One-Dimensional Variational Retrieval of Temperature and Humidity Profiles from the FY4A GIIRS

A physical retrieval approach based on the one-dimensional variational(1 D-Var) algorithm is applied in this paper to simultaneously retrieve atmospheric temperature and humidity profiles under both clear-sky and partly cloudy conditions from FY-4 A GIIRS(geostationary interferometric infrared sounder) observations. Radiosonde observations from upper-air stations in China and level-2 operational products from the Chinese National Satellite Meteorological Center(NSMC)during the periods from December 2019 to January 2020(winter) and from July 2020 to August 2020(summer) are used to validate the accuracies of the retrieved temperature and humidity profiles. Comparing the 1 D-Var-retrieved profiles to radiosonde data, the accuracy of the temperature retrievals at each vertical level of the troposphere is characterized by a root mean square error(RMSE) within 2 K, except for at the bottom level of the atmosphere under clear conditions. The RMSE increases slightly for the higher atmospheric layers, owing to the lack of temperature sounding channels there.Under partly cloudy conditions, the temperature at each vertical level can be obtained, while the level-2 operational products obtain values only at altitudes above the cloud top. In addition, the accuracy of the retrieved temperature profiles is greatly improved compared with the accuracies of the operational products. For the humidity retrievals, the mean RMSEs in the troposphere in winter and summer are both within 2 g kg^(–1). Moreover, the retrievals performed better compared with the ERA5 reanalysis data between 800 h Pa and 300 h Pa both in summer and winter in terms of RMSE.

Comparisons of the temperature and humidity profiles of reanalysis products with shipboard GPS sounding measurements obtained during the 2018 Eastern Indian Ocean Open Cruise

It is important to be able to characterize the thermal conditions over the equatorial Indian Ocean for both weather forecasting and climate prediction. This study compared the equatorial eastern Indian Ocean (EEIO) temperature and relative humidity profiles from three reanalysis products (JRA-55, MERRA2, and FGOALS-f2) with shipboard global positioning system (GPS) sounding measurements obtained during the Eastern Indian Ocean Open Cruise in spring 2018. The FGOALS-f2 reanalysis product is based on the initialization module of a sub-seasonal to seasonal prediction system with a nudging-based data assimilation method. The results indicated that:(1) both JRA-55 and MERRA2 were reliable in characterizing the temperature profile from 850 to 600 hPa, with a maximum deviation of about <0.5℃. Both datasets showed a large negative deviation below 825 hPa, with a maximum bias of about 2℃ at 1000 hPa and 1.5℃ at 900 hPa, respectively.(2) JRA-55 showed good performance in characterizing the relative humidity profile above 850 hPa, with a maximum deviation of < 8%, while it showed much wetter conditions below 850 hPa. MERRA2 overestimated the relative humidity in the middle to lower troposphere, with a maximum deviation of about 15% at 925 hPa.(3) The FGOALS-f2 reanalysis product more accurately reproduced the temperature profile in the marine atmospheric boundary layer over the EEIO than that in JRA-55 and MERRA2, but showed much wetter conditions than the GPS sounding observations, with a maximum deviation of up to 20% at 600 hPa. Future applications of GPS sounding datasets are discussed.

ACCURACY OF THE RETRIEVED TEMPERATURE AND HUMIDITY FIELDS FOR TYPHOON HAIYAN UTILIZING THE ADVANCED TECHNOLOGY MICROWAVE SOUNDER

One-dimensional retrieval was performed on Typhoon Haiyan utilizing the advanced technology microwave sounder onboard the satellite Suomi NPP to retrieve the temperature and water vapor profiles of the typhoon.Comparisons of the retrieved profiles and ECMWF reanalysis were made to assess the results. The main conclusions are as follows.(1) The results have high spatial resolution and therefore can precisely represent the temperature and humidity distribution of the typhoon.(2) The retrieved temperature is low in the areas of low temperature and high in the areas of high temperature; similar patterns are observed for humidity. This means that systematic revision may be needed during routine application.(3) The results of the retrieved temperature and humidity profiles are generally accurate, which is quite important for typhoon monitoring.

Operational Implementation of the ATOVS Processing Procedure in KMA and Its Validation

The Korea Meteorological Administration (KMA) has processed the data from the advanced TOVS (ATOVS) onboard NOAA-16 satellite since May 2001. The operational production utilizes the AAPP (ATOVS and AVHRR Processing Package) of EUMETSAT and IAPP (International ATOVS Processing Package) of the University of Wisconsin. For the initial guess profiles, the predicted fields (usually 6 to 12 hour forecasted fields) from the global aviation model of NOAA/NCEP are used. The average number of profiles retrieved from the ATOVS data is about 1,300 for each morning and afternoon orbit at about 18 and 06 UTC, respectively. The retrieved temperature and dew point temperatures are provided to forecasters in real time and used for initialization of prediction models. With the advanced microwave sensor (AMSU; Advanced Microwave Sounding Unit), accuracy of the ATOVS products is expected to be better than that of the TOVS products, especially in cloudy conditions. Indeed, the preliminary results from a validation study with the collocated radiosonde data during a 8-month period, from May to December 2001, for the East Asia region show an improved accuracy of the ATOVS products for cloudy skies versus the TOVS, especially for higher altitudes. The RMS (Root Mean Square) difference between the ATOVS products and radiosonde data is about 1.3°C for both clear and cloudy conditions, except for near the ground and at higher altitudes, at around 200 hPa. There is no significant temporal variation of the error statistics at all pressure levels. In case of the water vapor mixing ratio, the largest difference is shown at lower altitudes, while the accuracy is much better for the clear sky cases than the cloudy sky cases. The bias and RMSE at lower altitudes is about 0.557 g kg−1 and 2.5 g kg−1 and decrease significantly with increasing altitude.

Research on the Application of the Radiative Transfer Model Based on Deep Neural Network in One-dimensional Variational Algorithm

As a typical physical retrieval algorithm for retrieving atmospheric parameters,one-dimensional variational(1 DVAR)algorithm is widely used in various climate and meteorological communities and enjoys an important position in the field of microwave remote sensing.Among algorithm parameters affecting the performance of the 1 DVAR algorithm,the accuracy of the microwave radiative transfer model for calculating the simulated brightness temperature is the fundamental constraint on the retrieval accuracies of the 1 DVAR algorithm for retrieving atmospheric parameters.In this study,a deep neural network(DNN)is used to describe the nonlinear relationship between atmospheric parameters and satellite-based microwave radiometer observations,and a DNN-based radiative transfer model is developed and applied to the 1 DVAR algorithm to carry out retrieval experiments of the atmospheric temperature and humidity profiles.The retrieval results of the temperature and humidity profiles from the Microwave Humidity and Temperature Sounder(MWHTS)onboard the Feng-Yun-3(FY-3)satellite show that the DNN-based radiative transfer model can obtain higher accuracy for simulating MWHTS observations than that of the operational radiative transfer model RTTOV,and also enables the 1 DVAR algorithm to obtain higher retrieval accuracies of the temperature and humidity profiles.In this study,the DNN-based radiative transfer model applied to the 1 DVAR algorithm can fundamentally improve the retrieval accuracies of atmospheric parameters,which may provide important reference for various applied studies in atmospheric sciences.