Yao et al. (2025) Thinning Methods and Assimilation Applications for FY-4B/GIIRS Observations

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Identification

• Journal: Remote Sensing
• Year: 2025
• Date: 2025-12-29
• Authors: Shuhan Yao, Li Guan
• DOI: 10.3390/rs18010119

Research Groups

Not explicitly listed in the provided text.

Short Summary

This study evaluates the impact of different data thinning schemes for FY-4B/GIIRS observations within the GSI assimilation system on atmospheric analysis and forecasts, finding that the Wavelet Transform Modulus Maxima (WTMM) scheme significantly improves temperature, humidity, typhoon track, intensity, and precipitation predictions for Super Typhoon Doksuri.

Objective

• To evaluate the sensitivity of analysis fields to five different data thinning schemes (Wavelet Transform Modulus Maxima, 30 km, 60 km, 120 km grid-distance, and center field of view) for FY-4B/GIIRS observations within the GSI assimilation system.
• To assess the impact of the most effective thinning schemes on 72-hour cycling assimilation and forecast experiments, specifically focusing on temperature, humidity, typhoon track, intensity, and precipitation forecasts, using Super Typhoon Doksuri (2023) as a case study.

Study Configuration

• Spatial Scale: Regional (Super Typhoon Doksuri, South China Sea, land precipitation), with observations from a geostationary satellite.
• Temporal Scale:
  • Sensitivity analysis: 7 days (22 to 28 July 2023) at four daily assimilation times.
  • Cycling assimilation and forecast experiments: 72 hours.

Methodology and Data

• Models used: GSI (Gridpoint Statistical Interpolation) assimilation system.
• Data sources:
  • FY-4B/GIIRS (Geostationary Interferometric Infrared Sounder) observations.
  • ERA5 reanalysis fields (used as "true" for sensitivity analysis).
  • IMERG Final precipitation products (for precipitation verification).
  • Actual observations (for typhoon track and intensity verification).

Main Results

• The Wavelet Transform Modulus Maxima (WTMM) scheme yielded the smallest mean error for temperature and humidity analysis fields, followed by the 120 km thinning scheme, when compared against ERA5 reanalysis.
• Forecast experiments without any thinning scheme resulted in the largest root mean square error (RMSE) profiles for temperature and humidity across all pressure levels and forecast times.
• Data thinning led to a decrease in temperature forecast error at altitudes below 300 hPa.
• The WTMM scheme, by assimilating more observations, progressively increased the accuracy of temperature and humidity forecast fields with increasing forecast time.
• All thinning schemes underestimated typhoon intensity before landfall and overestimated it after landfall.
• The WTMM scheme demonstrated increasingly superior performance in typhoon track and intensity forecasts with longer forecast times, providing results closest to actual observations.
• Forecasted 24-hour accumulated precipitation over land was overestimated after typhoon landfall compared to IMERG Final products.
• The no-thinning scheme simulated precipitation locations that were generally more westward.
• Thinning improved the forecast accuracy of severe precipitation core locations and intensities, as well as the typhoon’s outer spiral rain bands over the South China Sea.
• The WTMM thinning scheme achieved the highest Equitable Threat Scores (ETSs) for most precipitation intensity thresholds.

Contributions

• Development and integration of a GIIRS data assimilation module within the GSI system for a new-generation Chinese geostationary meteorological satellite.
• Comprehensive evaluation and comparison of various data thinning schemes for hyperspectral infrared sounder observations (FY-4B/GIIRS) in the context of high-impact weather forecasting.
• Identification of the Wavelet Transform Modulus Maxima (WTMM) scheme as the most effective method for improving atmospheric analysis and forecasts (temperature, humidity, typhoon track, intensity, and precipitation) using GIIRS data.
• Quantification of the benefits of advanced data thinning techniques for geostationary hyperspectral sounder data in enhancing operational numerical weather prediction, particularly for typhoon events.

Funding

Not explicitly listed in the provided text.

Citation

@article{Yao2025Thinning,
  author = {Yao, Shuhan and Guan, Li},
  title = {Thinning Methods and Assimilation Applications for FY-4B/GIIRS Observations},
  journal = {Remote Sensing},
  year = {2025},
  doi = {10.3390/rs18010119},
  url = {https://doi.org/10.3390/rs18010119}
}