A method for regridding lying snow for blending of multiple forecast sources in IMPROVER

IMPROVER (Integrated Model Post-Processing and Verification) has been developed by the Met Office as an open-source probability-based post-processing system to fully exploit our convection permitting, hourly cycling ensemble forecasts. Post-processed MOGREPS-UK model forecasts are blended with deterministic UKV model forecasts and data from the coarser resolution global ensemble, MOGREPS-G as well as ECMWF, to produce seamless probabilistic forecasts from now out to 14 days. For precipitation, an extrapolation nowcast is also blended in at the start. Forecasts are converted to probabilities at the start, and all initial stages of post-processing are performed on gridded data, with site-specific forecasts extracted as a final step, helping to ensure consistency. Data are processed on a 10km global grid and on a 2km UK-centred grid. Physical and statistical corrections are applied to the data to ensure the probability distribution functions for each source model are sufficiently similar for blending into a seamless probabilistic forecast.

 

In order to blend data from different model sources, it is necessary that the data represent the same quantity. The most common inconsistency is altitude - different model configurations have different representations of the orography of mountains and valleys which can lead to very large differences in altitude-dependent diagnostics such as temperature, wind and lying snow, which is particularly relevant for driving hydrological models.

 

In this talk we present a novel method for adjusting lying snow data from one orography representation to another and demonstrate the effectiveness of this in creating data with similar characteristics from ensemble numerical models with different horizontal resolution so that they can be blended together into a single probabilistic data set.