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  • Author or Editor: Michael B. Ek x
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Anil Kumar, Fei Chen, Michael Barlage, Michael B. Ek, and Dev Niyogi

Abstract

The impact of 8-day-averaged data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor—namely, the 1-km leaf area index, absorbed photosynthetic radiation, and land-use data—is investigated for use in the Weather Research and Forecasting (WRF) model for regional weather prediction. These high-resolution, near-real-time MODIS data are hypothesized to enhance the representation of land–atmosphere interactions and to potentially improve the WRF model forecast skill for temperature, surface moisture, surface fluxes, and soil temperature. To test this hypothesis, the impact of using MODIS-based land surface data on surface energy and water budgets was assessed within the “Noah” land surface model with two different canopy-resistance schemes. An ensemble of six model experiments was conducted using the WRF model for a typical summertime episode over the U.S. southern Great Plains that occurred during the International H2O Project (IHOP_2002) field experiment. The six model experiments were statistically analyzed and showed some degree of improvement in surface latent heat flux and sensible heat flux, as well as surface temperature and moisture, after land use, leaf area index, and green vegetation fraction data were replaced by remotely sensed data. There was also an improvement in the WRF-simulated temperature and boundary layer moisture with MODIS data in comparison with the default U.S. Geological Survey land-use and leaf area index inputs. Overall, analysis suggests that recalibration and improvements to both the input data and the land model help to improve estimation of surface and soil parameters and boundary layer moisture and led to improvement in simulating convection in WRF runs. Incorporating updated land conditions provided the most notable improvements, and the mesoscale model performance could be further enhanced when improved land surface schemes become available.

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Youlong Xia, Trent W. Ford, Yihua Wu, Steven M. Quiring, and Michael B. Ek

Abstract

The North American Soil Moisture Database (NASMD) was initiated in 2011 to provide support for developing climate forecasting tools, calibrating land surface models, and validating satellite-derived soil moisture algorithms. The NASMD has collected data from over 30 soil moisture observation networks providing millions of in situ soil moisture observations in all 50 states, as well as Canada and Mexico. It is recognized that the quality of measured soil moisture in NASMD is highly variable because of the diversity of climatological conditions, land cover, soil texture, and topographies of the stations, and differences in measurement devices (e.g., sensors) and installation. It is also recognized that error, inaccuracy, and imprecision in the data can have significant impacts on practical operations and scientific studies. Therefore, developing an appropriate quality control procedure is essential to ensure that the data are of the best quality. In this study, an automated quality control approach is developed using the North American Land Data Assimilation System, phase 2 (NLDAS-2), Noah soil porosity, soil temperature, and fraction of liquid and total soil moisture to flag erroneous and/or spurious measurements. Overall results show that this approach is able to flag unreasonable values when the soil is partially frozen. A validation example using NLDAS-2 multiple model soil moisture products at the 20-cm soil layer showed that the quality control procedure had a significant positive impact in Alabama, North Carolina, and west Texas. It had a greater impact in colder regions, particularly during spring and autumn. Over 433 NASMD stations have been quality controlled using the methodology proposed in this study, and the algorithm will be implemented to control data quality from the other ~1200 NASMD stations in the near future.

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