Probabilistic Forecast of Low Level Wind Shear of Gimpo, Gimhae, Incheon and Jeju International Airports Using Ensemble Model Output Statistics

Wind shear can occur at all flight levels; however, it is particularly dangerous at low levels, from the ground up to approximately 2000 feet. If this phenomenon can occur during the take-off and landing of an aircraft, it may interfere with the normal altitude change of the aircraft, causing delay and cancellation of the aircraft, as well as economic damage. In this paper, to estimate the probabilistic forecasts of low-level wind shear at Gimpo, Gimhae, Incheon and Jeju International Airports, an Ensemble Model Output Statistics (EMOS) model based on a left-truncated normal distribution with a cutoff zero was applied. Observations were obtained from Gimpo, Gimhae, Incheon and Jeju International Airports and 13 ensemble member forecasts generated from the Limited-Area Ensemble Prediction System (LENS), for the period December 2018 to February 2020. Prior to applying to EMOS models, statistical consistency was analyzed by using a rank histogram and kernel density estimation to identify the uniformity of ensembles with corresponding observations. Performances were evaluated by mean absolute error, continuous ranked probability score and probability integral transform. The results showed that probabilistic forecasts obtained from the EMOS model exhibited better prediction skills when compared to the raw ensembles.

Spatially coherent postprocessing of cloud cover ensemble forecasts

Statistical postprocessing is commonly applied to reduce location and dispersion errors of probabilistic forecasts provided by numerical weather prediction (NWP) models. If postprocessed forecast scenarios are required, the combination of ensemble model output statistics (EMOS) for univariate postprocessing with ensemble copula coupling (ECC) or the Schaake shuffle (ScS) to retain the dependence structure of the raw ensemble is a state-of-the-art approach. However, modern machine learning methods may lead to both, a better univariate skill and more realistic forecast scenarios. In this study, we postprocess multi-model ensemble forecasts of cloud cover over Switzerland provided by COSMO-E and ECMWF-IFS using (a) EMOS + ECC, (b) EMOS + ScS, (c) dense neural networks (dense NN) + ECC, (d) dense NN + ScS, and (e) conditional generative adversarial networks (cGAN). The different methods are verified using EUMETSAT satellite data. Dense NN shows the best univariate skill, but cGAN performed only slightly worse. Furthermore, cGAN generates realistic forecast scenario maps, while not relying on a dependence template like ECC or ScS, which is particularly favorable in the case of complex topography.

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

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).

On the implementation of post-processing of runoff forecast ensembles

Different post-processing techniques are frequently employed to improve the outcome of ensemble forecasting models. The main reason is to compensate for biases caused by errors in model structure or initial conditions, and as a correction for under- or overdispersed ensembles. Here we use the Ensemble Model Output Statistics method to post-process the ensemble output from a continental scale hydrological model, LISFLOOD, as used in the European Flood Awareness System (EFAS). We develop a method for local calibration and interpolation of the post-processing parameters and compare it with a more traditional global calibration approach for 678 stations in Europe based on long term observations of runoff and meteorological variables. For the global calibration we also test a reduced model with only a variance inflation factor. 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. This was the case both for the local and global calibration methods. As the post-processing is based on assumptions about the distribution of forecast errors, we also present an analysis of the ensemble output that provides some indications of what to expect from the post-processing.

Skewed and Mixture of Gaussian Distributions for Ensemble Postprocessing

The implementation of statistical postprocessing of ensemble forecasts is increasingly developed among national weather services. The so-called Ensemble Model Output Statistics (EMOS) method, which consists of generating a given distribution whose parameters depend on the raw ensemble, leads to significant improvements in forecast performance for a low computational cost, and so is particularly appealing for reduced performance computing architectures. However, the choice of a parametric distribution has to be sufficiently consistent so as not to lose information on predictability such as multimodalities or asymmetries. Different distributions are applied to the postprocessing of the European Centre for Medium-range Weather Forecast (ECMWF) ensemble forecast of surface temperature. More precisely, a mixture of Gaussian and skewed normal distributions are tried from 3- up to 360-h lead time forecasts, with different estimation methods. For this work, analytical formulas of the continuous ranked probability score have been derived and appropriate link functions are used to prevent overfitting. The mixture models outperform single parametric distributions, especially for the longest lead times. This statement is valid judging both overall performance and tolerance to misspecification.

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

When 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.

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

<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>

Calibration of ECMWF seasonal ensemble precipitation reforecasts in Java (Indonesia) using bias-corrected precipitation and climate indices

The seasonal precipitation forecast is one of the essential inputs for economic and agricultural activities and has significant impact on decision making. Large-scale modes of climate variability have strong relationships with seasonal rainfall in Java and are natural candidates for use as potential predictors in a statistical post-processing application. We explore whether using climate indices as additional predictors in the statistical post-processing of ECMWF Seasonal Forecast System 5 (SEAS5) precipitation can improve skill. We use parametric statistical post-processing by applying a logistic distribution-based Ensemble Model Output Statistics (EMOS) technique. We add a variety of potential predictors in the analysis, namely SEAS5 raw and Empirical Quantile Mapping (EQM) bias-corrected precipitation, Nino3.4 index, Dipole Mode Index (DMI), Madden Julian Oscillation (MJO) indices, Sea Surface Temperature (SST) around Java, and several other predictors. We analyze the period of 1981-2010, focusing on July, August, September, and October. We use the Continuous Ranked Probability Skill Score (CRPSS) and Brier Skill Score (BSS) in a comparative verification of raw, EQM and EMOS seasonal precipitation forecasts. We have found that it is essential to use EQM-corrected precipitation as a predictor instead of raw precipitation in the latter. Besides, Nino3.4 and DMI forecasts are not needed as extra predictors to improve monthly precipitation forecasts for the first lead month, except for September. However, for somewhat longer lead months, in September and October when there is more skill than climatology, the model that includes only Nino3.4 and DMI forecasts as potential predictors performs about the same compared to the model that uses only EQM-corrected precipitation as a predictor.

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

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).