Error Analysis and Correction of FENGYUN-4A GIIRS Temperature Profile Products in Summer over the Qinghai–Tibet Plateau

• Published in: Remote sensing (Basel, Switzerland) Vol. 16; no. 11; p. 1881
• Main Authors: Jiang, Xiaofei, Wang, Qiguang, Niu, Ning
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.06.2024
• Subjects: Accuracy; Altitude; Artificial satellites in remote sensing; Bias; Calibration; Comparative analysis; Earth temperature; Environmental aspects; Error analysis; Error correction; FY-4A GIIRS; High altitude; Humidity; Infrared detectors; Long short-term memory; LSTM; Mean square errors; Mean square values; Measurement; Meteorological satellites; Precipitation; Qinghai–Tibet Plateau; Quality control; Radiosondes; Remote sensing; Root-mean-square errors; Summer; Technology application; Temperature; Temperature profiles; Usability; Weather forecasting; China; Tibetan Plateau; South China Sea; East China Sea
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs16111881

To understand the applicability of the temperature profile product of the FENGYUN-4A (FY-4A) geostationary interferometric infrared detector (GIIRS) in summer over the Qinghai–Tibet Plateau and to improve product quality, the error of GIIRS temperature products was analyzed based on radiosonde data. A long short-term memory network model (LSTM) was used to correct the GIIRS temperature profile product in summer over the plateau at a high altitude (above 500 hPa), and further evaluation of the corrected product was conducted. The results show that summertime GIIRS temperature retrievals over the Qinghai–Tibet Plateau had a positive bias above 150 hPa and a negative bias below 150 hPa, resulting in an overall negative bias. The root mean square error was between 2 and 2.9 K, and the root mean square error was relatively large at 100 hPa and above. The LSTM established in this study could effectively correct the GIIRS temperature over the plateau. The correlation and root mean square error of the corrected GIIRS temperature and the radiosonde observation temperature were significantly improved. Using a trained LSTM correction model to correct the hourly GIIRS temperature can improve the accuracy and usability of the product. After correction, the average bias of the GIIRS temperature compared to the ERA5 temperature product was reduced from −0.26 K to 0.06 K, and the root mean square error was reduced from 2.25 K to 1.25 K. The correction model can be applied to different seasons, and it can also correct the GIIRS temperature in larger areas based on other high-precision and high-resolution data, achieving good results, thus indicating that the correction model has universal applicability.