scholarly journals Calibrated ensemble forecasts of the height of new snow using quantile regression forests and ensemble model output statistics

2021 ◽  
Vol 28 (3) ◽  
pp. 467-480
Author(s):  
Guillaume Evin ◽  
Matthieu Lafaysse ◽  
Maxime Taillardat ◽  
Michaël Zamo
Keyword(s):  
Quantile Regression ◽  
Specific Humidity ◽  
Ensemble Model ◽  
Model Output ◽  
Post Processing ◽  
Ensemble Forecasts ◽  
Mountain Areas ◽  
Processing Methods ◽  
Ensemble Model Output Statistics ◽  
Model Output Statistics

Abstract. Height of new snow (HN) forecasts help to prevent critical failures of infrastructures in mountain areas, e.g. transport networks and ski resorts. The French national meteorological service, Météo-France, operates a probabilistic forecasting system based on ensemble meteorological forecasts and a detailed snowpack model to provide ensembles of HN forecasts. These forecasts are, however, biased and underdispersed. As for many weather variables, post-processing methods can be used to alleviate these drawbacks and obtain meaningful 1 to 4 d HN forecasts. In this paper, we compare the skill of two post-processing methods. The first approach is an ensemble model output statistics (EMOS) method, which can be described as a nonhomogeneous regression with a censored shifted Gamma distribution. The second approach is based on quantile regression forests, using different meteorological and snow predictors. Both approaches are evaluated using a 22 year reforecast. Thanks to a larger number of predictors, the quantile regression forest is shown to be a powerful alternative to EMOS for the post-processing of HN ensemble forecasts. The gain of performance is large in all situations but is particularly marked when raw forecasts completely miss the snow event. This type of situation happens when the rain–snow transition elevation is overestimated by the raw forecasts (rain instead of snow in the raw forecasts) or when there is no precipitation in the forecast. In that case, quantile regression forests improve the predictions using the other weather predictors (wind, temperature, and specific humidity).

scholarly journals Calibrated ensemble forecasts of the height of new snow using quantile regression forests and Ensemble Model Output Statistics

2021 ◽  
Author(s):  
Guillaume Evin ◽  
Matthieu Lafaysse ◽  
Maxime Taillardat ◽  
Michaël Zamo
Keyword(s):  
Quantile Regression ◽  
Specific Humidity ◽  
Ensemble Model ◽  
Model Output ◽  
Post Processing ◽  
Ensemble Forecasts ◽  
Mountain Areas ◽  
Processing Methods ◽  
Ensemble Model Output Statistics ◽  
Model Output Statistics

Abstract. Height of new snow (HN) forecasts help to prevent critical failures of infrastructures in mountain areas, e.g. transport networks, ski resorts. The French national meteorological service, Meteo-France, operates a probabilistic forecasting system based on ensemble meteorological forecasts and a detailed snowpack model to provide ensembles of HN forecasts. These forecasts are however significantly biased and underdispersed. As for many weather variables, post-processing methods can be used to alleviate these drawbacks and obtain meaningful 1-day to 4-day HN forecasts. In this paper, we compare the skill of two post-processing methods. The first approach is an ensemble model output statistics (EMOS) method, which can be described as a Nonhomogeneous Regression with a Censored Shifted Gamma distribution. The second approach is based on quantile regression forests, using different meteorological and snow predictors. Both approaches are evaluated using a 22-year reforecast. Thanks to a larger number of predictors, the quantile regression forest is shown to be a powerful alternative to EMOS for the post-processing of HN ensemble forecasts. The gain of performance is important in all situations but is particularly marked when raw forecasts completely miss the snow event. This type of situations happens when the rain-snow transition elevation is overestimated by the raw forecasts (rain instead of snow in the raw forecasts) or when there is no precipitation in the forecast. In that case, quantile regression forests improve the predictions using the other weather predictors (wind, temperature, specific humidity).

scholarly journals Calibrated Ensemble Forecasts Using Quantile Regression Forests and Ensemble Model Output Statistics

2016 ◽  
Vol 144 (6) ◽  
pp. 2375-2393 ◽  
Author(s):  
Maxime Taillardat ◽  
Olivier Mestre ◽  
Michaël Zamo ◽  
Philippe Naveau
Keyword(s):  
Quantile Regression ◽  
Weather Forecasting ◽  
Real Data ◽  
Ensemble Model ◽  
Model Output ◽  
General Measure ◽  
Ensemble Forecasts ◽  
Probabilistic Forecasts ◽  
Ensemble Model Output Statistics ◽  
Model Output Statistics

Abstract Ensembles used for probabilistic weather forecasting tend to be biased and underdispersive. This paper proposes a statistical method for postprocessing ensembles based on quantile regression forests (QRF), a generalization of random forests for quantile regression. This method does not fit a parametric probability density function (PDF) like in ensemble model output statistics (EMOS) but provides an estimation of desired quantiles. This is a nonparametric approach that eliminates any assumption on the variable subject to calibration. This method can estimate quantiles using not only members of the ensemble but any predictor available including statistics on other variables. The method is applied to the Météo-France 35-member ensemble forecast (PEARP) for surface temperature and wind speed for available lead times from 3 up to 54 h and compared to EMOS. All postprocessed ensembles are much better calibrated than the PEARP raw ensemble and experiments on real data also show that QRF performs better than EMOS, and can bring a real gain for human forecasters compared to EMOS. QRF provides sharp and reliable probabilistic forecasts. At last, classical scoring rules to verify predictive forecasts are completed by the introduction of entropy as a general measure of reliability.

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.

scholarly journals Ensemble Model Output Statistics for Wind Vectors

2012 ◽  
Vol 140 (10) ◽  
pp. 3204-3219 ◽  
Author(s):  
Nina Schuhen ◽  
Thordis L. Thorarinsdottir ◽  
Tilmann Gneiting
Keyword(s):  
Pacific Northwest ◽  
Ensemble Model ◽  
Model Output ◽  
Ensemble Forecasts ◽  
The North ◽  
Normal Probability ◽  
Density Forecasts ◽  
Ensemble Model Output Statistics ◽  
Model Output Statistics ◽  
The University

Abstract A bivariate ensemble model output statistics (EMOS) technique for the postprocessing of ensemble forecasts of two-dimensional wind vectors is proposed, where the postprocessed probabilistic forecast takes the form of a bivariate normal probability density function. The postprocessed means and variances of the wind vector components are linearly bias-corrected versions of the ensemble means and ensemble variances, respectively, and the conditional correlation between the wind components is represented by a trigonometric function of the ensemble mean wind direction. In a case study on 48-h forecasts of wind vectors over the North American Pacific Northwest with the University of Washington Mesoscale Ensemble, the bivariate EMOS density forecasts were calibrated and sharp, and showed considerable improvement over the raw ensemble and reference forecasts, including ensemble copula coupling.

scholarly journals How Suitable is Quantile Mapping For Postprocessing GCM Precipitation Forecasts?

2017 ◽  
Vol 30 (9) ◽  
pp. 3185-3196 ◽  
Author(s):  
Tongtiegang Zhao ◽  
James C. Bennett ◽  
Q. J. Wang ◽  
Andrew Schepen ◽  
Andrew W. Wood ◽  
...  
Keyword(s):  
Joint Probability ◽  
Ensemble Model ◽  
Ensemble Spread ◽  
Quantile Mapping ◽  
Ensemble Forecasts ◽  
Probability Modeling ◽  
Satisfactory Method ◽  
Coherent Forecasts ◽  
Ensemble Model Output Statistics ◽  
Model Output Statistics

GCMs are used by many national weather services to produce seasonal outlooks of atmospheric and oceanic conditions and fluxes. Postprocessing is often a necessary step before GCM forecasts can be applied in practice. Quantile mapping (QM) is rapidly becoming the method of choice by operational agencies to postprocess raw GCM outputs. The authors investigate whether QM is appropriate for this task. Ensemble forecast postprocessing methods should aim to 1) correct bias, 2) ensure forecasts are reliable in ensemble spread, and 3) guarantee forecasts are at least as skillful as climatology, a property called “coherence.” This study evaluates the effectiveness of QM in achieving these aims by applying it to precipitation forecasts from the POAMA model. It is shown that while QM is highly effective in correcting bias, it cannot ensure reliability in forecast ensemble spread or guarantee coherence. This is because QM ignores the correlation between raw ensemble forecasts and observations. When raw forecasts are not significantly positively correlated with observations, QM tends to produce negatively skillful forecasts. Even when there is significant positive correlation, QM cannot ensure reliability and coherence for postprocessed forecasts. Therefore, QM is not a fully satisfactory method for postprocessing forecasts where the issues of bias, reliability, and coherence pre-exist. Alternative postprocessing methods based on ensemble model output statistics (EMOS) are available that achieve not only unbiased but also reliable and coherent forecasts. This is shown with one such alternative, the Bayesian joint probability modeling approach.

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