Utilizing high spatiotemporal resolution soil moisture for dust storm modeling

Manzhu Yu, Chaowei Yang, Qunying Huang, Zhipeng Gui, Jizhe Xia

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publication2013 2nd International Conference on Agro-Geoinformatics
Subtitle of host publicationInformation for Sustainable Agriculture, Agro-Geoinformatics 2013
Pages176-181
Number of pages6
DOIs
Publication statusPublished - Dec 6 2013
Event2013 2nd International Conference on Agro-Geoinformatics: Information for Sustainable Agriculture, Agro-Geoinformatics 2013 - Fairfax, VA, United States
Duration: Aug 12 2013Aug 16 2013

Publication series

Name2013 2nd International Conference on Agro-Geoinformatics: Information for Sustainable Agriculture, Agro-Geoinformatics 2013

Conference

Conference2013 2nd International Conference on Agro-Geoinformatics: Information for Sustainable Agriculture, Agro-Geoinformatics 2013
CountryUnited States
CityFairfax, VA
Period8/12/138/16/13

    Fingerprint

All Science Journal Classification (ASJC) codes

  • Agronomy and Crop Science

Cite this

Yu, M., Yang, C., Huang, Q., Gui, Z., & Xia, J. (2013). Utilizing high spatiotemporal resolution soil moisture for dust storm modeling. In 2013 2nd International Conference on Agro-Geoinformatics: Information for Sustainable Agriculture, Agro-Geoinformatics 2013 (pp. 176-181). [6621903] (2013 2nd International Conference on Agro-Geoinformatics: Information for Sustainable Agriculture, Agro-Geoinformatics 2013). https://doi.org/10.1109/Argo-Geoinformatics.2013.6621903