Quality Assessment of Operational Fengyun-4B/GIIRS Atmospheric Temperature and Humidity Profile Products
Abstract
:1. Introduction
2. Data and Methods
2.1. Data
2.1.1. Operational Fengyun-4B/GIIRS ATP and AHP Products
2.1.2. Operational Fengyun-4A/GIIRS ATP Products
2.1.3. Radiosonde Observations
2.1.4. ERA5 Reanalysis
2.2. Strategy of Assessment and Comparison
3. Results
3.1. Analysis of Quality Flag for Operational Fengyun-4b/GIIRS ATP and AHPProducts
3.2. Assessment of Fengyun-4B/GIIRS ATP and AHP Products with Radiosonde Observations
3.3. Comparison of Fengyun-4B/GIIRS ATP and AHP Products with ERA5 Reanalysis
3.4. Dependence Analysis of Fengyun-4B/GIIRS ATP and AHP Products with Satellite Scan Angle
4. Discussion
- (1)
- In this study, due to the spatiotemporal distribution discontinuity of radiosonde observations, the ERA5 reanalysis is used to analyze vertical and temporal bias characteristics of the Fengyun-4B/GIIRS ATP and AHP products. Although the ERA5 reanalysis is considered to be an advanced atmospheric product, it may not be a substitute for radiosonde observations [30]. Thus, in future studies, in the comparison of the Fengyun-4B/GIIRS products and the ERA5 reanalysis, the uncertainty of the ERA5 reanalysis should be considered.
- (2)
- The Fengyun-4B/GIIRS ATP and AHP products were assessed in this study over a period of 11 months (from March 2023 to January 2024). Such an assessment should be performed for a longer time (for example, two years) to investigate the stability of the operational retrieval algorithm.
- (3)
- With the development of the artificial intelligence technology, new retrieval algorithms, such as convolutional neural networks [31,32,33], etc., should be fully tested and compared with the current operational algorithm of the NSMC, which may help improve the accuracy of the operational Fengyun-4B/GIIRS ATP and AHP products.
5. Conclusions
- When compared to Fengyun-4A/GIIRS, Fengyun-4B/GIIRS can provide more usable ATP products (with the quality flags of “very good” and “good”) under both clear-sky and cloudy-sky conditions. The percentage of usable Fengyun-4B/GIIRS AHP products can be over 86%. The interference of the surface leads to a low usability of Fengyun-4B/GIIRS ATP products below 1000 hPa, which should be avoided in relevant applications.
- The assessment based on radiosonde observations demonstrates that the current quality flag algorithm can classify the Fengyun-4B/GIIRS ATP and AHP products well with different accuracies. Biases of Fengyun-4B/GIIRS ATP products with the best quality (with the quality flags of “very good”) vary from −0.837 K to 0.192 K vertically, and their RMSEs are mainly around 1.5 K, statistically outperforming the Fengyun-4A/GIIRS ATP products.
- Using the ERA5 reanalysis as reference, the usable Fengyun-4B/GIIRS ATP products mainly underestimate and overestimate atmospheric conditions under clear-sky and cloudy-sky conditions, respectively. A similar phenomenon is also observed in Fengyun-4A/GIIRS ATP products, which suggests that the current NSMC operational retrieval algorithm may need further improvement to avoid systematic errors. However, for usable Fengyun-4B/GIIRS AHP products, a major underestimation exists under both clear-sky and cloudy-sky conditions.
- Biases and RMSEs of the usable Fengyun-4B/GIIRS and Fengyun-4A/GIIRS ATP products at the low-level, the mid-level, and the high-level troposphere all show a significant positive relationship with the satellite zenith angles when the angles are over 50°. For the usable Fengyun-4B/GIIRS AHP products, biases and RMSEs at the low-level troposphere seem to have a positive correlation with the satellite zenith angles when the angles are over 50°; however, for biases and RMSEs at the mid-level and the high-level troposphere, this relationship is insignificant. These findings suggest that the influence of the atmospheric slant path should be considered for ATP and AHP retrieval when the satellite zenith angles are over 50°.
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- National Satellite Meteorological Center. FY-4B Satellite. Available online: http://www.nsmc.org.cn/nsmc/en/satellite/FY4B.html (accessed on 22 January 2025).
- Liu, H.; Bai, W.; Ma, G.; Wang, G.; Zhang, P.; Zhang, W.; Li, J.; Wang, X.; Ao, Y.; Shen, Q. Neural network temperature and moisture retrieval technique for real-time processing of FengYun-4B/GIIRS hyperspectral radiances. Meteorol. Atmos. Phys. 2024, 136, 36. [Google Scholar] [CrossRef]
- Niu, Z.; Zhang, L.; Han, Y.; Dong, P.; Huang, W. Performances between the FY-4A/GIIRS and FY-4B/GIIRS Long-Wave InfraRed (LWIR) channels under clear-sky and all-sky conditions. Q. J. R. Meteorol. Soc. 2023, 149, 1612–1628. [Google Scholar] [CrossRef]
- Wang, W.; Huang, P.; Xu, N.; Li, J.; Di, D.; Zhang, Z.; Gao, L.; Ji, Z.; Min, M. Evaluating the First Year On-Orbit Radiometric Calibration Performance of GIIRS Onboard Fengyun-4B. IEEE Geosci. Remote Sens. Lett. 2024, 21, 1–5. [Google Scholar] [CrossRef]
- Niu, Z.; Wang, L.; Kumar, P. Scale Analysis of Typhoon In-Fa (2021) Based on FY-4A Geostationary Interferometric Infrared Sounder (GIIRS) Observed and All-Sky-Simulated Brightness Temperature. Remote Sens. 2023, 15, 4035. [Google Scholar] [CrossRef]
- Cai, X.; Bao, Y.; Petropoulos, G.P.; Lu, F.; Lu, Q.; Zhu, L.; Wu, Y. Temperature and Humidity Profile Retrieval from FY4-GIIRS Hyperspectral Data Using Artificial Neural Networks. Remote Sens. 2020, 12, 1872. [Google Scholar] [CrossRef]
- He, M.; Wang, D.H.; Ding, W.Y.; Wan, Y.J.; Chen, Y.H.; Zhang, Y. A Validation of Fengyun4A Temperature and Humidity Profile Products by Radiosonde Observations. Remote Sens 2019, 11, 2039. [Google Scholar] [CrossRef]
- Di, D.; Li, J.; Han, W.; Bai, W.G.; Wu, C.Q.; Menzel, W.P. Enhancing the Fast Radiative Transfer Model for FengYun-4 GIIRS by Using Local Training Profiles. J. Geophys. Res. Atmos. 2018, 123, 12583–12596. [Google Scholar] [CrossRef]
- Xue, Q.; Guan, L.; Shi, X. One-Dimensional Variational Retrieval of Temperature and Humidity Profiles from the FY4A GIIRS. Adv. Atmos. Sci. 2022, 39, 471–486. [Google Scholar] [CrossRef]
- Huang, P.Y.; Guo, Q.; Han, C.P.; Zhang, C.M.; Yang, T.H.; Huang, S. An Improved Method Combining ANN and 1D-Var for the Retrieval of Atmospheric Temperature Profiles from FY-4A/GIIRS Hyperspectral Data. Remote Sens. 2021, 13, 481. [Google Scholar] [CrossRef]
- Yao, S.H.; Guan, L. Comparison of Three Convolution Neural Network Schemes to Retrieve Temperature and Humidity Profiles from the FY4A GIIRS Observations. Remote Sens 2022, 14, 5112. [Google Scholar] [CrossRef]
- Yin, R.; Han, W.; Gao, Z.; Li, J. Impact of High Temporal Resolution FY-4A Geostationary Interferometric Infrared Sounder (GIIRS) Radiance Measurements on Typhoon Forecasts: Maria (2018) Case with GRAPES Global 4D-Var Assimilation System. Geophys. Res. Lett. 2021, 48, e2021GL093672. [Google Scholar] [CrossRef]
- Feng, J.; Qin, X.H.; Wu, C.Q.; Zhang, P.; Yang, L.; Shen, X.S.; Han, W.; Liu, Y.Z. Improving typhoon predictions by assimilating the retrieval of atmospheric temperature profiles from the FengYun-4A’s Geostationary Interferometric Infrared Sounder (GIIRS). Atmos. Res. 2022, 280, 106391. [Google Scholar] [CrossRef]
- Guo, Q.; Yang, J.; Wei, C.; Chen, B.; Wang, X.; Han, C.; Hui, W.; Xu, W.; Wen, R.; Liu, Y. Spectrum calibration of the first hyperspectral infrared measurements from a geostationary platform: Method and preliminary assessment. Q. J. R. Meteorol. Soc. 2021, 147, 1562–1583. [Google Scholar] [CrossRef]
- Wang, S.; Lu, F.; Feng, Y. An Investigation of the Fengyun-4A/B GIIRS Performance on Temperature and Humidity Retrievals. Atmos. 2022, 13, 1830. [Google Scholar] [CrossRef]
- Gao, Y.; Mao, D.; Wang, X.; Qin, D. Evaluation of FY-4A Temperature Profile Products and Application to Winter Precipitation Type Diagnosis in Southern China. Remote Sens. 2022, 14, 2363. [Google Scholar] [CrossRef]
- Ren, S.; Jiang, J.; Fang, X.; Liu, H.; Cao, Z. FY-4A/GIIRS Temperature Validation in Winter and Application to Cold Wave Monitoring. J. Meteor. Res. 2022, 36, 658–676. [Google Scholar] [CrossRef]
- Gao, Y.; Wang, X.; Mao, D. Performance of FY-4B GIIRS temperature products under cloudy skies and their enhancement of surface precipitation type forecasting. Atmos. Res. 2024, 302, 107305. [Google Scholar] [CrossRef]
- Yan, X.S.; Chen, Y.D.; Ma, G.; Qin, L.Y.; Zhang, P.; Gong, X.Y. A 3-D Cloud Detection Method for FY-4A GIIRS and Its Application in Operational Numerical Weather Prediction System. IEEE Trans. Geosci. Remote Sens. 2023, 61, 4105213. [Google Scholar] [CrossRef]
- Yin, R.; Han, W. Impact of Targeted Sounding Observations from FY-4B GIIRS on Two Super Typhoon Forecasts in 2024. IEEE Geosci. Remote Sens. Lett. 2025, 22, 5500805. [Google Scholar] [CrossRef]
- Shou, Y.X. Introduction to FY-4B Atmospheric Parameter Products. Available online: http://img.nsmc.org.cn/PORTAL/NSMC/VIDEO/FY4B/20230818/09-Atmosphere_Properties.pdf (accessed on 7 March 2025).
- Wang, G.; Han, W.; Yuan, S.; Wang, J.; Yin, R.Y.; Ye, S.; Xie, F. Retrieval of High-Frequency Temperature Profiles by FY-4A/GIIRS Based on Generalized Ensemble Learning. J. Meteorol. Soc. Jpn. 2024, 102, 241–264. [Google Scholar] [CrossRef]
- National Satellite Meteorological Center. Announcement on FY-4A and FY-4B Position Drift and Operation Service Pending. Available online: http://www.nsmc.org.cn/nsmc/en/news/130451.html (accessed on 22 January 2025).
- Yuan, F.; Zhou, Z.; Liao, J.; Li, Q. A quality control method for high-resolution radiosonde temperature and winddata. Atmos. Ocean. Sci. Lett. 2021, 14, 100029. [Google Scholar] [CrossRef]
- Hersbach, H.; Bell, B.; Berrisford, P.; Hirahara, S.; Horanyi, A.; Munoz-Sabater, J.; Nicolas, J.; Peubey, C.; Radu, R.; Schepers, D.; et al. The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. 2020, 146, 1999–2049. [Google Scholar] [CrossRef]
- Soci, C.; Hersbach, H.; Simmons, A.; Poli, P.; Bell, B.; Berrisford, P.; Horanyi, A.; Munoz-Sabater, J.; Nicolas, J.; Radu, R.; et al. The ERA5 global reanalysis from 1940 to 2022. Q. J. R. Meteorol. Soc. 2024, 150, 4014–4048. [Google Scholar] [CrossRef]
- China Meteorological Administration. Statistical Methods for Upperair Climate Data. Available online: https://www.cma.gov.cn/zfxxgk/gknr/flfgbz/bz/202102/t20210210_2720389.html (accessed on 22 January 2025).
- National Satellite Meteorological Center. Geostationary Interferometric Infrared Sounder (GIIRS). Available online: http://www.nsmc.org.cn/nsmc/cn/instrument/GIIRS.html (accessed on 7 March 2025).
- Huang, P.; Li, J.; Min, M.; Li, Z.; Di, D.; Anantharaj, V.; Ahn, M.-H. Influence of Atmospheric Slant Path on Geostationary Hyperspectral Infrared Sounder Radiance Simulations. Geophys. Res. Lett. 2024, 51, e2024GL110579. [Google Scholar] [CrossRef]
- Jacobs, A.; Top, S.; Vergauwen, T.; Suomi, J.; Käyhkö, J.; Caluwaerts, S. Filling gaps in urban temperature observations by debiasing ERA5 reanalysis data. Urban Clim. 2024, 58, 102226. [Google Scholar] [CrossRef]
- Jiang, P.; Liu, R.Y.; Huo, Y.F.; Wu, Y.L.; Ye, S.R.; Wang, S.C.; Mu, X.; Zhu, L. Retrieving the Atmospheric Water Vapor Profile Combining FY-4A/GIIRS and Ground-Based GNSS PWV in Hong Kong Region. IEEE Trans. Geosci. Remote Sens. 2025, 63, 4100809. [Google Scholar] [CrossRef]
- Renju, R.; Raju, C.S.; Swathi, R.; Milan, V.G. Retrieval of atmospheric temperature and humidity profiles over a tropical coastal station from ground-based Microwave Radiometer using deep learning technique. J. Atmos. Sol.-Terr. Phys. 2023, 249, 106094. [Google Scholar] [CrossRef]
- Zhang, L.W.; Ma, Y.Y.; Lei, L.F.; Wang, Y.J.; Jin, S.K.; Gong, W. Improving Atmospheric Temperature and Relative Humidity Profiles Retrieval Based on Ground-Based Multichannel Microwave Radiometer and Millimeter-Wave Cloud Radar. Atmosphere 2024, 15, 1064. [Google Scholar] [CrossRef]
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Zhu, Z.; Gu, J.; Yuan, F.; Shi, C. Quality Assessment of Operational Fengyun-4B/GIIRS Atmospheric Temperature and Humidity Profile Products. Remote Sens. 2025, 17, 1353. https://doi.org/10.3390/rs17081353
Zhu Z, Gu J, Yuan F, Shi C. Quality Assessment of Operational Fengyun-4B/GIIRS Atmospheric Temperature and Humidity Profile Products. Remote Sensing. 2025; 17(8):1353. https://doi.org/10.3390/rs17081353
Chicago/Turabian StyleZhu, Zhi, Junxia Gu, Fang Yuan, and Chunxiang Shi. 2025. "Quality Assessment of Operational Fengyun-4B/GIIRS Atmospheric Temperature and Humidity Profile Products" Remote Sensing 17, no. 8: 1353. https://doi.org/10.3390/rs17081353
APA StyleZhu, Z., Gu, J., Yuan, F., & Shi, C. (2025). Quality Assessment of Operational Fengyun-4B/GIIRS Atmospheric Temperature and Humidity Profile Products. Remote Sensing, 17(8), 1353. https://doi.org/10.3390/rs17081353