Improving the Non-Hydrostatic Numerical Dust Model by Assimilating Different Spatiotemporal Resolutions of Soil Moisture and Greenness Vegetation Fraction Data

Abstract:

Dust storm is one of the most devastating natural disasters that cost billions of dollars and many human lives every year. Improving the simulation accuracy of dust storm models could help better prepare and reduce the cost. Taken the Non-Hydrostatic Numerical Dust Model (NMM-dust) as an example, this paper studies how different spatiotemporal resolutions of two input parameters (soil moisture and greenness vegetation fraction) impact dust model’s sensitivity and accuracy. We used ground-base and satellite observation to validate the temporal evolution and spatial distribution of dust storm output from the NMM-dust. Measurements from four evaluation metrics (the mean bias error, the root mean square error, the correlation coefficient and the fractional gross error) found that the model is highly sensitive to both input parameters and adjusting spatiotemporal resolution of greenness vegetation fraction may increase model error while increasing soil moisture spatiotemporal resolution will decrease the error. And proper chosen of the two parameter’s spatiotemporal resolution will reduce overall model overestimation. Overall, the evaluation result indicates that NMM-dust is able to qualitatively reproduce the observed variations in Aerosol Optical Depth (AOD), while adjusting proper input parameter enables NMM-dust to perform the reproduction quantitatively.Dust storm is one of the most devastating natural disasters that cost billions of dollars and many human lives every year. Improving the simulation accuracy of dust storm models could help better prepare and reduce the cost. Taken the Non-Hydrostatic Numerical Dust Model (NMM-dust) as an example, this paper studies how different spatiotemporal resolutions of two input parameters (soil moisture and greenness vegetation fraction) impact dust model’s sensitivity and accuracy. We used ground-base and satellite observation to validate the temporal evolution and spatial distribution of dust storm output from the NMM-dust. Measurements from four evaluation metrics (the mean bias error, the root mean square error, the correlation coefficient and the fractional gross error) found that the model is highly sensitive to both input parameters and adjusting spatiotemporal resolution of greenness vegetation fraction may increase model error while increasing soil moisture spatiotemporal resolution will decrease the error. And proper chosen of the two parameter’s spatiotemporal resolution will reduce overall model overestimation. Overall, the evaluation result indicates that NMM-dust is able to qualitatively reproduce the observed variations in Aerosol Optical Depth (AOD), while adjusting proper input parameter enables NMM-dust to perform the reproduction quantitatively.