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The method discussed in this paper aims to be a versatile assessment method that can be applied to any energy model using input variables.
For this reason, we need to measure output data on curve driving with random input in order to design the curve model using input and output data sets.
The predictions of the applied droplet breakup model using input values from the numerical simulations showed very good agreement with the experimental data.
To that end, we applied a chemistry transport model, using input data from detailed emission inventories, meteorology, and land use variables, to estimate the surface concentration of air pollutants.
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When controlling for elevation, the NASA-CASA model using inputs of 30-m resolution Landsat 7 EVI for the month of peak growing season temperatures captures a substantial portion (> 67%) of landscape scale variation in the controls (such as foliar N content and moisture stress effects) on annual NPP at BEF.
The model uses input for tree cover, soil chemistry, soil physical properties, and vegetation chemistry to estimate biomass, carbon, and nitrogen accumulation on the landscape with woodland expansion.
The model uses input data from the weather forecast, the expected yield, the availability of required recourses, and machinery related specifications.
The model uses input parameters that can be determined visually (channel profile and patterns) and by accessing readily available historical meteorological data (winter intensity) or climate maps.
The downscaling fusion model uses input data from two sources: (1) monitoring networks and (2) numerical models.
The model uses input variables derived from regional tree species-specific yield tables, forest inventory data, regional climate variables and forestry sector statistics.
The EURAD model uses input data from official emission inventories on a scale of 1 km, including industrial sources, household heating, traffic, and agriculture data on hourly meteorology and regional topography.
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