Dr. Shu-Hua Chen Profile

Dr. Shu-Hua Chen

at Univ of California Davis

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Author

Publications (1)

Proceedings Article | 30 November 2006 Paper

Study of MODIS retrieved total precipitable water data and their impact on weather simulations

Shu-Hua Chen, Zhan Zhao, Jennifer Haase, Aidong Chen, Francois Vandenberghe

Proceedings Volume 6404, 640404 (2006) https://doi.org/10.1117/12.693752

KEYWORDS: MODIS, Global Positioning System, Data modeling, Data corrections, Clouds, Satellites, Infrared imaging, Error analysis, Infrared radiation, Knowledge management

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The comparison between Moderate Resolution Imaging Spectrometer (MODIS) Total Precipitable Water (TPW) and Global Positioning System (GPS) TPW showed that the standard deviation for differences between these two data sets was about 3.3 and 5.2 mm for near-infrared (nIR) and infrared (IR) TPW, respectively. The comparison also showed that there were biases for both retrieved nIR and IR TPW data. The MODIS nIR values were slightly underestimated in a dry atmosphere and overestimated in a moist atmosphere, and the overestimation increased as the column water vapor content increased. This makes it possible to correct the bias associated with these data. The bias correction and trend removal of MODIS nIR TPW reduced the standard deviation of differences from 3.3 mm to about 2 mm. A similar trend of differences between MODIS TPW and radiosonde TPW was also obtained, and a dry bias was found in the radisonde measurements. Two severe weather simulations, a severe thunderstorm (2004) over land and Hurricane Isidore (2002) over ocean, were used to assess the impact of assimilating MODIS nIR TPW data on severe weather simulations. The assimilation of conventional observations alone had a slightly positive impact on both weather simulations. The addition of assimilating original or bias-corrected MODIS TPW had no impact on simulated rainfall for the thunderstorm over the southern US. However, for Hurricane Isidore, MODIS nIR TPW with or without bias correction started influencing the simulated storm intensity positively after a one-day integration. There was almost no impact for the first day of simulation because almost no MODIS data were available due to cloudiness over the storm region and its vicinity. While this work is still too preliminary to draw conclusions on the impact of MODIS TPW on forecast improvement, it shows the type of results that may be expected. When assimilating MODIS TPW, severe weather simulations were improved over ocean but not over land since the quality of global analysis over land is usually better than over ocean. When over ocean, the assimilation of MODIS data can have a positive impact during the early simulation period if cloud-free data are available over the region of interest, while the impact can be delayed to a later simulation period if data are available only away from the region.

Conference Committee Involvement (3)

Remote Sensing and Modeling of the Atmosphere, Oceans, and Interactions III

14 October 2010 | Incheon, Korea, Republic of

Remote Sensing and Modeling of the Atmosphere, Oceans, and Interactions II

19 November 2008 | Noumea, New Caledonia

Remote Sensing and Modeling of the Atmosphere, Oceans, and Interactions

13 November 2006 | Goa, India

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