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Nobuyuki Utsumi, Hyungjun Kim, F. Joseph Turk, and Ziad. S. Haddad


Quantifying time-averaged rain rate, or rain accumulation, on subhourly time scales is essential for various application studies requiring rain estimates. This study proposes a novel idea to estimate subhourly time-averaged surface rain rate based on the instantaneous vertical rain profile observed from low-Earth-orbiting satellites. Instantaneous rain estimates from the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) are compared with 1-min surface rain gauges in North America and Kwajalein atoll for the warm seasons of 2005–14. Time-lagged correlation analysis between PR rain rates at various height levels and surface rain gauge data shows that the peak of the correlations tends to be delayed for PR rain at higher levels up to around 6-km altitude. PR estimates for low to middle height levels have better correlations with time-delayed surface gauge data than the PR’s estimated surface rain rate product. This implies that rain estimates for lower to middle heights may have skill to estimate the eventual surface rain rate that occurs 1–30 min later. Therefore, in this study, the vertical profiles of TRMM PR instantaneous rain estimates are averaged between the surface and various heights above the surface to represent time-averaged surface rain rate. It was shown that vertically averaged PR estimates up to middle heights (~4.5 km) exhibit better skill, compared to the PR estimated instantaneous surface rain product, to represent subhourly (~30 min) time-averaged surface rain rate. These findings highlight the merit of additional consideration of vertical rain profiles, not only instantaneous surface rain rate, to improve subhourly surface estimates of satellite-based rain products.

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Nobuyuki Utsumi, F. Joseph Turk, Ziad S. Haddad, Pierre-Emmanuel Kirstetter, and Hyungjun Kim


Precipitation estimation based on passive microwave (MW) observations from low-Earth-orbiting satellites is one of the essential variables for understanding the global climate. However, almost all validation studies for such precipitation estimation have focused only on the surface precipitation rate. This study investigates the vertical precipitation profiles estimated by two passive MW-based retrieval algorithms, i.e., the emissivity principal components (EPC) algorithm and the Goddard profiling algorithm (GPROF). The passive MW-based condensed water content profiles estimated from the Global Precipitation Measurement Microwave Imager (GMI) are validated using the GMI + Dual-Frequency Precipitation Radar combined algorithm as the reference product. It is shown that the EPC generally underestimates the magnitude of the condensed water content profiles, described by the mean condensed water content, by about 20%–50% in the middle-to-high latitudes, while GPROF overestimates it by about 20%–50% in the middle-to-high latitudes and more than 50% in the tropics. Part of the EPC magnitude biases is associated with the representation of the precipitation type (i.e., convective and stratiform) in the retrieval algorithm. This suggests that a separate technique for precipitation type identification would aid in mitigating these biases. In contrast to the magnitude of the profile, the profile shapes are relatively well represented by these two passive MW-based retrievals. The joint analysis between the estimation performances of the vertical profiles and surface precipitation rate shows that the physically reasonable connections between the surface precipitation rate and the associated vertical profiles are achieved to some extent by the passive MW-based algorithms.

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F. Joseph Turk, Sarah E. Ringerud, Yalei You, Andrea Camplani, Daniele Casella, Giulia Panegrossi, Paolo Sanò, Ardeshir Ebtehaj, Clement Guilloteau, Nobuyuki Utsumi, Catherine Prigent, and Christa Peters-Lidard


A fully global satellite-based precipitation estimate that can transition across the changing Earth surface and complex land/water conditions is an important capability for many hydrological applications, and for independent evaluation of the precipitation derived from weather and climate models. This capability is inherently challenging owing to the complexity of the surface geophysical properties upon which the satellite-based instruments view. To date, these satellite observations originate primarily from a variety of wide-swath passive microwave (MW) imagers and sounders. In contrast to open ocean and large water bodies, the surface emissivity contribution to passive MW measurements is much more variable for land surfaces, with varying sensitivities to near-surface precipitation. The NASA–JAXA Global Precipitation Measurement (GPM) spacecraft (2014–present) is equipped with a dual-frequency precipitation radar and a multichannel passive MW imaging radiometer specifically designed for precipitation measurement, covering substantially more land area than its predecessor Tropical Rainfall Measuring Mission (TRMM). The synergy between GPM’s instruments has guided a number of new frameworks for passive MW precipitation retrieval algorithms, whereby the information carried by the single narrow-swath precipitation radar is exploited to recover precipitation from a disparate constellation of passive MW imagers and sounders. With over 6 years of increased land surface coverage provided by GPM, new insight has been gained into the nature of the microwave surface emissivity over land and ice/snow-covered surfaces, leading to improvements in a number of physically and semiphysically based precipitation retrieval techniques that adapt to variable Earth surface conditions. In this manuscript, the workings and capabilities of several of these approaches are highlighted.

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