Summary
This study evaluates the utility of NASA's GEOS global precipitation forecast for informing landslide hazard prediction by comparing it against near-real time satellite precipitation estimates (IMERG) and gauge-corrected radar data. The analysis reveals that GEOS-Forecast detects extreme rainfall more frequently than IMERG in specific regions and events, with performance influenced by storm type and seasonality. The findings suggest that a 24-hour accumulated precipitation forecast threshold of >100 mm from GEOS-Forecast aligns reasonably well with observed IMERG estimates, indicating potential viability for global landslide forecasting.
Regional applicability
The study was primarily conducted over the contiguous United States using US-based gauge-corrected radar reference data, though it includes validation against global case studies (Colombia, southeast Asia, Tajikistan). Transferability to United Kingdom conditions would depend on local precipitation patterns and landslide susceptibility; the study's methods and satellite-forecast comparison approach could be adapted to UK contexts, though performance metrics may differ due to distinct climatic and topographic characteristics.
Key measures
Probability of detection, success ratio, critical success index, hit bias, seasonal performance variation, 24-hour accumulated precipitation forecasts (>100 mm threshold), tropical cyclone versus other storm type comparisons
Outcomes reported
The study compared NASA's GEOS precipitation forecast with satellite-based IMERG estimates and gauge-corrected radar data to assess performance in detecting extreme rainfall events relevant to landslide prediction. Performance was evaluated using multiple metrics including probability of detection, critical success index, and continuous statistics, with assessment of 24-hour forecast lead time and global case studies.
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