LQ-Warn – Entwicklung optimierter Vorhersagen verschiedener Luftqualitätsparameter

<p>Schlechte Luftqualität gefährdet die Gesundheit der Bevölkerung. Zur Information und zur Ergreifung kurzfristiger Maßnahmen zur Luftqualitätsverbesserung (z.B. Verkehrslenkung) ist eine möglichst genaue und – insbesondere in städtischen Gebieten – möglichst räumlich hochaufgelöste Luftqualitätsvorhersage notwendig. Numerische Luftqualitätsmodelle haben für diese Aufgabe in der Regel eine zu geringe räumliche Auflösung.</p> <p>Daher ist es Ziel des Projektes „LQ-Warn“ die Luftqualitätsvorhersage insbesondere im Hinblick auf die Überschreitung von Grenzwerten zu verbessern. Basierend auf den Modellergebnissen für Luftqualitätsparameter des Copernicus Atmospheric Monitoring Service (CAMS) werden zwei Ansätze verfolgt: Einerseits werden Vorhersagen mit dem regionalen chemischen Transportmodell „REM-CALGRID“ (RCG) unter Einbeziehung von CAMS-Ergebnissen und regionalen Emissionsdaten berechnet. Dabei kann eine hohe horizontale Auflösung von 2 km erzielt werden und Prognosen können für verschiedene Luftschadstoffe in stündlicher Auflösung mit bis zu 72 Stunden Vorlaufzeit erstellt werden, unter anderem für NO<sub>2</sub>, O<sub>3</sub>, PM<sub>10</sub> und PM<sub>2.5</sub>. Andererseits wird die statistische Post-Processing-Methode „Model Output Statistics“ (MOS) angewandt, um Punktvorhersagen für die Massenkonzentration der Spezies NO<sub>2</sub>, O<sub>3</sub>, PM<sub>10</sub> und PM<sub>2.5</sub> mit einer Vorlaufzeit von bis zu 96 Stunden zu berechnen. Dafür werden luftqualitätsbezogene Messungen, CAMS-Modellergebnisse und meteorologische Parameter aus dem numerischen Wettervorhersagemodell des ECMWF als Prädiktoren verwendet.</p> <p>Es werden erste Ergebnisse der mit den o.g. Ansätzen errechneten Vorhersagen präsentiert und die Vor- und Nachteile der jeweiligen Verfahren hervorgehoben. Durch die statistische Post-Processing-Methode MOS wird an den Vorhersagepunkten vor allem für die Massenkonzentration von O<sub>3 </sub>und NO<sub>2</sub> eine signifikante Verringerung des RMSE (Root Mean Square Error) im Vergleich zu den Vorhersagen des numerischen CAMS-Modells erreicht. Diese deutliche Verbesserung der Luftqualitätsvorhersage sinnvoll auf die Fläche auszudehnen ist jedoch noch eine Herausforderung. Im Gegensatz dazu zeigt die Vorhersage mit dem RCG-Modell eine geringere Verbesserung der Vorhersagegüte an einzelnen Vorhersagepunkten als der MOS-Ansatz. Stattdessen bietet das RCG-Modell zeitlich und räumlich konsistente Vorhersagen an allen Modellgitterpunkten. Kleinskalige Konzentrationsunterschiede können aufgrund der höheren Modellauflösung deutlich realistischer vorhergesagt werden als mit den CAMS-Vorhersagen. Ein weiterführendes Ziel des LQ-Warn-Projektes ist es die beiden Ansätze zu kombinieren, um die Vorteile beider nutzen zu können und eine präzise Luftqualitätsvorhersage flächendeckend für Deutschland bereitstellen zu können.</p>

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.

Model Output Statistics (MOS) applied to CAMS O₃ forecasts: trade-offs between continuous and categorical skill scores

Air quality (AQ) forecasting systems are usually built upon physics-based numerical models that are affected by a number of uncertainty sources. In order to reduce forecast errors, first and foremost the bias, they are often coupled with Model Output Statistics (MOS) modules. MOS methods are statistical techniques used to correct raw forecasts at surface monitoring station locations, where AQ observations are available. In this study, we investigate to what extent AQ forecasts can be improved using a variety of MOS methods, including persistence (PERS), moving average (MA), quantile mapping (QM), Kalman Filter (KF), analogs (AN), and gradient boosting machine (GBM). We apply our analysis to the Copernicus Atmospheric Monitoring Service (CAMS) regional ensemble median O3 forecasts over the Iberian Peninsula during 2018–2019. A key aspect of our study is the evaluation, which is performed using a very comprehensive set of continuous and categorical metrics at various time scales (hourly to daily), along different lead times (1 to 4 days), and using different meteorological input data (forecast vs reanalyzed). Our results show that O3 forecasts can be substantially improved using such MOS corrections and that this improvement goes much beyond the correction of the systematic bias. Although it typically affects all lead times, some MOS methods appear more adversely impacted by the lead time. When considering MOS methods relying on meteorological information and comparing the results obtained with IFS forecasts and ERA5 reanalysis, the relative deterioration brought by the use of IFS is minor, which paves the way for their use in operational MOS applications. Importantly, our results also clearly show the trade-offs between continuous and categorical skills and their dependencies on the MOS method. The most sophisticated MOS methods better reproduce O3 mixing ratios overall, with lowest errors and highest correlations. However, they are not necessarily the best in predicting the highest O3 episodes, for which simpler MOS methods can give better results. Although the complex impact of MOS methods on the distribution and variability of raw forecasts can only be comprehended through an extended set of complementary statistical metrics, our study shows that optimally implementing MOS in AQ forecast systems crucially requires selecting the appropriate skill score to be optimized for the forecast application of interest.

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).

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.

MODEL OUTPUT STATISTICS DENGAN PRINCIPAL COMPONENT REGRESSION, PARTIAL LEAST SQUARE REGRESSION, DAN RIDGE REGRESSION UNTUK KALIBRASI PRAKIRAAN CUACA JANGKA PENDEK

Penelitian ini merupakan upaya pengembangan Model Output Statistics (MOS) yang akan digunakan sebagai alat kalibrasi prakiraan cuaca jangka pendek. Informasi mengenai prakiraan cuaca yang akurat diharapkan dapat meminimalkan risiko kecelakaan yang disebabkan oleh cuaca, khususnya dalam bidang transportasi udara dan laut. Metode yang akan dikembangkan mencakup beberapa stasiun pengamatan cuaca di Indonesia. MOS merupakan sebuah metode berbasis regresi yang mengoptimalkan hubungan antara observasi cuaca dan luaran model Numerical Weather Predictor (NWP). Beberapa masalah yang muncul kaitannya dengan MOS adalah; mereduksi dimensi luaran NWP, mendapatkan variabel prediktor yang mampu menjelaskan variabilitas variabel respon, dan menentukan metode statistik yang sesuai dengan karakteristik data, sehingga dapat menggambarkan hubungan antara variabel respon dan variabel prediktor. Tujuan dari penelitian ini yaitu untuk mendapatkan pemodelan MOS yang sesuai untuk variabel respon suhu maksimum, suhu minimum, dan kelembapan udara. Metode regresi yang digunakan adalah Principal Component Regression (PCR), Partial Least Square Regression (PLSR), dan ridge regression. Selanjutnya, model MOS yang terbentuk divalidasi dengan kriteria Root Mean Square Error (RMSE) dan Percentage Improval (IM%). MOS mampu mengoreksi bias prakiraan NWP hingga lebih dari 50%. Berdasarkan RMSE terkecil pada penelitian ini, suhu maksimum lebih akurat diprakirakan menggunakan model PLSR, sementara suhu minimum dan kelembapan udara lebih akurat diprakirakan menggunakan ridge regression.Kata Kunci: cuaca, MOS, NWP.

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.

Automatic Regional Interpretation and Forecasting System Supported by Machine Learning

The Model Output Statistics (MOS) model is a dynamic statistical weather forecast model based on multiple linear regression technology. It is greatly affected by the selection of parameters and predictors, especially when the weather changes drastically, or extreme weather occurs. We improved the traditional MOS model with the machine learning method to enhance the capabilities of self-learning and generalization. Simultaneously, multi-source meteorological data were used as the input to the model to improve the data quality. In the experiment, we selected the four areas of Nanjing, Beijing, Chengdu, and Guangzhou for verification, with the numerical weather prediction (NWP) products and observation data from automatic weather stations (AWSs) used to predict the temperature and wind speed in the next 24 h. From the experiment, it can be seen that the accuracy of the prediction values and speed of the method were improved by the ML-MOS. Finally, we compared the ML-MOS model with neural networks and support vector machine (SVM), the results show that the prediction result of the ML-MOS model is better than that of the above two models.

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>