Editorial Type:
Article
Article Type:
Research Article

Automated Quality Control of In Situ Soil Moisture from the North American Soil Moisture Database Using NLDAS-2 Products

Youlong Xia National Centers for Environmental Prediction/Environmental Modeling Center, and I. M. Systems Group, College Park, Maryland

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Trent W. Ford Department of Geography, Texas A&M University, College Station, Texas

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Yihua Wu National Centers for Environmental Prediction/Environmental Modeling Center, and I. M. Systems Group, College Park, Maryland

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Steven M. Quiring Department of Geography, Texas A&M University, College Station, Texas

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Michael B. Ek National Centers for Environmental Prediction/Environmental Modeling Center, College Park, Maryland

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Print Publication:
01 Jun 2015
DOI:
https://doi.org/10.1175/JAMC-D-14-0275.1
Page(s):
1267–1282
Restricted access

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.

Corresponding author address: Youlong Xia, IMSG at NCEP/EMC, NOAA Center for Weather and Climate Prediction (NCWCP), 5830 University Research Court, College Park, MD 20740. E-mail: youlong.xia@noaa.gov

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.

Corresponding author address: Youlong Xia, IMSG at NCEP/EMC, NOAA Center for Weather and Climate Prediction (NCWCP), 5830 University Research Court, College Park, MD 20740. E-mail: youlong.xia@noaa.gov
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Cover Journal of Applied Meteorology and Climatology
Journal of Applied Meteorology and Climatology

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