urban flood management teams

Abstract: Flash floods in Hanoi submerge roads within minutes of heavy rain onset, yet public flood advisories remain retrospective. Hydrodynamic models could supply forward-looking depth guidance, but they are typically run offline for historical events, and prior studies have not evaluated whether physics-based simulation, spatial serving, and vehicle-aware routing can operate together within a fixed refresh cycle using a cloud solver and a single commodity server. This paper evaluates an end-to-end prototype of an automated flood digital twin designed to meet a 15-minute refresh cycle under data scarcity. The architecture organizes execution into decoupled, event-driven workflows that chain weather-gated 3Di simulations through hydraulic restart states, serve flood depths through a dual-track spatial layer, and publish road-edge depths via standardized output contracts. Evaluated on a 57-run storm replay in the My Dinh and Tay Mo sector of Hanoi driven by satellite rainfall estimates, every cycle completed within the 15-minute deadline, publishing a ready edge table at a median of 537 s after slot start. Indexed raster tiles resolved point-depth lookups in a median of 0.54 ms (277 times faster than un-tiled rasters), while pre-rendered vector tiles served web maps without database queries. These findings demonstrate that a commodity server coupled to a cloud solver can sustain the 15-minute operational cycle and decouple routing policies from simulation internals. Because simulated depths were not validated against field gauges and the replay used observed satellite rainfall, the results establish system feasibility rather than forecast accuracy or route safety.

Abstract: This note is a technical companion to a previously published preprint describing an Attention Residual U-Net that postprocesses deterministic forecasts from The Weather Company's Global and Regional Atmospheric Forecast (GRAF) model into probabilistic hourly precipitation forecasts. It documents what has changed in that method since publication. Feature-wise Linear Modulation conditioning on calendar season and forecast lead time is used to produce a single trained model for each season, replacing 192 separately trained per-month, per-lead checkpoints. Lead time is extended from 48 to 72 h. Two new input channels are used, per-pixel local solar hour and a static, monthly-varying precipitation climatology. During verification, the climatological reference against which the Brier Skill Score is computed now has an added diurnal dimension, on top of the monthly resolution it already had. Brier Skill Score and reliability are compared between the new vs. the previous training. Forecasts generated with the new training show a modest, consistent improvement of the current training over the original.