Abstract

The high relevance of satellites for collecting information regarding precipitation at global scale implies the need of a continuous validation of satellite products to ensure good data quality over time and to provide feedback for updating and improving retrieval algorithms. However, validating satellite products using measurements collected by sensors at ground is still a challenging task. To date, the Dual-frequency Precipitation Radar (DPR) aboard the Core Satellite of the Global Precipitation Measurement (GPM) mission is the only active sensor able to provide, at global scale, vertical profiles of rainfall rate, radar reflectivity, and Drop Size Distribution (DSD) parameters from space. In this study, we compare near surface GPM retrievals with long time series of measurements collected by seven laser disdrometers in Italy since the launch of the GPM mission. The comparison shows limited differences in the performances of the different GPM algorithms, be they dual- or single-frequency, although in most cases, the dual-frequency algorithms present the better performances. Furthermore, the agreement between satellite and ground-based estimates depends on the considered precipitation variable. The agreement is very promising for rain rate, reflectivity factor, and the mass-weighted mean diameter (Dm), while the satellite retrievals need to be improved for the normalized gamma DSD intercept parameter (Nw).

Highlights

  • Satellite data are crucial to detect, measure, and monitor the precipitation amount and its characteristics at global scale

  • The core satellite of the Global Precipitation Measurement (GPM) mission was launched more than seven years ago, and since a big effort has been made to validate GPM products with ground-based devices

  • This study has presented the first validation of the GPM Dual-frequency Precipitation Radar (DPR) observations in Italy with disdrometer data

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Summary

Introduction

Satellite data are crucial to detect, measure, and monitor the precipitation amount and its characteristics at global scale. Validation of DPR products Version 5 can be found in [6], where rainfall estimates obtained from Ku radar data were compared with different raingauge networks, showing a better performance with respect to the GPM microwave radiometer estimator. Radar-based validation can consider either rainfall retrievals referred to the ground or other estimates, including microphysical parameters, resampled at a common resolution with DPR Using the latter approach, in [8], four years of GPM-DPR Version 5 products were compared with the data obtained by five NEXRAD S-band radars in the U.S, finding good agreement in terms of co-located reflectivity with a correlation up to 0.9 at Ku-band and 0.85 at Ka-band. Radar network (5 at C-band) radar and raingauge network radar network (18 at C-band) radar network (17 at C-band) radars at S- and X-band

OTT Parsivel disdrometers
GPM DPR Data
Disdrometer Data
Precipitation Characteristics from Disdrometer Data
Comparison Approach
GPM DPR and Disdrometer Comparison
Findings
Conclusions
Full Text
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