Evaluation of the Microphysical Assumptions within GPM-DPR Using Ground-Based Observations of Rain and Snow

The Global Precipitation Measurement Dual-Frequency Precipitation Radar (GPM-DPR) provides an opportunity to investigate hydrometeor properties. Here, an evaluation of the microphysical framework used within the GPM-DPR retrieval was undertaken using ground-based disdrometer measurements in both rai...

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Bibliographic Details
Main Authors: Randy J. Chase, Stephen W. Nesbitt, Greg M. McFarquhar
Format: Article
Language:English
Published: MDPI AG 2020-06-01
Series:Atmosphere
Subjects:
Online Access:https://www.mdpi.com/2073-4433/11/6/619
Description
Summary:The Global Precipitation Measurement Dual-Frequency Precipitation Radar (GPM-DPR) provides an opportunity to investigate hydrometeor properties. Here, an evaluation of the microphysical framework used within the GPM-DPR retrieval was undertaken using ground-based disdrometer measurements in both rain and snow with an emphasis on the evaluation of snowfall retrieval. Disdrometer measurements of rain show support for the two separate prescribed relations within the GPM-DPR algorithm between the precipitation rate (<i>R</i>) and the mass weighted mean diameter (<inline-formula> <math display="inline"> <semantics> <msub> <mi>D</mi> <mi>m</mi> </msub> </semantics> </math> </inline-formula>) with a mean absolute percent error (<inline-formula> <math display="inline"> <semantics> <mrow> <mi>M</mi> <mi>A</mi> <mi>P</mi> <mi>E</mi> </mrow> </semantics> </math> </inline-formula>) on <i>R</i> of <inline-formula> <math display="inline"> <semantics> <mn>29%</mn> </semantics> </math> </inline-formula> and <inline-formula> <math display="inline"> <semantics> <mn>47%</mn> </semantics> </math> </inline-formula> and a mean bias percentage (<inline-formula> <math display="inline"> <semantics> <mrow> <mi>M</mi> <mi>B</mi> <mi>P</mi> </mrow> </semantics> </math> </inline-formula>) of <inline-formula> <math display="inline"> <semantics> <mrow> <mo>−</mo> <mn>6%</mn> </mrow> </semantics> </math> </inline-formula> and <inline-formula> <math display="inline"> <semantics> <mrow> <mo>−</mo> <mn>20%</mn> </mrow> </semantics> </math> </inline-formula> for the stratiform and convective relation, respectively. Ground-based disdrometer measurements of snow show higher MAPE and MBP values in the retrieval of <i>R</i>, at <inline-formula> <math display="inline"> <semantics> <mn>77%</mn> </semantics> </math> </inline-formula> and <inline-formula> <math display="inline"> <semantics> <mrow> <mo>−</mo> <mn>52%</mn> </mrow> </semantics> </math> </inline-formula>, respectively, compared to the stratiform rain relation. An investigation using the disdrometer-measured fall velocity and mass in the calculation of <i>R</i> and <inline-formula> <math display="inline"> <semantics> <msub> <mi>D</mi> <mi>m</mi> </msub> </semantics> </math> </inline-formula> illustrates that the variability found in hydrometeor mass causes a poor correlation between <i>R</i> and <inline-formula> <math display="inline"> <semantics> <msub> <mi>D</mi> <mi>m</mi> </msub> </semantics> </math> </inline-formula> in snowfall. The results presented here suggest that <inline-formula> <math display="inline"> <semantics> <mrow> <mi>R</mi> <mo>−</mo> <msub> <mi>D</mi> <mi>m</mi> </msub> </mrow> </semantics> </math> </inline-formula> retrieval is likely not optimal in snowfall, and other retrieval techniques for <i>R</i> should be explored.
ISSN:2073-4433