EVALUATING THE PHYSICAL CONSISTENCY BETWEEN GMI AND DPR BASED RETRIEVALS OF PRECIPITATION SYSTEMS

V. Chandrasekar ¹, C. Radhakrishnan¹, S. C. Reising¹

¹Colorado State University

Spaceborne microwave sensors provide vital observations of the global atmosphere, particularly over remote regions and the open oceans, where conventional ground-based measurements are limited or unavailable. These instruments are generally categorized into two groups: microwave radiometers and active radar systems. Radiometers detect natural thermal emission at a variety of frequencies, which provide information about integrated and sounding properties of the atmosphere, such as water vapor, cloud liquid water, and precipitation. Radars transmit microwave pulses and measure the energy scattered back from hydrometeors, thereby capturing the detailed vertical structure of precipitation with range profiling. Together, these complementary observing systems form the foundation for comprehensive monitoring of precipitation and related atmospheric processes.

The Global Precipitation Measurement (GPM) Core Observatory, launched in 2014, is unique in combining both passive and active microwave sensors on a single platform. It carries the GPM Microwave Imager (GMI) and the Dual-frequency Precipitation Radar (DPR), which together provide synergistic information on precipitation systems across the globe. DPR operates at Ku- and Ka-band frequencies, providing three-dimensional profiles of radar reflectivity. These profiles reveal critical features such as the vertical distribution of rain and snow, the presence and location of the melting layer, as well as hydrometeor microphysical characteristics. In contrast, GMI observes at 13 frequencies ranging from 10 to 183 GHz, offering a wide swath of coverage sensitive to surface emission and scattering from hydrometeors. By combining these capabilities, GPM enables physically consistent retrievals of precipitation properties that no single instrument could provide on its own. The GPM combined product [1] integrates GMI radiances with DPR vertical reflectivity to generate improved global precipitation estimates, capitalizing on the complementary strengths of the two instruments [2].

The present study aims to evaluate the physical consistency between GMI and DPR measurements. Specifically, we examine coincident observations during precipitation events and study how these instruments yield mutually consistent descriptions of precipitation structures. Our approach employs a combination of observational and physical models to tie the observations together. It is achieved by using the Monochromatic Radiative Transfer Model (MonoRTM) to retrieve hydrometeor profiles from GMI brightness temperatures. The newly developed Version 8 GPM DPR Level-2 algorithm [3] provides vertical profiles of hydrometeors. In its initial phase, five hydrometeor types are used: dry snow/ice crystals (DS/ICE), wet snow (WS), graupel (GPL), hail (Hail), and rain (Rain). DS/ICE, GPL, and Hail represent low-density, medium-density, and high-density particles, respectively. This study integrates microphysics modeling, the newly developed vertical hydrometeor profiles, and atmospheric reanalysis data from numerical weather prediction models as input to radiative transfer model simulations. The radiometer-based retrievals are then compared with newly-developed vertical hydrometeor distributions obtained from DPR. This comparison provides a framework to assess the degree of consistency between passive and active microwave retrievals.

Evaluating consistency between retrievals from GMI and DPR is important for several reasons.

First, it builds confidence in the GPM combined products, which are used in hydrological applications, climate studies, and model validation. Second, it supports the development of multi-sensor retrieval frameworks that integrate both active and passive data streams. Such integration enhances the accuracy of precipitation and atmospheric property estimates. Third, it offers new insights into the microphysical processes of precipitation by linking vertically resolved radar information with radiometer observations sensitive to integrated column properties.

1. Olson, Willam S., Hirohiko Masunaga, and Combined Radar-Radiometer Algorithm Team Gpm. “GPM combined radar-radiometer precipitation algorithm theoretical basis document (version 4).” NASA: Washington, DC, USA (2016).

2. Grecu, M., W. S. Olson, S. J. Munchak, S. Ringerud, L. Liao, Z. Haddad, B. L. Kelley, and S. F. McLaughlin, 2016: The GPM Combined Algorithm. J. Atmos. Oceanic Technol., 33, 2225–2245.

3. Chandrasekar, C. V., Le, M., and Harri, A.-M.: THE HYDROMETEOR IDENTIFICATION FOR THE GPM DPR: Version 8 Updates, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13739.