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Direct comparisons between GPM-DPR and CloudSat snowfall retrievals

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  • 1 School of Computer Science, University of Oklahoma, Norman, OK 73072, USA School of Meteorology, University of Oklahoma, Norman, OK 73072, USA NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography, University of Oklahoma, Norman OK 73072, USA
  • | 2 Department of Atmospheric Sciences, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA;
  • | 3 Cooperative Institute for Severe and High-Impact Weather Research and Operations and School of Meteorology, University of Oklahoma, Norman, OK 73072, USA;
  • | 4 Space Science and Engineering Center, University of Wisconsin-Madison, Madison, WI 53706, USA;
  • | 5 Goddard Space Flight Center, NASA, Greenbelt, MD 20771, USA;
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Abstract

Two spaceborne radars currently in orbit enable the sampling of snowfall near the surface and throughout the atmospheric column, namely CloudSat’s Cloud Profiling Radar (CPR) and the Global Precipitation Measurement mission’s Dual-Frequency Precipitation Radar (GPM-DPR). In this paper a direct comparison of the CPR’s 2C-SNOW-PROFILE (2CSP), the operational GPM-DPR algorithm (2ADPR) and a neural network (NN) retrieval applied to the GPM-DPR data is performed using coincident observations between both radars. Examination of over 3500 profiles within moderate to strong precipitation (Ka-band ≥ 18 dBZ) show that the NN retrieval provides the closest retrieval of liquid equivalent precipitation rate (R) immediately above the melting level to the R retrieved just below the melting layer, agreeing within 5%. Meanwhile, 2CSP retrieves a maximum value of R at −15◦C, decreases by 35% just above the melting layer, and is about 50% smaller than the GPM-DPR retrieved R below the melting layer. CPR measured reflectivity shows median reduction of 2-3 dB from −15°C to −2.5°C, likely the reason for the 2CSP retrieval reduction of R. Two case studies from NASA field campaigns (i.e., OLYMPEX and IMPACTS) provide analogues to the type of precipitating systems found in the comparison between retrieval products. For the snowfall events that GPM-DPR can observe, this work suggests that the 2CSP retrieval is likely underestimating the unattenuated reflectivity, resulting in a potential low bias in R. Future work should investigate how frequently the underestimated reflectivity profiles occur within the CPR record and quantify its potential effects on global snowfall accumulation estimation.

Corresponding author: Randy J. Chase, randychase@ou.edu

Abstract

Two spaceborne radars currently in orbit enable the sampling of snowfall near the surface and throughout the atmospheric column, namely CloudSat’s Cloud Profiling Radar (CPR) and the Global Precipitation Measurement mission’s Dual-Frequency Precipitation Radar (GPM-DPR). In this paper a direct comparison of the CPR’s 2C-SNOW-PROFILE (2CSP), the operational GPM-DPR algorithm (2ADPR) and a neural network (NN) retrieval applied to the GPM-DPR data is performed using coincident observations between both radars. Examination of over 3500 profiles within moderate to strong precipitation (Ka-band ≥ 18 dBZ) show that the NN retrieval provides the closest retrieval of liquid equivalent precipitation rate (R) immediately above the melting level to the R retrieved just below the melting layer, agreeing within 5%. Meanwhile, 2CSP retrieves a maximum value of R at −15◦C, decreases by 35% just above the melting layer, and is about 50% smaller than the GPM-DPR retrieved R below the melting layer. CPR measured reflectivity shows median reduction of 2-3 dB from −15°C to −2.5°C, likely the reason for the 2CSP retrieval reduction of R. Two case studies from NASA field campaigns (i.e., OLYMPEX and IMPACTS) provide analogues to the type of precipitating systems found in the comparison between retrieval products. For the snowfall events that GPM-DPR can observe, this work suggests that the 2CSP retrieval is likely underestimating the unattenuated reflectivity, resulting in a potential low bias in R. Future work should investigate how frequently the underestimated reflectivity profiles occur within the CPR record and quantify its potential effects on global snowfall accumulation estimation.

Corresponding author: Randy J. Chase, randychase@ou.edu
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