IMPROVER: The New Probabilistic Postprocessing System at the Met Office

Roberts, Nigel; Ayliffe, Ben; Evans, Gavin R.; Moseley, Stephen; Rust, Fiona; Sandford, Caroline; Trzeciak, Tomasz M.; Abernethy, Paul; Beard, Laurence; Crosswaite, Neil; Fitzpatrick, Ben; Flowerdew, Jonathan; Gale, Thomas; Holly, Leigh; Hopkinson, Aaron; Hurst, Katharine; Jackson, Simon; Jones, Chris; Mylne, Ken; Sampson, Christopher; Sharpe, Michael; Wright, Bruce; Backhouse, Simon; Baker, M. D.; Brierley, Daniel; Booton, Anna; Bysouth, Clare E.; Coulson, Robert; Coultas, Sean; Crocker, Ric; Harbord, Roger; Howard, Kathryn; Hughes, Teressa; Mittermaier, Marion; Petch, Jon; Pillinger, Tim; Smart, Victoria; Smith, Eleanor R.; Worsfold, Mark

doi:10.1175/bams-d-21-0273.1

Cited by 19 publications

(17 citation statements)

References 32 publications

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“…Furthermore, due to the smooth nature of the field, neighborhoods would have limited impacts (e.g., Mittermaier, 2014). However, it is acknowledged that there still remains research into what a “representative” temperature in a neighborhood means (e.g., Roberts et al, 2023) and so could be an important future avenue of investigation.…”

Section: Methodsmentioning

confidence: 99%

Stratification of the vertical spread‐skill relation by radiosonde drift in a convective‐scale ensemble

Flack

2023

Atmospheric Science Letters

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Ensemble forecasting systems provide useful insight into the uncertainty in the prediction of the atmosphere. However, most analysis considers ensembles in latitude, longitude, and time. Here, the vertical aspects of the spread‐skill relation are considered in a convective‐scale ensemble via comparisons with radiosonde ascents. The specific focus is on the impact of stratifying the spread‐skill relation by radiosonde drift. The drift acts as a proxy for the mobility of the atmosphere. The overall spread‐skill relation shows the temperature has a better relation than the dewpoint. However, the total variance comparisons between model and observations indicates that the dewpoint is underspread throughout the atmosphere, whilst the temperature is overspread through the lower atmosphere and underspread aloft. This suggests that the model bias is influencing the spread‐skill relation. Stratifying these results by the radiosonde drift indicates that the spread‐skill relation, and model bias, for both temperature and dewpoint degrades with increased mobility. For the most mobile situations, the ensemble is underspread throughout the atmosphere. These results have implications for ensemble design in terms of the role and influence of the driving ensemble in regional systems as more mobile situations will have a stronger dependence on the lateral boundary conditions. Longer term it may also imply that different strategies are required depending on the mobility of the synoptic conditions. Therefore, it argues for more consideration of “on‐demand” ensemble forecasting systems to allow a fairer representation of the uncertainty in different situations.

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Section: Methodsmentioning

confidence: 99%

Stratification of the vertical spread‐skill relation by radiosonde drift in a convective‐scale ensemble

Flack

2023

Atmospheric Science Letters

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“…The non-parametric nature of Reliability Calibration makes it attractive for a range of diagnostics, including temperature, if combined with other simple calibration techniques such as those applied here. Reliability Calibration was implemented using IMPROVER (Roberts et al, 2022), an open-source codebase developed by the Met Office and collaborators.…”

Section: Reliability Calibration (Rc)mentioning

confidence: 99%

The EUPPBench postprocessing benchmark dataset v1.0

Demaeyer

Bhend

Lerch

et al. 2023

Preprint

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Abstract. 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, 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. We provide examples on how to download and use the data, propose a set of evaluation methods, and perform a first benchmark of several methods for the correction of 2-meter temperature forecasts.

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“…February and March sample occurrences of frontal precipitation and cloud in wintery regimes, whilst June and July sample convective events. MOGREPS-UK and MOGREPS-G forecasts are provided on standard grids, rather than the raw model grid, see further details in Roberts et al (2023), for usage in all systems that post-process model output. To create a seamless forecast across lead times, MOGREPS-G forecasts are nearest neighbour interpolated onto the UK standard grid to match the MOGREPS-UK forecasts, which is a Lambert Azimuthal Equal Area grid with a 2 km horizontal resolution.…”

Section: Datasetsmentioning

confidence: 99%

“…To create a seamless forecast across lead times, MOGREPS‐G forecasts are nearest neighbour interpolated onto the UK standard grid to match the MOGREPS‐UK forecasts, which is a Lambert Azimuthal Equal Area grid with a 2 km horizontal resolution. The forecasts then undergo a series of IMPROVER post‐processing steps (Roberts et al, 2023) prior to Reliability Calibration. Probability forecasts are created by computing the proportion of ensemble members that have exceeded each threshold for a pre‐defined set of thresholds; these ensembles comprise 3 and 18 members for MOGREPS‐UK and MOGREPS‐G, respectively.…”

Section: Datamentioning

confidence: 99%

“…The approach aims to address visual oddities that can arise, particularly when blending different forecast sources, such as a convection‐permitting ensemble model and an ensemble with parameterized convection. This study is set within the framework of IMPROVER, which is the new post‐processing system running operationally at the Met Office (Roberts et al, 2023) using open‐source algorithms (https://github.com/metoppv/improver). Within the post‐processing system, multiple post‐processing steps are applied in series to individual model forecasts, as well as creating a multi‐model blend.…”

Section: Introductionmentioning

confidence: 99%

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Improving the blend of multiple weather forecast sources by Reliability Calibration

Rust,

Evans,

Ayliffe

2023

Meteorological Applications

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Creating a forecast that is seamless across time yet is optimal at each forecast validity time is often achieved by blending forecasts from multiple Numerical Weather Prediction models (or using other forecast sources, such as an extrapolation nowcast). With the increasing usage of convection‐permitting ensemble models at shorter lead times, the blending of these forecasts with longer‐range ensemble models with parameterized convection can lead to a clear transition from one forecast source to another. This is particularly noticeable when visualizing the evolution of the gridded forecast. Calibrating the forecast sources with a common truth prior to blending provides a method of improving forecast skill whilst also unifying the characteristics of the forecasts to create a smoother blend throughout the evolution of the forecast. In this work, a non‐parametric method for calibrating the reliability of the forecast without degrading the forecast resolution is assessed for its usability for gridded precipitation rate and total cloud amount forecasts. Reliability is markedly improved resulting in a similar skill between forecast sources during the blending period. Further refinements to the technique removed artefacts in the gridded forecasts. Caveats, including a reduction in sharpness following calibration, are also presented.

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IMPROVER: The New Probabilistic Postprocessing System at the Met Office

Cited by 19 publications

References 32 publications

Stratification of the vertical spread‐skill relation by radiosonde drift in a convective‐scale ensemble

Stratification of the vertical spread‐skill relation by radiosonde drift in a convective‐scale ensemble

The EUPPBench postprocessing benchmark dataset v1.0

Improving the blend of multiple weather forecast sources by Reliability Calibration

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