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A technique called Model Output Enhancement (MOE) has been developed for the generation and display of mesoscale weather forecasts. The MOE technique derives mesoscale or high-resolution (order of 1 km) weather forecasts from synoptic-scale numerical weather-prediction models by modifying model output with geophysical and land-cover data. Mesoscale forecasts generated by the MOE technique are displayed as color-class maps overlaid on perspective plots of terrain. The MOE technique has been demonstrated in the generation of mesoscale maximum-temperature and minimum-temperature forecasts for case-study days of clear-sky conditions over the Commonwealth of Pennsylvania. The generated forecasts were evaluated using data from selected climatological stations.
A technique called Model Output Enhancement (MOE) has been developed for the generation and display of mesoscale weather forecasts. The MOE technique derives mesoscale or high-resolution (order of 1 km) weather forecasts from synoptic-scale numerical weather-prediction models by modifying model output with geophysical and land-cover data. Mesoscale forecasts generated by the MOE technique are displayed as color-class maps overlaid on perspective plots of terrain. The MOE technique has been demonstrated in the generation of mesoscale maximum-temperature and minimum-temperature forecasts for case-study days of clear-sky conditions over the Commonwealth of Pennsylvania. The generated forecasts were evaluated using data from selected climatological stations.
Abstract
The Soil Hydrology Model (SHM) was modified, and daily simulations of soil volumetric water content were made at 38 Oklahoma Mesonet sites for July 1997. These model results were compared with soil moisture observations made at the mesonet sites at depths of 5, 25, 60, and 75 cm. This work is believed to be the first time that a hydrological model has been evaluated with in situ soil moisture measurements over such an extensive area spanning several climate zones.
Comparisons of time series between the observed and modeled domain-averaged volumetric water content at 5 cm revealed similar phase and amplitude changes, a coefficient of determination (R 2) of 0.64, and small mean bias and root-mean-square errors (MBE and rmse) of 0.03 and 0.09, respectively. At 25, 60, and 75 cm, the model performance was slightly worse, with R 2 values between 0.27 and 0.40, MBE between −0.01 and 0.02, and rmse between 0.11 and 0.13. The model response to changes in soil water at these levels was sluggish, possibly because of, among other things, a lack of ability to model preferential downward water flow through cracks in the soil.
The results of this study suggest that SHM can be used effectively to initialize 5-cm soil moisture values in numerical prediction models. At deeper soil levels, however, the relatively small R 2 values and negligible MBE suggest that the model may be better suited for initializing a regionally averaged soil moisture value rather than unique gridbox values. These results illustrate the difficulty in using point measurements to validate a hydrological model, especially deeper in the soil where moisture values are more dependent on soil properties (which can vary sharply over small distances) and are less dependent on recent rainfall.
Abstract
The Soil Hydrology Model (SHM) was modified, and daily simulations of soil volumetric water content were made at 38 Oklahoma Mesonet sites for July 1997. These model results were compared with soil moisture observations made at the mesonet sites at depths of 5, 25, 60, and 75 cm. This work is believed to be the first time that a hydrological model has been evaluated with in situ soil moisture measurements over such an extensive area spanning several climate zones.
Comparisons of time series between the observed and modeled domain-averaged volumetric water content at 5 cm revealed similar phase and amplitude changes, a coefficient of determination (R 2) of 0.64, and small mean bias and root-mean-square errors (MBE and rmse) of 0.03 and 0.09, respectively. At 25, 60, and 75 cm, the model performance was slightly worse, with R 2 values between 0.27 and 0.40, MBE between −0.01 and 0.02, and rmse between 0.11 and 0.13. The model response to changes in soil water at these levels was sluggish, possibly because of, among other things, a lack of ability to model preferential downward water flow through cracks in the soil.
The results of this study suggest that SHM can be used effectively to initialize 5-cm soil moisture values in numerical prediction models. At deeper soil levels, however, the relatively small R 2 values and negligible MBE suggest that the model may be better suited for initializing a regionally averaged soil moisture value rather than unique gridbox values. These results illustrate the difficulty in using point measurements to validate a hydrological model, especially deeper in the soil where moisture values are more dependent on soil properties (which can vary sharply over small distances) and are less dependent on recent rainfall.
Abstract
A new approach to simulating the urban environment with a mesocale model has been developed to identify efficient strategies for mitigating increases in surface air temperatures associated with the urban heat island (UHI). A key step in this process is to define a “global” roughness for the cityscape and to use this roughness to diagnose 10-m temperature, moisture, and winds within an atmospheric model. This information is used to calculate local exchange coefficients for different city surface types (each with their own “local roughness” lengths); each surface’s energy balances, including surface air temperatures, humidity, and wind, are then readily obtained. The model was run for several summer days in 2001 for the New York City five-county area. The most effective strategy to reduce the surface radiometric and 2-m surface air temperatures was to increase the albedo of the city (impervious) surfaces. However, this caused increased thermal stress at street level, especially noontime thermal stress. As an alternative, the planting of trees reduced the UHI’s adverse effects of high temperatures and also reduced noontime thermal stress on city residents (and would also have reduced cooling energy requirements of small structures). Taking these results together, the analysis suggests that the best mitigation strategy is planting trees at street level and increasing the reflectivity of roofs.
Abstract
A new approach to simulating the urban environment with a mesocale model has been developed to identify efficient strategies for mitigating increases in surface air temperatures associated with the urban heat island (UHI). A key step in this process is to define a “global” roughness for the cityscape and to use this roughness to diagnose 10-m temperature, moisture, and winds within an atmospheric model. This information is used to calculate local exchange coefficients for different city surface types (each with their own “local roughness” lengths); each surface’s energy balances, including surface air temperatures, humidity, and wind, are then readily obtained. The model was run for several summer days in 2001 for the New York City five-county area. The most effective strategy to reduce the surface radiometric and 2-m surface air temperatures was to increase the albedo of the city (impervious) surfaces. However, this caused increased thermal stress at street level, especially noontime thermal stress. As an alternative, the planting of trees reduced the UHI’s adverse effects of high temperatures and also reduced noontime thermal stress on city residents (and would also have reduced cooling energy requirements of small structures). Taking these results together, the analysis suggests that the best mitigation strategy is planting trees at street level and increasing the reflectivity of roofs.
