The LQ-WARN Project – Development of a Model Output Statistics Product for Air Quality Warnings

2021 ◽  
Author(s):  
Sabine Robrecht ◽  
Andreas Lambert ◽  
Stefan Gilge
Keyword(s):  
Air Quality ◽  
Meteorological Parameters ◽  
Phase 1 ◽  
Weather Prediction ◽  
Model Output ◽  
Operational Mode ◽  
Quality Models ◽  
Air Quality Forecast ◽  
Direct Model ◽  
Model Output Statistics

<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.  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>

MOSMIX-SNOW – A Model Output Statistics Product for Fresh Snow Forecasts at Mountain Locations

2020 ◽  
Author(s):  
Andreas Lambert ◽  
Sebastian Trepte ◽  
Franziska Ehrnsperger
Keyword(s):  
Lead Time ◽  
Weather Prediction ◽  
Winter Season ◽  
Dew Point ◽  
Model Output ◽  
Flow Modification ◽  
Direct Model ◽  
Fresh Snow ◽  
Model Output Statistics ◽  
Avalanche Risk

<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>

Towards operational postprocessing of cloud cover at MeteoSwiss

2020 ◽  
Author(s):  
Stephan Hemri ◽  
Christoph Spirig ◽  
Jonas Bhend ◽  
Lionel Moret ◽  
Mark Liniger
Keyword(s):  
Cloud Cover ◽  
Prediction Models ◽  
Weather Prediction ◽  
Weather Forecast ◽  
Forecast Errors ◽  
Observation Data ◽  
Model Output ◽  
Ensemble Forecasts ◽  
Direct Model ◽  
Ensemble Model Output Statistics

<p>Over the last decades ensemble approaches have become state-of-the-art for the quantification of weather forecast uncertainty. Despite ongoing improvements, ensemble forecasts issued by numerical weather prediction models (NWPs) still tend to be biased and underdispersed. Statistical postprocessing has proven to be an appropriate tool to correct biases and underdispersion, and hence to improve forecast skill. Here we focus on multi-model postprocessing of cloud cover forecasts in Switzerland. In order to issue postprocessed forecasts at any point in space, ensemble model output statistics (EMOS) models are trained and verified against EUMETSAT CM SAF satellite data with a spatial resolution of around 2 km over Switzerland. Training with a minimal record length of the past 45 days of forecast and observation data already produced an EMOS model improving direct model output (DMO). Training on a 3 years record of the corresponding season further improved the performance. We evaluate how well postprocessing corrects the most severe forecast errors, like missing fog and low level stratus in winter. For such conditions, postprocessing of cloud cover benefits strongly from incorporating additional predictors into the postprocessing suite. A quasi-operational prototype has been set up and was used to explore meteogram-like visualizations of probabilistic cloud cover forecasts.</p>

scholarly journals The Use of Gridded Model Output Statistics (GMOS) in Energy Forecasting of a Solar Car

Energies ◽  
2020 ◽  
Vol 13 (8) ◽  
pp. 1984
Author(s):  
Christiaan Oosthuizen ◽  
Barend Van Wyk ◽  
Yskandar Hamam ◽  
Dawood Desai ◽  
Yasser Alayli
Keyword(s):  
Weather Forecasting ◽  
Forecast Error ◽  
Weather Prediction ◽  
Weather Conditions ◽  
Model Output ◽  
Energy Forecasting ◽  
Weather Forecasts ◽  
Improved Performance ◽  
Model Output Statistics ◽  
Auxiliary Services

For many years, primary weather forecasting services (Global Forecast System (GFS) and the European Centre for Medium-Range Weather Forecasts (ECMWF)) have been made available to the public through global Numerical Weather Prediction (NWP) models estimating a multitude of general weather variables in a variety of resolutions. Secondary services such as weather experts Meteomatics AG use data and improve the forecasts through various methods. They tailor for the specific needs of customers in the wind and solar power generation sector as well as data scientists, analysts, and meteorologists in all areas of business. These auxiliary services have improved performance and provide reliable data. However, this work extended these auxiliary services to so-called tertiary services in which the weather forecasts were further conditioned for the very niche application environment of mobile solar technology in solar car energy management. The Gridded Model Output Statistics (GMOS) Global Horizontal Irradiance (GHI) model developed in this work utilizes historical data from various ground station locations in South Africa to reduce the mean forecast error of the GHI component. An average Root Mean Square Error (RMSE) improvement of 11.28% was shown across all locations and weather conditions. It was also shown how the incorporation of the GMOS model could have increased the accuracy in regard to the State of Charge (SoC) energy simulation of a solar car during the Sasol Solar Challenge 2018 and the possible range benefits thereof.

Improved model output statistics of numerical weather prediction based irradiance forecasts for solar power applications

Solar Energy ◽  
2015 ◽  
Vol 118 ◽  
pp. 634-645 ◽  
Author(s):  
Remco A. Verzijlbergh ◽  
Petra W. Heijnen ◽  
Stephan R. de Roode ◽  
Alexander Los ◽  
Harm J.J. Jonker
Keyword(s):  
Numerical Weather Prediction ◽  
Solar Power ◽  
Weather Prediction ◽  
Model Output ◽  
Numerical Weather ◽  
Model Output Statistics ◽  
Improved Model

Urbanization of Weather Forecast, Air-Quality Prediction and Climate Scenarios - Project URBI PRAGENSI

2021 ◽  
Author(s):  
Tomas Halenka ◽  
Michal Belda ◽  
Peter Huszar ◽  
Jan Karlicky ◽  
Tereza Novakova
Keyword(s):  
Air Quality ◽  
Weather Prediction ◽  
Regional Climate ◽  
Weather Forecast ◽  
Quality Prediction ◽  
Climate Scenarios ◽  
Air Quality Prediction ◽  
Air Quality Forecast ◽  
The City ◽  
Very High

<p>The ratio of population living in cities is growing and this is especially true for the largest ones, megacities. However, even smaller cities like the City of Prague  (about 1.5 M) can suffer significantly and the night time temperature difference under summer heat wave can achieve more than 5°C. To assess the impact of cities and urban structures on weather, climate and air-quality, modelling approach is commonly used and the inclusion of urban parameterization in land-surface interactions is of primary importance to capture the urban effects properly. This is especially important when going to higher resolution, which is common trend in operational weather forecast, air-quality prediction as well as regional climate modeling. This represents the rapidly developing research, motivated by specific risks in urban environment, with strong impacts on vulnerable communities there, leading to the tools to assess properly impacts within the cities and the effectiveness of adaptation and mitigation options applied there by the city authorities. Under the action towards the Smart Cities and within the framework for developing adequate climate services, such supporting tools for decission making are inevitable. It is valid not only for extreme heat waves impact prediction, but as well in air-quality forecast and in long term perspective in connection to climate change impacts assessment. This provides the background for the project within Operational Program Prague - The Pole of Growth “Urbanization of weather forecast, air-quality prediction and climate scenarios for Prague”, shortly URBI PRAGENSI.</p><p> </p><p>There are four main tasks within the project. First, urbanization of weather forecast, i.e. involving and testing the urban parameterization scheme in the weather prediction model can provide in very high resolution localized weather prediction and especially under the heat wave condition it can well capture the temperature differences in the city center with respect to the remote areas. There are applications, which can use such localized prediction for planning and decision making on e.g. public services for some specific groups of population in risks. Further, air-quality forecast based on such urbanized weather condition forecast can benefit from better estimates of temperature for chemical reactions, mixing height for dispersion conditions etc. Third, urbanized scenarios of climate change can provide better description of future conditions in the city for adaptation and mitigation options, moreover, in connection to urban heat island urbanized regional climate model in very high resolution is good tool for estimates of efficiency  of potential adaptation or mitigation measures which might be applied by the city administration. Last, but not least, microscale simulations using LES methods are supposed to be used for selected local hot-spots to solve them.</p>

scholarly journals Operational Consensus Forecasts

2005 ◽  
Vol 20 (1) ◽  
pp. 101-111 ◽  
Author(s):  
Frank Woodcock ◽  
Chermelle Engel
Keyword(s):  
Weighted Average ◽  
Ground Level ◽  
Absolute Error ◽  
Model Output ◽  
Consensus Forecasts ◽  
Sunshine Hours ◽  
Operational Forecasting ◽  
Direct Model ◽  
Forecasting System ◽  
Model Output Statistics

Abstract The objective consensus forecasting (OCF) system is an automated operational forecasting system that adapts to underlying numerical model upgrades within 30 days and generally outperforms direct model output (DMO) and model output statistics (MOS) forecasts. It employs routinely available DMO and MOS guidance combined after bias correction using a mean absolute error (MAE)-weighted average algorithm. OCF generates twice-daily forecasts of screen-level temperature maxima and minima, ground-level temperature minima, evaporation, sunshine hours, and rainfall and its probability for day 0 to day 6 for up to 600 Australian sites. Extensive real-time trials of temperature forecasts yielded MAEs at days 0–2 about 40% lower than those from its component MOS and DMO forecasts. MAEs were also lower at day 1 than matching official forecasts of maxima and minima by 8% and 10% and outperformed official forecasts at over 71% and 75% of sites, respectively. MAEs of weighted average consensus outperformed simple average forecasts by about 5%.

scholarly journals A revised parameterization for gaseous dry deposition in air-quality models

2003 ◽  
Vol 3 (6) ◽  
pp. 2067-2082 ◽  
Author(s):  
L. Zhang ◽  
J. R. Brook ◽  
R. Vet
Keyword(s):  
Air Quality ◽  
Dry Deposition ◽  
Stomatal Resistance ◽  
Model Output ◽  
Gaseous Species ◽  
Area Index ◽  
Quality Models ◽  
Deposition Velocities ◽  
Study Results ◽  
Land Types

Abstract. A parameterization scheme for calculating gaseous dry deposition velocities in air-quality models is revised based on recent study results on non-stomatal uptake of O3 and SO2 over 5 different vegetation types. Non-stomatal resistance, which includes in-canopy aerodynamic, soil and cuticle resistances, for SO2 and O3 is parameterized as a function of friction velocity, relative humidity, leaf area index, and canopy wetness. Non-stomatal resistance for other chemical species is scaled to those of SO2 and O3 based on their chemical and physical characteristics. Stomatal resistance is calculated using a two-big-leaf stomatal resistance sub-model for all gaseous species of interest. The improvements in the present model compared to its earlier version include a newly developed non-stomatal resistance formulation, a realistic treatment of cuticle and ground resistance in winter, and the handling of seasonally-dependent input parameters. Model evaluation shows that the revised parameterization can provide more realistic deposition velocities for both O3 and SO2, especially for wet canopies. Example model output shows that the parameterization provides reasonable estimates of dry deposition velocities for different gaseous species, land types and diurnal and seasonal variations. Maximum deposition velocities from model output are close to reported measurement values for different land types. The current parameterization can be easily adopted into different air-quality models that require inclusion of dry deposition processes.

MOS, Perfect Prog, and Reanalysis

2006 ◽  
Vol 134 (2) ◽  
pp. 657-663 ◽  
Author(s):  
Caren Marzban ◽  
Scott Sandgathe ◽  
Eugenia Kalnay
Keyword(s):  
Mean Squared Error ◽  
Weather Prediction ◽  
Error Variance ◽  
Numerical Weather Prediction Model ◽  
Reanalysis Data ◽  
Point Of View ◽  
Model Output ◽  
Squared Error ◽  
Error Bias ◽  
Model Output Statistics

Abstract Statistical postprocessing methods have been successful in correcting many defects inherent in numerical weather prediction model forecasts. Among them, model output statistics (MOS) and perfect prog have been most common, each with its own strengths and weaknesses. Here, an alternative method (called RAN) is examined that combines the two, while at the same time utilizes the information in reanalysis data. The three methods are examined from a purely formal/mathematical point of view. The results suggest that whereas MOS is expected to outperform perfect prog and RAN in terms of mean squared error, bias, and error variance, the RAN approach is expected to yield more certain and bias-free forecasts. It is suggested therefore that a real-time RAN-based postprocessor be developed for further testing.

scholarly journals A Comparative Verification of Localized Aviation Model Output Statistics Program (LAMP) and Numerical Weather Prediction (NWP) Model Forecasts of Ceiling Height and Visibility

2010 ◽  
Vol 25 (4) ◽  
pp. 1161-1178 ◽  
Author(s):  
David E. Rudack ◽  
Judy E. Ghirardelli
Keyword(s):  
United States ◽  
Short Range ◽  
Mesoscale Model ◽  
Weather Prediction ◽  
Weather Research And Forecasting ◽  
Model Output ◽  
Probabilistic Forecasts ◽  
Ceiling Height ◽  
Nwp Model ◽  
Model Output Statistics

Abstract In an effort to support aviation forecasting, the National Weather Service’s Meteorological Development Laboratory (MDL) has recently redeveloped the Localized Aviation Model Output Statistics (MOS) Program (LAMP) system. LAMP is designed to run hourly in NWS operations and produce short-range aviation forecast guidance at 1-h projections out to 25 h. This paper compares and contrasts LAMP ceiling height and visibility forecasts with forecasts produced by the 20-km Rapid Update Cycle model (RUC20), the Weather Research and Forecasting Nonhydrostatic Mesoscale Model (WRF-NMM), and the Short-Range Ensemble Forecast system (SREF). RUC20 and WRF-NMM forecasts of continuous ceiling height and visibility were interpolated to stations and converted into categorical forecasts. These interpolated forecasts were also categorized into instrument flight rule (IFR) or lower conditions and verified against LAMP forecasts at stations in the contiguous United States. LAMP and SREF probabilistic forecasts of ceiling height and visibility from LAMP and the SREF system were also verified. This study demonstrates that for the 0000 and 1200 UTC cycles over the contiguous United States, LAMP station-based categorical forecasts of ceiling height, visibility, and IFR conditions or lower are more accurate than the RUC20 and WRF-NMM ceiling height and visibility forecasts interpolated to stations. Moreover, for the 0900 and 2100 UTC forecast cycles and verification periods studied here, LAMP ceiling height and visibility probabilities exhibit better reliability and skill than the SREF system.

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