precipitation occurring globally is obtained.

High-altitude atmospheric electricity measurements have been used to calculate the conduction (Wilson) currents that are supplied to the global electric circuit (GEC) by individual electrified clouds. Quantifying the global average current and assessing its temporal variability is a challenge, however, because it requires measurements in every stormy region of the world. Thus, a retrieval algorithm has been developed to infer the electric fields and Wilson currents above electrified weather from NASA ER-2 passive microwave high-altitude aircraft observations that are also common satellite products.This study documents the adaptation of the passive microwave electric field and the Wilson current retrieval algorithm for use with satellite platforms. Three distinct variants on the algorithm are produced to respond to specific use cases that differ in 1) whether swath or microwave feature data are available to describe the lateral extent of electrified clouds, 2) the availability of coincident radar data to characterize the vertical structure of electrified clouds, and 3) the prioritization of scientific accuracy or computational expense and product latency. The Wilson currents produced by the satellite retrievals are compared with each other and also with coincident lightning measurements and the Carnegie curve. The advantages, caveats, and limitations of each variant are discussed.

Evolution of precipitation structures are simulated and compared with radar observations for the November Madden‐Julian Oscillation (MJO) event during the DYNAmics of the MJO (DYNAMO) field campaign. Three ground‐based, ship‐borne, and spaceborne precipitation radars and three cloud‐resolving models (CRMs) driven by observed large‐scale forcing are used to study precipitation structures at different locations over the central equatorial Indian Ocean. Convective strength is represented by 0‐dBZ echo‐top heights, and convective organization by contiguous 17‐dBZ areas. The multi‐radar and multi‐model framework allows for more stringent model validations. The emphasis is on testing models' ability to simulate subtle differences observed at different radar sites when the MJO event passed through. The results show that CRMs forced by site‐specific large‐scale forcing can reproduce not only common features in cloud populations but also subtle variations observed by different radars. The comparisons also revealed common deficiencies in CRM simulations where they underestimate radar echo‐top heights for the strongest convection within large, organized precipitation features. Cross validations with multiple radars and models also enable quantitative comparisons in CRM sensitivity studies using different large‐scale forcing, microphysical schemes and parameters, resolutions, and domain sizes. In terms of radar echo‐top height temporal variations, many model sensitivity tests have better correlations than radar/model comparisons, indicating robustness in model performance on this aspect. It is further shown that well‐validated model simulations could be used to constrain uncertainties in observed echo‐top heights when the low‐resolution surveillance scanning strategy is used.

Using a 3-yr Global Precipitation Mission (GPM) Ku-band Precipitation Radar (KuPR) dataset, snow features (SFs) are defined by grouping the contiguous area of nonzero solid precipitation. The near-surface wet bulb temperatures calculated from ERA-Interim reanalysis data are used to verify that SFs are colder than 1°C to omit snowfall that melts before reaching the surface. The properties of SFs are summarized to understand the global distribution and characteristics of snow systems. The seasonal and diurnal variations of SFs and their properties are analyzed over Northern and Southern Hemispheric land and ocean separately. To quantify the amount of snow missed by the GPM KuPR and the amount of snow underestimated by the CloudSat Cloud Profiling (CPR), 3-yr KuPR pixel-level data are compared with 4-yr CloudSat CPR observations. The overall underestimation of snowfall during heavy snow events by CPR is less than 3% compared to the combined CPR and KuPR estimates. KuPR underestimates about 52% of weak snow. Only a small percentage of SFs have sizes greater than 10 000 km2 (0.35%), maximum near-surface reflectivity above 30 dBZ (5.1%), or echo top above 5 km (1.6%); however, they contribute 40%, 49.5%, or 30.4% of the global volumetric snow detected by KuPR. Snow in the Northern Hemisphere has stronger diurnal and seasonal variation compared to the Southern Hemisphere. Most of the SFs over the ocean are found with relatively smaller, less intense, and shallower echo tops than over land.

Two methods for deriving relationships between the equivalent radar reflectivity factor Ze and the snowfall rate S at three radar wavelengths are described. The first method uses collocations of in situ aircraft (microphysical observations) and overflying aircraft (radar observations) from two field programs to develop Ze–S relationships. In the second method, measurements of Ze at the top of the melting layer (ML), from radars on the Tropical Rainfall Measuring Mission (TRMM), Global Precipitation Measurement (GPM), and CloudSat satellites, are related to the retrieved rainfall rate R at the base of the ML, assuming that the mass flux through the ML is constant. Retrievals of R are likely to be more reliable than S because far fewer assumptions are involved in the retrieval and because supporting ground-based validation data are available. The Ze–S relationships developed here for the collocations and the mass-flux technique are compared with those derived from level 2 retrievals from the standard satellite products and with a number of relationships developed and reported by others. It is shown that there are substantial differences among them. The relationships developed here promise improvements in snowfall-rate retrievals from satellite-based radar measurements.

In previous studies, remote sensing properties of hailstorms have been discussed using various spaceborne sensors. Relationships between hail occurrence and strong passive microwave brightness temperature depressions have been established. Using a 16-yr precipitation-feature database derived from the Tropical Rainfall Measuring Mission (TRMM) satellite, the performance of the TRMM Precipitation Radar and TRMM Microwave Imager is further investigated for hail detection. Detection criteria for hail larger than 19 mm are separately developed from Ku-band radar reflectivity and microwave brightness temperature properties of precipitation features that are collocated with surface hail reports over the southeastern and south-central United States. A threshold of 44 dBZ at −22°C is found to have the highest critical success index and Heidke skill score. The threshold of 230 K at 37 GHz yields the best scores among passive microwave properties. Using these two thresholds, global distributions of possible hail events are generated over 65°S–65°N using two years of observations from the Global Precipitation Measurement Core Observatory satellite. Differences in the derived hail geographical distributions are found between radar and passive microwave methods over tropical South America, the “Maritime Continent,” west-central Africa, Argentina, and South Africa. These discrepancies result from different vertical structures of the maximum radar reflectivity profiles over these regions relative to the southeastern and south-central United States, where the thresholds were established. Those differences generally led to overestimates in the tropics from the passive microwave methods, relative to the radar-based methods.

The response of hailstorm intensity to climate variability/change has become a topic of community interest recently. But the lack of persistent and homogenous observations makes it difficult to confidently describe its interannual variations. Hail size, as a common indicator of hailstorm intensity, displays distinct regional variability. Here, for the first time, we show robust evidence of a decrease in hail size using continuous and coherent hail size records from 2,254 manned stations in China since 1980. The stations were categorized based on their elevation: plateaus (above 2000 m), foothills (between 500 and 2000 m), and plains (below 500 m). Compared with 1980–1997, the hail size spectra from 1998 to 2015 all shifted toward smaller sizes significantly in plateaus, foothills, and plains. The proportion of overall hail events with maximum sizes of at least 5 and 20 mm significantly decreased since 1980. Meanwhile, the annual mean size of hail above 10 and 20 mm significantly decreased during the research period, especially after 1990. These changes in the hail size spectra may imply a weakened intensity of hailstorms in China in recent decades.

In the early 1900's, J.W. Whipple began to validate C.T.R. Wilson's Global Electric Circuit (GEC) hypothesis by correlating the diurnal variation of global thunder days with the diurnal variation of the fair weather electric field measured by the Carnegie Cruise. This study applies 16+ years of Precipitation Feature (PF) data from the Tropical Rainfall Measuring Mission (TRMM), including lightning data from the Lightning Imaging Sensor (LIS), alongside 12-years of electric field measurements from Vostok, Antarctica to further examine this relationship. Joint diurnal-seasonal variations of the electric field are introduced and compared with a variety of PF parameters that are potentially related to the GEC. All tested PF parameters showed significant correlations to the electric field on the joint seasonal-diurnal timescale, with the flash rate and volume of 30 dBZ between the -5oC and -35oC isotherms showing the best linear correlations with R2 values of 0.67 and 0.62 respectively. Furthermore, these relationships are analyzed during the two different phases of the El Nino Southern Oscillation (ENSO). Results show different seasonal-diurnal variations of the electric field during El Nino and La Nina periods, with enhancements in the electric field between the months of January through April at 16-24 UTC in La Nina years. A similar trend is shown in global PF parameters, indicating relationshipsbetween the variations seen in the fair weather electric field, and the variations of global PFs at diurnal, seasonal and interannual timescales. This provides further evidence that PFs around the globe have a direct connection to the GEC.

Trends of tropical (30° N-30° S) mean daytime and nighttime outgoing longwave radiation (OLR) from CERES and AIRS are analyzed using data from 2003 to 2013. Both the daytime and nighttime OLR from these instruments show decreasing trends because of El Nino conditions early in the period and La Nina conditions at the end. However, the daytime and nighttime OLR decrease at different rates with the OLR decreasing faster during daytime than nighttime. The daytime-nighttime OLR trend is consistent across CERES Terra, Aqua observations, and computed OLR based upon AIRS and MODIS retrievals. To understand the cause of the differing decreasing rates of daytime and nighttime OLR, high cloud fraction and effective temperature are examined using cloud retrievals from MODIS and AIRS. Unlike the very consistent OLR trends between CERES and AIRS, the trends in cloud properties are not as consistent, which is likely due to the different cloud retrieval methods used. When MODIS and AIRS cloud properties are used to compute OLR, the daytime and nighttime OLR trends based upon MODIS cloud properties are approximately half as large as the trends from AIRS cloud properties, but their daytime-nighttime OLR trends are in agreement. This demonstrates that though the current cloud retrieval algorithms lack the accuracy to pinpoint the changes of daytime and nighttime clouds in the tropics, they do provide a radiatively-consistent view for daytime and nighttime OLR changes. The causes for the larger decreasing daytime OLR trend than that for nighttime OLR are not clear and further studies are needed.

In this study, TRMM-observed precipitation in the tropics is decomposed according to the horizontal area of radar precipitation features, with special emphasis on large systems (rain area > 104 km2) that contribute roughly half of tropical rainfall. Statistical associations of rain-weighted radar precipitation feature (RPF) size distributions with atmospheric variables on the 1.5° grid of ERA-Interim data are explored. In one-predictor distributions, the association with total precipitable water vapor (TPWV) is the strongest, while relative humidity at low and midlevels and low-level wind shear are also positively related to large-RPF rain fraction. Standard CAPE and CIN variables computed from grid-mean thermodynamic profiles are only weakly related to the size of rain systems. Joint distributions over two variables are also reported. The relative importance of predictors varies over different regions. The eastern Pacific is distinctive for having large rain systems in environments with a moist boundary layer but a dry midtroposphere, with strong shallow wind shear and small CAPE. In contrast, the large-storm environment over the western Pacific is found to be moister in the whole troposphere, with relatively weaker wind shear and larger CAPE. Over tropical land, the Sahel and central Africa stand out as having a great fractional rainfall contributed by large RPFs. Their associated environment is characterized by lower TPWV but stronger shallow wind shear and larger CIN and CAPE, in comparison to the equatorial Amazon basin and the Maritime Continent. Based on these associations, statistical reconstructions of the geographical distribution of large-RPF rain fraction from grid-mean atmospheric predictors are attempted.

Optical lightning sensors like the Optical Transient Detector (OTD) and Lightning Imaging Sensor (LIS) measure total lightning across large swaths of the globe with high detection efficiency. With two upcoming missions that employ these sensors – LIS on the International Space Station (ISS-LIS) and the Geostationary Lightning Mapper (GLM) on the GOES-R satellite – there has been increased interest in what these measurements can reveal about lightning and thunderstorms in addition to total flash activity. Optical lightning imagers are capable of observing the characteristics of individual flashes that include their sizes, durations, and radiative energies. However, it is important to exercise caution when interpreting trends in optical flash measurements because they can be affected by the scene. This study uses coincident measurements from the Tropical Rainfall Measuring Mission (TRMM) satellite to examine the properties of LIS flashes and the surrounding cloud regions they illuminate. These combined measurements are used to assess to what extent optical flash characteristics can be used to make inferences about flash structure and energetics. Clouds illuminated by lightning over land and ocean regions that are otherwise similar based on TRMM measurements are identified. Even when LIS flashes occur in similar clouds and background radiances, oceanic flashes are still shown to be larger, brighter, longer lasting, more prone to horizontal propagation and to contain more groups than their land-based counterparts. This suggests that the optical trends noted in literature are not entirely the result of radiative transfer effects, but rather stem from physical differences in the flashes.

This study uses 16-yr Tropical Rainfall Measuring Mission (TRMM) radar precipitation feature (RPF) data to characterize warm rain systems in the tropics with large horizontal extensions, referred to as warm organized rain systems. The systems are selected by specifying the RPFs with minimum infrared brightness temperature warmer than 0°C and rain area greater than 500 km2. ERA-Interim atmospheric fields and SST from NOAA are analyzed to highlight the environmental characteristics of warm organized rain systems. Warm organized systems occur over specific oceanic regions, including the eastern Pacific ITCZ, the eastern part of the SPCZ, and coastal regions. In contrast with ubiquitous warm isolated RPFs, warm organized systems have greater near-surface radar reflectivity. The rainfall amounts generated by warm organized systems are greater in winter than in summer. Composite analyses indicate that warm organized RPFs prefer to coexist with a dry midtroposphere associated with a strong upper-level descent, an enhanced near-surface moisture convergence, and a strong low-level large-scale ascent. The shallow meridional circulation in the eastern Pacific is significantly stronger for warm organized RPFs compared to the circulation for warm isolated RPFs. Warm organized systems over the tropical eastern Pacific occur at warm SSTs with mean value of about 27°C and a strong SST meridional gradient. The warm organized RPFs in the tropical eastern Pacific are found to be at the southern edge of deep ITCZ cores. This is probably related to the meridional asymmetrical thermodynamic structure over the eastern Pacific ITCZ with a higher low-level humidity to the south. Similar favorable large-scale environments for the warm organized RPFs are also found over the SPCZ and other regions.

Hailstorms have caused damages in billions of dollars to industrial, electronic, and mechanical properties such as automobiles, buildings, roads, and aircrafts, as well as life threats to crop and cattle populations, due to their hazardous nature. Hence, the relevance of predicting hailstorms in the future has significant scientific, economic, and societal benefits. However, climate models do not have adequate resolutions to explicitly resolve these subscale phenomena. One solution is to estimate the probability of these storms by using large-scale atmospheric thermodynamic environment variables from climate model outputs, but the existing methods only carried out experiments on small datasets limited to a region, country, or location and a large number of input features. Using one year of Tropical Rainfall Measuring Mission (TRMM) observations and European Center for Medium-Range Weather Forecasts (ECMWF) Re-Analysis Interim (ERA-Interim) reanalysis on a global scale, this paper develops two deep-learning-based models (an autoencoder and convolutional neural network (CNN)) as well as a machine learning approach (random forest) for hailstorm prediction by using only four attributes—convective potential energy, convective inhibition, 1–3 km wind shear, and warm cloud depth. In the experiments, the random forest approach produces the best hailstorm prediction performance compared to the other two methods.

By grouping the contiguous precipitating area detected by the precipitation radar on board the Global Precipitation Mission (GPM) core satellite, snapshots of precipitation systems are summarized as precipitation features (PFs), and their properties are cataloged from 1 year GPM observations. These PFs are categorized by their area and depth and convective intensity based on the 20 and 40 dBZ radar echo tops, respectively. The largest PFs are found mainly over ocean at the mid-high latitudes, especially over southern ocean. The deepest PFs are mainly over tropical land, the West Pacific Warm Pool, and the Great Plains of the United States and Argentina. The most convectively intense PFs are dominant over land regions, including midlatitude and high latitude. The zonal precipitation contribution from extremely large precipitation systems is greater in midlatitude and high latitude than in the tropics. These extreme precipitation systems are rare but contribute significantly to the global precipitation. It is important to include their impacts in global climate models to correctly describe the global water cycle.

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Latent heating (LH) from precipitation systems with different sizes, depths, and convective intensities is quantified with 15 years of LH retrievals from version 7 Precipitation Radar (PR) products of the Tropical Rainfall Measuring Mission (TRMM). Organized precipitation systems, such as mesoscale convective systems (MCSs; precipitation area > 2000 km2), contribute to 88% of the LH above 7 km over tropical land and 95% over tropical oceans. LH over tropical land is mainly from convective precipitation, and has one vertical mode with a peak from 4 to 7 km. There are two vertical modes of LH over tropical oceans. The shallow mode from about 1 to 4 km results from small, shallow, and weak precipitation systems, and partially from congestus clouds with radar echo top between 5 and 8 km. The deep mode from 5 to 9 km is mainly from stratiform precipitation in MCSs. MCSs of different regions and seasons have different LH vertical structure mainly due to the different proportion of stratiform precipitation. MCSs over ocean have a larger fraction of stratiform precipitation and a top-heavy LH structure. MCSs over land have a higher percentage of convective versus stratiform precipitation, which results in a relatively lower-level peak in LH compared to MCSs over the ocean. MCSs during monsoons have properties of LH in between those typical land and oceanic MCSs. Consistent with the diurnal variation of precipitation, tropical land has a stronger LH diurnal variation than tropical oceans with peak LH in the late afternoon. Over tropical oceans in the early morning, the shallow mode of LH peaks slightly earlier than the deep mode. There are almost no diurnal changes of MCSs LH over oceans. However, the small convective systems over land contribute a significant amount of LH at all vertical levels in the afternoon, when the contribution of MCSs is small.

With 15 yr of the Tropical Rainfall Measuring Mission (TRMM) observations, the passive microwave radiometers [TRMM Microwave Imager (TMI)] and the precipitation radar (PR) report a close geographical distribution of annual precipitation between 36°S and 36°N. However, large discrepancies between PR and TMI precipitation retrievals are also found over several specific regions, such as central Africa, the Amazon, the tropical east Pacific, and north Indian Ocean. To understand these discrepancies, the PR near-surface and the TMI surface precipitation retrievals are compared at both pixel and precipitation system levels using collocated pixels and a precipitation feature database from 1998 to 2012. Over land, the TMI overestimates precipitation in deep and intense convective systems, but misses significant amounts of warm rainfall in shallow systems. Over the ocean, because of the partial beam filling of large footprints of the lower-frequency sensors, the TMI reports a larger precipitation area than the PR and underestimates the precipitation rate in the convective precipitation region. The TMI tends to overestimate precipitation compared to the PR in a large proportion of shallow systems over the tropical east Pacific and trade wind regions with large-scale descent. The PR tends to overestimate precipitation compared to the TMI in a large proportion of shallow systems over rainy oceans, such as the west Pacific and the Atlantic ITCZ. All these findings imply that there are still large uncertainties in the precipitation climatology over some regions. Further ground validation campaigns are still needed, especially over the ocean.

The effect of the environment on individual clouds makes it difficult to isolate the signal of the aerosol indirect effect, particularly at larger spatial and temporal scales. This study uses observations from the Tropical Rainfall Measuring Mission (TRMM), CloudSat, and Aqua satellites to identify convective cloud systems in clean and dirty environments. The Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol index is collocated with radar precipitation features (RPFs) from TRMM and congestus cloud features (CFs) from CloudSat. The Interim ECMWF Re-Analysis (ERA-Interim) is interpolated to identify the environmental profile surrounding each feature. Regions in Africa, the Amazon, the Atlantic Ocean, and the southwestern United States are examined. TRMM features in the Africa and Amazon regions are more intense and have higher lightning flash rates under dirty background conditions. RPFs in the southwestern United States are more intense under clean background conditions. The Atlantic region shows little difference in intensity. The differences found in the mean thermodynamic profile for RPFs forming in clean and dirty environments could explain these differences in convective intensity.Congestus identified with CloudSat show smaller differences between clouds forming in clean and dirty environments in Africa and the Amazon. Congestus in clean environments have higher reflectivities and generally larger widths, but no trend is seen in cloud-top height. The signal of the aerosol indirect effect is so small that it is very difficult to detect confidently using these methods. The environment must be considered in any study of the aerosol indirect effect, because important environmental changes can occur as aerosols are introduced to an air mass.

This study adopts a "precipitation object" approach by using 14 years of Tropical Rainfall Measuring Mission (TRMM) Precipitation Feature (PF) and National Centers for Environmental Prediction (NCEP) reanalysis data to study rainfall structure and environmental factors associated with extreme heavy rain events. Characteristics of instantaneous extreme volumetric PFs are examined and compared to those of intermediate and small systems. It is found that instantaneous PFs exhibit a much wider scale range compared to the daily gridded precipitation accumulation range. The top 1% of the rainiest PFs contribute over 55% of total rainfall and have 2 orders of rain volume magnitude greater than those of the median PFs. We find a threshold near the top 10% beyond which the PFs grow exponentially into larger, deeper, and colder rain systems. NCEP reanalyses show that midlevel relative humidity and total precipitable water increase steadily with increasingly larger PFs, along with a rapid increase of 500 hPa upward vertical velocity beyond the top 10%. This provides the necessary moisture convergence to amplify and sustain the extreme events. The rapid increase in vertical motion is associated with the release of convective available potential energy (CAPE) in mature systems, as is evident in the increase in CAPE of PFs up to 10% and the subsequent dropoff. The study illustrates distinct stages in the development of an extreme rainfall event including: (1) a systematic buildup in large-scale temperature and moisture, (2) a rapid change in rain structure, (3) explosive growth of the PF size, and (4) a release of CAPE before the demise of the event.

[1] Cumulus congestus clouds have long been identified as an important part of the spectrum of convective clouds in the tropics. These clouds—which range in size from growing cumulus to slightly smaller than a cumulonimbus—make important contributions to precipitation and latent heat fluxes in the tropics. Past studies have used numerical simulations and satellite observations to examine these clouds globally, although definitions of congestus vary between studies. In this study, congestus in the tropics are identified using contiguous cloud area with cloud tops between 5 and 8 km from 5 years of CloudSat reflectivity data. Alternatively, congestus clouds are defined by contiguous cloud areas with infrared brightness temperature ranging from 273 to 260 K and radar-detected surface rainfall from 14 years of Tropical Rainfall Measuring Mission (TRMM) data. Due to the resolution of CloudSat, the congestus identified using this method represent groups of congestus clouds rather than individual turrets. The regional, seasonal variations of the population of congestus are presented globally. Congestus clouds are found most frequently over the Amazon. There is a strong diurnal variation of congestus clouds over land with a peak in the early afternoon shown by TRMM. General differences are found between the properties of congestus over land and those over ocean, especially the shapes of groups of congestus over land and ocean. Ocean congestus clusters are more bell shaped, while land congestus clusters tend to have flatter sides and larger area above the freezing level. These differences have important implications in the proper representation of congestus in numerical models.

[1] Both ground and space borne radars have shown that radar reflectivity profiles below the freezing level have different slopes over land and ocean in general. This is critical in correctly estimating the surface precipitation rate in the usual situation in which the radar reflectivity cannot be measured as close to the surface as one would like. Using 14 years of Tropical Rainfall Measuring Mission precipitation radar observations, the variations of slopes of the radar reflectivity in the low troposphere are examined over the stratiform and convective precipitation regions. Radar reflectivity below the freezing level usually decreases toward the surface over land, but increases toward the surface over the ocean. Increasing reflectivity toward the surface is hypothesized to occur mainly when raindrops grow while falling through low clouds, which is favored by high humidity at low levels, and by updraft speeds lower than the fall speed of raindrops, both more likely over oceans. Other things being equal, proxy evidence is presented that the more intense the convection, the more likely reflectivity is to decrease toward the surface, and that this is at least as important as low-level relative humidity. Over monsoon regions with more moderate convection but higher humidity, such as southeast China and the Amazon, there are more profiles with reflectivity increasing toward the surface than over other continental regions such as Africa. Radar reflectivity tends to increase toward the surface in shallow warm rain systems in trade cumulus regions, but tends to decrease toward the surface when high reflectivity values are present at or above the freezing level.

Passive microwave (PMW) satellite-based precipitation over land algorithms rely on physical models to define the most appropriate channel combinations to use in the retrieval, yet typically require considerable empirical adaptation of the model for use with the satellite measurements. Although low-frequency channels are better suited to measure the emission due to liquid associated with rain, most techniques to date rely on high-frequency, scattering-based schemes since the low-frequency methods are limited to the highly variable land surface background, whose radiometric contribution is substantial and can vary more than the contribution of the rain signal. Thus, emission techniques are generally useless over the majority of the Earth's surface. As a first step toward advancing to globally useful physical retrieval schemes, an intercomparison project was organized to determine the accuracy and variability of several emissivity retrieval schemes. A three-year period (July 2004-June 2007) over different targets with varying surface characteristics was developed. The PMW radiometer data used includes the Special Sensor Microwave Imagers, SSMI Sounder, Advanced Microwave Scanning Radiometer (AMSR-E), Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), Advanced Microwave Sounding Units, and Microwave Humidity Sounder, along with land surface model emissivity estimates. Results from three specific targets in North America were examined. While there are notable discrepancies among the estimates, similar seasonal trends and associated variability were noted. Because of differences in the treatment surface temperature in the various techniques, it was found that comparing the product of temperature and emissivity yielded more insight than when comparing the emissivity alone. This product is the major contribution to the overall signal measured by PMW sensors and, if it can be properly retrieved, will improve the utility of emission techniques for over land precipitation retrievals. As a more rigorous means of comparison, these emissivity time series were analyzed jointly with precipitation data sets, to examine the emissivity response immediately following rain events. The results demonstrate that while the emissivity structure can be fairly well characterized for certain surface types, there are other more complex surfaces where the underlying variability is more than can be captured with the PMW channels. The implications for Global Precipitation Measurement-era algorithms suggest that physical retrievals are feasible over vegetated land during the warm seasons.

Electrified clouds are known to play a major role in the Global Electric Circuit. These clouds produce upward currents which maintain the potential difference between earth's surface and the upper atmosphere. In this study, model output from two simulations of the Community Earth System Model (CESM) are compared with conduction currents and other data derived from the Tropical Rainfall Measuring Mission (TRMM) satellite, including both the lightning imaging sensor (LIS) and precipitation radar (PR). The intention is to determine CESM's skill at representing these microphysical and dynamical properties of clouds. Then, these cloud properties are used to develop a model parameterization to compute conduction currents from electrified clouds. Specifically, we evaluate the ability of global mean convective mass flux, ice water path and convective precipitation to represent conduction current sources. Parameterizations using these variables yield derived global mean currents that agree well with the geographical patterns of TRMM currents. In addition, comparing the diurnal variations of modeled global mean current to the observed diurnal variations of electric potential gradient, root mean square (RMS) errors range between 6.5% and 8.1%, but the maximum occurs 4 to 6 hours early in all three variables. Output currents derived from the model variables generally match well to the currents derived from TRMM, and the total global current estimates agree well with past studies. This suggests that cloud parameters are well suited for representing the global distribution and strength of currents in a global model framework.

This study investigates a long-duration mesoscale system with extremely heavy rainfall over southwest Taiwan during the Terrain-influenced Monsoon Rainfall Experiment (TiMREX). This mesoscale convective system develops offshore and stays quasi-stationary over the upstream ocean and southwest coast of Taiwan. New convection keeps developing upstream offshore but decays or dies after moving into the island, dropping the heaviest rain over the upstream ocean and coastal regions. Warm, moist, unstable conditions and a low-level jet (LLJ) are found only over the upstream ocean, while the island of Taiwan is under the control of a weak cold pool. The LLJ is lifted upward at the boundary between the cold pool and LLJ. Most convective clusters supporting the long-lived rainy mesoscale system are initiated and develop along that boundary. The initiation and maintenance is thought to be a “back-building–quasi-stationary” process. The cold pool forms from previous persistent precipitation with a temperature depression of 2°–4°C in the lowest 500 m, while the high terrain in Taiwan is thought to trap the cold pool from spreading or moving. As a result, the orography of Taiwan is “extended” to the upstream ocean and plays an indirect effect on the long-duration mesoscale system.

Using 13 yr of data from the Tropical Rainfall Measuring Mission (TRMM) satellite, a regional climatology of monsoonal precipitation is created for portions of the southwest United States. The climatology created using precipitation features defined from the TRMM precipitation radar (PR) shows that the population of features includes a large number of small, weak features that do not produce much rain and are very shallow. A lesser percentage of large, stronger features contributes most of the region’s rainfall. Dividing the features into categories based on the median values of volumetric rainfall and maximum height of the 30-dBZ echo is a useful way to visualize the population of features, and the categories selected reflect the life cycle of monsoonal convection. An examination of the top rain-producing features at different elevations reveals that extreme features tend to occur at lower elevations later in the day. A comparison with the region studied in the North American Monsoon Experiment (NAME) shows that similar diurnal patterns occur in the Sierra Madre Occidental region of Mexico. The population of precipitation features in both regions is similar, with the NAME region producing slightly larger precipitation systems on average than the southwest United States. Both regions on occasion demonstrate the pattern of convection initiating at high elevations and moving downslope while growing upscale through the afternoon and evening; however, there are also days on which convection remains over the high terrain.

A specialized satellite version of the passive microwave electric field retrieval algorithm (Peterson et al., 2015) is applied to observations from the Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Measurement (GPM) satellites to estimate the generator current for the Global Electric Circuit (GEC) and compute its temporal variability. By integrating retrieved Wilson currents from electrified clouds across the globe, we estimate a total mean current of between 1.4 kA (assuming the 7% fraction of electrified clouds producing downward currents measured by the ER-2 is representative) to 1.6 kA (assuming all electrified clouds contribute to the GEC). These current estimates come from all types of convective weather without preference, including Electrified Shower Clouds (ESCs). The diurnal distribution of the retrieved generator current is in excellent agreement with the Carnegie curve (RMS difference: 1.7%). The temporal variability of the total mean generator current ranges from 110% on semi-annual timescales (29% on an annual timescale) to 7.5% on decadal timescales with notable responses to the Madden-Julian Oscillation and El Nino Southern Oscillation. The geographical distribution of current includes significant contributions from oceanic regions in addition to the land-based tropical chimneys. The relative importance of the Americas and Asia chimneys compared to Africa is consistent with the best modern ground-based observations and further highlights the importance of ESCs for the GEC.

During the past few decades, the Sahara Desert has become even hotter. Satellite observations suggest that this warming has led to a rise in the frequency of extreme storms in the Sahel region of West Africa. See Letter p.475 A warming climate is expected to increase the frequency of extreme rainfall events because warmer air can support higher humidity, but other mechanisms may also be at work. Christopher Taylor et al. show that increases in extreme rainfall in the western Sahel region of Africa since 1982 are driven by a larger gradient in temperature across the region caused by a warmer Sahara desert to the north. In turn, the sharper gradient increases wind shear and seems to have led to the frequency of mesoscale convective systems—which are exactly the kind of weather systems likely to generate extreme rainfall—tripling since 1982. The spatial patterns of global warming are likely to further increase the temperature gradient and intensify mesoscale convective systems, leading to more extreme rainfall events.

The rainfall contributions from precipitation features (PFs) with full spectra of different sizes and convective intensities over the tropics and subtropics are summarized using 12 yr of version 6 Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) and Microwave Imager (TMI) observations. Regional, seasonal, and diurnal variations of the rainfall contributions from various PFs are shown, with the global distribution of the sizes, PR echo tops, maximum heights of 30 dBZ, and minimum TMI 85-GHz brightness temperatures of PFs above which contribute half of the rainfall in each 2° × 2° region. Though the results from radar and microwave observations generally agree with each other, some large differences exist over land. Seasonal variations of sizes and intensities of precipitation systems are found over the northeast Pacific, northern SPCZ, and some land areas in addition to the well-known monsoon regions. The diurnal cycles of rainfall over land and ocean are interpreted with the combinations of life cycles of various precipitation systems, using the diurnal variations of rainfall contributions from precipitation systems with different sizes and intensities. The long-duration rainfall events with more than four consecutive 3-h periods with rain at a grid point are identified from 11 yr of TRMM 3B42 products. These “12-h rain events” contribute a larger proportion of the total rainfall over ocean than over land. They are mostly correlated with precipitation systems with large sizes and intense convection. However, they can also be caused by some shallow persistent precipitation systems, such as those over the northeast slope of the Andes in Peru in spring and fall and over the west coast of India in summer.

The Tropical Rainfall Measuring Mission (TRMM) satellite has provided invaluable data for tropical cyclone (TC) research since December 1997. The challenge, however, is how to analyze and efficiently utilize all of the information from several instruments on TRMM that observe the same target. In this study, a tropical cyclone precipitation, cloud, and convective cell feature (TCPF) database has been developed by using observations of the TRMM precipitation radar (PR), Microwave Imager (TMI), Visible and Infrared Scanner (VIRS), Lightning Imaging System (LIS), and the TRMM 3B42 rainfall product. The database is based on an event-based method that analyzes the measurements from multiple sensors. This method condenses the original information of pixel-level measurements into the properties of events, which can significantly increase the efficiency of searching and sorting the observed historical TCs. With both convective and rainfall properties included, the database offers the potential to aid the research aiming to improve both TC intensification and rainfall forecasts. Using the TRMM TCPF database, regional variations of TC convection and diurnal variations of TC rainfall are examined. In terms of absolute number, the northwest Pacific Ocean basin has the deepest and most intense TCPFs according to IR, radar, and 85-GHz microwave measurements. However, the North Atlantic TCPFs appear to have the highest lightning production. Globally, TC rainfall has a maximum at 0430–0730 local solar time (LST) and a minimum around 1930–2230 LST. However, after separating ocean from land, a distinct difference is seen. Over land, the diurnal variation of TC rainfall shows double peaks: one around 0130–0730 LST and the other at 1630–1930 LST. The minimum is at 1030–1330 LST.

Moist convection is well known to be generally more intense over continental than maritime regions, with larger updraft velocities, graupel, and lightning production. This study explores the transition from maritime to continental convection by comparing the trends in Tropical Rainfall Measuring Mission (TRMM) radar and microwave (37 and 85 GHz) observations over islands of increasing size to those simulated by a cloud-resolving model. The observed storms were essentially maritime over islands of 10 000 km2, with a gradual transition in between. Equivalent radar and microwave quantities were simulated from cloud-resolving runs of the Weather Research and Forecasting model via offline radiation codes. The model configuration was idealized, with islands represented by regions of uniform surface heat flux without orography, using a range of initial sounding conditions without strong horizontal winds or aerosols. Simulated storm strength varied with initial sounding, as expected, but also increased sharply with island size in a manner similar to observations. Stronger simulated storms were associated with higher concentrations of large hydrometeors. Although biases varied with different ice microphysical schemes, the trend was similar for all three schemes tested and was also seen in 2D and 3D model configurations. The successful reproduction of the trend with such idealized forcing supports previous suggestions that mesoscale variation in surface heating—rather than any difference in humidity, aerosol, or other aspects of the atmospheric state—is the main reason that convection is more intense over continents and large islands than over oceans. Some dynamical storm aspects, notably the peak rainfall and minimum surface pressure low, were more sensitive to surface forcing than to the atmospheric sounding or ice scheme. Large hydrometeor concentrations and simulated microwave and radar signatures, however, were at least as sensitive to initial humidity levels as to surface forcing and were more sensitive to the ice scheme. Issues with running the TRMM simulator on 2D simulations are discussed, but they appear to be less serious than sensitivities to model microphysics, which were similar in 2D and 3D. This supports the further use of 2D simulations to economically explore modeling uncertainties.

This paper describes improvements to the Tropical Rainfall Measurement Mission (TRMM) Microwave Imager (TMI) land rainfall algorithm in version 7 (v7) of the TRMM data products. The correlations between rain rates and TMI 85-GHz brightness temperatures (Tb) for convective and stratiform rain are generated using 7 years of collocated TMI and TRMM precipitation radar (PR) data. The TMI algorithm for estimating the convective ratio of rainfall is also modified. This paper highlights both the improvements in the v7 algorithm and the continuing problems with the land rainfall retrievals. It is demonstrated that the proposed changes to the algorithm significantly lower the overestimation by TMI globally and over large sections of central Africa and South America. Also highlighted are the problems with the 2A12 land algorithm that have not been addressed in the version 7 algorithm, such as large regional and seasonal dependence of biases in the TMI rain estimates, and potential changes to the algorithm to resolve these problems are discussed.

The long-standing mainstay of support for C. T. R. Wilson’s global circuit hypothesis is the similarity between the diurnal variation of thunderstorm days in universal time and the Carnegie curve of electrical potential gradient. This rough agreement has sustained the widespread view that thunderstorms are the “batteries” for the global electrical circuit. This study utilizes 10 years of Tropical Rainfall Measuring Mission (TRMM) observations to quantify the global occurrence of thunderstorms with much better accuracy and to validate the comparison by F. J. W. Whipple 80 years ago. The results support Wilson’s original ideas that both thunderstorms and electrified shower clouds contribute to the DC global circuit by virtue of negative charge carried downward by precipitation. First, the precipitation features (PFs) are defined by grouping the pixels with rain using 10 years of TRMM observations. Thunderstorms are identified from these PFs with lightning flashes observed by the Lightning Imaging Sensor. PFs without lightning flashes but with a 30-dBZ radar echo-top temperature lower than −10°C over land and −17°C over ocean are selected as possibly electrified shower clouds. The universal diurnal variation of rainfall, the raining area from the thunderstorms, and possibly electrified shower clouds in different seasons are derived and compared with the diurnal variations of the electric field observed at Vostok, Antarctica. The result shows a substantially better match from the updated diurnal variations of the thunderstorm area to the Carnegie curve than Whipple showed. However, to fully understand and quantify the amount of negative charge carried downward by precipitation in electrified storms, more observations of precipitation current in different types of electrified shower clouds are required.

Comparisons between cloud model simulations and observations are crucial in validating model performance and improving physical processes represented in the mod Tel.hese modeled physical processes are idealized representations and almost always have large rooms for improvements. In this study, we use data from two different sensors onboard TRMM (Tropical Rainfall Measurement Mission) satellite to improve the microphysical scheme in the Goddard Cumulus Ensemble (GCE) model. TRMM observed mature-stage squall lines during late spring, early summer in central US over a 9-year period are compiled and compared with a case simulation by GCE model. A unique aspect of the GCE model is that it has a state-of-the-art spectral bin microphysical scheme, which uses 33 different bins to represent particle size distribution of each of the seven hydrometeor species. A forward radiative transfer model calculates TRMM Precipitation Radar (PR) reflectivity and TRMM Microwave Imager (TMI) 85 GHz brightness temperatures from simulated particle size distributions. Comparisons between model outputs and observations reveal that the model overestimates sizes of snow/aggregates in the stratiform region of the squall line. After adjusting temperature-dependent collection coefficients among ice-phase particles, PR comparisons become good while TMI comparisons worsen. Further investigations show that the partitioning between graupel (a high-density form of aggregate), and snow (a low-density form of aggregate) needs to be adjusted in order to have good comparisons in both PR reflectivity and TMI brightness temperature. This study shows that long-term satellite observations, especially those with multiple sensors, can be very useful in constraining model microphysics. It is also the first study in validating and improving a sophisticated spectral bin microphysical scheme according to long-term satellite observations.

Rainfall characteristics and mesoscale properties of precipitation systems in mei-yu seasons over South China, Taiwan, and the South China Sea (SCS) during 1998–2007 are investigated in this study. Mei-yu rainbands are defined using the Tropical Rainfall Measuring Mission 3B42 rainfall product and then applied to divide the mei-yu season into the mei-yu and break periods. In the 10-yr “climatology,” on average, the mei-yu rainbands have a lifetime of 4–5 days and most frequently occur near the South China coast. During the mei-yu periods, rainfall maxima are found over the Pearl River Delta, the foothills of the Yun-Gui Plateau, and Wuyi Mountain, with the first two maxima corresponding to especially heavy rainfall. Intraseasonal variations on the convective structures, especially over land, are distinct among the mei-yu, break, pre-mei-yu, and post-mei-yu, based on analysis of convection intensity proxies and vertical radar reflectivity profiles of precipitation features. Lightning flash rates are consistent with the convective structure. The most frequent lightning over South China and Taiwan is in the pre-mei-yu and the least is during the mei-yu, which suggests different microphysical structures. Therefore, the discrimination of intraseasonal transitions on convective vertical structures may have important implications to the problems of cumulus parameterization, model validation, rainfall estimation, and latent heat retrievals. Intraseasonal variations of convective structures over the SCS are less evident than those over land. Storms over the SCS during the mei-yu are slightly convectively stronger than those in the break. Oceanic features with strong ice scattering have much lower lightning flash rates than their counterparts over land.