Bias Correction Of ATMS Radiance Data And Experimental Study Of Typhoon Forecast

ATMS is the new microwave temperature and humidity sounding radiometer, building on the NOAA series AMSU-A and MHS heritage, the spatial resolution and humidity detecting channels has greatly improved. For the data assimilation applications of the new microwave sounding radiometer, data bias correction is particularly critical. In this paper, the data bias revision of a new microwave sounding radiometer (ATMS) carried on the NPP satellite were studied. Based on WRFDA3.7 and radiative transfer model RTTOV 11.2, construct bias correction system, prepare bias correction procedures and bias correction factor is applied to test a typhoon forecast.One bias correction system has been established with the ATMS observation data in 2015 January 1-15 in winter as the research subjects. According to the characteristics of ATMS data, a new bias correction scheme for ATMS radiance data is developed referencing the classical off-line scheme which had been used in European Centre for Medium-Range Weather Forecasts (ECMWF), In the framework of WRFDA, brightness temperature deviation of 10 (6-10,18-22) ATMS channel were revised which play a major role in the numerical prediction, demonstrate the effectiveness of bias corrections. Taking into account the impact of sea ice on the sea surface temperature predictor in winter and summer, in order to make a more accurate forecast of typhoon path, bias correction coefficients obtained from the July 21,2015 August 5 ATMS radiance data are applied to the typhoon forecast. And analyze the impact of assimilation on forecast results.The main conclusions are as follows:(1) In this paper, the bias of NPP ATMS radiance data is corrected. Based on WRFDA3.7 and radiative transfer model RTTOV 11.2, construct bias correction system, ATMS data were revised in six latitude, statistical results showed that:Bias correction results were good and the observation residuals of ATMS data after the whole bias correction procedure fit a normal distribution (mean=0). After the bias correction, standard deviation of observation residuals decreased in most channels, indicates that ATMS data has higher quality.(2) Compared with bias correction coefficient from NOAA-18 AMSUA/MHS, typhoon forecast path was closer to the actual path using the coefficient statisticed offline, results shows that using the coefficient statisticed offline was better than the coefficient obtained directly from NOAA-18 AMSUA/MHS or equivalent.(3) The assimilation of ATMS data made a dramatical change in first guess field applying to the typhoon path forecast which compared with AMSUA/MHS data, and the results were better than AMSUA/MHS data. The results showed that ATMS data have good prospects in numerical weather prediction.