Abstract
The microwave humidity and temperature sounder (MWHTS) on the Fengyun (FY)-3C satellite measures the outgoing radiance from the Earth’s surface and atmospheric constituents. MWHTS, which makes measurements in the isolated oxygen absorption line near 118 GHz and the vicinity of the strong water vapor absorption line around 183 GHz, can provide fine vertical distribution structures of both atmospheric humidity and temperature. However, in order to obtain the accurate soundings of humidity and temperature by physical retrieval methods, the bias between the observed and simulated radiance calculated by the radiative transfer model from the background or first guess profiles must be corrected. In this study, two bias correction methods are developed through the correlation analysis between MWHTS measurements and air mass identified by the first guess profiles of the physical inversion; one is the linear regression correction (LRC), and the other is the neural network correction (NNC), representing the linear and nonlinear relationships between MWHTS measurements and air mass, respectively. The correction methods have been applied to MWHTS observed brightness temperatures over the geographic area (180° W–180° E, 60° S–60° N). The corrected results are evaluated by the probability density function of the differences between corrected observations and simulated values and the root mean square errors (RMSE) with respect to simulated observations. The numerical results show that the NNC method has better performance, especially in MWHTS Channels 1 and 7–9, whose peak weight function heights are close to the surface. In order to assess the effects of bias correction methods proposed in this study on the retrieval accuracy, a one-dimensional variational system was built and applied to the MWHTS brightness temperatures to estimate the atmospheric temperature and humidity profiles. The retrieval results also show that NNC has better performance. An indication of the stability and robustness of the NNC method is given, which suggests that the NNC method has promising application perspectives in the physical retrieval.
Highlights
Atmospheric temperature and humidity profiles play important roles in a wide range of atmospheric applications, such as climate monitoring, weather forecasting, initialization and evaluation of numerical weather prediction (NWP) models, assessing the atmospheric stability and nowcasting the intense convective weather, to name a few, and will clearly continue to be important for the Atmosphere 2016, 7, 156; doi:10.3390/atmos7120156 www.mdpi.com/journal/atmosphereAtmosphere 2016, 7, 156 foreseeable future [1,2]
The inversion of microwave humidity and temperature sounder (MWHTS) measurements, including the brightness temperatures with and without bias correction and simulated brightness temperatures in the testing dataset in Section 3.1, into atmospheric temperature and relative humidity profiles was carried out to investigate the influences of the linear regression correction (LRC) and neural network correction (NNC) methods on the inversion accuracy
root mean square errors (RMSE) is considered as the standard quantification to validate the retrievals with European Center for Medium-Range Weather Forecast (ECMWF) ERA-Interim reanalysis, which is used as the truth
Summary
Atmospheric temperature and humidity profiles play important roles in a wide range of atmospheric applications, such as climate monitoring, weather forecasting, initialization and evaluation of numerical weather prediction (NWP) models, assessing the atmospheric stability and nowcasting the intense convective weather, to name a few, and will clearly continue to be important for the Atmosphere 2016, 7, 156; doi:10.3390/atmos7120156 www.mdpi.com/journal/atmosphere. The development of atmospheric temperature and humidity profile measurement using satellite-borne microwave sounders has a history of over 50 years and the improvement of inversion approaches are actively continuing [6], the retrieval strategies can be put into two categories: statistical methods and physical methods. Many efforts based on statistical methods have been made to develop the bias correction scheme for the NWP radiances’ assimilation system and the physical retrieval system These two systems are based on variational approaches, which have a similar cost function to find the optimal solution. Many previous studies have developed empirical correction methods, which remove the systemic biases by an empirical factor varying with instrument, radiative transfer model, observation conditions, etc.
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