Development of SOFT CUBE: A synthesis framework for urban 3D flow and air temperature using precomputed CFD scenarios and mesoscale forecasts

In this study, we developed SOFT CUBE, a scenario-based method to rapidly generate building-resolving three-dimensional wind and air temperature fields by combining a precomputed CFD database with operational mesoscale forecasts. For this, we constructed a CFD scenario library for a 2 km × 2 km urban domain by varying inflow wind speed and direction and surface thermal forcing, and supplemented it with auxiliary cases to represent background vertical wind structure and temperature stratification. Then, for each forecast time, we selected and linearly interpolated scenarios consistent with LDAPS boundary-layer conditions and synthesized the full 3D fields by performing layer-by-layer synthesis across the vertical levels. For validation of the developed method, we used LDAPS forecasts as background forcing and compared SOFT CUBE outputs with LDAPS-driven CFD simulations and observations from four urban stations during July–December 2021. The results showed that SOFT CUBE substantially improved near-surface wind-speed estimates compared with LDAPS, reduced air-temperature errors on average, and reproduced spatial patterns similar to those from the coupled LDAPS–CFD model for most cases. Finally, SOFT CUBE reduced the per-case runtime from 141 min for coupled CFD simulations to 3 min, supporting operational-scale high-resolution urban meteorological field production.