101 Enhancement on GPM DPR Dual-Frequency Profile Classification Module

Tuesday, 29 August 2017
Zurich (Swissotel Chicago)
Minda Le, Colorado State Univ., Fort Collins, CO; and C. V. Chandra, S. K. Biswas, and T. Iguchi

Dual-frequency profile classification module of GPM (Global Precipitation Measurement) DPR (Dual-frequency Precipitation Radar) has gone through expensive validations using both TRMM (Tropical Rainfall Measurement Mission) radar products and ground based radar system since launch and shows promising results [1][2].

For further enhancement on the dual-frequency classification module, we have developed an algorithm to identify surface snowfall based on dual-frequency radar observations. This algorithm has been implemented as an experimental version in the upcoming version 5 of the GPM-DPR level 2 algorithm [3][4]. The algorithm has been validated using NEXRAD, NPOL as well as X band Alaska radars. In this paper, we present study cases showing validation results of the surface snowfall algorithm with NPOL radar during OLYMPEX field campaign, a NASA-led field campaign took place in Washington State from November 2015 through February 2016. Other validation sources such as airborne radar are also used to validate surface snowfall.

Compared to snow profiles, graupel and hail profiles are with higher density and they give noticeable different features on dual-frequency observations. Figure 1 here shows [5] a theoretical simulation of dual-frequency ratio versus drop size for snow, aggregates and graupel. We can observe different value ranges of dual-frequency ratio for different precipitation types. This is valuable feature that we will use to perform high-density profile classification such as graupel and hail.

High-density profiles feature large reflectivity values and even more, they are sometimes accompanied with multiple scattering features on its vertical profiles. Abnormal features on dual-frequency ratio profile which cannot be explained by single scatting theory are found, most of the time, among hail precipitation [6]. Algorithms to identify this kind of high-density precipitation profile are also considered as further enhancements of dual-frequency classification module in the GPM level 2 algorithm.



[1] M. Le and V. Chandrasekar, Evaluation and Validation of GPM Dual-frequency Classification Module after Launch, J. Atmos. Oceanic Technol., Special Collection: Precipitation retrieval algorithms for GPM. December, 2016.

[2] Jun Awaka, Minda Le, V. Chandrasekar, Naofumi Yoshida, Tomohiko Higashiuwatoko, Takuji Kubota and Toshio Iguchi, Rain type classification algorithm module for GPM dual-frequency precipitation radar, J. Atmos. Oceanic Technol., Special Collection: Precipitation retrieval algorithms for GPM. May, 2016.

[3] M. Le, V. Chandrasekar and B. Sounak, An Algorithm to Identify Surface Snowfall from GPM DPR Observations, Accepted by Geoscience and Remote Sensing, IEEE Transactions, March, 2017. Publishing soon.

[4] Toshio Iguchi, Shinta Seto, Robert Meneghini, Naofumi Yoshida, Jun Awaka, Minda Le,
V. Chandrasekar, and Takuji Kubota, GPM/DPR Level-2 Algorithm Theoretical Basis Document, revised April, 2017 for V05.

[5] J. Tyynelä and V. Chandrasekar, Characterizing falling snow using multi-frequency dual polarization measurements, Journal of Geophysical Research, Atmospheres. Volume 119, Issue 13, 2014.

[6] A.Battaglia, S.Tanelli, K.Mroz, and F.Tridon, Multiple scattering in observations of the GPM dual-frequency precipitation radar: Evidence and impact on retrievals: J. Geophys. Res. Atmos., 120, 2015.

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