[1] Short term precipitation forecasts based on Lagrangian advection of radar echoes are robust and have more skill than numerical weather prediction models over time scales of several hours. This is because the models do not generally capture well the initial precipitation distribution. We will refer to the advection-based methods as radar nowcasts. Over longer time scales, we expect the models to perform better than nowcast methods as they resolve dynamically the large scale flow. We verify this conceptual picture of the relative accuracy of radar nowcasts and model forecasts using conventional skill scores. We identify the cross-over point in time where model forecasts start to have more skill than nowcast methods. This occurs at about 6 hours after the forecast is initiated. Citation: Lin, C., S. Vasić, A. Kilambi, B. Turner, and I. Zawadzki (2005), Precipitation forecast skill of numerical weather prediction models and radar nowcasts, Geophys. Res. Lett., 32, L14801,

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Precipitation is evaluated from two weather prediction models and satellites, taking radar-retrieved values as a reference. The domain is over the central and eastern United States, with hourly accumulated precipitation over 21 days for the models and radar, and 13 days for satellite. Conventional statistical measures and scale decomposition methods are used. The models generally underestimate strong precipitation and show nearly constant modest skill over a 24-h forecast period. The scale decomposition results show that the effective model resolution for precipitation is many times the grid size. The model predictability extends beyond a few hours for only the largest scales.

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