Hourly Regional Precipitation Forecasting Using GNSS PWV and the ConvLSTM-ED Algorithm

By Liu, Yao, Zhang +5Remote SensingOct 29, 2026

Score████░░░░░░4.3

VerdictWorth a reader's time today.

Abstract

Accurate hourly regional precipitation forecasting is essential for flood warning and weather-risk management, but station-scale global navigation satellite system (GNSS) precipitation models cannot fully describe the spatial evolution of rainfall between stations. This study proposes an hourly regional precipitation forecasting (HRPF) model that combines gridded GNSS-derived precipitable water vapor (PWV) and gridded precipitation with a convolutional long short-term memory (ConvLSTM) encoder–decoder network. Missing values in the hourly PWV and precipitation records from 69 GNSS-collocated meteorological stations in Taiwan Province during 2008–2012 were reconstructed using singular spectrum analysis and then resampled onto a 0.06° latitude–longitude grid using nearest-neighbor interpolation. The model uses PWV and precipitation fields from the preceding 1–6 h as two input channels and predicts the precipitation field one hour ahead. Lagged autocorrelation analysis showed strong spatiotemporal dependence in both precipitation and PWV fields, with coefficients above 0.86 and 0.85, respectively. In annual-scale external validation, the HRPF model maintained probability of detection (POD) values above 90%, false alarm rate (FAR) values below 55%, and threat score (TS) values of 46.22–46.58%. Seasonal external validation showed POD values of 88.8–97.8%, with the highest TS in summer (52.8–53.3%) owing to lower FAR values (45.8–46.4%). These results indicate that GNSS-derived PWV can provide useful spatial water-vapor information for one-hour-ahead regional precipitation forecasting, while model skill remains season-dependent over Taiwan’s complex terrain.

Yang Liu, Yibin Yao, Bao Zhang, Qingzhi Zhao, Mingxian Hu, Junyu Li, Zhao hui Xiong, Jing Zhang

The editor's rubric

Heuristic review

Leverage3 / 5: A method or resource many groups across the field will adopt within a year.; 16% of this desk’s rubric
Magnitude2 / 5: Solid incremental gain on a meaningful problem.; 20% of this desk’s rubric
Evidence3 / 5: Solid: multiple benchmarks or cohorts, ablations, fair baselines, released code or data.; 20% of this desk’s rubric
Novelty2 / 5: A new combination of known ideas.; 10% of this desk’s rubric
Trajectory3 / 5: A clear path to scale.; 14% of this desk’s rubric
Stakes3 / 5: Meaningful benefit to many people within a few years.; 20% of this desk’s rubric

Dimension	Level	Weight	What that level means
Leverage	███░░3/5	16%	A method or resource many groups across the field will adopt within a year.
Magnitude	██░░░2/5	20%	Solid incremental gain on a meaningful problem.
Evidence	███░░3/5	20%	Solid: multiple benchmarks or cohorts, ablations, fair baselines, released code or data.
Novelty	██░░░2/5	10%	A new combination of known ideas.
Trajectory	███░░3/5	14%	A clear path to scale.
Stakes	███░░3/5	20%	Meaningful benefit to many people within a few years.

Editor’s rationale

Heuristic triage from title and abstract text only, not a reading of the paper. Cues found: breadth (many tasks); verification (false alarm rate); scale (scalable); stakes (global scale, energy, climate).

How the score was computed

rank-2026-10-18

Score████░░░░░░4.3

Score = 10 × (75% × adjusted merit / 10 + 15% × attention + 10% × freshness)

Desks place stronger verdicts first, then use this score to order work within the same verdict.

Merit: 5.4 / 10; Weighted rubric, evidence-gated.
Adjusted merit: 4.6 / 10; Shrunk toward the desk prior by editor confidence (42%).
Attention: 0%; Citations, upvotes, points, mentions.
Freshness: 85%; Half-life decay since publication.

Citations0 (reference 15, via openalex, Oct 31, 2026, 07:20 UTC)

The record

Reviewed by heuristic-v10 on Oct 31, 2026, 07:20 UTC. Paper type: empirical.
Categories: Precipitation Measurement and Analysis, Hydrological Forecasting Using AI, GNSS positioning and interference, Atmospheric Science, Earth and Planetary Sciences
BRIEF, No.1 in the Climate & Energy edition of October 31, 2026.