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Diane Strassberg
,
Margaret A. LeMone
,
Thomas T. Warner
, and
Joseph G. Alfieri

Abstract

Comparisons of 10-m above ground level (AGL) wind speeds from numerical weather prediction (NWP) models to point observations consistently show that model daytime wind speeds are slow compared to observations, even after improving model physics and going to smaller grid spacing. Previous authors have attributed the discrepancy to differences between the areas represented by model and observations, and the small surface roughness upstream of wind vanes compared with the corresponding model grid value. Using daytime fair-weather data from the May–June 2002 International H2O Experiment (IHOP_2002), the effect of wind-vane exposure is explored by comparing observed 10-m winds from nine surface-flux towers in well-exposed locations to modeled 10-m winds found by applying Monin–Obukhov (MO) similarity for unstable conditions to flight-track-averaged data collected by the University of Wyoming King Air over flat to rolling terrain with occasional trees and buildings. In the calculations, King Air winds and fluxes are supplemented with thermodynamic means and fluxes from the surface-flux towers. After exercising considerable care in characterizing and reducing biases in aircraft winds and fluxes, the authors found that MO-based surface winds averaged 0.5–0.7 ± 0.2 m s−1 less than those measured—about the same as the smaller reported discrepancies between NWP models and observed winds.

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Margaret A. LeMone
,
Mukul Tewari
,
Fei Chen
,
Joseph G. Alfieri
, and
Dev Niyogi

Abstract

Sources of differences between observations and simulations for a case study using the Noah land surface model–based High-Resolution Land Data Assimilation System (HRLDAS) are examined for sensible and latent heat fluxes H and LE, respectively; surface temperature Ts ; and vertical temperature difference T 0Ts , where T 0 is at 2 m. The observational data were collected on 29 May 2002, using the University of Wyoming King Air and four surface towers placed along a sparsely vegetated 60-km north–south flight track in the Oklahoma Panhandle. This day had nearly clear skies and a strong north–south soil-moisture gradient, with wet soils and widespread puddles at the south end of the track and drier soils to the north. Relative amplitudes of H and LE horizontal variation were estimated by taking the slope of the least squares best-fit straight line ΔLE/ΔH on plots of time-averaged LE as a function of time-averaged H for values along the track. It is argued that observed H and LE values departing significantly from their slope line are not associated with surface processes and, hence, need not be replicated by HRLDAS. Reasonable agreement between HRLDAS results and observed data was found only after adjusting the coefficient C in the Zilitinkevich equation relating the roughness lengths for momentum and heat in HRLDAS from its default value of 0.1 to a new value of 0.5. Using C = 0.1 and adjusting soil moisture to match the observed near-surface values increased horizontal variability in the right sense, raising LE and lowering H over the moist south end. However, both the magnitude of H and the amplitude of its horizontal variability relative to LE remained too large; adjustment of the green vegetation fraction had only a minor effect. With C = 0.5, model-input green vegetation fraction, and our best-estimate soil moisture, H, LE, ΔLE/ΔH, and T 0Ts , were all close to observed values. The remaining inconsistency between model and observations—too high a value of H and too low a value of LE over the wet southern end of the track—could be due to HRLDAS ignoring the effect of open water. Neglecting the effect of moist soils on the albedo could also have contributed.

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Joseph G. Alfieri
,
Dev Niyogi
,
Peter D. Blanken
,
Fei Chen
,
Margaret A. LeMone
,
Kenneth E. Mitchell
,
Michael B. Ek
, and
Anil Kumar

Abstract

Vegetated surfaces, such as grasslands and croplands, constitute a significant portion of the earth’s surface and play an important role in land–atmosphere exchange processes. This study focuses on one important parameter used in describing the exchange of moisture from vegetated surfaces: the minimum canopy resistance (r c min ). This parameter is used in the Jarvis canopy resistance scheme that is incorporated into the Noah and many other land surface models. By using an inverted form of the Jarvis scheme, r c min is determined from observational data collected during the 2002 International H2O Project (IHOP_2002). The results indicate that r c min is highly variable both site to site and over diurnal and longer time scales. The mean value at the grassland sites in this study is 96 s m−1 while the mean value for the cropland (winter wheat) sites is one-fourth that value at 24 s m−1. The mean r c min for all the sites is 72 s m−1 with a standard deviation of 39 s m−1. This variability is due to both the empirical nature of the Jarvis scheme and a combination of changing environmental conditions, such as plant physiology and plant species composition, that are not explicitly considered by the scheme. This variability in r c min has important implications for land surface modeling where r c min is often parameterized as a constant. For example, the Noah land surface model parameterizes r c min for the grasslands and croplands types in this study as 40 s m−1. Tests with the coupled Weather Research and Forecasting (WRF)–Noah model indicate that the using the modified values of r c min from this study improves the estimates of latent heat flux; the difference between the observed and modeled moisture flux decreased by 50% or more. While land surface models that estimate transpiration using Jarvis-type relationships may be improved by revising the r c min values for grasslands and croplands, updating the r c min will not fully account for the variability in r c min observed in this study. As such, it may be necessary to replace the Jarvis scheme currently used in many land surface and numerical weather prediction models with a physiologically based estimate of the canopy resistance.

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