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  • Cosgrove, Brian  (4)
  • 2000-2004  (4)
  • 1
    Online Resource
    Online Resource
    Evaluation of the North American Land Data Assimilation System over the southern Great Plains during the warm season
    American Geophysical Union (AGU) ; 2003
    In:  Journal of Geophysical Research: Atmospheres Vol. 108, No. D22 ( 2003-11-27)
    Details
    In: Journal of Geophysical Research: Atmospheres, American Geophysical Union (AGU), Vol. 108, No. D22 ( 2003-11-27)
    Abstract: North American Land Data Assimilation System (NLDAS) land surface models have been run for a retrospective period forced by atmospheric observations from the Eta analysis and actual precipitation and downward solar radiation to calculate land hydrology. We evaluated these simulations using in situ observations over the southern Great Plains for the periods of May–September of 1998 and 1999 by comparing the model outputs with surface latent, sensible, and ground heat fluxes at 24 Atmospheric Radiation Measurement/Cloud and Radiation Testbed stations and with soil temperature and soil moisture observations at 72 Oklahoma Mesonet stations. The standard NLDAS models do a fairly good job but with differences in the surface energy partition and in soil moisture between models and observations and among models during the summer, while they agree quite well on the soil temperature simulations. To investigate why, we performed a series of experiments accounting for differences between model‐specified soil types and vegetation and those observed at the stations, and differences in model treatment of different soil types, vegetation properties, canopy resistance, soil column depth, rooting depth, root density, snow‐free albedo, infiltration, aerodynamic resistance, and soil thermal diffusivity. The diagnosis and model enhancements demonstrate how the models can be improved so that they can be used in actual data assimilation mode.
    Type of Medium: Online Resource
    ISSN: 0148-0227
    URL: Article
    DOI: 10.1029/2002JD003245
    Language: English
    Publisher: American Geophysical Union (AGU)
    Publication Date: 2003
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    Online Resource
  • 2
    Online Resource
    Online Resource
    Snow process modeling in the North American Land Data Assimilation System (NLDAS): 1. Evaluation of model‐simulated snow cover extent
    American Geophysical Union (AGU) ; 2003
    In:  Journal of Geophysical Research: Atmospheres Vol. 108, No. D22 ( 2003-11-27)
    Details
    In: Journal of Geophysical Research: Atmospheres, American Geophysical Union (AGU), Vol. 108, No. D22 ( 2003-11-27)
    Abstract: This study evaluates the cold season process modeling in the North American Land Data Assimilation System (NLDAS) and consists of two parts: (1) assessment of land surface model simulations of snow cover extent and (2) evaluation of snow water equivalent. In this first part, simulations of snow cover extent from the four land surface models (Noah, MOSAIC, Sacramento land surface model (SAC), and variable infiltration capacity land surface model (VIC)) in the NLDAS were compared with observational data from the Interactive Multisensor Snow and Ice Mapping System for a 3 year retrospective period over the conterminous United States. In general, all models simulate reasonably well the regional‐scale spatial and seasonal dynamics of snow cover. Systematic biases are seen in the model simulations, with consistent underestimation of snow cover extent by MOSAIC (−19.8% average bias) and Noah (−22.5%), and overestimation by VIC (22.3%), with SAC being essentially unbiased on average. However, the level of bias at the regional scale varies with geographic location and elevation variability. Larger discrepancies are seen over higher elevation regions of the northwest of the United States that may be due, in part, to errors in the meteorological forcings and also at the snow line boundary, where most temporal and spatial variability in snow cover extent is likely to occur. The spread between model simulations is fairly low and generally envelopes the observed data at the mean regional scale, indicating that the models are quite capable of simulating the general behavior of snow processes at these scales. Intermodel differences can be explained to some extent by differences in the model representations of subgrid variability and parameterizations of snow cover extent.
    Type of Medium: Online Resource
    ISSN: 0148-0227
    URL: Article
    DOI: 10.1029/2002JD003274
    Language: English
    Publisher: American Geophysical Union (AGU)
    Publication Date: 2003
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    Online Resource
  • 3
    Online Resource
    Online Resource
    Snow process modeling in the North American Land Data Assimilation System (NLDAS): 2. Evaluation of model simulated snow water equivalent
    American Geophysical Union (AGU) ; 2003
    In:  Journal of Geophysical Research: Atmospheres Vol. 108, No. D22 ( 2003-11-27)
    Details
    In: Journal of Geophysical Research: Atmospheres, American Geophysical Union (AGU), Vol. 108, No. D22 ( 2003-11-27)
    Abstract: This is the second part of a study on the cold season process modeling in the North American Land Data Assimilation System (NLDAS). The first part concentrates on the assessment of model simulated snow cover extent. In this second part, the focus is on the evaluation of simulated snow water equivalent (SWE) from the four land surface models (Noah, MOSAIC, SAC and VIC) in the NLDAS. Comparisons are made with observational data from the Natural Resources Conservation Service's Snowpack Telemetry (SNOTEL) network for a 3‐year retrospective period at selected sites in the mountainous regions of the western United States. All models show systematic low bias in the maximum annual simulated SWE that is most notable in the Cascade and Sierra Nevada regions where differences can approach 1000 mm. Comparison of NLDAS precipitation forcing with SNOTEL measurements revealed a large bias in the NLDAS annual precipitation which may be lower than the SNOTEL record by up to 2000 mm at certain stations. Experiments with the VIC model indicated that most of the bias in SWE is removed by scaling the precipitation by a regional factor based on the regression of the NLDAS and SNOTEL precipitation. Individual station errors may be reduced further still using precipitation scaled to the local station SNOTEL record. Furthermore, the NLDAS air temperature is shown to be generally colder in winter months and biased warmer in spring and summer when compared to the SNOTEL record, although the level of bias is regionally dependent. Detailed analysis at a selected station indicate that errors in the air temperature forcing may cause the partitioning of precipitation into snowfall and rainfall by the models to be incorrect and thus may explain some of the remaining errors in the simulated SWE.
    Type of Medium: Online Resource
    ISSN: 0148-0227
    URL: Article
    DOI: 10.1029/2003JD003994
    Language: English
    Publisher: American Geophysical Union (AGU)
    Publication Date: 2003
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    SSG: 16,13
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    Online Resource
  • 4
    Online Resource
    Online Resource
    Validation of the North American Land Data Assimilation System (NLDAS) retrospective forcing over the southern Great Plains
    American Geophysical Union (AGU) ; 2003
    In:  Journal of Geophysical Research: Atmospheres Vol. 108, No. D22 ( 2003-11-27)
    Details
    In: Journal of Geophysical Research: Atmospheres, American Geophysical Union (AGU), Vol. 108, No. D22 ( 2003-11-27)
    Abstract: Atmospheric forcing used by land surface models is a critical component of the North American Land Data Assimilation System (NLDAS) and its quality crucially affects the final product of NLDAS and our work on model improvement. A three‐year (September 1996–September 1999) retrospective forcing data set was created from the Eta Data Assimilation System and observations and used to run the NLDAS land surface models for this period. We compared gridded NLDAS forcing with station observations obtained from networks including the Oklahoma Mesonet and Atmospheric Radiation Measurement/Cloud and Radiation Testbed at the southern Great Plains. Differences in all forcing variables except precipitation between the NLDAS forcing data set and station observations are small at all timescales. While precipitation data do not agree very well at an hourly timescale, they do agree better at longer timescales because of the way NLDAS precipitation forcing is generated. A small high bias in downward solar radiation and a low bias in downward longwave radiation exist in the retrospective forcing. To investigate the impact of these differences on land surface modeling we compared two sets of model simulations, one forced by the standard NLDAS product and one with station‐observed meteorology. The differences in the resulting simulations of soil moisture and soil temperature for each model were small, much smaller than the differences between the models and between the models and observations. This indicates that NLDAS retrospective forcing provides an excellent state‐of‐the‐art data set for land surface modeling, at least over the southern Great Plains region.
    Type of Medium: Online Resource
    ISSN: 0148-0227
    URL: Article
    DOI: 10.1029/2002JD003246
    Language: English
    Publisher: American Geophysical Union (AGU)
    Publication Date: 2003
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    SSG: 16,13
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    BibTip Others were also interested in ...
    Online Resource
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