Using IMPROVER as a toolbox

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. Each step in the IMPROVER processing suite is a separate tool that can be chained together in novel configurations to achieve a different end result. In this talk we present the Enhancing Post Processing (EPP) project which is reusing IMPROVER tools with small amounts of new code to reproduce many traditional operational forecasting parameters using robust modern python code that can continue to be supported for years to come. By separating the science and technical requirements, we will show how simple and complex recipes can be constructed for application to one or more NWP models, and executed using a Python networkx graph and scaled to fill a cloud-based compute node.