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passive microwave (PMW) observations and use other sources only when PMW observations are not available. PMW precipitation retrievals are prioritized first because the microwave signal interacts with precipitation-sized hydrometeors and not cloud-sized hydrometeors, so—compared to other sources such as infrared observations—PMW provides a more direct observation of the precipitation. Nevertheless, the PMW retrieval process is challenging due to the underconstrained nature of the problem ( Stephens and
passive microwave (PMW) observations and use other sources only when PMW observations are not available. PMW precipitation retrievals are prioritized first because the microwave signal interacts with precipitation-sized hydrometeors and not cloud-sized hydrometeors, so—compared to other sources such as infrared observations—PMW provides a more direct observation of the precipitation. Nevertheless, the PMW retrieval process is challenging due to the underconstrained nature of the problem ( Stephens and
to the five different 10° latitude bins indicated in the legend. The extremely variable snow-cover extent and snow radiative properties in the MW are one of the main issues in the detection and quantification of snowfall by passive microwave observations, which remain among the most challenging tasks in global precipitation retrieval ( Bennartz and Bauer 2003 ; Skofronick-Jackson et al. 2004 , 2019 ; Noh et al. 2009 ; Levizzani et al. 2011 ; Kongoli and Helfrich 2015 ; Chen et al. 2016
to the five different 10° latitude bins indicated in the legend. The extremely variable snow-cover extent and snow radiative properties in the MW are one of the main issues in the detection and quantification of snowfall by passive microwave observations, which remain among the most challenging tasks in global precipitation retrieval ( Bennartz and Bauer 2003 ; Skofronick-Jackson et al. 2004 , 2019 ; Noh et al. 2009 ; Levizzani et al. 2011 ; Kongoli and Helfrich 2015 ; Chen et al. 2016
1. Introduction The Global Precipitation Measurement Core Observatory (GPM CO ) satellite, launched in February 2014, offers unprecedented spaceborne observations of the three-dimensional structure of precipitating systems ( Hou et al. 2014 ). The satellite detects rain rates in the range 0.2–110.0 mm h −1 and travels in a sun-asynchronous orbit, providing coverage between 68°N and 68°S, thus augmenting the 37°N/S coverage of the predecessor Tropical Rainfall Measuring Mission (TRMM
1. Introduction The Global Precipitation Measurement Core Observatory (GPM CO ) satellite, launched in February 2014, offers unprecedented spaceborne observations of the three-dimensional structure of precipitating systems ( Hou et al. 2014 ). The satellite detects rain rates in the range 0.2–110.0 mm h −1 and travels in a sun-asynchronous orbit, providing coverage between 68°N and 68°S, thus augmenting the 37°N/S coverage of the predecessor Tropical Rainfall Measuring Mission (TRMM
-changing climate. Despite a long, albeit sparse, record [first known observations date back 2000 BCE ( Wang and Zhang 1988 )], globally complete precipitation measurements did not become available until the modern era of satellite Earth-observing systems that employ infrared and microwave radiometric techniques (e.g., Atlas and Thiele 1981 ). Achieving measurement standards of rainfall in atypical (i.e., extreme) environments on small spatiotemporal scales across the globe, however, has turned out to be more
-changing climate. Despite a long, albeit sparse, record [first known observations date back 2000 BCE ( Wang and Zhang 1988 )], globally complete precipitation measurements did not become available until the modern era of satellite Earth-observing systems that employ infrared and microwave radiometric techniques (e.g., Atlas and Thiele 1981 ). Achieving measurement standards of rainfall in atypical (i.e., extreme) environments on small spatiotemporal scales across the globe, however, has turned out to be more
1. Introduction Many societal applications that use precipitation information require high-quality data with fine spatiotemporal resolution ( Kirschbaum et al. 2017 ). Satellite observations of high-quality precipitation data are typically derived from passive microwave (PMW) sensors on board low-Earth-orbiting satellites because microwave radiation interacts directly with precipitation-sized particles ( Kidd and Huffman 2011 ; Kidd and Levizzani 2011 ; Tapiador et al. 2012 ). However, even
1. Introduction Many societal applications that use precipitation information require high-quality data with fine spatiotemporal resolution ( Kirschbaum et al. 2017 ). Satellite observations of high-quality precipitation data are typically derived from passive microwave (PMW) sensors on board low-Earth-orbiting satellites because microwave radiation interacts directly with precipitation-sized particles ( Kidd and Huffman 2011 ; Kidd and Levizzani 2011 ; Tapiador et al. 2012 ). However, even
precipitation and cloud radars have extremely sparse revisit times, so long-term global satellite precipitation products use passive microwave (PMW) sensor observations. These long-term precipitation records help us understand the hydrological cycle and climate change impacts at global and regional scales. These records include satellite precipitation data such as the Global Precipitation Climatology Project (GPCP; Huffman et al. 1997 ; Adler et al. 2018 ) and the TRMM Multisatellite Precipitation
precipitation and cloud radars have extremely sparse revisit times, so long-term global satellite precipitation products use passive microwave (PMW) sensor observations. These long-term precipitation records help us understand the hydrological cycle and climate change impacts at global and regional scales. These records include satellite precipitation data such as the Global Precipitation Climatology Project (GPCP; Huffman et al. 1997 ; Adler et al. 2018 ) and the TRMM Multisatellite Precipitation
1980 ; Hallikainen et al. 1986 , 1987 ). Hence, snow cover has a time-varying effect on snowfall upwelling signal. Physical and empirical approaches have been developed for microwave retrievals of snowfall. Skofronick-Jackson et al. (2004) presented a physical method to retrieve snowfall during a blizzard over the eastern United States using high-frequency observations from the Advanced Microwave Sounding Unit B (AMSU-B) instrument. Kim et al. (2008) simulated atmospheric profiles of a
1980 ; Hallikainen et al. 1986 , 1987 ). Hence, snow cover has a time-varying effect on snowfall upwelling signal. Physical and empirical approaches have been developed for microwave retrievals of snowfall. Skofronick-Jackson et al. (2004) presented a physical method to retrieve snowfall during a blizzard over the eastern United States using high-frequency observations from the Advanced Microwave Sounding Unit B (AMSU-B) instrument. Kim et al. (2008) simulated atmospheric profiles of a
regions, and are fraught with problems like undercatch and wind-blown snow biases ( Fassnacht 2004 ). This measurement gap can be bridged by spaceborne active and passive microwave (PMW) sensors that are tailored to detect and quantify snowfall thanks to their ability to probe within clouds ( Levizzani et al. 2011 ; Skofronick-Jackson et al. 2017 ). Two spaceborne radars paved the way toward ground-breaking vertically resolved observations of falling snow over much of the globe: the CloudSat Cloud
regions, and are fraught with problems like undercatch and wind-blown snow biases ( Fassnacht 2004 ). This measurement gap can be bridged by spaceborne active and passive microwave (PMW) sensors that are tailored to detect and quantify snowfall thanks to their ability to probe within clouds ( Levizzani et al. 2011 ; Skofronick-Jackson et al. 2017 ). Two spaceborne radars paved the way toward ground-breaking vertically resolved observations of falling snow over much of the globe: the CloudSat Cloud
estimates from various GPM partner satellites with passive microwave (PMW) sensors on board are combined. Additionally, a morphing algorithm is applied to fill time gaps between PMW observations using motion vectors. If the time gap between two subsequent PMW observations is larger than ~30 min, infrared (IR) observations are additionally included to update the final precipitation estimates ( Huffman et al. 2019 , 2020 ). The key difference between IMERG V06B and its previous versions is a modification
estimates from various GPM partner satellites with passive microwave (PMW) sensors on board are combined. Additionally, a morphing algorithm is applied to fill time gaps between PMW observations using motion vectors. If the time gap between two subsequent PMW observations is larger than ~30 min, infrared (IR) observations are additionally included to update the final precipitation estimates ( Huffman et al. 2019 , 2020 ). The key difference between IMERG V06B and its previous versions is a modification
) has been used by several retrieval algorithms ( You et al. 2015 ; Kummerow et al. 2015 ). To largely avoid the possible surface contamination, instead of using the signatures from window channels (e.g., 85 GHz), Staelin and Chen (2000) developed a rainfall retrieval algorithm solely dependent on the microwave observations near opaque water vapor and oxygen absorption channels (183 and 52 GHz). Brocca et al. (2014) proposed a conceptually different rainfall retrieval algorithm by using the soil
) has been used by several retrieval algorithms ( You et al. 2015 ; Kummerow et al. 2015 ). To largely avoid the possible surface contamination, instead of using the signatures from window channels (e.g., 85 GHz), Staelin and Chen (2000) developed a rainfall retrieval algorithm solely dependent on the microwave observations near opaque water vapor and oxygen absorption channels (183 and 52 GHz). Brocca et al. (2014) proposed a conceptually different rainfall retrieval algorithm by using the soil
