scholarly journals Accounting for skew when post-processing MOGREPS-UK temperature forecast fields

2021 ◽  
Author(s):  
Sam Allen ◽  
Gavin R Evans ◽  
Piers Buchanan ◽  
Frank Kwasniok
Keyword(s):  
Ensemble Prediction ◽  
Processing Temperature ◽  
Prediction System ◽  
Post Processing ◽  
Ensemble Prediction System ◽  
Mountainous Regions ◽  
Temperature Forecast ◽  
Post Process ◽  
Ensemble Model Output Statistics ◽  
Model Output Statistics

AbstractWhen statistically post-processing temperature forecasts, it is almost always assumed that the future temperature follows a Gaussian distribution conditional on the output of an ensemble prediction system. Recent studies, however, have demonstrated that it can at times be beneficial to employ alternative parametric families when post-processing temperature forecasts, that are either asymmetric or heavier-tailed than the normal distribution. In this article, we compare choices of the parametric distribution used within the Ensemble Model Output Statistics (EMOS) framework to statistically post-process 2m temperature forecast fields generated by the Met Office’s regional, convection-permitting ensemble prediction system, MOGREPS-UK. Specifically, we study the normal, logistic and skew-logistic distributions. A flexible alternative is also introduced that first applies a Yeo-Johnson transformation to the temperature forecasts prior to post-processing, so that they more readily conform to the assumptions made by established post-processing methods. It is found that accounting for the skewness of temperature when post-processing can enhance the performance of the resulting forecast field, particularly during summer and winter and in mountainous regions.

scholarly journals Forest-Based and Semiparametric Methods for the Postprocessing of Rainfall Ensemble Forecasting

2019 ◽  
Vol 34 (3) ◽  
pp. 617-634 ◽  
Author(s):  
Maxime Taillardat ◽  
Anne-Laure Fougères ◽  
Philippe Naveau ◽  
Olivier Mestre
Keyword(s):  
Heavy Rainfall ◽  
Hybrid Methods ◽  
Ensemble Prediction ◽  
Ensemble Forecasts ◽  
Ensemble Prediction System ◽  
Wide Range ◽  
Selection Step ◽  
Heavy Tailed ◽  
Ensemble Model Output Statistics ◽  
Model Output Statistics

Abstract To satisfy a wide range of end users, rainfall ensemble forecasts have to be skillful for both low precipitation and extreme events. We introduce local statistical postprocessing methods based on quantile regression forests and gradient forests with a semiparametric extension for heavy-tailed distributions. These hybrid methods make use of the forest-based outputs to fit a parametric distribution that is suitable to model jointly low, medium, and heavy rainfall intensities. Our goal is to improve ensemble quality and value for all rainfall intensities. The proposed methods are applied to daily 51-h forecasts of 6-h accumulated precipitation from 2012 to 2015 over France using the Météo-France ensemble prediction system called Prévision d’Ensemble ARPEGE (PEARP). They are verified with a cross-validation strategy and compete favorably with state-of-the-art methods like analog ensemble or ensemble model output statistics. Our methods do not assume any parametric links between the variables to calibrate and possible covariates. They do not require any variable selection step and can make use of more than 60 predictors available such as summary statistics on the raw ensemble, deterministic forecasts of other parameters of interest, or probabilities of convective rainfall. In addition to improvements in overall performance, hybrid forest-based procedures produced the largest skill improvements for forecasting heavy rainfall events.

Probabilistic Weather Prediction with an Analog Ensemble

2013 ◽  
Vol 141 (10) ◽  
pp. 3498-3516 ◽  
Author(s):  
Luca Delle Monache ◽  
F. Anthony Eckel ◽  
Daran L. Rife ◽  
Badrinath Nagarajan ◽  
Keith Searight
Keyword(s):  
Current Model ◽  
Weather Prediction ◽  
Computational Cost ◽  
Ensemble Prediction ◽  
Ensemble Prediction System ◽  
Forecast Lead Time ◽  
Ensemble Model Output Statistics ◽  
Model Output Statistics ◽  
Statistical Consistency ◽  
State Of The Science

Abstract This study explores an analog-based method to generate an ensemble [analog ensemble (AnEn)] in which the probability distribution of the future state of the atmosphere is estimated with a set of past observations that correspond to the best analogs of a deterministic numerical weather prediction (NWP). An analog for a given location and forecast lead time is defined as a past prediction, from the same model, that has similar values for selected features of the current model forecast. The AnEn is evaluated for 0–48-h probabilistic predictions of 10-m wind speed and 2-m temperature over the contiguous United States and against observations provided by 550 surface stations, over the 23 April–31 July 2011 period. The AnEn is generated from the Environment Canada (EC) deterministic Global Environmental Multiscale (GEM) model and a 12–15-month-long training period of forecasts and observations. The skill and value of AnEn predictions are compared with forecasts from a state-of-the-science NWP ensemble system, the 21-member Regional Ensemble Prediction System (REPS). The AnEn exhibits high statistical consistency and reliability and the ability to capture the flow-dependent behavior of errors, and it has equal or superior skill and value compared to forecasts generated via logistic regression (LR) applied to both the deterministic GEM (as in AnEn) and REPS [ensemble model output statistics (EMOS)]. The real-time computational cost of AnEn and LR is lower than EMOS.

Will post-processing always improve my forecasts?

2020 ◽  
Author(s):  
Jon Olav Skøien ◽  
Peter Salamon ◽  
Fredrik Wetterhall
Keyword(s):  
River Basin ◽  
Ensemble Model ◽  
Model Output ◽  
Post Processing ◽  
Continental Scale ◽  
Post Process ◽  
Basin Scale ◽  
Verification Period ◽  
Ensemble Model Output Statistics ◽  
Model Output Statistics

<p>Different statistical techniques are frequently employed to post-process the outcome of ensemble forecasting models. The main reason is to compensate for biases due to errors in model structure or initial conditions, and as a correction for under- or overdispersed ensembles.</p><p>Here we present analyses of the results from one these methods. We use the Ensemble Model Output Statistics method (EMOS; Gneiting et al., 2005) to post-process the ensemble output from a continental scale hydrological model - LISFLOOD (Van Der Knijff et al., 2010; De Roo et al., 2000). The model was calibrated at approximately 700 stations based on long term observations of runoff and meteorological variables. We use the same locations for calibration and verification of the 1-10 days forecasts of the model, based on ensemble and deterministic meteorological forecasts from ECMWF (51 ensemble members + 1 high-resolution), DWD (1 member) and COMSO-LEPS (16 ensemble members).</p><p>We calibrated the EMOS-parameters using the Continuous ranked probability score (CRPS). Whereas the post-processing improved the results for the first 1-2 days lead time, the improvement was less for increasing lead times of the verification period. As the post-processing is based on assumptions about the forecast errors, we will here present analyses of the ensemble output that can give some indications of what to expect from the post-processing.</p><p> </p><p>Gneiting, T., Raftery, A. E., Westveld, A. H. and Goldman, T.: Calibrated Probabilistic Forecasting Using Ensemble Model Output Statistics and Minimum CRPS Estimation, Mon. Weather Rev., 133(5), 1098–1118, doi:10.1175/MWR2904.1, 2005.</p><p>Van Der Knijff, J. M., Younis, J. and De Roo, A. P. J.: LISFLOOD: a GIS‐based distributed model for river basin scale water balance and flood simulation, Int. J. Geogr. Inf. Sci., 24(2), 189–212, doi:10.1080/13658810802549154, 2010.</p><p>De Roo, A. P. J., Wesseling, C. G. and Van Deursen, W. P. A.: Physically based river basin modelling within a GIS: The LISFLOOD model, in Hydrological Processes, vol. 14, pp. 1981–1992, John Wiley & Sons Ltd. [online] Available from: http://www.scopus.com/inward/record.url?eid=2-s2.0-0034254644&partnerID=tZOtx3y1, 2000.</p><p> </p>

Analog-Based Ensemble Model Output Statistics

2015 ◽  
Vol 143 (7) ◽  
pp. 2909-2917 ◽  
Author(s):  
Constantin Junk ◽  
Luca Delle Monache ◽  
Stefano Alessandrini
Keyword(s):  
Rare Events ◽  
Training Dataset ◽  
Ensemble Prediction ◽  
Brier Score ◽  
Ensemble Model ◽  
Model Output ◽  
Ensemble Forecasts ◽  
Ensemble Prediction System ◽  
Ensemble Model Output Statistics ◽  
Model Output Statistics

Abstract An analog-based ensemble model output statistics (EMOS) is proposed to improve EMOS for the calibration of ensemble forecasts. Given a set of analog predictors and corresponding weights, which are optimized with a brute-force continuous ranked probability score (CRPS) minimization, forecasts similar to a current ensemble forecast (i.e., analogs) are searched. The best analogs and the corresponding observations form the training dataset for estimating the EMOS coefficients. To test the new approach for renewable energy applications, wind speed measurements at 100-m height from six measurement towers and wind ensemble forecasts at 100-m height from the European Centre for Medium-Range Weather Forecasts (ECMWF) Ensemble Prediction System (EPS) are used. The analog-based EMOS is compared against EMOS, an adaptive and recursive wind vector calibration (AUV), and an analog ensemble applied to ECMWF EPS. It is shown that the analog-based EMOS outperforms EMOS, AUV, and the analog ensemble at all measurement sites in terms of CRPS and Brier score for common and rare events. The CRPS improvements relative to EMOS reach up to 11% and are statistically significant at almost all sites. The reliability of the analog-based EMOS ensemble for rare events is better compared to EMOS and AUV and is similar compared to the analog ensemble.

Study of perturbing method in regional BGM ensemble prediction system

2012 ◽  
Vol 4 (1) ◽  
pp. 65
Author(s):  
Xiao Yu-Hua ◽  
He Guang-Bi ◽  
Chen Jing ◽  
Deng Guo
Keyword(s):  
Ensemble Prediction ◽  
Prediction System ◽  
Ensemble Prediction System

Extended Prediction of North Indian Ocean Tropical Cyclones

2012 ◽  
Vol 27 (3) ◽  
pp. 757-769 ◽  
Author(s):  
James I. Belanger ◽  
Peter J. Webster ◽  
Judith A. Curry ◽  
Mark T. Jelinek
Keyword(s):  
Tropical Cyclone ◽  
Indian Ocean ◽  
Tropical Cyclones ◽  
North Indian Ocean ◽  
Ensemble Prediction ◽  
Tropical Cyclone Genesis ◽  
Lead Times ◽  
Prediction System ◽  
Ensemble Prediction System ◽  
The North

Abstract This analysis examines the predictability of several key forecasting parameters using the ECMWF Variable Ensemble Prediction System (VarEPS) for tropical cyclones (TCs) in the North Indian Ocean (NIO) including tropical cyclone genesis, pregenesis and postgenesis track and intensity projections, and regional outlooks of tropical cyclone activity for the Arabian Sea and the Bay of Bengal. Based on the evaluation period from 2007 to 2010, the VarEPS TC genesis forecasts demonstrate low false-alarm rates and moderate to high probabilities of detection for lead times of 1–7 days. In addition, VarEPS pregenesis track forecasts on average perform better than VarEPS postgenesis forecasts through 120 h and feature a total track error growth of 41 n mi day−1. VarEPS provides superior postgenesis track forecasts for lead times greater than 12 h compared to other models, including the Met Office global model (UKMET), the Navy Operational Global Atmospheric Prediction System (NOGAPS), and the Global Forecasting System (GFS), and slightly lower track errors than the Joint Typhoon Warning Center. This paper concludes with a discussion of how VarEPS can provide much of this extended predictability within a probabilistic framework for the region.

scholarly journals A Probabilistic Multimodel Ensemble Approach to Seasonal Prediction

2009 ◽  
Vol 24 (3) ◽  
pp. 812-828 ◽  
Author(s):  
Young-Mi Min ◽  
Vladimir N. Kryjov ◽  
Chung-Kyu Park
Keyword(s):  
Real Time ◽  
Gaussian Approximation ◽  
Seasonal Prediction ◽  
Asia Pacific ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Sampling Errors ◽  
Seasonal Forecasts ◽  
Multimodel Ensemble ◽  
Ensemble Prediction System

Abstract A probabilistic multimodel ensemble prediction system (PMME) has been developed to provide operational seasonal forecasts at the Asia–Pacific Economic Cooperation (APEC) Climate Center (APCC). This system is based on an uncalibrated multimodel ensemble, with model weights inversely proportional to the errors in forecast probability associated with the model sampling errors, and a parametric Gaussian fitting method for the estimate of tercile-based categorical probabilities. It is shown that the suggested method is the most appropriate for use in an operational global prediction system that combines a large number of models, with individual model ensembles essentially differing in size and model weights in the forecast and hindcast datasets being inconsistent. Justification for the use of a Gaussian approximation of the precipitation probability distribution function for global forecasts is also provided. PMME retrospective and real-time forecasts are assessed. For above normal and below normal categories, temperature forecasts outperform climatology for a large part of the globe. Precipitation forecasts are definitely more skillful than random guessing for the extratropics and climatological forecasts for the tropics. The skill of real-time forecasts lies within the range of the interannual variability of the historical forecasts.

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