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The Met Office in the United Kingdom has developed a completely new probabilistic postprocessing system called IMPROVER to operate on outputs from its operational numerical weather prediction (NWP) forecasts and precipitation nowcasts. The aim is to improve weather forecast information to the public and other stakeholders while better exploiting the current and future generations of underpinning kilometer-scale NWP ensembles. We wish to provide seamless forecasts from nowcasting to medium range, provide consistency between gridded and site-specific forecasts, and be able to verify every stage of the processing. The software is written in a modern modular framework that is easy to maintain, develop, and share. IMPROVER allows forecast information to be provided with greater spatial and temporal detail and a faster update frequency than previous postprocessing. Independent probabilistic processing chains are constructed for each meteorological variable consisting of a series of processing stages that operate on predefined grids and blend outputs from several NWP inputs to give a frequently updated, probabilistic forecast solution. Probabilistic information is produced as standard, with the option of extracting a most likely or yes–no outcome if required. Verification can be performed at all stages, although it is only currently switched on for the most significant stages when run in real time. IMPROVER has been producing real-time output since March 2021 and became operational in spring 2022.

The UK Met Office is developing an open-source probability-based post-processing system called IMPROVER (Integrated Model Post-Processing and Verification) 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.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. The system achieved operational acceptance in spring 2022 and will be used in operational products from spring 2023.Weather symbols provide the general public with a simple, pictorial view of the weather for a time of interest and include sun and cloud conditions, mist and fog, hail and lightning, and three phases of precipitation, both as showers or continuous, and light or heavy. This talk describes how a deterministic most-likely weather type code is generated using a decision tree approach from probabilistic multi model IMPROVER data for 1 hour, 3 hour and daytime periods that are consistent with each other. Recent work to make these weather codes representative of a time-window, rather than an instant, will be discussed. We will present some verification, comparing IMPROVER weather symbols and the current operational Met Office symbols with SYNOP present weather reports.

Statistical postprocessing of forecasts from numerical weather prediction systems is an important component of modern weather forecasting systems. A growing variety of postprocessing methods has been proposed, but a comprehensive, community-driven comparison of their relative performance is yet to be established. Important reasons for this lack include the absence of a fair intercomparison protocol, and, the difficulty of constructing a common comprehensive dataset that can be used to perform such intercomparison. Here we introduce the first version of the EUPPBench, a dataset of time-aligned medium-range forecasts and observations over Central Europe, with the aim to facilitate and standardize the intercomparison of postprocessing methods. This dataset is publicly available [1], includes station and gridded data, ensemble forecasts for training (20 years) and validation (2 years) based on the ECMWF system. The initial dataset is the basis of an ongoing activity to establish a benchmarking platform for postprocessing of medium-range weather forecasts. We showcase a first benchmark of several methods for the adjustment of near-surface temperature forecasts and outline the future plans for the benchmark activity. [1] https://github.com/EUPP-benchmark/climetlab-eumetnet-postprocessing-benchmark

Statistical postprocessing of medium-range weather forecasts is an important component of modern forecasting systems. Since the beginning of modern data science, numerous new postprocessing methods have been proposed, complementing an already very diverse field. However, one of the questions that frequently arises when considering different methods in the framework of implementing operational postprocessing is the relative performance of the methods for a given specific task. It is particularly challenging to find or construct a common comprehensive dataset that can be used to perform such comparisons. Here, we introduce the first version of EUPPBench (EUMETNET postprocessing benchmark), a dataset of time-aligned forecasts and observations, with the aim to facilitate and standardize this process. This dataset is publicly available at https://github.com/EUPP-benchmark/climetlab-eumetnet-postprocessing-benchmark (31 December 2022) and on Zenodo (https://doi.org/10.5281/zenodo.7429236, Demaeyer, 2022b and https://doi.org/10.5281/zenodo.7708362, Bhend et al., 2023). We provide examples showing how to download and use the data, we propose a set of evaluation methods, and we perform a first benchmark of several methods for the correction of 2 m temperature forecasts.

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.

Three families living in an urban area in the Midwest with elementary-age children diagnosed with disabilities were interviewed about their family involvement in special education programming and intervention. The study explored how three mothers used social networks to advocate and care for their child's special education programming. Using Brigg's distinction of social networks that serve as social supports and ones that serve as social leverage as a conceptual framework, we aim to help further understand how parents advocate for their children in the parent—school relationship.