Retrieval of Atmospheric Water Vapor Content in the Environment from AHI/H8 Using Both Physical and Random Forest Methods—A Case Study for Typhoon Maria (201808)
In one sentence. Random forest retrieves Himawari-8 AHI water vapor 35–45% more accurately than 1DVAR at much lower cost, demonstrated for Typhoon Maria.
Abstract
The advanced imagers onboard the new generation of geostationary satellites could provide multilayer atmospheric moisture information with unprecedented high spatial and temporal resolutions, while the physical retrieval algorithm (One-Dimensional Variational, 1DVAR) is performed for operational atmospheric water vapor products with reduced resolutions, which is due to the limited computational efficiency of the physical retrieval algorithm. In this study, a typical cost-efficient machine learning (Random Forecast, RF) algorithm is adopted and compared with the physical retrieval algorithm for retrieving the atmospheric moisture from the measurements of Advance Himawari Imager (AHI) onboard the Himawari-8 satellite during the typhoon Maria (201808). It is found that the accuracy of the RF-based algorithm has much high computational efficiency and provides moisture retrievals with accuracy 35–45% better than that of 1DVAR, which means the retrieval process can be conducted at full spatial resolution for potential operational application. Both the Global Forecast System (GFS) forecasts and the AHI measurements are necessary information for moisture retrievals; they provide added value for each other.
Labels
- Topic
- Observations & remote sensing
- Type
- New model or method
- Method
- Classical ML (trees, GP, linear, kernels)
- Domain
- Regional
- Data
- Satellite, NWP analysis or forecasts (IFS, GFS, ...)
- Status
- Research
- Identifiers
- DOI:10.3390/rs15020498 OpenAlex:W4316664557
- Added
- 2026-10-22
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