Atmospheric Retrievals and Assessment for Microwave Observations from Chinese FY-3C Satellite during Hurricane Matthew

Jieying He, Haonan Chen

Remote Sensing · 12 April 2019 · NOAA (incl. GFDL, PSL, GSL, NCEP/EMC), Colorado State University (incl. CIRA)

In one sentence. Neural network combined with 1D-variational retrieval of FY-3C microwave temperature and humidity profiles during Hurricane Matthew, improving WRF data-assimilation track forecasts.

Abstract

The evolution process of hurricane Matthew (NO. 8, 2016) was simulated using the mesoscale Weather Research and Forecasting (WRF) model at temporal resolution of 5 min and spatial resolution of 15 km. The atmospheric temperature and humidity profiles were retrieved accordingly for diagnostic analysis of the short-term heavy rainstorm. The satellite-based microwave observations from Microwave Humidity and Temperature Sounder (MWHTS) instrument on board the FY-3C polar-orbiting satellite were matched with the WRF grid points. In particular, the in-orbit calibration and data quality control are detailed, and an innovative method combining artificial neural network (ANN) and 1-D variational approach is presented to derive the high-performance retrieval profiles. Results show that the root-mean-square errors of the retrieved temperature and water vapor density profiles are 0.75 K and 0.41 g/m3, respectively. In addition, this study used both the retrievals and radiance from MWHTS as input to the WRF Data Assimilation (WRFDA) model to forecast the track and intensity of hurricane Matthew. The forecast results were cross-compared with the best track to verify the radiance quality and performance of the retrievals, especially for the 118 GHz channel, which was firstly used in meteorological satellite.

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