Validating Dust Storm Model Using Satellite Aerosol Retrievals and Ground-based Observations
Abstract
Dust storm is a meteorological phenomenon with high dust concentration and strong striking force affecting transportation and causing disease. Considering the negative impacts of dust storm, the accuracy of dust storm forecasting is critical for, especially, responding to the emergencies. However, it is challenge to validating the forecasting limited by availability of observation data. The complexity is partially caused by the inconsistency in spatial and temporal resolutions between model simulation and field observation. In addition, the accuracy and reliability of observation data are not guaranteed. Therefore, in order to complement observation data in terms of temporal resolution and enhance the accuracy of observation data, validation methods should be based on data assimilation between satellite and ground-based observations. The dust storm simulation and forecasting model, NMM-dust, coupling Dust Regional Atmospheric Model (DREAM) and Non-hydrostatic Mesoscale Model (NMM) meteorological module, produces higher resolution results for weather forecasting and enabling executability in parallel mode on distributed systems. So far, NMM-dust has been validated in the southwestern US only by comparison with measurement from AIRNOW data and with barely acceptable results. Observation data used for validation includes MODIS and SeaWiFS Deep Blue aerosol products, and ground-based observations from EPA-AQS. Results from comparisons between satellite data and model output show similar dust distribution patterns. Besides, the temporal resolution of satellite data is improved by using both MODIS and SeaWiFS. Quantitative analysis including time-series analysis and diagnose analysis are also examined to investigate the stability and consistency of the model.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2013
- Bibcode:
- 2013AGUFMIN31B1506Y
- Keywords:
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- 0305 ATMOSPHERIC COMPOSITION AND STRUCTURE Aerosols and particles;
- 1627 GLOBAL CHANGE Coupled models of the climate system;
- 1910 INFORMATICS Data assimilation;
- integration and fusion;
- 4315 NATURAL HAZARDS Monitoring;
- forecasting;
- prediction