Chemical and biological (CB) agent detection and effective use of these observations in hazard assessment models are
key elements of our nation's CB defense program that seeks to ensure that Department of Defense (DoD) operations are
minimally affected by a CB attack. Accurate hazard assessments rely heavily on the source term parameters necessary
to characterize the release in the transport and dispersion (T&D) simulation. Unfortunately, these source parameters are
often not known and based on rudimentary assumptions. In this presentation we describe an algorithm that utilizes
variational data assimilation techniques to fuse CB and meteorological observations to characterize agent release source
parameters and provide a refined hazard assessment. The underlying algorithm consists of a combination of modeling
systems, including the Second order Closure Integrated PUFF model (SCIPUFF), its corresponding Source Term
Estimation (STE) model, a hybrid Lagrangian-Eulerian Plume Model (LEPM), its formal adjoint, and the software
infrastructure necessary to link them. SCIPUFF and its STE model are used to calculate a "first guess" source estimate.
The LEPM and corresponding adjoint are then used to iteratively refine this release source estimate using variational
data assimilation techniques. This algorithm has undergone preliminary testing using virtual "single realization" plume
release data sets from the Virtual THreat Response Emulation and Analysis Testbed (VTHREAT) and data from the
FUSION Field Trials 2007 (FFT07). The end-to-end prototype of this system that has been developed to illustrate its
use within the United States (US) Joint Effects Model (JEM) will be demonstrated.
By tracking a GPS satellite with an antenna of receivers, it is possible to estimate the difference between the satellite
elevation angle and the actual arrival angle of the transmitted signal in the line of sight of the antenna. Those
measurements are assimilated through the use of a fast ray-tracing observation operator and its adjoint into a high
resolution version of the Weather Research and Forecast model. Such assimilation has the potential to improve the
description and prediction of the local refractivity field, through improved pressure, temperature and humidity, around
We introduce a formalism for computing the Cramer-Rao lower bound (CRLB) for a general dynamical system,
develop an approach to bounding the process noise for a general dynamical system, and discuss the application
of this formalism in the context of a prototypical forecasting model. This model consists of a simple transport
diffusion process with assimilation updates based on point source measurements. We investigate the use of Krylov
subspace techniques for efficient computation of two point correlation functions, and the use of this technique in
generating a coarse-grained state covariance.
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
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.
Thermodynamic profiling provides continuous temperature, humidity and cloud liquid profiles during clear and cloudy conditions. The thermodynamic profiler radiometrically observes microwave radiation intensity at multiple frequencies, along with infrared and surface meteorological measurements. Historical radiosonde and neural network or regression methods are used for profile retrieval. Wind profiling radar provides horizontal winds. We compare radiosonde, thermodynamic and wind soundings to evaluate continuous profiling accuracy. Forecast and observed thermodynamic profiles are also compared. Thermodynamic profiling, particularly when combined with wind profiling radar and advanced assimilation methods, provides continuous soundings needed for improved local high resolution modeling and forecasting. We also describe "slant" observations of integrated GPS signal delay and their potential to extend local forecast improvements to regional scale. Applications include improved forecasting of high resolution dispersion and transport, short-term precipitation and fog.