Stomatal resistance (R s) calculation has a major impact on the surface energy partitioning that influences diverse boundary layer processes. Present operational limited area or mesoscale models have the Jarvis-type parameterization, whereas the microscale and the climate simulation models prefer physiological schemes for estimating R s. The pivotal question regarding operational mesoscale models is whether an iterative physiological scheme needs to be adopted ahead of the analytical Jarvis-type formulation. This question is addressed by comparing the ability of three physiological schemes along with a typical Jarvistype scheme for predicting R s using observations made during FIFE. The data used is typical of a C4-type vegetation, predominant in regions of high convective activity such as the semiarid Tropics and the southern United States grasslands. Data from three different intensive field campaigns are analyzed to account for vegetation and hydrological diversity. It is found that the Jarvis-type approach has low variance in the outcome due to a poor feedback for the ambient changes. The physiological models, on the other hand, are found to be quite responsive to the external environment. All three physiological schemes have a similar performance qualitatively, which suggests that the vapor pressure deficit approach or the relative humidity descriptor used in the physiological schemes may not yield different results for routine meteorological applications. For the data considered, the physiological schemes had a consistently better performance compared to the Jarvis-type scheme in predicting R s outcome. All four schemes can, however, provide a reasonable estimate of the ensemble mean of the samples considered. A significant influence of the seasonal change in the minimum R s in the Jarvis-type scheme was also noticed, which suggests the use of nitrogen-based information for improving the performance of the Jarvis-type scheme. A possible interactive influence of soil moisture on the capabilities of the four schemes is also discussed. Overall, the physiological schemes performed better under higher moisture availability.

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A soil-vegetation-atmospheric boundary layer model was developed to study the performance of two localclosure and two nonlocal-closure boundary layer mixing schemes for use in meteorological and air quality simulation models. Full interaction between the surface and atmosphere is achieved by representing surface characteristics and associated processes using a prognostic soil-vegetation scheme and atmospheric boundary layer schemes. There are 30 layers in the lowest 3 km of the model with a high resolution near the surface. The four boundary layer schemes are tested by simulating atmospheric boundary layer structures over densely and sparsely vegetated regions using the observational data from the First ISLSCP (International Satellite Land Surface Climatology Project) Field Experiment (FIFE) and from Wangara. Simulation results indicate that the near-surface turbulent fluxes predicted by the four boundary layer schemes differ from each other, even though the formulation used to represent the surface-layer processes is the same. These differences arise from the differing ways of representing subgrid-scale vertical mixing processes. Results also indicate that the vertical profiles of predicted parameters (i.e., temperature, mixing ratio, and horizontal winds) from the four mixed-layer schemes differ from each other, particularly during the daytime growth of the mixed layer. During the evening hours, after the mixed layer has reached its maximum depth, the differences among these respective predicted variables are found to be insignificant. There were some general features that were associated with each of the schemes in all of the simulations. Compared with observations, in all of the cases the simulated maximum depths of the boundary layer for each scheme were consistently either lower or higher, superadiabatic lapse rates were consistently either stronger or weaker, and the intensity of the vertical mixing was either stronger or weaker. Also, throughout the simulation period in all case studies, most of the differences in the predicted parameters are present in the surface layer and near the top of the mixed layer.

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Large errors in atmospheric boundary layer (ABL) simulations can be caused by inaccuracies in the specification of surface characteristics in addition to assumptions and simplifications made in boundary layer formulations or other model deficiencies. For certain applications, such as air quality studies, these errors can have significant effects. To reduce such errors, a continuous surface data assimilation technique is developed. In this technique, surface-layer temperature and water vapor mixing ratio are directly assimilated by using the analyzed surface data. Then, the difference between the observations and model results is used to calculate adjustments to the surface fluxes of sensible and latent heat. These adjustments are then used to calculate a new estimate of the ground temperature, thereby affecting the simulated surface fluxes on the subsequent time step. This indirect data assimilation is applied simultaneously with the direct assimilation of surface data in the model's lowest layer, thereby maintaining greater consistency between the ground temperature and the surface-layer mass-field variables. A one-dimensional model was used to study the improvements that result from applying this technique for ABL simulations in two cases. It was found that application of the new technique led to significant reductions in ABL modeling errors.

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This study compares aerosol optical depths (AOD) products from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Goddard Chemistry Aerosol Radiation and Transport (GOCART) model and their integrated products with ground measurements across the eastern U.S. from March 1, 2000 to December 31, 2001. The Terra MODIS Level‐3 (collection 4) AOD at 0.55 μm has better correlation, but consistently overestimates the values of the Aerosol Robotic Network (AERONET) measurements. GOCART has small biases for a 22‐month integration, and slight positive biases appeared for the cold season. These results are also supported by the comparison with the IMPROVE (Interagency Monitoring of Protected Visual Environments) light extinction index. The optimal interpolation improves the daily‐scale RMSE from either MODIS or GOCART alone. However, the regional biases in the aerosol products constitute a major constraint to the optimal estimate of AOD.

The National Weather Service's Cooperative Observer Program (COOP) is a valuable climate data resource that provides manually observed information on temperature and precipitation across the nation. These data are part of the climate dataset and continue to be used in evaluating weather and climate models. Increasingly, weather and climate information is also available from automated weather stations. A comparison between these two observing methods is performed in North Carolina, where 13 of these stations are collocated. Results indicate that, without correcting the data for differing observation times, daily temperature observations are generally in good agreement (0.96 Pearson product–moment correlation for minimum temperature, 0.89 for maximum temperature). Daily rainfall values recorded by the two different systems correlate poorly (0.44), but the correlations are improved (to 0.91) when corrections are made for the differences in observation times between the COOP and automated stations. Daily rainfall correlations especially improve with rainfall amounts less than 50 mm day−1. Temperature and rainfall have high correlation (nearly 1.00 for maximum and minimum temperatures, 0.97 for rainfall) when monthly averages are used. Differences of the data between the two platforms consistently indicate that COOP instruments may be recording warmer maximum temperatures, cooler minimum temperatures, and larger amounts of rainfall, especially with higher rainfall rates. Root-mean-square errors are reduced by up to 71% with the day-shift and hourly corrections. This study shows that COOP and automated data [such as from the North Carolina Environment and Climate Observing Network (NCECONet)] can, with simple corrections, be used in conjunction for various climate analysis applications such as climate change and site-to-site comparisons. This allows a higher spatial density of data and a larger density of environmental parameters, thus potentially improving the accuracy of the data that are relayed to the public and used in climate studies.