Fig 1.
The framework diagram of the overall research process.
(a) shows the training data and target data used in the experiment, (b) shows the temporal and spatial domain of the experiment and the globe map shows 99th percentile of precipitation in each region over a 10-year (2015–2024) period. (c) shows the model and analysis. Panel b base map coastlines were generated using the GSHHS (Global Self-consistent Hierarchical High-resolution Shorelines) dataset (https://www.ngdc.noaa.gov/mgg/shorelines/gshhs.html), which is distributed under the GNU Lesser General Public License (LGPL) (https://www.gnu.org/licenses/lgpl-3.0.html).
Table 1.
Physical characteristics of the GK2A AMI single channel observations used as input variables for the machine learning model.
Fig 2.
The contribution of each variable to the precipitation estimation in the East Asian mid-latitude region demonstrated by SHAP value.
The importance of variables is ranked by the average absolute SHAP value of the variables. Only the top 20 variables with high contributions are shown. The distribution of SHAP values for light-to-moderate rain estimation (a) and heavy rain estimation (b).
Fig 3.
Spatial distribution of variables and precipitation distribution in the East Asian mid-latitude region.
An arbitrarily selected case on August 15, 2023 at 03:00 UTC. (a) Spatial distribution of IWP, the most influential variable for light-to-moderate rain estimation. (b) Spatial distribution of the reflectance difference between 1.37 µm and 1.61 µm, the most influential variable for heavy rain estimation. (c) Distribution of target precipitation. Base map coastlines were generated using the GSHHS dataset (https://www.ngdc.noaa.gov/mgg/shorelines/gshhs.html), which is distributed under the GNU LGPL (https://www.gnu.org/licenses/lgpl-3.0.html).
Fig 4.
The same as Fig 2 except for the tropical warm pool region.
Fig 5.
The same as Fig 3 except for the tropical warm pool region.
Base map coastlines were generated using the GSHHS dataset (https://www.ngdc.noaa.gov/mgg/shorelines/gshhs.html), which is distributed under the GNU LGPL (https://www.gnu.org/licenses/lgpl-3.0.html).
Fig 6.
Accuracy graph of the model trained cumulatively in order of feature importance.
Graphs showing how the model’s POD, FAR, and CSI change as it trains by sequentially accumulating the most important features from geostationary satellite data. (a) and (b) show results for the East Asian mid-latitude and TWP regions, respectively.