scholarly journals Post-processing and its effect on relative-extreme event identification

2021 ◽  
Author(s):  
Michael Sharpe ◽  
Joseph Battershill ◽  
Katherine Hurst
Keyword(s):  
Extreme Events ◽  
Deterministic Model ◽  
Extreme Weather Events ◽  
Ensemble Prediction ◽  
Model Output ◽  
Post Processing ◽  
Ensemble Prediction System ◽  
Site Specific ◽  
The Public ◽  
The Uk

<p>The UK Met Office manages its commitment to the public through the Public Weather Service and an important factor in public safety and concern is extreme weather events. Therefore, a new Key Performance Indicator is being introduced, related to the ability with which extreme events are correctly identified. The Threshold Weighted Continuous Ranked Probability Score (twCRPS) is used to make this assessment by determining how well site-specific Met Office ensemble-based probabilistic forecast solutions predict relative-extreme events. The threshold weighted version of the Mean Absolute Error (twMAE) is the deterministic equivalent to the twCRPS. The twMAE is used for the assessment of the deterministic model output that currently appears on the Met Office App and website.</p><p>Gridded numerical ensemble model data is generated by MOGREPS (the Met Office Global and Regional Ensemble Prediction System). A new program of post-processing work has been undertaken in recent years (IMPROVER) to replace the system of post-processing currently employed by the Met Office. IMPROVER applies a series of post-processing steps to generate both probabilistic and deterministic forecasts and site-specific data is generated from these model fields. Verification of the model output is undertaken at each post-processing stage to ensure that every step is having the expected impact on the performance of the model. To date however, these assessments have concentrated on the performance of more typical conditions rather than the ability with which more extreme events are identified.</p><p>This session outlines very recent work to assess the ability with which raw MOGREPS data and data generated by various of the post-processing stages of IMPROVER, predict relative-extreme events at observation sites throughout the UK. The twMAE and twCRPS are used for this assessment, where in both cases, the threshold weighting function is defined in terms of a distribution formed by sampling the numerical value corresponding to a chosen relatively extreme percentile from the observed 30-year climatology of each UK site.</p>

scholarly journals Deep learning for post-processing ensemble weather forecasts

2021 ◽  
Vol 379 (2194) ◽  
pp. 20200092 ◽  
Author(s):  
Peter Grönquist ◽  
Chengyuan Yao ◽  
Tal Ben-Nun ◽  
Nikoli Dryden ◽  
Peter Dueben ◽  
...  
Keyword(s):  
Numerical Models ◽  
Computational Cost ◽  
Extreme Weather ◽  
Extreme Weather Events ◽  
Ensemble Prediction ◽  
Climate Modelling ◽  
Post Processing ◽  
Ensemble Prediction System ◽  
Weather Forecasts ◽  
Weather Events

Quantifying uncertainty in weather forecasts is critical, especially for predicting extreme weather events. This is typically accomplished with ensemble prediction systems, which consist of many perturbed numerical weather simulations, or trajectories, run in parallel. These systems are associated with a high computational cost and often involve statistical post-processing steps to inexpensively improve their raw prediction qualities. We propose a mixed model that uses only a subset of the original weather trajectories combined with a post-processing step using deep neural networks. These enable the model to account for non-linear relationships that are not captured by current numerical models or post-processing methods. Applied to the global data, our mixed models achieve a relative improvement in ensemble forecast skill (CRPS) of over 14%. Furthermore, we demonstrate that the improvement is larger for extreme weather events on select case studies. We also show that our post-processing can use fewer trajectories to achieve comparable results to the full ensemble. By using fewer trajectories, the computational costs of an ensemble prediction system can be reduced, allowing it to run at higher resolution and produce more accurate forecasts. This article is part of the theme issue ‘Machine learning for weather and climate modelling’.

scholarly journals Using probabilistic model data to generate area marine forecasts

2021 ◽  
Author(s):  
Michael Sharpe ◽  
Thomas Dodds ◽  
Ruth Steele ◽  
Caroline Jones
Keyword(s):  
Deterministic Model ◽  
Weather Conditions ◽  
Ensemble Prediction ◽  
Wind Component ◽  
Ensemble Prediction System ◽  
Wind Speeds ◽  
High Profile ◽  
Post Process ◽  
Inshore Waters ◽  
The Uk

<p>The UK Inshore Waters Forecast predicts wind speeds, sea states, weather conditions and visibilities for marine areas within 12 nautical miles of the UK coast. In addition to the now-common web-based outlets of most public forecast products, this very high profile forecast product is also broadcast by the BBC on national radio and television. It is the enviable task of Operational Meteorologists, based at UK Met Office sites in Exeter and Aberdeen, to issue these forecasts every six hours for the vitally important purpose of protecting lives in the coastal waters surrounding the UK. Currently, the production process involves a marine forecaster comprehensively inspecting deterministic model fields, prior to manual text generation. However, direct utilisation of an ensemble model-based product has the potential to make this task considerably more efficient and possibly make the forecast more accurate.</p><p>Raw output from the Met Office Global and Regional Ensemble Prediction System (MOGREPS) is used routinely throughout the Met Office to assist forecasters. Furthermore, a recent project to develop and improve the techniques used to statistically post-process this data (IMPROVER) is now employed to further reduce identified errors within MOGREPS data.</p><p>This session describes the latest work to exploit both raw MOGREPS and post-processed data for the generation of the wind component to the Inshore Waters Forecast. This component is verified against post-processed nowcast analysis fields to determine its accuracy and the results are compared against the equivalent performance currently achieved by Operational Maritime Meteorologists. The outcome of this assessment will help to determine whether either of these data-sources are suitable as a guide for the production of this high-profile forecast product.</p>

scholarly journals Hydrometeorological Short-Range Ensemble Forecasts in Complex Terrain. Part II: Economic Evaluation

2008 ◽  
Vol 23 (4) ◽  
pp. 557-574 ◽  
Author(s):  
Doug McCollor ◽  
Roland Stull
Keyword(s):  
High Resolution ◽  
Utility Function ◽  
Short Range ◽  
Deterministic Model ◽  
Ensemble Average ◽  
Ensemble Prediction ◽  
Value Assessment ◽  
Ensemble Forecasts ◽  
Ensemble Prediction System ◽  
Hydroelectric Reservoir

Abstract Two economic models are employed to perform a value assessment of short-range ensemble forecasts of 24-h precipitation probabilities for hydroelectric reservoir operation. Using a static cost–loss model, the value of the probability information is compared to the values of a deterministic control high-resolution forecast and of an ensemble-average forecast for forecast days 1 and 2. It is found that the probabilistic ensemble forecast provides value to a much wider range of hydroelectric operators than either the deterministic high-resolution forecast or the ensemble-average forecast, although for a small subset of operators the value of the three forecasts is the same. Forecasts for day-1 precipitation provide measurably higher value than forecasts for day-2 precipitation because of the loss of skill in the longer-range forecasts. A decision theory model provides a continuous-variable weighting of a user-specific utility function. The utility function weights are supplied by the ensemble prediction system, and the outcome is compared with weights calculated from a deterministic model, from the ensemble average, and from climatology. It is found that the methods employing the full ensemble and the ensemble average outperform the single deterministic model and climatology for the hydroelectric reservoir scenario studied.

scholarly journals From research to applications – examples of operational ensemble post-processing in France using machine learning

2020 ◽  
Vol 27 (2) ◽  
pp. 329-347 ◽  
Author(s):  
Maxime Taillardat ◽  
Olivier Mestre
Keyword(s):  
Machine Learning ◽  
Heat Waves ◽  
Computation Time ◽  
Ensemble Prediction ◽  
Post Processing ◽  
Ensemble Forecasts ◽  
Ensemble Prediction System ◽  
Practical Applications ◽  
Medium Resolution ◽  
Hourly Rainfall

Abstract. Statistical post-processing of ensemble forecasts, from simple linear regressions to more sophisticated techniques, is now a well-known procedure for correcting biased and poorly dispersed ensemble weather predictions. However, practical applications in national weather services are still in their infancy compared to deterministic post-processing. This paper presents two different applications of ensemble post-processing using machine learning at an industrial scale. The first is a station-based post-processing of surface temperature and subsequent interpolation to a grid in a medium-resolution ensemble system. The second is a gridded post-processing of hourly rainfall amounts in a high-resolution ensemble prediction system. The techniques used rely on quantile regression forests (QRFs) and ensemble copula coupling (ECC), chosen for their robustness and simplicity of training regardless of the variable subject to calibration. Moreover, some variants of classical techniques used, such as QRF and ECC, were developed in order to adjust to operational constraints. A forecast anomaly-based QRF is used for temperature for a better prediction of cold and heat waves. A variant of ECC for hourly rainfall was built, accounting for more realistic longer rainfall accumulations. We show that both forecast quality and forecast value are improved compared to the raw ensemble. Finally, comments about model size and computation time are made.

scholarly journals Evaluation of the ECMWF System 4 climate forecasts for streamflow forecasting in the Upper Hanjiang River Basin

2018 ◽  
Vol 49 (6) ◽  
pp. 1864-1879 ◽  
Author(s):  
Fuqiang Tian ◽  
Yilu Li ◽  
Tongtiegang Zhao ◽  
Hongchang Hu ◽  
Florian Pappenberger ◽  
...  
Keyword(s):  
River Basin ◽  
Processing Method ◽  
Water Diversion ◽  
Ensemble Prediction ◽  
Distributed Hydrological Model ◽  
Streamflow Forecasting ◽  
Post Processing ◽  
Ensemble Prediction System ◽  
Streamflow Forecasts ◽  
Hanjiang River

Abstract This paper assesses the potential of the European Centre for Medium-Range Weather Forecasts (ECMWF) System 4 forecasts and investigates the post-processing precipitation to enhance the skill of streamflow forecasts. The investigation is based on hydrological modelling and is conducted through the case study of the Upper Hanjiang River Basin (UHRB). A semi-distributed hydrological model, TsingHua Representative Elementary Watershed (THREW), is implemented to simulate the rainfall–runoff processes, with the help of hydrological ensemble prediction system (HEPS) approach. A post-processing method, quantile mapping method, is applied to bias correct the raw precipitation forecasts. Then we evaluate the performance of raw and post-processed streamflow forecasts for the four hydrological stations along the mainstream of Hanjiang River from 2001 to 2008. The results show that the performance of the streamflow forecasts is greatly enhanced with post-processing precipitation forecasts, especially in pre-dry season (November and December), thus providing useful information for water supply management of the central route of South to North Water Diversion Project (SNWDP). The raw streamflow forecasts tend to overpredict and present similarly to forecast accuracy with the extended streamflow prediction (ESP) approach. Streamflow forecast skill is considerably improved when applying post-processing method to bias correct the ECMWF System 4 precipitation forecasts.

scholarly journals Representation of model error in a convective-scale ensemble prediction system

2014 ◽  
Vol 21 (1) ◽  
pp. 19-39 ◽  
Author(s):  
L. H. Baker ◽  
A. C. Rudd ◽  
S. Migliorini ◽  
R. N. Bannister
Keyword(s):  
Model Error ◽  
Ensemble Prediction ◽  
Initial Condition ◽  
Prediction System ◽  
Ensemble Prediction System ◽  
Near Surface ◽  
Skill Scores ◽  
The Uk ◽  
Parameter Values ◽  
The Impact

Abstract. In this paper ensembles of forecasts (of up to six hours) are studied from a convection-permitting model with a representation of model error due to unresolved processes. The ensemble prediction system (EPS) used is an experimental convection-permitting version of the UK Met Office's 24-member Global and Regional Ensemble Prediction System (MOGREPS). The method of representing model error variability, which perturbs parameters within the model's parameterisation schemes, has been modified and we investigate the impact of applying this scheme in different ways. These are: a control ensemble where all ensemble members have the same parameter values; an ensemble where the parameters are different between members, but fixed in time; and ensembles where the parameters are updated randomly every 30 or 60 min. The choice of parameters and their ranges of variability have been determined from expert opinion and parameter sensitivity tests. A case of frontal rain over the southern UK has been chosen, which has a multi-banded rainfall structure. The consequences of including model error variability in the case studied are mixed and are summarised as follows. The multiple banding, evident in the radar, is not captured for any single member. However, the single band is positioned in some members where a secondary band is present in the radar. This is found for all ensembles studied. Adding model error variability with fixed parameters in time does increase the ensemble spread for near-surface variables like wind and temperature, but can actually decrease the spread of the rainfall. Perturbing the parameters periodically throughout the forecast does not further increase the spread and exhibits "jumpiness" in the spread at times when the parameters are perturbed. Adding model error variability gives an improvement in forecast skill after the first 2–3 h of the forecast for near-surface temperature and relative humidity. For precipitation skill scores, adding model error variability has the effect of improving the skill in the first 1–2 h of the forecast, but then of reducing the skill after that. Complementary experiments were performed where the only difference between members was the set of parameter values (i.e. no initial condition variability). The resulting spread was found to be significantly less than the spread from initial condition variability alone.

scholarly journals From research to applications – Examples of operational ensemble post-processing in France using machine learning

2020 ◽  
Author(s):  
Maxime Taillardat ◽  
Olivier Mestre
Keyword(s):  
Machine Learning ◽  
Heat Waves ◽  
Computation Time ◽  
Ensemble Prediction ◽  
Post Processing ◽  
Ensemble Forecasts ◽  
Ensemble Prediction System ◽  
Practical Applications ◽  
Medium Resolution ◽  
Hourly Rainfall

Abstract. Statistical post-processing of ensemble forecasts, from simple linear regressions to more sophisticated techniques, is now a well-known procedure in order to correct biased and misdispersed ensemble weather predictions. However, practical applications in National Weather Services is still in its infancy compared to deterministic post-processing. This paper presents two different applications of ensemble post-processing using machine learning at an industrial scale. The first is a station-based post-processing of surface temperature in a medium resolution ensemble system. The second is a gridded post-processing of hourly rainfall amounts in a high resolution ensemble prediction system. The techniques used rely on quantile regression forests (QRF) and ensemble copula coupling (ECC), chosen for their robustness and simplicity of training whatever the variable subject to calibration. Moreover, some variants of classical techniques used such as QRF or ECC have been developed in order to adjust to operational constraints. A forecast anomaly-based QRF is used for temperature for a better prediction of cold and heat waves. A variant of ECC for hourly rainfall is built, accounting for more realistic longer rainfall accumulations. It is shown that forecast quality as well as forecast value is improved compared to the raw ensemble. At last, comments about model size and computation time are made.

scholarly journals Can an ensemble give anything more than Gaussian probabilities?

2003 ◽  
Vol 10 (6) ◽  
pp. 469-475 ◽  
Author(s):  
J. C. W. Denholm-Price
Keyword(s):  
Weather Prediction ◽  
Ensemble Prediction ◽  
Ensemble Prediction System ◽  
Gaussian Probability Density Function ◽  
Weather Forecasts ◽  
Skill Scores ◽  
Gaussian Fit ◽  
Probability Forecasts ◽  
The Uk ◽  
Probability Density Function Pdf

Abstract. Can a relatively small numerical weather prediction ensemble produce any more forecast information than can be reproduced by a Gaussian probability density function (PDF)? This question is examined using site-specific probability forecasts from the UK Met Office. These forecasts are based on the 51-member Ensemble Prediction System of the European Centre for Medium-range Weather Forecasts. Verification using Brier skill scores suggests that there can be statistically-significant skill in the ensemble forecast PDF compared with a Gaussian fit to the ensemble. The most significant increases in skill were achieved from bias-corrected, calibrated forecasts and for probability forecasts of thresholds that are located well inside the climatological limits at the examined sites. Forecast probabilities for more climatologically-extreme thresholds, where the verification more often lies within the tails or outside of the PDF, showed little difference in skill between the forecast PDF and the Gaussian forecast.

scholarly journals The Global Influence of the Madden–Julian Oscillation on Extreme Temperature Events*

2015 ◽  
Vol 28 (10) ◽  
pp. 4141-4151 ◽  
Author(s):  
Satoko Matsueda ◽  
Yuhei Takaya
Keyword(s):  
Extreme Events ◽  
Time Lag ◽  
Extreme Temperature ◽  
Forecast Skill ◽  
Japan Meteorological Agency ◽  
Ensemble Prediction ◽  
Boreal Winter ◽  
Madden Julian Oscillation ◽  
Ensemble Prediction System ◽  
Extreme Temperature Events

Abstract The authors investigated the influence of the Madden–Julian oscillation (MJO) on extreme warm and cold events, which may have large social and economic impacts. The frequencies of extreme temperature events were analyzed and compared between active and inactive MJO periods by using the 7-day running average of the 850-hPa temperature during the extended boreal winter (November–April). The results show that the frequency of extreme events is significantly modulated (i.e., increased by a factor of more than 2) by the MJO with a time lag over some areas in the extratropics as well as in the tropics. In the extratropics, the modulation of the frequency of the extreme events is roughly associated with midlatitude wave responses to tropical forcing and anomalous lower-level circulation due to the MJO. The relationship between the MJO and forecast skill of extreme temperature events was also investigated by using a suite of hindcasts made with the operational one-month ensemble prediction system of the Japan Meteorological Agency. Forecast skill of extreme events occurring after active MJO periods tend to be better over some areas, compared with after inactive MJO periods. These results suggest that a realistic representation of the MJO and of the atmospheric response to the MJO in forecast models is important for providing reliable early warning information about extreme events.

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