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Analysis and Mitigation of Striping Noise in Two Water Vapor Sounding Channels of Global Precipitation Measurement (GPM) Microwave Imageral. 1981 ; Hersman and Poe 1981 ). No amplifier was placed in front of a receiver of AMSU-A. In this study, we will show that striping noise also existed in brightness temperature observations of two water vapor sounding channels at the frequencies 183.31 3 and 183 7 GHz from the Global Precipitation Measurement (GPM) Microwave Imager (GMI). The GMI serves as the successor of the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) to continuously provide global precipitation
al. 1981 ; Hersman and Poe 1981 ). No amplifier was placed in front of a receiver of AMSU-A. In this study, we will show that striping noise also existed in brightness temperature observations of two water vapor sounding channels at the frequencies 183.31 3 and 183 7 GHz from the Global Precipitation Measurement (GPM) Microwave Imager (GMI). The GMI serves as the successor of the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) to continuously provide global precipitation
for the evaluation of atmospheric numerical model simulations and their parameterized ice-phase microphysics (e.g., Delanoë et al. 2011 ; Stein et al. 2015 ; Ori et al. 2020 ). Despite many advances in satellite remote sensing techniques and sensors in the past few decades, the uncertainty in the estimate of the atmosphere’s ice water path remains large, and there is poor agreement between observational retrievals and numerical models (e.g., Duncan and Eriksson 2018 ). The best way to retrieve
for the evaluation of atmospheric numerical model simulations and their parameterized ice-phase microphysics (e.g., Delanoë et al. 2011 ; Stein et al. 2015 ; Ori et al. 2020 ). Despite many advances in satellite remote sensing techniques and sensors in the past few decades, the uncertainty in the estimate of the atmosphere’s ice water path remains large, and there is poor agreement between observational retrievals and numerical models (e.g., Duncan and Eriksson 2018 ). The best way to retrieve
about the meteorological sensors installed on the tower and the BSRN site in general can be found in Maturilli et al. (2013) . AWI’s humidity and temperature profiler (HATPRO), a microwave radiometer, provides information on vertically integrated liquid water in the atmospheric column, that is, the liquid water path (LWP; Nomokonova et al. 2019a ), which is used in this study to detect precipitation and indicate riming. Details on the LWP retrieval can be found in Nomokonova et al. (2019a) . The
about the meteorological sensors installed on the tower and the BSRN site in general can be found in Maturilli et al. (2013) . AWI’s humidity and temperature profiler (HATPRO), a microwave radiometer, provides information on vertically integrated liquid water in the atmospheric column, that is, the liquid water path (LWP; Nomokonova et al. 2019a ), which is used in this study to detect precipitation and indicate riming. Details on the LWP retrieval can be found in Nomokonova et al. (2019a) . The
Intercalibration of the GPM Microwave Radiometer Constellationintercalibration efforts have focused on the extension of the DMSP data record ( Yan and Weng 2008 ; Yang et al. 2011 ; Sapiano et al. 2013 ), incorporating newer and more capable sensors for more robust longer-term data records ( Wilheit 2013 ; Wentz et al. 2001 ; Wentz 2015 ), developing improved techniques ( Ruf 2000 ; Brown and Ruf 2005 ), and addressing calibration differences with cross-track temperature and water vapor sounding radiometers ( Zou and Wang 2011 ; John et al. 2012 , 2013 ; Chung et
intercalibration efforts have focused on the extension of the DMSP data record ( Yan and Weng 2008 ; Yang et al. 2011 ; Sapiano et al. 2013 ), incorporating newer and more capable sensors for more robust longer-term data records ( Wilheit 2013 ; Wentz et al. 2001 ; Wentz 2015 ), developing improved techniques ( Ruf 2000 ; Brown and Ruf 2005 ), and addressing calibration differences with cross-track temperature and water vapor sounding radiometers ( Zou and Wang 2011 ; John et al. 2012 , 2013 ; Chung et
-free MODIS temperature data. As the liquid water content of global snowpack is not available, we define dry (wet) snow when the skin and air temperature are both below (above) 0°C ( Baggi and Schweizer 2009 ). To account for atmospheric radiometric signals, we also added the integrated liquid and ice water content of the clouds, as well as the integrated water vapor content of the atmospheric column from the second version of the Modern-Era Retrospective Analysis for Research and Applications (MERRA-2-M2
-free MODIS temperature data. As the liquid water content of global snowpack is not available, we define dry (wet) snow when the skin and air temperature are both below (above) 0°C ( Baggi and Schweizer 2009 ). To account for atmospheric radiometric signals, we also added the integrated liquid and ice water content of the clouds, as well as the integrated water vapor content of the atmospheric column from the second version of the Modern-Era Retrospective Analysis for Research and Applications (MERRA-2-M2
The GPM mission collects essential rain and snow data for scientific studies and societal benefit. Water is essential to our planet. It literally moves mountains through erosion, transports heat in Earth’s oceans and atmosphere, keeps our planet from freezing as a result of radiative impacts of atmospheric water vapor, and causes catastrophes through droughts, floods, landslides, blizzards, and severe storms, but most importantly water is vital for nourishing all life on Earth. Precipitation as
The GPM mission collects essential rain and snow data for scientific studies and societal benefit. Water is essential to our planet. It literally moves mountains through erosion, transports heat in Earth’s oceans and atmosphere, keeps our planet from freezing as a result of radiative impacts of atmospheric water vapor, and causes catastrophes through droughts, floods, landslides, blizzards, and severe storms, but most importantly water is vital for nourishing all life on Earth. Precipitation as
NASA’s precipitation measurement missions provide critical precipitation information to end users that improves understanding of Earth’s water cycle and enhances decision-making at local to global scales. Precipitation is the fundamental source of freshwater in the water cycle. If one could collect all of the water in the atmosphere, including water vapor, clouds, and precipitation, it would account for 4% of the total freshwater and 0.01% of the total water on Earth ( USGS 2016 ). Despite its
NASA’s precipitation measurement missions provide critical precipitation information to end users that improves understanding of Earth’s water cycle and enhances decision-making at local to global scales. Precipitation is the fundamental source of freshwater in the water cycle. If one could collect all of the water in the atmosphere, including water vapor, clouds, and precipitation, it would account for 4% of the total freshwater and 0.01% of the total water on Earth ( USGS 2016 ). Despite its
Resolving Surface Rain from GMI High-Frequency Channels: Limits Imposed by the Three-Dimensional Structure of Precipitationmeasured radiances are the product of the interaction of surface-emitted radiation with water vapor, liquid, and solid hydrometeors in the atmosphere. The radiances are converted into brightness temperatures (TBs) for physical interpretation. The 183-GHz channels are primarily sensitive to water vapor absorption and ice scattering. While channels between 80 and 170 GHz are also most sensitive to ice scattering, channels between 10 and 40 GHz are most sensitive to emission by liquid raindrops (and by
measured radiances are the product of the interaction of surface-emitted radiation with water vapor, liquid, and solid hydrometeors in the atmosphere. The radiances are converted into brightness temperatures (TBs) for physical interpretation. The 183-GHz channels are primarily sensitive to water vapor absorption and ice scattering. While channels between 80 and 170 GHz are also most sensitive to ice scattering, channels between 10 and 40 GHz are most sensitive to emission by liquid raindrops (and by
vapor, cloud liquid water, and ice content. McKague et al. (1998) , Berg et al. (2006) , and Bennartz and Petty (2001) describe a strong correlation between these factors and the three criteria listed above. In this process, surface types are defined using SSM/I observed emissivity climatology ( Aires et al. 2011 ) updated daily by NOAA’s AutoSnow product ( Romanov et al. 2000 ), while TPW and 2-m temperature come from reanalysis datasets such as ECMWF ( Dee et al. 2011 ) and JMA’s global
vapor, cloud liquid water, and ice content. McKague et al. (1998) , Berg et al. (2006) , and Bennartz and Petty (2001) describe a strong correlation between these factors and the three criteria listed above. In this process, surface types are defined using SSM/I observed emissivity climatology ( Aires et al. 2011 ) updated daily by NOAA’s AutoSnow product ( Romanov et al. 2000 ), while TPW and 2-m temperature come from reanalysis datasets such as ECMWF ( Dee et al. 2011 ) and JMA’s global
Americas. The weather cooperated by providing a large representative sample of storms under both warm and cold conditions. Several precipitation events were of the “atmospheric river” type in which a long fetch of warm-sector water vapor influx impacted the Olympic Mountains, while others were dominated by warm-/cold-frontal dynamics or unstable postfrontal conditions. The OLYMPEX dataset will serve both NASA’s need for validation of its satellite-based precipitation retrieval algorithms over
Americas. The weather cooperated by providing a large representative sample of storms under both warm and cold conditions. Several precipitation events were of the “atmospheric river” type in which a long fetch of warm-sector water vapor influx impacted the Olympic Mountains, while others were dominated by warm-/cold-frontal dynamics or unstable postfrontal conditions. The OLYMPEX dataset will serve both NASA’s need for validation of its satellite-based precipitation retrieval algorithms over
