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Andrea Camplani, Daniele Casella, Paolo Sanò, and Giulia Panegrossi

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

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Daniel Watters, Alessandro Battaglia, Kamil Mroz, and Frédéric Tridon

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

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Veljko Petković, Christian D. Kummerow, David L. Randel, Jeffrey R. Pierce, and John K. Kodros

-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

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Jackson Tan, George J. Huffman, David T. Bolvin, Eric J. Nelkin, and Manikandan Rajagopal

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

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Manikandan Rajagopal, Edward Zipser, George Huffman, James Russell, and Jackson Tan

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

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Zeinab Takbiri, Ardeshir Ebtehaj, Efi Foufoula-Georgiou, Pierre-Emmanuel Kirstetter, and F. Joseph Turk

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

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Kamil Mroz, Mario Montopoli, Alessandro Battaglia, Giulia Panegrossi, Pierre Kirstetter, and Luca Baldini

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

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Linda Bogerd, Aart Overeem, Hidde Leijnse, and Remko Uijlenhoet

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

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Yalei You, S. Joseph Munchak, Christa Peters-Lidard, and Sarah Ringerud

) 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

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Yagmur Derin, Pierre-Emmanuel Kirstetter, and Jonathan J. Gourley

Multisatellite Retrievals for Global Precipitation Measurement (IMERG). IMERG merges available Level-2 passive microwave (PMW) and infrared (IR) precipitation retrievals at a high spatial (0.1°) and temporal resolution (30 min). Yet, multiple uncertainties are associated with Level-2 PMW and IR precipitation retrievals and Level-3 merged SPPs, especially in coastal regions. Precipitation retrievals from PMW observations highly depend on the ability to separate emission and scattering radiances of rain, ice

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