Dr. Francois C. Vandenberghe Profile

Dr. Francois C. Vandenberghe

at National Ctr for Atmospheric Research

SPIE Involvement:

Author

Publications (5)

Proceedings Article | 7 June 2011 Paper

Fusion of chemical, biological, and meteorological observations for agent source term estimation and hazard refinement

Paul Bieringer, Luna Rodriguez, Ian Sykes, Jonathan Hurst, Francois Vandenberghe, Jeffrey Weil, George Bieberbach, Steve Parker, Ryan Cabell

Proceedings Volume 8064, 80640H (2011) https://doi.org/10.1117/12.884221

KEYWORDS: Solar thermal energy, Atmospheric modeling, Sensors, Systems modeling, Environmental sensing, Data modeling, Chemical analysis, Data fusion, Biological research, Defense and security

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Proceedings Article | 13 September 2007 Paper

Assimilation of angle of arrival measurements from an antenna of GPS receivers in the WRF model

F. Vandenberghe, Clayton Kerce, Robert Bock

Proceedings Volume 6685, 66850A (2007) https://doi.org/10.1117/12.740690

KEYWORDS: Global Positioning System, Antennas, Receivers, Atmospheric modeling, Data modeling, Satellites, Atmospheric propagation, Earth's atmosphere, Humidity, Refraction

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Proceedings Article | 12 September 2007 Paper

A lower bound for prediction uncertainty in nowcasting/forecasting models

J. Clayton Kerce, Francois Vandenberghe

Proceedings Volume 6685, 668505 (2007) https://doi.org/10.1117/12.740665

KEYWORDS: Dynamical systems, Filtering (signal processing), Electronic filtering, Differential equations, Matrices, Atmospheric modeling, Diffusion, Correlation function, Sensors, Weather forecasting

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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.

Proceedings Article | 30 April 2003 Paper

Thermodynamic and wind profiling for high-resolution dispersion, precipitation, and fog forecasting

Randolph Ware, Fredrick Solheim, Francois Vandenberghe

Proceedings Volume 4894, (2003) https://doi.org/10.1117/12.466277

KEYWORDS: Thermodynamics, Profiling, Fiber optic gyroscopes, Global Positioning System, Clouds, Radiometry, Liquids, Humidity, Neural networks, Radar

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