A fully probabilistic post processing system called IMPROVER has been developed at the UK Met Office. IMPROVER provides frequently updated probabilistic gridded forecasts, as well as forecasts for point locations, for input into automated forecast generation and for users such as operational meteorologists. Although the outputs are probabilistic, a deterministic interpretation can be extracted if required. The scientific rationale behind this endeavor is the need to make more optimal use of the current and future generations of convection-allowing Numerical Weather Prediction (NWP) models and ensembles. The aim is to provide seamless, calibrated, probabilistic forecasts that are a blend of NWP models/ensembles from nowcasting to medium range. Today’s NWP systems offer not only many ensemble members but also frequent updates making it very difficult for users to manage the data and exploit latest information, so a key capability of IMPROVER is the frequent cycling, providing a continuously updated forecast blending the most recent available data. Several key scientific benefits arise from the probabilistic approach on top of the capability to provide probabilistic outputs. Probabilities allow much simpler and effective blending with older forecasts or between different models/ensembles. We have introduced a variety of probabilistic neighbourhood methods to account for the inherent limited predictability at small scales. Some of these can incorporate topographic variation which is particularly important for variables such as rain, sleet and snow or fog. The ensemble-probabilistic approach has also enabled the use of ensemble calibration methods, which can not only improve skill and spread, but create a much more seamless transition between models/ensembles at different resolutions. The system is built with a modular software framework that allows flexibility for future development and includes verification at every stage of the processing. IMPROVER is now routinely running with operational support and is expected to become fully operational in 2022. This presentation will briefly describe the initial scientific vision and current IMPROVER capability and discuss where any compromise or re-evaluation had to be made along the way. Finally, thoughts about the future and lessons learnt will be shared.

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, to produce seamless probabilistic forecasts from now out to 7 days ahead. 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. This talk will briefly describe the post-processing sequence from raw NWP model data to fully-blended, gridded and spot forecasts as probabilities and percentiles of a broad range of meteorological diagnostics, with the application of physical and statistical post-processing techniques. The system became operational in early May 2022, and the this talk will focus on some of the work that has been undertaken in the last year to achieve this, including ensemble calibration of temperature and wind speed data at observed and non-observed sites. There will be discussion of some of the verification used to prove this system, as well as a brief look at the technical aspects of this complex system, the initial customers and collaborators and the planned future work, including the use of ECMWF forecast data to extend the range of IMPROVER out to 14 days.

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. 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. 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. 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. * This will include some discussion of the remaking of weather symbol products as period, rather than instantaneous, forecasts and the implications for their verification.

Existing surface water flood forecasting methods in Scotland are based on indicative depth‐duration rainfall thresholds with limited understanding of the likelihood of inundation or associated impacts. Innovative risk‐based solutions are urgently needed to advance surface water forecasting capabilities for improved flood resilience in urban centres. A new model‐based solution was developed for Glasgow, linking 24‐h ensemble rainfall predictions from the Met Office Global and Regional Ensemble Prediction System for the UK (MOGREPS‐UK) with static flood risk maps through the Grid‐to‐Grid hydrological model. This new forecasting capability was used operationally by the Scottish Flood Forecasting Service during the 2014 Commonwealth Games to provide bespoke surface water flooding guidance to responders. The operational trial demonstrated the benefits of being able to provide targeted information on real‐time surface water flood risk. It also identified the high staff resource requirement to support the service due to the greater uncertainty in surface water flood forecasting compared to established fluvial and coastal methods.