VERTICAL PROFILE OF HYDROMETEOR CLASS RETRIEVAL --- A NEW PRODUCT IN VERSION 8 OF GPM DPR

The Global Precipitation Measurement (GPM) core observatory spacecraft has continuously provided us valuable observations on a global scale since launch in year 2014. Since May 2018, the Dual-frequency Precipitation Radar (DPR) has operated in full scan mode in both the Ku-band and Ka-band. The full scan mode can enable about 245 km full swath dual-frequency processing, where algorithms beforehand only enabled dual-frequency processing in the narrower inner swath. Dual-frequency full swath data provides us a valuable chance to improve our knowledge of precipitation processes by providing greater dynamic range, more detailed information on microphysics, and better accuracies in rainfall and liquid water content retrievals [1][2].

The DPR Level-2 algorithms consist of several modules including the classification (CSF) module, where precipitation type is classified into three major types: stratiform, convective, and other [3][4]. Besides that, estimates of the melting layer top and bottom are provided in the classification module with product name as “binDFRmMLTop”, “binDFRmMLBottom” and the quality metric of “flagMLquality”. Three flags namely, identifiers of falling snow on the ground, graupel or hail along vertical profile termed, “flagSurfaceSnowfall”, “flagGraupelHail” and “flagHail” are recently developed in the DPR level-2 algorithm using a concept of precipitation type index (PTI). All these are currently developed products (version 7) in classification module of GPM DPR level-2 algorithm based on full-swath dual-frequency observations [5][6][7].

A vertical description of the profiles of precipitation is a long-term goal of atmospheric research and precipitation science. Although GPM DPR has fine vertical resolutions in dual-frequency observations, most of the algorithms or products developed are 2 dimensional with either a “flag” or “type” (or etc.) on a 2-dimentional surface. For future prospective, the products developed by our team described above allow us to have the potential to take a big step forward adding vertical profile of hydrometeors for DPR full swath data. By doing this, a vertical profile of hydrometeor type will be available for DPR resolution in full swath if precipitation is detected. Figure 1 illustrates a conceptual flow chart for initial implementation of the algorithm adding vertical profile of hydrometeor type.

Left side of figure 1 shows different hydrometeor portfolios as either stratiform or convective rain types. The judgements are made mainly on the DPR products developed by our team as listed in the “algorithm judgement box”. Mixed phase hydrometeors are judged with melting layer top and bottom information together with the 0° isotherm. Flag of surface snowfall is used to identify snow only profile, while flags for detecting graupel and hail help identify range bins with those hydrometeor types. The whole judgement box is a robust detection system to not only combine the products but enforce meteorologically meaningful. In the initial phase, five hydrometeor types will be introduced. They are dry snow/ice crystal (DS/ICE), wet snow (WS), graupel (GPL), hail (Hail) and rain (Rain). DS/ICE, GPL and Hail represent low-density, medium-density, high-density particles respectively.

The algorithm has been applied to various types of precipitation events over the globe and has been successfully validated with NPOL ground radar, CSU-CHILL radar and NEXRAD radar network. The validation has extended to GMI based retrievals especially for strong precipitation with graupel and hail. This new feature of adding vertical hydrometeor profile to GPM DPR classification module is planned to be implemented in the next version (version 8) of level-2 algorithm in the near future. This paper presents the initial results and validation of this new products in GPM-DPR.

Figure 1. Conceptual flow chart for algorithm adding vertical profile of hydrometeor type. https://docs.google.com/document/d/1H3Gmbm7DM0DIs3RXseOY-WuaPbgaRIUs/edit

REFERENCES

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[2] Iguchi, T., 2020: Dual-Frequency Precipitation Radar (DPR) on the Global Precipitation Measurement (GPM) mission’s core observatory. Satellite Precipitation Measurement. Levizzani, V., C. Kidd, D. Kirschbaum, C. Kummerow, K. Nakamura, and F. Turk (eds.), Advances in Global Change Research, 69, Springer, 183-192.

[3] Iguchi, T., S. Seto, R. Meneghini, N. Yoshida, J. Awaka, M. Le, V. Chandrasekar, S. Brodzik, T. Kubota, and N. Takahashi, 2020: GPM/DPR level-2 algorithm theoretical basis document (revised for V07).

[4] Awaka, J., M. Le, S. Brodzik, T. Kubota, T. Masaki, V. Chandrasekar, and T. Iguchi, 2021: Development of Precipitation Type Classification Algorithms for a Full Scan Mode of GPM Dual-frequency Precipitation Radar. Journal of the Meteorological Society of Japan. Ser. II, Vol 99, 5, 1253-1270.

[5] Le, M., V. Chandrasekar and S. Biswas, 2017: An Algorithm to Identify Surface Snowfall from GPM DPR Observations, in IEEE Transactions on Geoscience and Remote Sensing, vol. 55, no. 7, pp. 4059-4071.

[6] Le, M. and V. Chandrasekar, 2021: Graupel and Hail Identification Algorithm for the Dual-frequency Precipitation Radar (DPR) on the GPM Core Satellite. Journal of the Meteorological Society of Japan. 99, Issue 1, pp.49-65.

[7] Le, M. and V. Chandrasekar, 2022: Hail Identification Algorithm for DPR on board the GPM Satellite. Under review by IEEE Transactions on Geoscience and Remote Sensing.