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

Abstract

In this study, a new technique is proposed to retrieve temperature and relative humidity profiles under clear sky conditions in the Arctic region based on the artificial neural network (ANN) algorithm using Fengyun-3D (FY-3D) vertical atmospheric sounder suit (VASS: HIRAS, MWTS-II, and MWHS-II) observations. This technology combines infrared (IR) and microwave (MW) observations to improve retrieval accuracy in the middle and low troposphere by reducing the sensitivity of the neural networks (NNs) to cloud coverage. The approach was compared against other methods available in the literature on retrieving profiles only from FY-3D/HIRAS data. Furthermore, its retrieval performance was tested by comparing the NNs’ prediction accuracy versus the corresponding FY-3D/VASS and Aqua/AIRS L2 products. The results showed that: (1) NNs retrieval accuracy is higher during the warm season and over the ocean; (2) the retrieval accuracy of NNs has been significantly improved compared with satellite L2 products; (3) referring to radiosonde observations, the retrieval accuracy of NNs below 600 hPa is effectively improved by adding the information of the MW channel, especially on land where cloud clearing is more difficult. The root mean square error (RMSE) of temperature and relative humidity in the cold season were reduced by 0.3 K and 2%, respectively. The advanced NNs proposed herein offer a more stable retrieval performance compared with NNs built only by FY-3D/HIRAS data. The study results indicated the potential value in time and space domain of the NN algorithm in retrieving temperature and relative humidity profiles of the Arctic region from FY-3D/VASS observations under clear-sky conditions. All in all, this work enhances our knowledge towards improving operational use of FY-3D satellite data in the Arctic region.

Artificial Neural Network Models for the Estimation of Air Temperature Cooling and Warming Patterns Inside Urban Clusters: The Case of Courtyards in Athens, Greece

“…Mohmed et al [3] applied artificial neural networks (ANNs) to predict environmental conditions, such as temperature, CO 2 concentrations, air relative humidity and light radiation, to enhance crop production systems and improve plant performance and resource use efficiency in a Chinese Solar Greenhouse. Hu et al [4], in their study, proposed a new technique to retrieve temperature and relative humidity profiles under clear sky conditions in the Arctic region based on artificial neural network (ANN) modelling. Vouterakos et al [5] developed different ANNs models in order to forecast human thermal comfort/discomfort conditions three days ahead in different locations within the greater Athens area, Greece.…”