By S. Trivikrama Rao, Christian Hogrefe, Gopal Sistla, Shiang-Yuh Wu, Winston Hao (auth.), Sven-Erik Gryning, Ekaterina Batchvarova (eds.)
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Additional info for Air Pollution Modeling and Its Application XIII
In the preliminary step, the performance ofthe model REM3 as astand alone tool for the ozone forecast is tested. The test focusses on the impact of the meteorological input data on the model performance. Therefore, REM3 results gained by employing a numerically predicted data base will be compared to results gained by an analysed data base for the summer 1997. The forecast error of the prognostic data base will influence the model simulation. ) causes substantial differences in the' calculated ozone fields too.
It is different for different variables. The forecast of sub - scale depended parameters, such as c10ud cover, tends to be more erroneous than the one of large scale parameters such as pressure fields. The forecast error can be estimated by comparing the forecasted field with the analysed initial field ofthe numerical weather prediction (figure 1). 8. 8. Date in 1997 Error >= 5/8 19. 9 Error >= 2/8 Figure 1. Graph ofthe cloud cover forecast error ofthe EUROPA-model: Percentage of grid points over Germany predicted erroneously after 24 hours over Germany.
32, No. 8, 1998. pp 1255-1466. Systems Applications International. Users Guide to the Variable Grid Urban Airshed Model (UAM-V), San Rafael, CA, 1995. 1, February 12, 1997. html). F. y. Ku. Statistical characteristics of spectrally-decomposed ambient ozone time series data. wustl. edu/otag/reports). Y and RF. Henry. Dealing with the ozone non-attainment problem in the Eastern United States, Environ. , 1996, pp 17-31. Y and RF. Henry. Space and time scales in ambient ozone data. Bull. Amer. Meteor.