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.

Accurate quantification of snowfall rate from space is important, but has remained difficult. Four years (2007-2010) of NOAA-18 Microwave Humidity Sounder (MHS) data are trained and tested with snowfall estimates from coincident CloudSat Cloud Profiling Radar (CPR) observations using several machine learning methods. Among the studied methods, random forest using MHS (RF-MHS) is found the best for both detection and estimation of global snowfall. The RF-MHS estimates are tested using independent years of coincident CPR snowfall estimates and compared with snowfall rates from Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2), Atmospheric Infrared Sounder (AIRS), and MHS Goddard Profiling Algorithm (GPROF). It was found that RF-MHS algorithm can detect global snowfall with approximately 90% accuracy and a Heidke skill score of 0.48 compared to independent CloudSat samples. The surface wet bulb temperatures, brightness temperatures at 190 GHz, and 157 GHz channels are found to be the most important features to delineate snowfall areas. The RF-MHS retrieved global snowfall rates are well compared with CPR estimates and show generally better statistics than MERRA-2, AIRS, and GPROF ©2020 American Geophysical Union. All rights reserved. products. A case study over the US verifies that the RF-MHS estimated snowfall agrees well with the ground-based NCEP Stage-IV and MERRA-2 product whereas a relatively large underestimation is observed with the current GPROF product (V05). MHS snowfall estimated based on RF algorithm, however, shows some underestimation over cold and snow-covered surfaces (e.g., Greenland, Alaska, and Northern Russia), where improvements through new sensors or retrieval techniques are needed.

Precipitation retrieval is a challenging topic, especially in high latitudes (HL), and current precipitation products face ample challenges over these regions. This study investigates the potential of the Advanced Very High-Resolution Radiometer (AVHRR) for snowfall retrieval in HL using CloudSat radar information and machine learning (ML). With all the known limitations, AVHRR observations should be considered for HL snowfall retrieval because (1) AVHRR data have been continuously collected for about four decades on multiple platforms with global coverage, and similar observations will likely continue in the future; (2) current passive microwave satellite precipitation products have several issues over snow and ice surfaces; and (3) good coincident observations between AVHRR and CloudSat are available for training ML algorithms. Using ML, snowfall rate was retrieved from AVHRR’s brightness temperature and cloud probability, as well as auxiliary information provided by numerical reanalysis. The results indicate that the ML-based retrieval algorithm is capable of detection and estimation of snowfall with comparable or better statistical scores than those obtained from the Atmospheric Infrared Sounder (AIRS) and two passive microwave sensors contributing to the Global Precipitation Measurement (GPM) mission constellation. The outcomes also suggest that AVHRR-based snowfall retrievals are spatially and temporally reasonable and can be considered as a quantitatively useful input to the merged precipitation products that require frequent sampling or long-term records.

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.

Six‐years (2010–2015) of snow lightning characteristics and climatology, including seasonal, diurnal, and surface temperature distribution, are generated. The World Wide Lightning Location Network (WWLLN) and the National Lightning Detection Network lightning observations are collocated with Modern‐Era Retrospective Analysis for Research and Application (MERRA‐2) temperatures. Cold season lightning events are identified as lightning with the MERRA‐2 two‐meter surface temperature colder than 0 °C and then further classified as snow lightning or thundersnow, when the entire vertical temperature profile is below 2 °C, and as freezing rain lightning when there is a temperature warmer than 2 °C somewhere in the column above the freezing surface. The statistics of snow lightning events from WWLLN and National Lightning Detection Network are well matched and are consistent with the climatology of thundersnow days reported at ground‐based stations over the United States. Using 4 years of observations from the Global Precipitation Measuring Mission Ku band radar, 443 Thunder Snow Features (TSFs) are defined, having a contiguous area of nonzero near surface snow precipitation derived from the Ku band radar and MERRA‐2 data, along with collocated WWLLN lightning flashes. The majority (about 394) are found over high mountainous regions such the Himalayas, Tibet, the Andes, and the Zagros mountain regions. Low‐elevation TSFs (45) are observed over the continental and coastal regions. Though only a small number of TSFs are identified with 4 years of Global Precipitation Mission data, most TSFs have maximum radar reflectivity above 30 dBZ at temperature colder than −10 °C, which indicates the importance of the noninductive charging process in these events.

Four years (April 2014 to March 2018) of Global Precipitation Measurement (GPM) Precipitation Features data along with colocated the Modern Era Retrospective‐Analysis for Research and Applications‐2 model data are used to identify Freezing Rain Features (FRFs). A Precipitation Feature with presence of both melting layer (maximum temperature of the vertical column > 4 °C) and a layer of subfreezing air (2‐m temperature < 0 °C) adjacent to the surface is considered an FRF. During 4 years of observations, GPM and Modern Era Retrospective‐Analysis for Research and Applications‐2 identify approximately 3,096 FRFs globally (65°S–65°N). Most of them are observed over Northern Hemispheric land in the winter season. The majority of FRFs originates through the “melting process,” whereas only 35 features are associated with “warm rain” process. The locations and seasonal and diurnal distribution patterns of the FRFs over the United States are well matched with the ground‐based observations. The ground‐based observations verify approximately 70% of the FRFs over the United States. Ku‐band radar properties show that FRFs are found shallower (2–5 km) and less intense (<27 dBZ) than precipitation features in general but deeper and more intense than Snow Features. Passive microwave properties show that FRFs Tbs and Polarization‐Corrected Temperature are warmer than Snow Features at all GPM Microwave Imager channels with the largest differences in 166 GHz. The enhancement in Tbs are more distinct with warm rain FRFs. FRF Tb tends to decrease as echo top height increases at all GPM Microwave Imager channels except for 183 GHz, where Tbs have lack of dependence on echo top height.

Satellite-based precipitation estimates have been widely used in many research and application areas, including short-term weather and long-term climate prediction (Arkin & Ardanuy, 1989;Huffman et al., 1995). Precipitation estimates from satellite measurements are unique due to their global coverage, including oceanic and high terrain mountainous regions where ground-based observations are not always possible (Kidd et al., 2017). It is difficult to get broad spatial coverage in mountainous areas using rain gauges, and ground-based radar observation has limitations (Sapiano & Arkin, 2009). Despite the great coverage of satellite products, their precipitation retrieval accuracy in mountain regions is still a challenge (Mei et al., 2014;Shige et al., 2013). The primary types of sensors used to estimate precipitation from satellite measurements are radar, passive microwave imager/ sounder, and Infrared radiometers. The space-borne radar onboard satellites such as the Tropical Rainfall Measuring Mission (TRMM; Kummerow et al., 1998) and the Global Precipitation Measurement (GPM; Hou et al., 2014;Skofronick-Jackson et al., 2018) have shown success in measuring precipitations globally, but they still suffer from uncertainties especially in mountainous regions (Adhikari et al., 2019). One major source of uncertainty associated with space-borne radars is the contamination of near-surface reflectivity profiles due to ground-clutter (Arulraj & Barros, 2019;Liao et al., 2014). Passive microwave sensors (PMW) are popular and have been widely used in estimating precipitation for decades (Prigent, 2010;Wilheit, 1986). The benefit of using PMW sensors compared to radar is their higher sampling rate, although they can misclassify rainfall over mountainous regions (Yamamoto & Shige, 2015). For example, snow and ice-covered surfaces over the mountains could be classified wrongly as precipitating clouds resulting in an overestimation of precipitation. The PMW precipitation estimates mainly rely on the cumulative signal from the vertical column of cloud, hydrometeors and their emission or scattering properties. The heavy rainfall associated with mountainous regions can be generated from clouds with no or little ice aloft (You & Liu, 2012), resulting in an underestimation in PMW estimates (Dinku et al., 2010;Houze, 2012;Shige et al., 2013). Studies have shown that satellite-based precipitation products underestimate precipitation rate over several mountainous regions of the globe such as Japan (Kubota et al., 2009;Shige et al., 2013), Africa

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