Potential and Shortcomings of Numerical Weather Prediction Models in Providing Meteorological Data for Urban Air Pollution Forecasting

2002 ◽  
pp. 43-60 ◽  
Author(s):  
Alexander Baklanov ◽  
Alix Rasmussen ◽  
Barbara Fay ◽  
Erik Berge ◽  
Sandro Finardi
Keyword(s):  
Air Pollution ◽  
Numerical Weather Prediction ◽  
Prediction Models ◽  
Meteorological Data ◽  
Weather Prediction ◽  
Urban Air Pollution ◽  
Urban Air ◽  
Numerical Weather ◽  
Numerical Weather Prediction Models ◽  
Air Pollution Forecasting

scholarly journals Overview of the European project FUMAPEX

2005 ◽  
Vol 5 (6) ◽  
pp. 11679-11702 ◽  
Author(s):  
A. Baklanov
Keyword(s):  
Air Pollution ◽  
Prediction Models ◽  
Meteorological Data ◽  
Weather Prediction ◽  
Urban Air Pollution ◽  
Population Exposure ◽  
Urban Air ◽  
Exposure Models ◽  
Numerical Weather Prediction Models

Abstract. The quality of the urban air pollution forecast critically depends on the mapping of emissions, the urban air pollution models, and the meteorological data. The quality of the meteorological data should be largely enhanced by using downscaled data from advanced numerical weather prediction models. These different topics, as well as the application of population exposure models, have traditionally been treated in distinct scientific communities whose expertise needs to be combined to enhance the possibilities of forecasting air pollution episodes in European cities. For this purpose the EU project ''Integrated Systems for Forecasting Urban Meteorology, Air Pollution and Population Exposure'' (FUMAPEX) (http://fumapex.dmi.dk), involving 22 organizations from 10 European countries, was initiated. The main objectives of the project are the improvement of meteorological forecasts for urban areas, the connection of numerical weather prediction models to urban air pollution and population exposure models, the building of improved Urban Air Quality Information and Forecasting Systems, and their application in cities in various European climates. This paper overviews the project items and first two-years results, it is an introduction to the whole ACP issue.

scholarly journals Overview of the European project FUMAPEX

2006 ◽  
Vol 6 (7) ◽  
pp. 2005-2015 ◽  
Author(s):  
A. Baklanov
Keyword(s):  
Air Pollution ◽  
Prediction Models ◽  
Meteorological Data ◽  
Weather Prediction ◽  
Urban Air Pollution ◽  
Population Exposure ◽  
Urban Air ◽  
Exposure Models ◽  
Numerical Weather Prediction Models

Abstract. The quality of the urban air pollution forecast critically depends on the mapping of emissions, the urban air pollution models, and the meteorological data. The quality of the meteorological data should be largely enhanced by using downscaled data from advanced numerical weather prediction models. These different topics, as well as the application of population exposure models, have traditionally been treated in distinct scientific communities whose expertise needs to be combined to enhance the possibilities of forecasting air pollution episodes in European cities. For this purpose the EU project "Integrated Systems for Forecasting Urban Meteorology, Air Pollution and Population Exposure'' (FUMAPEX) (http://fumapex.dmi.dk), involving 22 organizations from 10 European countries, was initiated. The main objectives of the project are the improvement of meteorological forecasts for urban areas, the connection of numerical weather prediction models to urban air pollution and population exposure models, the building of improved Urban Air Quality Information and Forecasting Systems, and their application in cities in various European climates. This paper overviews the project items and first two-years results, it is an introduction to the whole ACP issue.

scholarly journals Parametrizations of Liquid and Ice Clouds’ Optical Properties in Operational Numerical Weather Prediction Models

Atmosphere ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 89
Author(s):  
Harel. B. Muskatel ◽  
Ulrich Blahak ◽  
Pavel Khain ◽  
Yoav Levi ◽  
Qiang Fu
Keyword(s):  
Optical Properties ◽  
Numerical Weather Prediction ◽  
Radiation Transfer ◽  
Prediction Models ◽  
Weather Prediction ◽  
Effective Size ◽  
Asymmetry Factor ◽  
Numerical Weather ◽  
Numerical Weather Prediction Models ◽  
Spectral Averaging

Parametrization of radiation transfer through clouds is an important factor in the ability of Numerical Weather Prediction models to correctly describe the weather evolution. Here we present a practical parameterization of both liquid droplets and ice optical properties in the longwave and shortwave radiation. An advanced spectral averaging method is used to calculate the extinction coefficient, single scattering albedo, forward scattered fraction and asymmetry factor (bext, v, f, g), taking into account the nonlinear effects of light attenuation in the spectral averaging. An ensemble of particle size distributions was used for the ice optical properties calculations, which enables the effective size range to be extended up to 570 μm and thus be applicable for larger hydrometeor categories such as snow, graupel, and rain. The new parameterization was applied both in the COSMO limited-area model and in ICON global model and was evaluated by using the COSMO model to simulate stratiform ice and water clouds. Numerical weather prediction models usually determine the asymmetry factor as a function of effective size. For the first time in an operational numerical weather prediction (NWP) model, the asymmetry factor is parametrized as a function of aspect ratio. The method is generalized and is available on-line to be readily applied to any optical properties dataset and spectral intervals of a wide range of radiation transfer models and applications.

Forecasting wave height probabilities with numerical weather prediction models

2005 ◽  
Vol 32 (14-15) ◽  
pp. 1841-1863 ◽  
Author(s):  
Mark S. Roulston ◽  
Jerome Ellepola ◽  
Jost von Hardenberg ◽  
Leonard A. Smith
Keyword(s):  
Wave Height ◽  
Numerical Weather Prediction ◽  
Prediction Models ◽  
Weather Prediction ◽  
Numerical Weather ◽  
Numerical Weather Prediction Models
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