IMPROVER : A probabilistic, multi-model post-processing system for meteorological forecasts

Abstract

<p>The UK Met Office is developing an open-source probability-based post-processing system called IMPROVER to exploit convection permitting, hourly cycling ensemble forecasts. The system is tasked with blending these forecasts with both deterministic nowcast data, and coarser resolution global ensemble model data, to produce seamless probabilistic forecasts from the very short to medium range.</p><p>A majority of the post-processing within IMPROVER is performed on gridded forecasts, with site-specific forecasts extracted as a final step, helping to ensure consistency. IMPROVER delivers a wide range of probabilistic products to both operational meteorologists and as input to automated forecast production. and this presentation will detail some of the work that has been undertaken in the past year to prepare, with a focus on the use of statistical post-processing.</p><p>Statistical post-processing plays two complimentary roles within IMPROVER; ensuring forecasts better reflect reality, and in so doing, bringing different models into better alignment, which improves the seamlessness of model transitions. For a selection of diagnostics, the gridded forecasts from different source models are calibrated independently using ensemble model output statistics (EMOS). Results of experiments looking at the calibration of gridded forecasts will be discussed briefly.</p><p>More recently calibration of site forecasts has been introduced as a final step for temperature and wind speed forecasts. Results of experiments using EMOS to perform calibration in a variety of different ways will be presented, including justifications and trade-offs made in choosing a final approach.</p><ul><li>This will include some discussion of the remaking of weather symbol products as period, rather than instantaneous, forecasts and the implications for their verification.</li> </ul>

Towards nowcasting in Europe in 2030

“…If the probabilities accurately represent the uncertainty of the observation‐based nowcast and that of the NWP, then they can be combined to produce skilful probability forecast over a wide range of lead times. Combination techniques for these probabilistic systems are under development, based on variational techniques or Kalman filters (Atencia et al, 2020), or by blending of probabilities (Moseley et al, 2022).…”