scholarly journals The new MeteoSwiss postprocessing scheme for medium-range surface weather forecasts: multi-model, probabilistic, seamless, and at any arbitrary location

2021 ◽  
Author(s):  
Christoph Spirig ◽  
Jonas Bhend ◽  
Stephan Hemri ◽  
Jan Rajczak ◽  
Daniele Nerini ◽  
...  
Keyword(s):  
Local Area ◽  
Limited Area ◽  
Weather Forecasts ◽  
Probabilistic Forecasts ◽  
Medium Range ◽  
Non Local ◽  
Model Ensembles ◽  
Arbitrary Location ◽  
Ensemble Model Output Statistics ◽  
Model Output Statistics

<p>MeteoSwiss has developed and is currently implementing a NWP postprocessing suite for providing  automated weather forecasts at any location in Switzerland. The aim is a combined postprocessing of high resolution limited area and global model ensembles with different forecast horizons to enable seamless probabilistic forecasts over two weeks leadtime. Further, the output should be coherent in space and provide predictions at any location of interest, including sites without observations. We use the full archive of MeteoSwiss’ operational local area models (COSMO-1 and COSMO-E) over the past four years and the corresponding IFS-ENS medium range predictions of ECMWF to develop postprocessing routines for temperature, precipitation, cloud cover and wind. Here we present selected key results on the performance of various postprocessing methods we applied but also on practical aspects of their implementation into operational production.</p><p>Both ensemble model output statistics (EMOS) and machine learning (ML) approaches are able to improve the forecasts in terms of CRPS by up to 30% as compared to the direct output of the local area model. The skill increase obtained by postprocessing varies depending on the parameter, region and season, with best results for temperature and wind in areas of complex orography and only marginal improvements for precipitation during seasons with a high fraction of convective situations. Particularly for temperature, the combined postprocessing of COSMO and IFS-ENS resulted in a skill benefit over postprocessing the COSMO models alone. Locally optimized postprocessing would allow further skill improvements, but only at sites where observations are available. However, the ability of non-local postprocessing approaches to provide calibrated forecast at any point in space is a key advantage for providing automated forecasts to the general public via the internet and smartphone app. Furthermore, the computational efficiency of these non-local approaches makes them attractive for operationalization in a realtime context. </p>

Comparison of Ensemble-MOS Methods Using GFS Reforecasts

2007 ◽  
Vol 135 (6) ◽  
pp. 2379-2390 ◽  
Author(s):  
Daniel S. Wilks ◽  
Thomas M. Hamill
Keyword(s):  
Lead Time ◽  
Ensemble Model ◽  
Ensemble Forecasts ◽  
Temperature And Precipitation ◽  
Training Samples ◽  
Probabilistic Forecasts ◽  
Medium Range ◽  
Logistic Regressions ◽  
Ensemble Model Output Statistics ◽  
Model Output Statistics

Abstract Three recently proposed and promising methods for postprocessing ensemble forecasts based on their historical error characteristics (i.e., ensemble-model output statistics methods) are compared using a multidecadal reforecast dataset. Logistic regressions and nonhomogeneous Gaussian regressions are generally preferred for daily temperature, and for medium-range (6–10 and 8–14 day) temperature and precipitation forecasts. However, the better sharpness of medium-range ensemble-dressing forecasts sometimes yields the best Brier scores even though their calibration is somewhat worse. Using the long (15 or 25 yr) training samples that are available with these reforecasts improves the accuracy and skill of these probabilistic forecasts to levels that are approximately equivalent to gains of 1 day of lead time, relative to using short (1 or 2 yr) training samples.

scholarly journals Verification of Ensemble-Based Uncertainty Circles around Tropical Cyclone Track Forecasts

2011 ◽  
Vol 26 (5) ◽  
pp. 664-676 ◽  
Author(s):  
Thierry Dupont ◽  
Matthieu Plu ◽  
Philippe Caroff ◽  
Ghislain Faure
Keyword(s):  
Tropical Cyclone ◽  
Large Error ◽  
Ensemble Prediction ◽  
Probabilistic Forecasting ◽  
Cyclone Track ◽  
Ensemble Prediction System ◽  
Weather Forecasts ◽  
Probabilistic Forecasts ◽  
Medium Range ◽  
Uncertainty Information

Abstract Several tropical cyclone forecasting centers issue uncertainty information with regard to their official track forecasts, generally using the climatological distribution of position error. However, such methods are not able to convey information that depends on the situation. The purpose of the present study is to assess the skill of the Ensemble Prediction System (EPS) from the European Centre for Medium-Range Weather Forecasts (ECMWF) at measuring the uncertainty of up to 3-day track forecasts issued by the Regional Specialized Meteorological Centre (RSMC) La Réunion in the southwestern Indian Ocean. The dispersion of cyclone positions in the EPS is extracted and translated at the RSMC forecast position. The verification relies on existing methods for probabilistic forecasts that are presently adapted to a cyclone-position metric. First, the probability distribution of forecast positions is compared to the climatological distribution using Brier scores. The probabilistic forecasts have better scores than the climatology, particularly after applying a simple calibration scheme. Second, uncertainty circles are built by fixing the probability at 75%. Their skill at detecting small and large error values is assessed. The circles have some skill for large errors up to the 3-day forecast (and maybe after); but the detection of small radii is skillful only up to 2-day forecasts. The applied methodology may be used to assess and to compare the skill of different probabilistic forecasting systems of cyclone position.

Predicting Wind Power with Reforecasts

2015 ◽  
Vol 30 (6) ◽  
pp. 1655-1662 ◽  
Author(s):  
Markus Dabernig ◽  
Georg J. Mayr ◽  
Jakob W. Messner
Keyword(s):  
Wind Power ◽  
Systematic Errors ◽  
Decision Makers ◽  
Energy Market ◽  
Limited Area ◽  
Ensemble Forecasts ◽  
Weather Forecasts ◽  
Medium Range ◽  
Operational Models ◽  
Nwp Model

Abstract Energy traders and decision-makers need accurate wind power forecasts. For this purpose, numerical weather predictions (NWPs) are often statistically postprocessed to correct systematic errors. This requires a dataset of past forecasts and observations that is often limited by frequent NWP model enhancements that change the statistical model properties. Reforecasts that recompute past forecasts with a recent model provide considerably longer datasets but usually have weaker setups than operational models. This study tests the reforecasts from the National Oceanic and Atmospheric Administration (NOAA) and the European Centre for Medium-Range Weather Forecasts (ECMWF) for wind power predictions. The NOAA reforecast clearly performs worse than the ECMWF reforecast, the operational ECMWF deterministic and ensemble forecasts, and a limited-area model of the Austrian weather service [Zentralanstalt für Meteorologie und Geodynamik (ZAMG)]. On the contrary, the ECMWF reforecast has, of all tested models, the smallest squared errors and one of the highest financial values in an energy market.

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.

Stochastic and Perturbed Parameter Representations of Model Uncertainty in Convection Parameterization*

2015 ◽  
Vol 72 (6) ◽  
pp. 2525-2544 ◽  
Author(s):  
H. M. Christensen ◽  
I. M. Moroz ◽  
T. N. Palmer
Keyword(s):  
Model Uncertainty ◽  
Ensemble Prediction ◽  
Uncertain Parameters ◽  
Convection Scheme ◽  
Weather Forecasts ◽  
Probabilistic Forecasts ◽  
Medium Range ◽  
Estimation System ◽  
Convection Parameterization ◽  
Parameter Scheme

Abstract It is now acknowledged that representing model uncertainty in atmospheric simulators is essential for the production of reliable probabilistic forecasts, and a number of different techniques have been proposed for this purpose. This paper presents new perturbed parameter schemes for use in the European Centre for Medium-Range Weather Forecasts (ECMWF) convection scheme. Two types of scheme are developed and implemented. Both schemes represent the joint uncertainty in four of the parameters in the convection parameterization scheme, which was estimated using the Ensemble Prediction and Parameter Estimation System (EPPES). The first scheme developed is a fixed perturbed parameter scheme, where the values of uncertain parameters are varied between ensemble members, but held constant over the duration of the forecast. The second is a stochastically varying perturbed parameter scheme. The performance of these schemes was compared to the ECMWF operational stochastic scheme, stochastically perturbed parameterization tendencies (SPPT), and to a model that does not represent uncertainty in convection. The skill of probabilistic forecasts made using the different models was evaluated. While the perturbed parameter schemes improve on the stochastic parameterization in some regards, the SPPT scheme outperforms the perturbed parameter approaches when considering forecast variables that are particularly sensitive to convection. Overall, SPPT schemes are the most skillful representations of model uncertainty owing to convection parameterization.

scholarly journals Winter Subseasonal Wind Speed Forecasts for Finland from ECMWF

2021 ◽  
Vol 18 ◽  
pp. 127-134
Author(s):  
Otto Hyvärinen ◽  
Terhi K. Laurila ◽  
Olle Räty ◽  
Natalia Korhonen ◽  
Andrea Vajda ◽  
...  
Keyword(s):  
Wind Speed ◽  
Lead Time ◽  
Reference Data ◽  
Data Sets ◽  
Weather Forecasts ◽  
The Third ◽  
Probabilistic Forecasts ◽  
Medium Range ◽  
Mean Wind Speed ◽  
Gaussian Regression

Abstract. The subseasonal forecasts from the ECMWF (European Centre for Medium-Range Weather Forecasts) were used to construct weekly mean wind speed forecasts for the spatially aggregated area in Finland. Reforecasts for the winters (November, December and January) of 2016–2017 and 2017–2018 were analysed. The ERA-Interim reanalysis was used as observations and climatological forecasts. We evaluated two types of forecasts, the deterministic forecasts and the probabilistic forecasts. Non-homogeneous Gaussian regression was used to bias-adjust both types of forecasts. The forecasts proved to be skilful until the third week, but the longest skilful lead time depends on the reference data sets and the verification scores used.

scholarly journals Calibrated Probabilistic Forecasting Using Ensemble Model Output Statistics and Minimum CRPS Estimation

2005 ◽  
Vol 133 (5) ◽  
pp. 1098-1118 ◽  
Author(s):  
Tilmann Gneiting ◽  
Adrian E. Raftery ◽  
Anton H. Westveld ◽  
Tom Goldman
Keyword(s):  
Sea Level ◽  
Ensemble Prediction ◽  
Ensemble Model ◽  
Model Output ◽  
Sea Level Pressure ◽  
Forecast Bias ◽  
Level Pressure ◽  
Probabilistic Forecasts ◽  
Ensemble Model Output Statistics ◽  
Model Output Statistics

Abstract Ensemble prediction systems typically show positive spread-error correlation, but they are subject to forecast bias and dispersion errors, and are therefore uncalibrated. This work proposes the use of ensemble model output statistics (EMOS), an easy-to-implement postprocessing technique that addresses both forecast bias and underdispersion and takes into account the spread-skill relationship. The technique is based on multiple linear regression and is akin to the superensemble approach that has traditionally been used for deterministic-style forecasts. The EMOS technique yields probabilistic forecasts that take the form of Gaussian predictive probability density functions (PDFs) for continuous weather variables and can be applied to gridded model output. The EMOS predictive mean is a bias-corrected weighted average of the ensemble member forecasts, with coefficients that can be interpreted in terms of the relative contributions of the member models to the ensemble, and provides a highly competitive deterministic-style forecast. The EMOS predictive variance is a linear function of the ensemble variance. For fitting the EMOS coefficients, the method of minimum continuous ranked probability score (CRPS) estimation is introduced. This technique finds the coefficient values that optimize the CRPS for the training data. The EMOS technique was applied to 48-h forecasts of sea level pressure and surface temperature over the North American Pacific Northwest in spring 2000, using the University of Washington mesoscale ensemble. When compared to the bias-corrected ensemble, deterministic-style EMOS forecasts of sea level pressure had root-mean-square error 9% less and mean absolute error 7% less. The EMOS predictive PDFs were sharp, and much better calibrated than the raw ensemble or the bias-corrected ensemble.

Extended-Range Probabilistic Fire-Weather Forecasting Based on Ensemble Model Output Statistics and Ensemble Copula Coupling

2020 ◽  
Vol 148 (2) ◽  
pp. 499-521 ◽  
Author(s):  
Rochelle P. Worsnop ◽  
Michael Scheuerer ◽  
Thomas M. Hamill
Keyword(s):  
Forecast Skill ◽  
Lead Times ◽  
Ensemble Model ◽  
Fire Weather ◽  
Model Output ◽  
Probabilistic Forecasts ◽  
Extended Range ◽  
Truncated Normal ◽  
Ensemble Model Output Statistics ◽  
Model Output Statistics

Abstract Probabilistic fire-weather forecasts provide pertinent information to assess fire behavior and danger of current or potential fires. Operational fire-weather guidance is provided for lead times fewer than seven days, with most products only providing day 1–3 outlooks. Extended-range forecasts can aid in decisions regarding placement of in- and out-of-state resources, prescribed burns, and overall preparedness levels. We demonstrate how ensemble model output statistics and ensemble copula coupling (ECC) postprocessing methods can be used to provide locally calibrated and spatially coherent probabilistic forecasts of the hot–dry–windy index (and its components). The univariate postprocessing fits the truncated normal distribution to data transformed with a flexible selection of power exponents. Forecast scenarios are generated via the ECC-Q variation, which maintains their spatial and temporal coherence by reordering samples from the univariate distributions according to ranks of the raw ensemble. A total of 20 years of ECMWF reforecasts and ERA-Interim reanalysis data over the continental United States are used. Skill of the forecasts is quantified with the continuous ranked probability score using benchmarks of raw and climatological forecasts. Results show postprocessing is beneficial during all seasons over CONUS out to two weeks. Forecast skill relative to climatological forecasts depends on the atmospheric variable, season, location, and lead time, where winter (summer) generally provides the most (least) skill at the longest lead times. Additional improvements of forecast skill can be achieved by aggregating forecast days. Illustrations of these postprocessed forecasts are explored for a past fire event.

Ensemble Prediction with Different Spatial Resolutions for the 2014 Sochi Winter Olympic Games: The Effects of Calibration and Multimodel Approaches

2016 ◽  
Vol 31 (6) ◽  
pp. 1833-1851 ◽  
Author(s):  
Inger-Lise Frogner ◽  
Thomas Nipen ◽  
Andrew Singleton ◽  
John Bjørnar Bremnes ◽  
Ole Vignes
Keyword(s):  
Complex Terrain ◽  
Olympic Games ◽  
Horizontal Resolution ◽  
Ensemble Prediction ◽  
Limited Area ◽  
Ensemble Prediction System ◽  
Weather Forecasts ◽  
Medium Range ◽  
Winter Olympic Games ◽  
Prediction Systems

Abstract Three ensemble prediction systems (EPSs) with different grid spacings are compared and evaluated with respect to their ability to predict wintertime weather in complex terrain. The experiment period was two-and-a-half winter months in 2014, coinciding with the Forecast and Research in the Olympic Sochi Testbed (FROST) project, which took place during the Winter Olympic Games in Sochi, Russia. The global, synoptic-scale ensemble system used is the IFS ENS from the European Centre for Medium-Range Weather Forecasts (ECMWF), and its performance is compared with both the operational pan-European Grand Limited Area Ensemble Prediction System (GLAMEPS) at 11-km horizontal resolution and the experimental regional convection-permitting HIRLAM–ALADIN Regional Mesoscale Operational NWP in Europe (HARMONIE) EPS (HarmonEPS) at 2.5 km. Both GLAMEPS and HarmonEPS are multimodel systems, and it is seen that a large part of the skill in these systems comes from the multimodel approach, as long as all subensembles are performing reasonably. The number of members has less impact on the overall skill measurement. The relative importance of resolution and calibration is also assessed. Statistical calibration was applied and evaluated. In contrast to what is seen for the raw ensembles, the number of members, as well as the number of subensembles, is important for the calibrated ensembles. HarmonEPS shows greater potential than GLAMEPS for predicting wintertime weather, and also has an advantage after calibration.

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