Dr. Andrew S. Jones - CIRA - Cooperative Institute for Research in the Atmosphere

Dr. Andrew S. Jones

Senior Research Scientist

Office: ACRC Room 002, Fort Collins
Phone: 970-491-8628
Fax: 970-491-8241
Email:

Mailing Address:

Dr. Andrew S. Jones
Cooperative Institute for Research in the Atmosphere
Colorado State University
1375 Campus Delivery
Fort Collins, CO 80523-1375

Professional Website: http://lamar.colostate.edu/~asjones/Jones/default.htm

Biography

Andrew Jones received the B.S. in Physics (minor in Mathematics), summa cum laude with University Honors, at Eastern Illinois Univ.; and M.S. and Ph.D. in Atmospheric Science, at Colorado State University. In the past, he has received 6 academic merit scholarships/fellowships from various organizations including the State of Illinois and the State of Colorado. He has more than 350 publications and reports in the areas of satellite data assimilation, multisensor satellite data merger techniques, satellite spatial filters, 1DVAR water vapor profile retrievals, measurement of microwave surface emissivity for determining surface wetness and radiometric boundary conditions, microwave detection of cloud liquid water and surface properties over land, satellite limb radiance, and radar and satellite intercomparison studies. He serves as the Principle Scientist and Project Leader for the DoD Center for Geosciences / Atmospheric Science Hydrometeorology Research Groups. He is PI on several satellite data assimilation projects and advanced data processing activities, including work and collaborations with NCAR’s WRF data assimilation project, NOAA/NESDIS, the U. S. Air Force Weather Agency (AFWA), the U.S. Army Research Laboratory (ARL), and the U.S. Army Corps of Engineers.

Recent Work

Above: The National Polar-orbiting Operational Environmental Satellite System (NPOESS) will include a Microwave Imager Sounder (MIS) sensor to detect surface soil moisture. Our work extends this capability to deep soil levels by using temporal data assimilation methods. This approach employs the wetting and drying tendencies of the soils to penetrate further into the soil as time progresses. Variational adjoint-based techniques are used to determine the deep soil moisture sensitivities. We have determined that approximately 14 days of adjoint integration are required for deep soil moisture retrievals. The above results are from the four-dimensional variational (4DVAR) Regional Atmospheric Model Data Assimilation System’s (RAMDAS) land surface model (LSM) for Hollis, OK, Sep. 17, 2003 - Oct. 7, 2003, and show normalized adjoint sensitivity results. Deep soil moisture at a depth of approximately 1 m has the largest sensitivity in the integration time period of 0-7 days – thus confirming our abilities to have meaningful deep soil moisture retrieval capabilities using the temporal variational method. This technique is designed to be used operationally by the U.S. Army within the Battlefield Terrain Reasoning and Analysis (BTRA) system and will leverage additional operational U.S. Air Force LSM data sources as a priori information.

Selected Publications

Jones, A. S., T. Vukicevic, and T. H. Vonder Haar, 2004: A microwave satellite observational operator for variational data assimilation of soil moisture. J. Hydrometeorology, 5, 213-229.

Jones, A. S., and T. H. Vonder Haar, 2002: A dynamic parallel data-computing environment for cross-sensor satellite data merger and scientific analysis. J. Atmos. and Oceanic Technol., 19, 1307-1317.

Kidder, S. Q., and A. S. Jones, 2007: Blended satellite products for operational forecasting. J. Atmos. Oceanic Technol., 24, 74-81.

Koyama, T., T. Vukicevic, M. Sengupta, T. H. Vonder Haar, and A. S. Jones 2006: Analysis of information content of IR sounding radiances in cloudy conditions, Mon. Wea. Rev., 134, 3657-3667.

Stephens, P. J., and A. S. Jones, 2007: Geometrical variations of gain patterns, IEEE Trans. Geosci. Remote Sens., 45, 376-382.

Stephens, P. J., and A. S. Jones, 2006: Bounds on the variance in the pattern matching criteria, IEEE Trans. Geosci. Remote Sens., 44, 2514-2522.

Stephens, P. J., and A. S. Jones, 2002: A computationally efficient discrete Backus-Gilbert footprint-matching algorithm. IEEE Trans. Geosci. Remote Sens., 40, 1865-1878.

Vukicevic, T., M. Sengupta, A. S. Jones, and T. H. Vonder Haar, 2006: Cloud-resolving satellite data assimilation: Information content of IR window observations and uncertainties in estimation, J. Atmos. Sci., 63, 901-919.

Vukicevic, T., T. Greenwald, M. Zupanski, D. Zupanski, T. Vonder Haar, and A. S. Jones, 2004: Mesoscale cloud state estimation from visible and infrared satellite radiance. Mon. Wea. Rev., 132, 3066-3077.

General Themes