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Remote Sens. 2019, 11(1), 54;

An Assessment of Satellite Radiance Data Assimilation in RMAPS

Institute of Urban Meteorology, China Meteorological Administration, Beijing 100089, China
State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China
Joint Center for Global Change Studies (JCGCS), Beijing 100875, China
Author to whom correspondence should be addressed.
Received: 30 October 2018 / Revised: 19 December 2018 / Accepted: 27 December 2018 / Published: 29 December 2018
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Due to the availability of observations and the effectiveness of bias correction, it is still a challenge to assimilate data from the polar orbit satellites into a limited-area and frequently updated model. This study assessed the initial application of satellite radiance data from multiple platforms in the Rapid-refresh Multi-scale Analysis and Prediction System (RMAPS). Satellite radiance data from the advanced microwave sounding unit-A (AMSU-A) and microwave humidity sounding (MHS) were used. Two 12-day retrospective runs were conducted to evaluate the impact of assimilating satellite radiance data on 0–24 h forecasts using RMAPS. The forecasts, initialized from analyses with and without satellite radiance data, were verified against observations. The results showed that satellite radiance data from AMSU-A and MHS had a positive impact on the initial conditions and the forecasts of RMAPS, even over the relatively data-rich area of North China. Compared to the control run that only assimilated conventional observations, an improvement of about 36.8% can be obtained for the temperature bias between 300 hPa and 850 hPa and 0.65% for the average RMSE. Satellite radiance observations from 1200 UTC contribute relatively significantly (77.8%) to the bias improvement of the initial temperature field. For the wind at 10 m, the bias and root-mean-square error (RMSE) both had a reduction for the 0–12 h forecast range. An improvement can be also found for the skill score of the 3-h accumulated rainfall below 10.0 mm in the first 12 h of the forecast range. There was a slight improvement in the skill score of the 6-h accumulated rainfall above 50 mm over North China, with a 20.7% improvement for the first 12 h of the forecast. The inclusion of satellite radiance observations was found to be beneficial for the initial temperature, which consequently improved the forecast skill of the 0–12 h range in the RMAPS. View Full-Text
Keywords: satellite radiance; data assimilation; assessment; initial conditions; forecast skill satellite radiance; data assimilation; assessment; initial conditions; forecast skill

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Xie, Y.; Fan, S.; Chen, M.; Shi, J.; Zhong, J.; Zhang, X. An Assessment of Satellite Radiance Data Assimilation in RMAPS. Remote Sens. 2019, 11, 54.

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