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William P. Kustas and Karen S. Humes

Abstract

Radiometric surface temperature images from aircraft observations over the Walnut Gulch Experimental Watershed, a semiarid rangeland watershed, were used with ground-based meteorological data at a reference site for extrapolating estimates of surface sensible heat flux across the basin. Two approaches were used. One method assumed that the resistance to heat transport and other meteorological data at a reference site were constant over the watershed. This resulted in a simple scheme (constant resistance approach) for computing spatially distributed sensible heat flux since the variation in sensible heat flux was directly proportional to surface temperature differences from the reference site. The second approach (the variable resistance approach) used spatially distributed estimates of the surface roughness for momentum and heat, as well as air temperature and wind speed. The sensible heat flux values derived by both techniques were compared to measurements made at several other locations in the watershed for three different days. The environmental conditions for these days ranged from uniformly dry surface soil moisture to variably wet conditions caused by several high intensity and spatially variable rainfall events. Comparisons between these two schemes with observations indicated that the more detailed method of accounting for changes in surface roughness over the basin gave significantly better agreement than the simpler scheme. The average percentage of difference with measured values was 30% for the constant resistance approach compared to approximately 20% for the variable resistance method.

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William P. Kustas, John H. Prueger, Karen S. Humes, and Patrick J. Starks

Abstract

Radiometric surface temperature observations T R(ϕ), near-surface meteorological/surface energy flux (METFLUX), and atmospheric boundary layer (ABL) data were collected during the Washita ’94 Experiment conducted in the Little Washita Experimental Watershed near Chickasha, Oklahoma. The T R(ϕ) measurements were made from ground and aircraft platforms near the METFLUX stations located over vegetated surfaces of varying amounts of cover and over bare soil. Continuous, half-hourly averaged ground-based T R(ϕ) measurements essentially at the point scale were calibrated with periodic ground transect and aircraft-based T R(ϕ) observations at coarser resolutions so that the continuous T R(ϕ) measurements would be representative of surface temperatures at the field scale (i.e., on the order of 104 m2). The METFLUX data were collected nominally at 2 m above the surface, while ABL measurements were made in the lower 8–10 km of the atmosphere. The “local” wind speed, u, and air temperature, T A, from the METFLUX stations, as well as the mixed-layer wind speed, U M, and potential temperature, ΘM, were used in a two-source energy balance model for computing fluxes with continuous T R(ϕ) measurements from the various surfaces. Standard Monin–Obukhov surface layer similarity was used with the “local” u and T A data from the METFLUX stations. Bulk similarity approaches were used with the U M and ΘM data referenced either to ABL height or the top of the surface layer. This latter approach of using mixed-layer data to drive model computations for the different sites is similar to the so-called flux-aggregation schemes or methods proposed to account for subgrid variability in atmospheric models, such as the“tile” or “mosaic” approach. There was less agreement between modeled and measured fluxes when using mixed-layer versus local meteorological variables data for driving the model, and the type of bulk formulation used (i.e., whether local or regional surface roughness was used) also had a significant impact on the results. Differences between the flux observations and model predictions using surface layer similarity with local u and T A data were about 25% on average, while using the bulk formulations with U M and ΘM differences averaged about 30%. This larger difference was caused by an increase in biases and scatter between modeled and measured fluxes for some sites. Therefore, computing spatially distributed local-scale fluxes with ABL observations of mixed-layer properties will probably yield less reliable flux predictions than using local meteorological data, if available. Given the uncertainty in flux observations is about 20%, these estimates are still considered reasonable and moreover permit the mapping of spatially distributed surface fluxes at regional scales using a single observation of U M and ΘM with high resolution T R(ϕ) data. Such T R(ϕ) observations with a 90-m pixel resolution will be available from the Advanced Spaceborne Thermal Emission and Reflection Radiometer to be launched on NASA’s Earth Observing System.

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Troy R. Blandford, Karen S. Humes, Brian J. Harshburger, Brandon C. Moore, Von P. Walden, and Hengchun Ye

Abstract

To accurately estimate near-surface (2 m) air temperatures in a mountainous region for hydrologic prediction models and other investigations of environmental processes, the authors evaluated daily and seasonal variations (with the consideration of different weather types) of surface air temperature lapse rates at a spatial scale of 10 000 km2 in south-central Idaho. Near-surface air temperature data (T max, T min, and T avg) from 14 meteorological stations were used to compute daily lapse rates from January 1989 to December 2004 for a medium-elevation study area in south-central Idaho. Daily lapse rates were grouped by month, synoptic weather type, and a combination of both (seasonal–synoptic). Daily air temperature lapse rates show high variability at both daily and seasonal time scales. Daily T max lapse rates show a distinct seasonal trend, with steeper lapse rates (greater decrease in temperature with height) occurring in summer and shallower rates (lesser decrease in temperature with height) occurring in winter. Daily T min and T avg lapse rates are more variable and tend to be steepest in spring and shallowest in midsummer. Different synoptic weather types also influence lapse rates, although differences are tenuous. In general, warmer air masses tend to be associated with steeper lapse rates for maximum temperature, and drier air masses have shallower lapse rates for minimum temperature. The largest diurnal range is produced by dry tropical conditions (clear skies, high solar input). Cross-validation results indicate that the commonly used environmental lapse rate [typically assumed to be −0.65°C (100 m)−1] is solely applicable to maximum temperature and often grossly overestimates T min and T avg lapse rates. Regional lapse rates perform better than the environmental lapse rate for T min and T avg, although for some months rates can be predicted more accurately by using monthly lapse rates. Lapse rates computed for different months, synoptic types, and seasonal–synoptic categories all perform similarly. Therefore, the use of monthly lapse rates is recommended as a practical combination of effective performance and ease of implementation.

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