Testing Passive Microwave-based Hail Retrievals using GPM DPR Ku-band Radar

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  • 1 NASA Postdoctoral Program, NASA Marshall Space Flight Center, Huntsville, Alabama, USA
  • 2 NASA Marshall Space Flight Center, Huntsville, Alabama, USA
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Abstract

Several studies in the literature have developed approaches to diagnose hail storms from satellite-borne passive-microwave imagery and build nearly global climatologies of hail. This paper uses spaceborne Ku-band radar measurements to validate several passive-microwave approaches. We assess the retrievals based on how tightly they constrain the radar reflectivity at -20°C, and how this measured radar reflectivity aloft varies geographically. The algorithm which combines Minimum 19-GHz polarization corrected temperature (PCT) with a 37-GHz PCT depression normalized by tropopause height constrains the radar reflectivity most tightly, and gives the least appearance of regional biases. A retrieval based on a 19- GHz PCT threshold of 261K also produces tightly clustered profiles of radar reflectivity, with little regional bias. An approach using regionally-adjusted Minimum 37-GHz PCT performs relatively well, but our results indicate it may overestimate hail in some subtropical and midlatitude regions. A threshold applied to the Minimum 37-GHz PCT (≤ 230K), without any scaling by region or probability of hail, overestimates hail in the tropics and underestimates beyond the tropics. For all retrieval approaches, storms identified as having hail tended to have radar reflectivity profiles that are consistent with general expectations for hailstorms (reflectivity > 50 dBZ below the 0°C level, and > 40 dBZ extending far above 0°C). Profiles from oceanic regions tended to have more rapidly decreasing reflectivity with height than profiles from other regions. Subtropical, high latitude, and high terrain land profiles had the slowest decreases of reflectivity with height.

Current Affiliation: NASA Marshall Space Flight Center, Huntsville, Alabama, USA

Corresponding author: Sarah D. Bang, sarah.d.bang@nasa.gov

This article is included in the Global Precipitation Measurement (GPM) special collection.

Abstract

Several studies in the literature have developed approaches to diagnose hail storms from satellite-borne passive-microwave imagery and build nearly global climatologies of hail. This paper uses spaceborne Ku-band radar measurements to validate several passive-microwave approaches. We assess the retrievals based on how tightly they constrain the radar reflectivity at -20°C, and how this measured radar reflectivity aloft varies geographically. The algorithm which combines Minimum 19-GHz polarization corrected temperature (PCT) with a 37-GHz PCT depression normalized by tropopause height constrains the radar reflectivity most tightly, and gives the least appearance of regional biases. A retrieval based on a 19- GHz PCT threshold of 261K also produces tightly clustered profiles of radar reflectivity, with little regional bias. An approach using regionally-adjusted Minimum 37-GHz PCT performs relatively well, but our results indicate it may overestimate hail in some subtropical and midlatitude regions. A threshold applied to the Minimum 37-GHz PCT (≤ 230K), without any scaling by region or probability of hail, overestimates hail in the tropics and underestimates beyond the tropics. For all retrieval approaches, storms identified as having hail tended to have radar reflectivity profiles that are consistent with general expectations for hailstorms (reflectivity > 50 dBZ below the 0°C level, and > 40 dBZ extending far above 0°C). Profiles from oceanic regions tended to have more rapidly decreasing reflectivity with height than profiles from other regions. Subtropical, high latitude, and high terrain land profiles had the slowest decreases of reflectivity with height.

Current Affiliation: NASA Marshall Space Flight Center, Huntsville, Alabama, USA

Corresponding author: Sarah D. Bang, sarah.d.bang@nasa.gov

This article is included in the Global Precipitation Measurement (GPM) special collection.

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