<p>Many Numerical Weather Prediction (NWP) models provide the parameter total snow depth as a Direct Model Output (DMO) surface variable. In mountain regions, however, the orographic flow modification significantly influences precipitation formation and preferential settling, leading to large model biases if DMO is directly compared to fresh snow point observations. Avalanche risk forecasts in turn require calibrated deterministic and probabilistic fresh snow forecasts, as the amount of fresh snow constitutes a crucial driver of avalanche risk.</p><p>In this study, MOSMIX-SNOW, a Model Output Statistics (MOS) product based on multiple linear regression is developed. Ground-based observations and operational forecast data of the two deterministic global NWP models ICON and ECMWF form the basis of the MOS system. MOSMIX-SNOW offers point forecasts for 20 deterministic as well as probabilistic forecast variables like the amount of fresh snow within 24h, the probability of more than 30cm of fresh snow within 24h and some basic variables like 2m temperature and dew point. The unique characteristic of MOSMIX-SNOW is the large number of observation-based, model-based and empirical predictors, which exceeds 200. Furthermore, a long historical data period of 9 years is applied for training of the MOS system. Thus, local orographic effects and large scale flow patterns are implicitly included in the MOS equations by a location and lead time specific choice of predictors. To avoid unrealistic jumps in the forecast, persistence predictors, which represent the forecast value of the previous forecast hour, are included in the MOS system. All forecasts feature a maximum lead time of +48h, have an hourly forecast resolution as well as update cycle and are available for about 15 mountain locations in the Bavarian Alps between 1100m and 2400m above sea level.</p><p>The verification analysis of the winter season 2018/19 shows that MOSMIX-SNOW forecasts offer a significantly higher forecast reliability than the raw ensemble of the regional NWP model COSMO-D2-EPS. The bias of the deterministic forecast parameters is smaller for MOSMIX-SNOW, especially for heavy snowfall events. MOSMIX-SNOW turned out to be a useful tool to support the avalanche risk forecasts on a daily basis during the snowy winter of 2018/19. Furthermore, the deterministic fresh snow forecast of MOSMIX-SNOW and other meteorological parameters like 2m-temperature serve as input for operational snowpack simulations. Measurement related noise and snow drift in the observations, however, are identified as an important source of uncertainty and the application of noise reduction techniques like a Savitzky-Golay filter are expected to have a beneficial impact on the forecast quality. MOSMIX-SNOW will become operational by end of 2020.</p>
<p>In order to reach legal air quality limits, several municipalities in Germany have decided to take actions if concentrations of NO<sub>2</sub> and Particulate Matter (PM) exceed certain thresholds. The decision for concrete measures is usually based on observations or use the Direct Model Output (DMO) of air quality models. However, due to large biases of state-of-the-art numerical air quality models, the skill of DMO forecasts to predict periods of polluted air up to four days ahead is very limited.</p><p>The project LQ-WARN aims to develop a system for warning of poor air quality based on Model Output Statistics (MOS). Therefore, air quality related observations, model results provided by the Copernicus Atmosphere Monitoring Service (CAMS) and meteorological parameters from the ECMWF numerical weather prediction model are used as predictors to forecast the air quality by applying Multiple Linear Regression (MLR). In this way MOS equations are calculated for four seasons. The final forecast product will comprise post-processed probabilistic as well as deterministic (e.g. mass concentration) parameters for the species NO<sub>2</sub>, O<sub>3</sub>, PM<sub>10</sub> and PM<sub>2.5</sub>. Forecasts will be available for several hundred German locations and cover lead times up to 96 hours.</p><p>Here, we show first results of our phase 1 MOS prototype, for which observational, meteorological and empirical predictors are applied. Despite of the preliminary exclusion of CAMS predictors, the verifications of the MOS equations imply a considerable reduction of variance and a significant reduction of RMSE (Root Mean Square Error) compared to the climatological values for all four species. Hence, the MOS system can already provide a reasonably good air quality forecast. Furthermore, our analysis of used meteorological predictors, enables a detailed analysis of the importance of specific meteorological parameters for improved statistical air quality forecasts. &#160;As an outlook we will provide detailed information about the final phase 2 LQ-WARN product, which will also include the MOS predictors of CAMS and is expected to be launched in pre-operational mode by 2022.</p>
<p>Schlechte Luftqualit&#228;t gef&#228;hrdet die Gesundheit der Bev&#246;lkerung. Zur Information und zur Ergreifung kurzfristiger Ma&#223;nahmen zur Luftqualit&#228;tsverbesserung (z.B. Verkehrslenkung) ist eine m&#246;glichst genaue und &#8211; insbesondere in st&#228;dtischen Gebieten &#8211; m&#246;glichst r&#228;umlich hochaufgel&#246;ste Luftqualit&#228;tsvorhersage notwendig. Numerische Luftqualit&#228;tsmodelle haben f&#252;r diese Aufgabe in der Regel eine zu geringe r&#228;umliche Aufl&#246;sung.</p> <p>Daher ist es Ziel des Projektes &#8222;LQ-Warn&#8220; die Luftqualit&#228;tsvorhersage insbesondere im Hinblick auf die &#220;berschreitung von Grenzwerten zu verbessern. Basierend auf den Modellergebnissen f&#252;r Luftqualit&#228;tsparameter des Copernicus Atmospheric Monitoring Service (CAMS) werden zwei Ans&#228;tze verfolgt: Einerseits werden Vorhersagen mit dem regionalen chemischen Transportmodell &#8222;REM-CALGRID&#8220; (RCG) unter Einbeziehung von CAMS-Ergebnissen und regionalen Emissionsdaten berechnet. Dabei kann eine hohe horizontale Aufl&#246;sung von 2 km erzielt werden und Prognosen k&#246;nnen f&#252;r verschiedene Luftschadstoffe in st&#252;ndlicher Aufl&#246;sung mit bis zu 72 Stunden Vorlaufzeit erstellt werden, unter anderem f&#252;r 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 &#8222;Model Output Statistics&#8220; (MOS) angewandt, um Punktvorhersagen f&#252;r 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. Daf&#252;r werden luftqualit&#228;tsbezogene Messungen, CAMS-Modellergebnisse und meteorologische Parameter aus dem numerischen Wettervorhersagemodell des ECMWF als Pr&#228;diktoren verwendet.</p> <p>Es werden erste Ergebnisse der mit den o.g. Ans&#228;tzen errechneten Vorhersagen pr&#228;sentiert und die Vor- und Nachteile der jeweiligen Verfahren hervorgehoben. Durch die statistische Post-Processing-Methode MOS wird an den Vorhersagepunkten vor allem f&#252;r 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 Luftqualit&#228;tsvorhersage sinnvoll auf die Fl&#228;che auszudehnen ist jedoch noch eine Herausforderung. Im Gegensatz dazu zeigt die Vorhersage mit dem RCG-Modell eine geringere Verbesserung der Vorhersageg&#252;te an einzelnen Vorhersagepunkten als der MOS-Ansatz. Stattdessen bietet das RCG-Modell zeitlich und r&#228;umlich konsistente Vorhersagen an allen Modellgitterpunkten. Kleinskalige Konzentrationsunterschiede k&#246;nnen aufgrund der h&#246;heren Modellaufl&#246;sung deutlich realistischer vorhergesagt werden als mit den CAMS-Vorhersagen. Ein weiterf&#252;hrendes Ziel des LQ-Warn-Projektes ist es die beiden Ans&#228;tze zu kombinieren, um die Vorteile beider nutzen zu k&#246;nnen und eine pr&#228;zise Luftqualit&#228;tsvorhersage fl&#228;chendeckend f&#252;r Deutschland bereitstellen zu k&#246;nnen.</p>
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