The Met Office in the UK has developed a completely new probabilistic post-processing 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 whilst 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 post-processing. Independent probabilistic processing chains are constructed for each meteorological variable consisting of a series of processing stages that operate on pre-defined 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.
Grazing incidence mirrors are now a standard optic for focusing X-ray beams. Both bimorph and mechanically bendable mirrors are widely used at Diamond Light Source because they permit a wide choice of focal lengths. They can also be deliberately set out of focus to enlarge the X-ray beam, and indeed many beamline teams now wish to generate uniform beam spots of variable size. However, progress has been slowed by the appearance of fine structure in these defocused beams. Measurements showing the relationship between the medium-frequency polishing error and this structure over a variety of beam sizes will be presented. A theoretical model for the simulations of defocused beams from general mirrors will then be developed. Not only the figure error and its first derivative the slope error, but also the second derivative, the curvature error, must be considered. In conclusion, possible ways to reduce the defocused beam structure by varying the actuators' configuration and settings will be discussed.
<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>
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 parameterised convection can lead to a clear transition from one forecast source to another. This is particularly noticeable when visualising 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. This presentation aims to describe a non-parametric method, utilising tools from the Met Office’s IMPROVER codebase (https://github.com/metoppv/improver), for calibrating the reliability of the forecast without degrading the forecast resolution. This approach is assessed for its usability for gridded precipitation rate and total cloud amount forecasts. Reliability is markedly improved resulting in similar skill between forecast sources during the blending period and therefore extends the lead time range at which the forecast is more skilful than climatology. This approach is also presented as a step within a series of steps to improve forecast skill therefore highlighting that this approach can be complementary to other techniques without significant tuning. Further refinements to the Reliability Calibration technique removed artefacts in the gridded forecasts. Caveats, including a reduction in sharpness following calibration, are also presented.
<p>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.</p><p>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.</p>
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