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Using Remote Sensing and Radar Meteorological Data to Support Watershed Assessments Comprising Integrated Environmental Modeling
Keewook, K., K. Price, G. Whelan, M. Galvin, K. Wolfe, P. Duda, M. Gray, AND Y. Pachepsky. Using Remote Sensing and Radar Meteorological Data to Support Watershed Assessments Comprising Integrated Environmental Modeling. Presented at International Environmental Modelling and Software Society (iEMSs), 7th Intl. Congress on Env. Modelling and Software, San Diego, CA, June 15 - 19, 2014.
For International Environmental Modelling and Software Society (iEMSs), 7th Intl. Congress on Env. Modelling and Software, San Diego, CA, USA Daniel P. Ames, Nigel W.T. Quinn and Andrea E. Rizzoli (Eds.) http://www.iemss.org/society/index.php/iemss-2014-proceedings
Meteorological (MET) data required by watershed assessments comprising Integrated Environmental Modeling (IEM) traditionally have been provided by land-based weather (gauge) stations, although these data may not be the most appropriate for adequate spatial and temporal resolution if MET stations are too few, too far away, or operating improperly. To complement land-based stations, remote sensing and radar satellite data are increasingly being used to obtain synoptic data with the spatial and temporal resolution required for site-specific and/or event-based assessments. This study compares the viability of automating radar satellite data and land-based gauge stations to support MET data collection for IEM applications, especially at locations where gauge stations are inadequate. North American Land Data Assimilation System (NLDAS) and NEXRAD (NEXt generation RADar) Multisensor Precipitation Estimates (MPE) are compared with gauge data at Milwaukee and Manitowoc, Wisconsin USA. NLDAS contains automatic quality control (QC), uses hourly gauge station data and modeled precipitation, provides estimates at hourly intervals with a 1/8 degree resolution, and provides time series at specified locations. MPE contains data QCed by human forecasters, combines radar-based estimates with hourly gauge station data on a 4-km grid, provides spatial data by time increment, and is based on algorithms newer than NLDAS. Results showed excellent correlation between gauge and radar data at Milwaukee, while Manitowoc results strongly suggest using radar over gauge.