A 4-year Global Precipitation Measurement (GPM) Precipitation Feature (PF) data set is used to quantify the frequency and global distribution of overshooting convection. In this study, overshooting convection is defined as PFs with maximum 20 dBZ echo top height (MAXHT20) greater than the height of the lapse rate tropopause, derived from ERA-Interim reanalysis data. The geographical distribution of overshooting convection exhibits a strong preference for specific land regions, such as over the central United States, Argentina, Central Africa, and Colombia. Larger areas and greater occurrence of 20 dBZ radar reflectivity at the tropopause are found in northern middle to high latitudes than in the tropics. The occurrence of 20 dBZ radar reflectivity reaching above the level of 380 K potential temperature (Z 380K ) in middle and high latitudes is found to be comparable to that in the tropics. Furthermore, a methodology is developed to detect overshooting convection using the GPM Microwave Imager measured brightness temperature at 183.31 ± 3 and 183.31 ± 7 GHz, and Polarization Corrected Temperature at 89 GHz. The geographical distribution of overshooting convection can be closely reproduced using the combinations of these brightness temperatures with an average Heidke skill score of 0.4 and probability of 0.38. This shows the possibility of identifying overshooting convection from microwave observations at high-frequency channels near the water vapor absorption line centered at 183.31 GHz from other satellite missions.

Mesoscale convective systems (MCSs) are an important source of precipitation and cloudiness in the tropics as well as at mid latitudes. They are capable of producing copious amounts of precipitation (over half the annual rainfall in some regions; Schumacher & Rasmussen, 2020) and contribute to mixing and heat transport, via the release of latent heat and the modulation of radiative heating and cooling . They are also large in size and can be long-lived, resulting in these impacts being distributed over widespread areas. Understanding the development of large-scale precipitation systems, as well as their properties throughout their lifetimes is important for better prediction of these storms and correctly and completely quantifying their environmental effects.Many previous studies follow the lifecycle of convective systems in order to understand processes associated with their growth and decay, as well as the processes occurring within the storm, which affect the environment through mixing, transport, the production of precipitation, and the release of latent heat. These studies have been conducted on a case study basis using ground based instruments such as field campaign and radar data (

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.

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 (<200 km2) due to the lack of ice scattering signatures. However, for large systems (>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.

Accurate, high‐resolution measurements of the precipitation diurnal cycle are important for understanding local variations in precipitation and the underlying processes which cause them. Combining 16 years of measurements from the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) and 4 years from the Global Precipitation Measurement (GPM) Dual‐frequency Precipitation Radar (DPR) creates a 20‐year mean of near surface precipitation rate at 0.1° × 0.1°, hourly resolution from 35°N to 35°S. Similar data sets are created using the same proportions of the TRMM Microwave Imager and GPM Microwave Imager measurements, and 18 years of the Integrated Multi‐satellitE Retrievals for GPM (IMERG) precipitation product. The phase and amplitude of the diurnal variation from each data set are calculated at each grid point using a fast Fourier transform (FFT). The satellite data are validated with 14 years of hourly National Climatic Data Center rain gauge measurements over the southeast United States by applying the same FFT method. Spaceborne radar best represents the unaveraged magnitude and diurnal phase measured by the gauges in this region. Time delays are found in precipitation retrievals from passive microwave observations as well as in the IMERG product. The strengths and weaknesses of these high‐resolution data sets in determining the diurnal cycle of precipitation on a climatological scale are discussed across the tropics and subtropics. In general, when compared to PR and DPR retrievals, IMERG overestimates the global precipitation and has varying time lags, which tend to be larger and earlier over regions where organized convective systems are common.