TY - GEN
T1 - Utilizing high spatiotemporal resolution soil moisture for dust storm modeling
AU - Yu, Manzhu
AU - Yang, Chaowei
AU - Huang, Qunying
AU - Gui, Zhipeng
AU - Xia, Jizhe
PY - 2013
Y1 - 2013
N2 - The resolution and accuracy of initial model input are two fundamental factors for numerical modeling of climate and weather. High quality initial input assimilated in the model has significant impact on dust storm forecasting accuracy, and hence significantly influences the effectiveness of public health services and emergency management. Previous work with the Non-hydrostatic Mesoscale Model (NMM-dust) has been using static input data for parameters like soil moisture content. Since these parameters are changing seasonally or even daily, static input will reduce the model accuracy. This research investigates the sensitivity of the NMM-dust model in response to dynamic inputs of soil moisture, and evaluates the improvement of the model accuracy. The soil moisture data used for this research is generated by the Noah LSM, part of the North American Land Data Assimilation System (NLDAS) modeling suite. Numerical analysis is conducted by comparing simulation results using near-real-time soil moisture data with original model output using static one and MODIS Aqua atmosphere product in Deep Blue band.
AB - The resolution and accuracy of initial model input are two fundamental factors for numerical modeling of climate and weather. High quality initial input assimilated in the model has significant impact on dust storm forecasting accuracy, and hence significantly influences the effectiveness of public health services and emergency management. Previous work with the Non-hydrostatic Mesoscale Model (NMM-dust) has been using static input data for parameters like soil moisture content. Since these parameters are changing seasonally or even daily, static input will reduce the model accuracy. This research investigates the sensitivity of the NMM-dust model in response to dynamic inputs of soil moisture, and evaluates the improvement of the model accuracy. The soil moisture data used for this research is generated by the Noah LSM, part of the North American Land Data Assimilation System (NLDAS) modeling suite. Numerical analysis is conducted by comparing simulation results using near-real-time soil moisture data with original model output using static one and MODIS Aqua atmosphere product in Deep Blue band.
UR - http://www.scopus.com/inward/record.url?scp=84888877207&partnerID=8YFLogxK
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U2 - 10.1109/Argo-Geoinformatics.2013.6621903
DO - 10.1109/Argo-Geoinformatics.2013.6621903
M3 - Conference contribution
AN - SCOPUS:84888877207
SN - 9781479908684
T3 - 2013 2nd International Conference on Agro-Geoinformatics: Information for Sustainable Agriculture, Agro-Geoinformatics 2013
SP - 176
EP - 181
BT - 2013 2nd International Conference on Agro-Geoinformatics
T2 - 2013 2nd International Conference on Agro-Geoinformatics: Information for Sustainable Agriculture, Agro-Geoinformatics 2013
Y2 - 12 August 2013 through 16 August 2013
ER -