scholarly journals Impact of Stochastic Physics in a Convection-Permitting Ensemble

2012 ◽  
Vol 140 (11) ◽  
pp. 3706-3721 ◽  
Author(s):  
François Bouttier ◽  
Benoît Vié ◽  
Olivier Nuissier ◽  
Laure Raynaud
Keyword(s):  
Stochastic Perturbation ◽  
Atmospheric Model ◽  
Ensemble Prediction ◽  
Ensemble Spread ◽  
Ensemble Prediction System ◽  
Stochastic Perturbations ◽  
Score Improvement ◽  
Time Correlations ◽  
Systematic Effects ◽  
Stochastic Physics

A stochastic physics scheme is tested in the Application of Research to Operations at Mesoscale (AROME) short-range convection-permitting ensemble prediction system. It is an adaptation of ECMWF’s stochastic perturbation of physics tendencies (SPPT) scheme. The probabilistic performance of the AROME model ensemble is found to be significantly improved, when verified against observations over two 2-week periods. The main improvement lies in the ensemble reliability and the spread–skill consistency. Probabilistic scores for several weather parameters are improved. The tendency perturbations have zero mean, but the stochastic perturbations have systematic effects on the model output, which explains much of the score improvement. Ensemble spread is an increasing function of the SPPT space and time correlations. A case study reveals that stochastic physics do not simply increase ensemble spread, they also tend to smooth out high-spread areas over wider geographical areas. Although the ensemble design lacks surface perturbations, there is a significant end impact of SPPT on low-level fields through physical interactions in the atmospheric model.

A Unified Scheme of Stochastic Physics and Bias Correction in an Ensemble Model to Reduce Both Random and Systematic Errors

2019 ◽  
Vol 34 (6) ◽  
pp. 1675-1691 ◽  
Author(s):  
Yu Xia ◽  
Jing Chen ◽  
Jun Du ◽  
Xiefei Zhi ◽  
Jingzhuo Wang ◽  
...  
Keyword(s):  
Bias Correction ◽  
Weather Prediction ◽  
Systematic Errors ◽  
Ensemble Prediction ◽  
Ensemble Model ◽  
Prediction System ◽  
Ensemble Prediction System ◽  
Probabilistic Forecasts ◽  
Ensemble Mean ◽  
Stochastic Physics

Abstract This study experimented with a unified scheme of stochastic physics and bias correction within a regional ensemble model [Global and Regional Assimilation and Prediction System–Regional Ensemble Prediction System (GRAPES-REPS)]. It is intended to improve ensemble prediction skill by reducing both random and systematic errors at the same time. Three experiments were performed on top of GRAPES-REPS. The first experiment adds only the stochastic physics. The second experiment adds only the bias correction scheme. The third experiment adds both the stochastic physics and bias correction. The experimental period is one month from 1 to 31 July 2015 over the China domain. Using 850-hPa temperature as an example, the study reveals the following: 1) the stochastic physics can effectively increase the ensemble spread, while the bias correction cannot. Therefore, ensemble averaging of the stochastic physics runs can reduce more random error than the bias correction runs. 2) Bias correction can significantly reduce systematic error, while the stochastic physics cannot. As a result, the bias correction greatly improved the quality of ensemble mean forecasts but the stochastic physics did not. 3) The unified scheme can greatly reduce both random and systematic errors at the same time and performed the best of the three experiments. These results were further confirmed by verification of the ensemble mean, spread, and probabilistic forecasts of many other atmospheric fields for both upper air and the surface, including precipitation. Based on this study, we recommend that operational numerical weather prediction centers adopt this unified scheme approach in ensemble models to achieve the best forecasts.

scholarly journals A Regional Ensemble Prediction System Based on Moist Targeted Singular Vectors and Stochastic Parameter Perturbations

2008 ◽  
Vol 136 (2) ◽  
pp. 443-462 ◽  
Author(s):  
Xiaoli Li ◽  
Martin Charron ◽  
Lubos Spacek ◽  
Guillem Candille
Keyword(s):  
Deep Convection ◽  
Horizontal Resolution ◽  
Forecast Skill ◽  
Convective Precipitation ◽  
Ensemble Prediction ◽  
Limited Area ◽  
Prediction System ◽  
Ensemble Prediction System ◽  
Stochastic Perturbations ◽  
Singular Vectors

Abstract A regional ensemble prediction system (REPS) with the limited-area version of the Canadian Global Environmental Multiscale (GEM) model at 15-km horizontal resolution is developed and tested. The total energy norm singular vectors (SVs) targeted over northeastern North America are used for initial and boundary perturbations. Two SV perturbation strategies are tested: dry SVs with dry simplified physics and moist SVs with simplified physics, including stratiform condensation and convective precipitation as well as dry processes. Model physics uncertainties are partly accounted for by stochastically perturbing two parameters: the threshold vertical velocity in the trigger function of the Kain–Fritsch deep convection scheme, and the threshold humidity in the Sundqvist explicit scheme. The perturbations are obtained from first-order Markov processes. Short-range ensemble forecasts in summer with 16 members are performed for five different experiments. The experiments employ different perturbation and piloting strategies, and two different surface schemes. Verification focuses on quantitative precipitation forecasts and is done using a range of probabilistic measures. Results indicate that using moist SVs instead of dry SVs has a stronger impact on precipitation than on dynamical fields. Forecast skill for precipitation is greatly influenced by the dominant synoptic weather systems. For stratiform precipitation caused by strong baroclinic systems, the forecast skill is improved in the moist SV experiments relative to the dry SV experiments. For convective precipitation rates in the range 15–50 mm (24 h)−1 produced by weak synoptic baroclinic systems, all experiments exhibit noticeably poorer forecast skills. Skill improvements due to the Interactions between Soil, Biosphere, and Atmosphere (ISBA) surface scheme and stochastic perturbations are also observed.

scholarly journals New Developments in Ensemble-based Probabilistic Forecasting of Solar Radiation: The WRF-Solar Ensemble Prediction System

2021 ◽  
Author(s):  
Manajit Sengupta ◽  
Pedro Jimenez ◽  
Jaemo Yang ◽  
Ju-Hye Kim ◽  
Yu Xie
Keyword(s):  
Sensitivity Analysis ◽  
Solar Radiation ◽  
Solar Irradiance ◽  
Solar Power ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Ensemble Prediction System ◽  
Stochastic Perturbations ◽  
Radiation Data ◽  
Solar Generation

<p>The demand for increased accuracy in predicting solar power has grown considerably over recent years due to a rapid growth in grid-tied solar generation both utility scale and distributed. To increase confidence in forecasting solar power there is a need to provide reliable probabilistic solar radiation information that also minimizes error and uncertainty. Funded by the U.S. Department of Energy, the Weather Research and Forecasting (WRF)-Solar ensemble prediction system (WRF-Solar EPS) has been recently developed by a collaboration between the National Renewable Energy Laboratory and the National Center for Atmospheric Research. The WRF-Solar EPS is now ready to be disseminated to support the integration of solar generation resources and improve accuracy of day-ahead and intraday probabilistic solar forecasts. The first stage of our framework in developing WRF-Solar EPS required a specially designed method using a tangent linear (TL) sensitivity analysis to efficiently investigate uncertainties of WRF-Solar variables in forecasting clouds and solar irradiance. For the second stage, we applied a methodology to introduce stochastic perturbations in 14 key variables ascertained through the TL sensitivity analysis in generating ensemble members. A user-friendly interface is provided in WRF-Solar EPS, in which the parameters of stochastic perturbations can be controlled by configuration files. Lastly, we implemented an analog technique as an ensemble post-processing method to improve the performance of ensemble solar irradiance forecasts. This presentation will summarize the work performed in the past 3 years to understand the interactions between cloud physics, land surface, boundary layer and radiative transfer models through the development of a probabilistic cloud optimized day-ahead forecasting system based on WRF-Solar. For evaluation of forecasts, we adapt and use satellite-derived solar radiation data, e.g., the National Solar Radiation Data Base (NSRDB) as well as ground-measured observations. A comprehensive analysis to assess gridded model outputs over the Contiguous U.S is performed. The importance of evaluation of the WRF-Solar model with the NSRDB lies in the fact that the knowledge of the cloud-caused uncertainties in predicting solar irradiance over a wide range of regions provides model developers a detailed understanding of model strength and weaknesses in predicting clouds. Overall, we will present the detailed research steps that resulted in the development of the WRF-Solar EPS. We will also present a detailed validation demonstrating the improvements provided by this model. Moreover, we will also introduce the user’s guide for WRF-Solar EPS and future extension of this research.</p>

scholarly journals Model Error Representation in an Operational Ensemble Kalman Filter

2009 ◽  
Vol 137 (7) ◽  
pp. 2126-2143 ◽  
Author(s):  
P. L. Houtekamer ◽  
Herschel L. Mitchell ◽  
Xingxiu Deng
Keyword(s):  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Model Error ◽  
Ensemble Prediction ◽  
Isotropic Model ◽  
Error Sources ◽  
Ensemble Prediction System ◽  
Stochastic Perturbations ◽  
Ensemble Mean ◽  
The Impact

Since 12 January 2005, an ensemble Kalman filter (EnKF) has been used operationally at the Meteorological Service of Canada to provide the initial conditions for the medium-range forecasts of the ensemble prediction system. One issue in EnKF development is how to best account for model error. It is shown that in a perfect-model environment, without any model error or model error simulation, the EnKF spread remains representative of the ensemble mean error with respect to a truth integration. Consequently, the EnKF can be used to quantify the impact of the various error sources in a data-assimilation cycle on the quality of the ensemble mean. Using real rather than simulated observations, but still not simulating model error in any manner, the rms ensemble spread is found to be too small by approximately a factor of 2. It is then attempted to account for model error by using various combinations of the following four different approaches: (i) additive isotropic model error perturbations; (ii) different versions of the model for different ensemble members; (iii) stochastic perturbations to physical tendencies; and (iv) stochastic kinetic energy backscatter. The addition of isotropic model error perturbations is found to have the biggest impact. The identification of model error sources could lead to a more realistic, likely anisotropic, parameterization. Using different versions of the model has a small but clearly positive impact and consequently both (i) and (ii) are used in the operational EnKF. The use of approaches (iii) and (iv) did not lead to further improvements.

scholarly journals Regional Differences in the Prediction of Extratropical Cyclones by the ECMWF Ensemble Prediction System

2009 ◽  
Vol 137 (3) ◽  
pp. 893-911 ◽  
Author(s):  
Lizzie S. R. Froude
Keyword(s):  
Regional Differences ◽  
Propagation Speed ◽  
Ensemble Prediction ◽  
Extratropical Cyclones ◽  
Spatial Distributions ◽  
Prediction System ◽  
Ensemble Spread ◽  
Ensemble Prediction System ◽  
Mean Error ◽  
Ensemble Mean

Abstract A regional study of the prediction of extratropical cyclones by the European Centre for Medium-Range Weather Forecasts (ECMWF) Ensemble Prediction System (EPS) has been performed. An objective feature-tracking method has been used to identify and track the cyclones along the forecast trajectories. Forecast error statistics have then been produced for the position, intensity, and propagation speed of the storms. In previous work, data limitations meant it was only possible to present the diagnostics for the entire Northern Hemisphere (NH) or Southern Hemisphere. A larger data sample has allowed the diagnostics to be computed separately for smaller regions around the globe and has made it possible to explore the regional differences in the prediction of storms by the EPS. Results show that in the NH there is a larger ensemble mean error in the position of storms over the Atlantic Ocean. Further analysis revealed that this is mainly due to errors in the prediction of storm propagation speed rather than in direction. Forecast storms propagate too slowly in all regions, but the bias is about 2 times as large in the NH Atlantic region. The results show that storm intensity is generally overpredicted over the ocean and underpredicted over the land and that the absolute error in intensity is larger over the ocean than over the land. In the NH, large errors occur in the prediction of the intensity of storms that originate as tropical cyclones but then move into the extratropics. The ensemble is underdispersive for the intensity of cyclones (i.e., the spread is smaller than the mean error) in all regions. The spatial patterns of the ensemble mean error and ensemble spread are very different for the intensity of cyclones. Spatial distributions of the ensemble mean error suggest that large errors occur during the growth phase of storm development, but this is not indicated by the spatial distributions of the ensemble spread. In the NH there are further differences. First, the large errors in the prediction of the intensity of cyclones that originate in the tropics are not indicated by the spread. Second, the ensemble mean error is larger over the Pacific Ocean than over the Atlantic, whereas the opposite is true for the spread. The use of a storm-tracking approach, to both weather forecasters and developers of forecast systems, is also discussed.

scholarly journals Why Should Ensemble Spread Match the RMSE of the Ensemble Mean?

2014 ◽  
Vol 15 (4) ◽  
pp. 1708-1713 ◽  
Author(s):  
V. Fortin ◽  
M. Abaza ◽  
F. Anctil ◽  
R. Turcotte
Keyword(s):  
Good Practice ◽  
Ensemble Prediction ◽  
Square Root ◽  
Prediction System ◽  
Mean Square ◽  
Ensemble Spread ◽  
Ensemble Prediction System ◽  
Correct Equation ◽  
Ensemble Mean ◽  
The Impact

Abstract When evaluating the reliability of an ensemble prediction system, it is common to compare the root-mean-square error of the ensemble mean to the average ensemble spread. While this is indeed good practice, two different and inconsistent methodologies have been used over the last few years in the meteorology and hydrology literature to compute the average ensemble spread. In some cases, the square root of average ensemble variance is used, and in other cases, the average of ensemble standard deviation is computed instead. The second option is incorrect. To avoid the perpetuation of practices that are not supported by probability theory, the correct equation for computing the average ensemble spread is obtained and the impact of using the wrong equation is illustrated.

Comparison of a 51-Member Low-Resolution (TL399L62) Ensemble with a 6-Member High-Resolution (TL799L91) Lagged-Forecast Ensemble

2008 ◽  
Vol 136 (9) ◽  
pp. 3343-3362 ◽  
Author(s):  
Roberto Buizza
Keyword(s):  
Forecast Error ◽  
Ensemble Prediction ◽  
Individual Member ◽  
Ensemble Spread ◽  
Error Statistics ◽  
Ensemble Prediction System ◽  
Optimal Weights ◽  
Probabilistic Forecasts ◽  
Ensemble Mean ◽  
Medium Range

Abstract The 51-member TL399L62 ECMWF ensemble prediction system (EPS51) is compared with a lagged ensemble system based on the six most recent ECMWF TL799L91 forecasts (LAG6). The EPS51 and LAG6 systems are compared to two 6-member ensembles with a “weighted” ensemble-mean: EPS6wEM and LAG6wEM. EPS6wEM includes six members of EPS51 and has the ensemble mean constructed giving optimal weights to its members, while LAG6wEM includes the LAG6 six members and has the ensemble mean constructed giving optimal weights to its members. In these weighted ensembles, the optimal weights are based on 50-day forecast error statistics of each individual member (in EPS51 and LAG6 the ensemble mean is constructed giving the same weight to each individual member). The EPS51, LAG6, EPS6wEM, and LAG6wEM ensembles are compared for a 7-month period (from 1 April to 30 October 2006—213 cases) and for two of the most severe storms that hit the Scandinavian countries since 1969. The study shows that EPS51 has the best-tuned ensemble spread, and provides the best probabilistic forecasts, with differences in predictability between EPS51 and LAG6 or LAG6wEM probabilistic forecasts of geopotential height anomalies of up to 24 h. In terms of ensemble mean, EPS51 gives the best forecast from forecast day 4, but before forecast day 4 LAG6wEM provides a slightly better forecast, with differences in predictability smaller than 2 h up to forecast day 6, and of about 6 h afterward. The comparison also shows that a larger ensemble size is more important in the medium range rather than in the short range. Overall, these results indicate that if the aim of ensemble prediction is to generate not only a single (most likely) scenario but also a probabilistic forecast, than EPS51 has a higher skill than the lagged ensemble system based on LAG6 or LAG6wEM.

scholarly journals Sensitivity of Ensemble Forecast Verification to Model Bias

2018 ◽  
Vol 146 (3) ◽  
pp. 781-796 ◽  
Author(s):  
Jingzhuo Wang ◽  
Jing Chen ◽  
Jun Du ◽  
Yutao Zhang ◽  
Yu Xia ◽  
...  
Keyword(s):  
Bias Correction ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Model Bias ◽  
Ensemble Spread ◽  
Ensemble Prediction System ◽  
Forecast Bias ◽  
Probabilistic Forecasts ◽  
Before And After

This study demonstrates how model bias can adversely affect the quality assessment of an ensemble prediction system (EPS) by verification metrics. A regional EPS [Global and Regional Assimilation and Prediction Enhanced System-Regional Ensemble Prediction System (GRAPES-REPS)] was verified over a period of one month over China. Three variables (500-hPa and 2-m temperatures, and 250-hPa wind) are selected to represent “strong” and “weak” bias situations. Ensemble spread and probabilistic forecasts are compared before and after a bias correction. The results show that the conclusions drawn from ensemble verification about the EPS are dramatically different with or without model bias. This is true for both ensemble spread and probabilistic forecasts. The GRAPES-REPS is severely underdispersive before the bias correction but becomes calibrated afterward, although the improvement in the spread’s spatial structure is much less; the spread–skill relation is also improved. The probabilities become much sharper and almost perfectly reliable after the bias is removed. Therefore, it is necessary to remove forecast biases before an EPS can be accurately evaluated since an EPS deals only with random error but not systematic error. Only when an EPS has no or little forecast bias, can ensemble verification metrics reliably reveal the true quality of an EPS without removing forecast bias first. An implication is that EPS developers should not be expected to introduce methods to dramatically increase ensemble spread (either by perturbation method or statistical calibration) to achieve reliability. Instead, the preferred solution is to reduce model bias through prediction system developments and to focus on the quality of spread (not the quantity of spread). Forecast products should also be produced from the debiased but not the raw ensemble.

scholarly journals The 2009–2010 Arctic stratospheric winter – general evolution, mountain waves and predictability of an operational weather forecast model

2012 ◽  
Vol 12 (8) ◽  
pp. 3659-3675 ◽  
Author(s):  
A. Dörnbrack ◽  
M. C. Pitts ◽  
L. R. Poole ◽  
Y. J. Orsolini ◽  
K. Nishii ◽  
...  
Keyword(s):  
Weather Prediction ◽  
Polar Vortex ◽  
Weather Forecast ◽  
Internal Variability ◽  
Forecast Model ◽  
The Arctic ◽  
Ensemble Prediction ◽  
Ensemble Spread ◽  
Ensemble Prediction System ◽  
Cold Vortex

Abstract. The relatively warm 2009–2010 Arctic winter was an exceptional one as the North Atlantic Oscillation index attained persistent extreme negative values. Here, selected aspects of the Arctic stratosphere during this winter inspired by the analysis of the international field experiment RECONCILE are presented. First of all, and as a kind of reference, the evolution of the polar vortex in its different phases is documented. Special emphasis is put on explaining the formation of the exceptionally cold vortex in mid winter after a sequence of stratospheric disturbances which were caused by upward propagating planetary waves. A major sudden stratospheric warming (SSW) occurring near the end of January 2010 concluded the anomalous cold vortex period. Wave ice polar stratospheric clouds were frequently observed by spaceborne remote-sensing instruments over the Arctic during the cold period in January 2010. Here, one such case observed over Greenland is analysed in more detail and an attempt is made to correlate flow information of an operational numerical weather prediction model to the magnitude of the mountain-wave induced temperature fluctuations. Finally, it is shown that the forecasts of the ECMWF ensemble prediction system for the onset of the major SSW were very skilful and the ensemble spread was very small. However, the ensemble spread increased dramatically after the major SSW, displaying the strong non-linearity and internal variability involved in the SSW event.

scholarly journals A comparison of predictors of the error of weather forecasts

2005 ◽  
Vol 12 (6) ◽  
pp. 1021-1032 ◽  
Author(s):  
M. S. Roulston
Keyword(s):  
Forecast Error ◽  
Absolute Error ◽  
Ensemble Prediction ◽  
Lead Times ◽  
Ensemble Spread ◽  
Spatial Error ◽  
Ensemble Prediction System ◽  
Error Covariance ◽  
The Mean ◽  
Local Gradient

Abstract. Three different potential predictors of forecast error - ensemble spread, mean errors of recent forecasts and the local gradient of the predicted field - were compared. The comparison was performed using the forecasts of 500hPa geopotential and 2-m temperature of the ECMWF ensemble prediction system at lead times of 96, 168 and 240h, over North America for each day in 2004. Ensemble spread was found to be the best overall predictor of absolute forecast error. The mean absolute error of recent forecasts (past 30 days) was found to contain some information, however, and the local gradient of the geopotential also provided some information about the error in the prediction of this variable. Ensemble spatial error covariance and the mean spatial error covariance of recent forecasts (past 30 days) were also compared as predictors of actual spatial error covariance. Both were found to provide some predictive information, although the ensemble error covariance was found to provide substantially more information for both variables tested at all three lead times. The results of the study suggest that past errors and local field gradients should not be ignored as predictors of forecast error as they can be computed cheaply from single forecasts when an ensemble is not available. Alternatively, in some cases, they could be used to supplement the information about forecast error provided by an ensemble to provide a better prediction of forecast skill.

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