Temperature and Relative Humidity Profile Retrieval from Fengyun-3D/HIRAS in the Arctic Region

Abstract

The acquisition of real-time temperature and relative humidity (RH) profiles in the Arctic is of great significance for the study of the Arctic’s climate and Arctic scientific research. However, the operational algorithm of Fengyun-3D only takes into account areas within 60°N, the innovation of this work is that a new technique based on Neural Network (NN) algorithm was proposed, which can retrieve these parameters in real time from the Fengyun-3D Hyperspectral Infrared Radiation Atmospheric Sounding (HIRAS) observations in the Arctic region. Considering the difficulty of obtaining a large amount of actual observation (such as radiosonde) in the Arctic region, collocated ERA5 data from European Centre for Medium-Range Weather Forecasts (ECMWF) and HIRAS observations were used to train the neural networks (NNs). Brightness temperature and training targets were classified using two variables: season (warm season and cold season) and surface type (ocean and land). NNs-based retrievals were compared with ERA5 data and radiosonde observations (RAOBs) independent of the NN training sets. Results showed that (1) the NNs retrievals accuracy is generally higher on warm season and ocean; (2) the root-mean-square error (RMSE) of retrieved profiles is generally slightly higher in the RAOB comparisons than in the ERA5 comparisons, but the variation trend of errors with height is consistent; (3) the retrieved profiles by the NN method are closer to ERA5, comparing with the AIRS products. All the results demonstrated the potential value in time and space of NN algorithm in retrieving temperature and relative humidity profiles of the Arctic region from HIRAS observations under clear-sky conditions. As such, the proposed NN algorithm provides a valuable pathway for retrieving reliably temperature and RH profiles from HIRAS observations in the Arctic region, providing information of practical value in a wide spectrum of practical applications and research investigations alike.All in all, our work has important implications in broadening Fengyun-3D’s operational implementation range from within 60°N to the Arctic region.

An Investigation of the Fengyun-4A/B GIIRS Performance on Temperature and Humidity Retrievals

“…The statistical retrieval method retrieves atmospheric parameters by establishing regression equations between parameters and radiation. It is fast and easy but it cannot deal with non-linearity [30] and its retrieval accuracy is limited [8,30]. The physical retrieval method solves the problem from the physical essence of the atmospheric radiation transfer equation which has high accuracy [46].…”

Temperature and Relative Humidity Profile Retrieval from Fengyun-3D/VASS in the Arctic Region Using Neural Networks

“…FY-3D/VASS L1 data and atmospheric profile data were first pre-processed. HIRAS' pixels with cloud amounts less than 5% were selected by using FY3D/MERSI-II L2 cloud products [20], which are considered as cloudless, and interpolated MWTS-II and MWHS-II observations to HIRAS clear sky pixels by using the inverse distance weight method [39,40]. The ERA5 data were matched with HIRAS data in time and space, which required that the distance between HIRAS FOVs and ERA5 was less than 0.1 • , and the time difference between ERA5 and HIRAS data was less than 1 h. In order to make the training set of different models more representative and reduce the training difficulty of the NNs, brightness temperature and training targets were classified using two variables: season (warm season: May 2019 to October 2019; cold season: November 2019 to April 2020) and surface type (ocean and land).…”

“…It is a relatively mature and widely used non-linear inversion algorithm. In a previous study by [20], the method of using 219 channels of HIRAS to obtain the optimal NNs with 220 input neurons (brightness temperature of 219 channels and the sensor zenith angle) was proposed. There are 250 input nodes of the improved NNs adding MW data studied in this work (NNs-250: 219 HIRAS channels, 13 MWTS channels, 15 MWHS channels, and 3 zenith angles).…”

“…The output layer of the NNs still has 42 nodes, which represent 42 vertical atmospheric layers from 1 to 1000 hPa. To ensure that changes in the accuracy of retrievals are due to the addition of MW data to the maximum extent, the same method of constructing NNs in our previous study [20] was used: selecting the hyperbolic tangent S-type tansig as the transfer function and trainscg as the training algorithm, and the number of hidden layer nodes increased from 300 to 360 using the same method [47].…”

“…A previous study by [20] demonstrated that temperature and relative humidity profiles under clear sky conditions in the Arctic region can be retrieved from FY-3D/HIRAS' 219 channel observations based on artificial neural networks (ANN). Authors showed that a root mean square error (RMSE) of NN retrievals can be reached within 1 K in the middle and upper troposphere.…”