MAGPIE-Net: Predicting short-duration heavy-rainfall events in station neighborhoods from multitemporal FY-4A AGRI observations

Xiang Lin; Yunying Li; Chengzhi Ye; Zitong Chen; Jing Sun

cs.LGAug 18, 2026

MAGPIE-Net: Predicting short-duration heavy-rainfall events in station neighborhoods from multitemporal FY-4A AGRI observations

Authors: Xiang Lin, Yunying Li, Chengzhi Ye, Zitong Chen, Jing Sun

Organizations: College of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, China · Key Laboratory of High Impact Weather (Special), China Meteorological Administration, Changsha 410073, China · Hunan Key Laboratory of Meteorological Disaster Prevention and Reduction, Changsha 410118, China · Institute of Meteorological Science of Hunan Province, Changsha 410118, China

Abstract

Short-duration heavy-rainfall warning determines whether 1 h rainfall will exceed a threshold within a target-station neighborhood over the next few hours. Multitemporal infrared and water-vapor observations from the Fengyun-4A Advanced Geostationary Radiation Imager (FY-4A AGRI) capture cloud-top cooling, moisture evolution, and cloud expansion before substantial surface rainfall develops. However, most deep-learning nowcasting methods convert these signals into local warnings by post-processing gridded precipitation predictions, preventing station-neighborhood event targets from directly supervising the satellite-to-station learning pathway. We propose MAGPIE-Net, which embeds a geographically adaptive, differentiable grid-to-station mapping in a pathway combining convection-initiation features, multiscale encoding, and auxiliary gridded precipitation diagnosis. Station-neighborhood event losses thereby constrain the satellite representation and its mapping to irregular station locations for 0-3 h event prediction. In independent 2023 warm-season tests over central and eastern China, critical success index (CSI) values under the primary 40 km/20 mm h-1 definition were 0.371, 0.304, and 0.238 at 0-1, 1-2, and 2-3 h. Across episodes, MAGPIE-Net achieved a detection rate of 65.1% and a mean lead time of 64.6 min, compared with 23.6% and 18.3 min for the best gridded-output baseline, and remained superior for smaller neighborhoods and the 50 mm h-1 threshold. During the critical early-warning stage, when antecedent 1 h rainfall within 40 km remained below 1 mm, MAGPIE-Net detected 51.9% of episodes with a mean lead time of 38.5 min. These results show that event-oriented satellite-to-station modeling converts multitemporal geostationary cloud and moisture observations into local heavy-rainfall warnings more effectively than gridded-precipitation modeling.

Figures & tables

Figure 1: Comparison of the conventional gridded-output pathway and the event-oriented satellite-to-station modeling pathway for satellite-based short-duration heavy-rainfall warning. Both pathways use regional multichannel geostationary satellite sequences. In the conventional pathway, model training and verification are based on gridded precipitation, and station-neighborhood events are subsequently derived through radius-based neighborhood aggregation. The event-oriented pathway incorporates a learnable grid-to-station mapping and uses station-neighborhood heavy-rainfall events as the common target for training, verification, and warning output.

Figure 2: Study domain and station networks. (a) Distribution of national meteorological stations, with marginal counts by longitude and latitude. (b) Location of the study domain within China. (c) Gaussian-smoothed relative density of regional automatic weather stations in the precipitation dataset.

Channel	Type	Central wavelength	Native spatial resolution
7	Mid-wave infrared (high gain)	$3.75\,\mu\mathrm{m}$	2 km a
8	Mid-wave infrared (low gain)	$3.75\,\mu\mathrm{m}$	4 km
9	Upper-tropospheric water vapor	$6.25\,\mu\mathrm{m}$	4 km
10	Mid-tropospheric water vapor	$7.10\,\mu\mathrm{m}$	4 km
11	Long-wave infrared	$8.50\,\mu\mathrm{m}$	4 km
12	Thermal-infrared window	$10.70\,\mu\mathrm{m}$	4 km

Table 1: FY-4A AGRI channels used as model input.

Figure 3: Overall architecture of MAGPIE-Net. The input AGRI sequence is used to construct CI features, which are combined with the original AGRI channels by the multiscale satellite encoder. The encoder produces a regional satellite representation, from which the auxiliary diagnosis head generates three gridded precipitation fields. GA-SetConv maps these fields to target stations, and the multi-head module predicts event probabilities for three forecast windows. CBR denotes a convolution–batch-normalization–ReLU block. For clarity, repeated indices are omitted from the GA-SetConv inset; the complete notation is defined in Section 3.4 .

Item	Specification or condition
Candidate times	$\tau=t_{0}-30\,\mathrm{min}$ and $t_{0}-15\,\mathrm{min}$ , each evaluated using the three scans at $\tau-30$ , $\tau-15$ , and $\tau$
Motion consistency	Backward trajectories remain within the grid, displacement in each 15-min step is at most 12 pixels, and forward–backward error is at most 1.5 pixels
Trajectory notation	$\widetilde{T}_{10.7}$ denotes a temperature sampled along the backward-traced cloud trajectory
Cold cloud top	$T_{10.7}(\tau,g)<273\,\mathrm{K}$
Continuous cooling	$\widetilde{T}_{10.7}(\tau-15,g)-\widetilde{T}_{10.7}(\tau-30,g)\leq-4\,\mathrm{K}$ and $T_{10.7}(\tau,g)-\widetilde{T}_{10.7}(\tau-15,g)\leq-4\,\mathrm{K}$
Water-vapor–infrared difference	$T_{7.1}(\tau,g)-T_{10.7}(\tau,g)>-30\,\mathrm{K}$

Table 2: Parameters and criteria for constructing CI candidate masks.

Figure 4: Station-neighborhood warning performance under the primary $40\,\mathrm{km}/20\,\mathrm{mm\,h^{-1}}$ event definition. Gridded-output predictions are converted into station-neighborhood events using the same radius-based neighborhood aggregation. (a)–(c) Sample-level CSI, POD, and FAR for the three forecast windows. (d) Episode detection rate. (e) Mean episode warning lead time, with undetected episodes assigned 0 min.

Figure 5: Episode-level warning performance stratified by antecedent rainfall within the 40-km neighborhood under the primary event definition.

P_{\mathrm{ante}}

denotes the maximum 1 h accumulated rainfall during the non-overlapping hour immediately preceding the first threshold-exceeding window. (a) Episode detection rate. (b) Mean episode warning lead time, with undetected episodes assigned 0 min. Numbers beneath the categories indicate the observed episode counts.

Model	$40/20$	$20/20$	$10/20$	$40/50$	$20/50$	$10/50$	Mean
Grid-trained backbone	0.0456	0.0291	0.0165	0.0007	0.0004	0.0002	0.0154
Bilinear event model	0.2873	0.1802	0.1086	0.1226	0.0703	0.0294	0.1331
MAGPIE-Net	0.3043	0.1909	0.1183	0.1315	0.0772	0.0376	0.1433

Table 3: Controlled comparison of the event-oriented pathway and grid-to-station decoding. Values are CSI averaged over the three forecast windows.

Figure 6: Cross-model CSI across neighborhood radii and rainfall thresholds. Gridded-output predictions are evaluated using the same radius-based neighborhood aggregation as the station-neighborhood event definition. Panels (a)–(c) correspond to

20\,\mathrm{mm\,h^{-1}}

, and panels (d)–(f) correspond to

50\,\mathrm{mm\,h^{-1}}

. Columns represent neighborhood radii of 40, 20, and 10 km. Each panel shows the 0–1, 1–2, and 2–3 h forecast windows.

Figure 7: Performance of MAGPIE-Net across temporal and regional subsets under the primary event definition. (a) Sample-level CSI, POD, FAR, and positive-event counts for daytime and nighttime samples. (b) Monthly sample-level metrics and positive-event counts for the three forecast windows, together with episode detection rate and mean episode warning lead time. (c) Regional partition and sample-level CSI, POD, and FAR across the five regions.

Figure 8: CI-feature ablation and inference-time spatial attribution under the primary event definition. (a)–(c) Detected episode count, episode detection rate, and mean warning lead time for independently trained MAGPIE-Net models without and with CI features. (d)–(f) CI-centered station-neighborhood event-probability profiles obtained with the full input and the CI-masked input. (g)–(i) Event-probability differences between the full and CI-masked inputs; the insets compare the mean differences for positive and negative station–time events.

Figure 9: Station-neighborhood event predictions for a localized convective heavy-rainfall episode over coastal southern Guangxi, initialized at 22:00 UTC on 10 August 2023 (06:00 BJT on 11 August). The top row shows the latest input AGRI $10.7\,\mu\mathrm{m}$ image at 21:45 UTC (05:45 BJT), observed positive events in the three forecast windows, and an enlarged view of the newly positive component during 1–2 h. The rightmost column uses the same AGRI $10.7\,\mu\mathrm{m}$ background for all rows. The remaining rows show predictions from PhyDNet, NPM, NowcastNet, and MAGPIE-Net. EarthFormer, omitted for clarity, also missed all three stations in the highlighted component. Cyan, orange, red, and blue markers denote hits, false alarms, misses, and observed positive events, respectively.

Figure 10: Station-neighborhood event predictions during the landfall of Typhoon Doksuri, initialized at 00:00 UTC (08:00 BJT) on 28 July 2023. The top row shows the latest input AGRI $10.7\,\mu\mathrm{m}$ image, observed positive events in the three forecast windows, and an enlarged coastal view during 2–3 h. The rightmost column uses the same AGRI $10.7\,\mu\mathrm{m}$ background for all rows. The remaining rows show predictions from PhyDNet, NPM, NowcastNet, and MAGPIE-Net. Cyan, orange, red, and blue markers denote hits, false alarms, misses, and observed positive events, respectively.

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