A three‐dimensional (3‐D) Global Assimilative Ionospheric Model (GAIM) is currently being developed by a joint University of Southern California and Jet Propulsion Laboratory (JPL) team. To estimate the electron density on a global grid, GAIM uses a first‐principles ionospheric physics model and the Kalman filter as one of its possible estimation techniques. Because of the large dimension of the state (i.e., electron density on a global 3‐D grid), implementation of a full Kalman filter is not computationally feasible. Of the possible suboptimal implementations of the Kalman filter, we have chosen a band‐limited Kalman filter where a full time propagation of the state error covariance is performed, but it is always kept sparse and banded. The effectiveness of ground GPS data for specifying the ionosphere is assessed by assimilating slant total electron content (TEC) data from 98 sites into the GAIM Kalman filter and validating the electron density field against independent measurements. A series of GAIM analyses are presented and validated by comparisons to JPL's global ionospheric maps (GIM) of vertical TEC (VTEC) and measurements from TOPEX. A statistical evaluation of GAIM and GIM against TOPEX VTEC indicates that GAIM accuracy is comparable or superior to GIM.

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[1] The Jet Propulsion Laboratory/University of Southern California Global Assimilation Ionospheric Model (JPL/USC GAIM) uses two data assimilation techniques to optimally combine ionospheric measurements with the physics model: a sparse, traditional Kalman filter to estimate the three-dimensional density state, and a four-dimensional variational approach (4DVAR) to estimate ionospheric drivers such as the equatorial E Â B drift or neutral winds. In this paper we study a specific implementation of the JPL/USC GAIM Kalman filter (single ion, low-resolution, and input data from 200 ground GPS sites) and validate its global accuracy over 137 days by comparisons to independent GPS slant total electron content (TEC) observations (''missing site'' tests) and independent JASON vertical TEC observations. The assimilation accuracy is robust with a slant TEC spatial prediction RMS error of 4 TECU (Total Electron Content Unit, 1 Â 10 16 e-/m 2 ) on average and a vertical TEC JASON RMS error of 7 TECU. Removing what appears to be a positive %4.4 TECU bias from the JASON observations, we obtain an improved performance of 5.3 TECU over the oceans. Comparisons with a single, thin shell global ionospheric map model and the International Reference Ionosphere and Bent ionospheric models are also provided.

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Biosensor measurement of transdermal alcohol oncentration in perspiration exhibits significant variance from subject to subject and device to device. Short duration data collected in a controlled clinical setting is used to calibrate a forward model for ethanol transport from the blood to the sensor. The calibrated model is then used to invert transdermal signals collected in the field (short or long duration) to obtain an estimate for breath measured blood alcohol concentration. A distributed parameter model for the forward transport of ethanol from the blood through the skin and its processing by the sensor is developed. Model calibration is formulated as a nonlinear least squares fit to data. The fit model is then used as part of a spline based scheme in the form of a regularized, non-negatively constrained linear deconvolution. Fully discrete, steepest descent based schemes for solving the resulting optimization problems are developed. The adjoint method is used to accurately and efficiently compute requisite gradients. Efficacy is demonstrated on subject field data.

The goal of the Multimodel Ensemble Prediction System (MEPS) program is to improve space weather specification and forecasting with ensemble modeling. Space weather can have detrimental effects on a variety of civilian and military systems and operations, and many of the applications pertain to the ionosphere and upper atmosphere. Space weather can affect over‐the‐horizon radars, HF communications, surveying and navigation systems, surveillance, spacecraft charging, power grids, pipelines, and the Federal Aviation Administration (FAA's) Wide Area Augmentation System (WAAS). Because of its importance, numerous space weather forecasting approaches are being pursued, including those involving empirical, physics‐based, and data assimilation models. Clearly, if there are sufficient data, the data assimilation modeling approach is expected to be the most reliable, but different data assimilation models can produce different results. Therefore, like the meteorology community, we created a Multimodel Ensemble Prediction System (MEPS) for the Ionosphere‐Thermosphere‐Electrodynamics (ITE) system that is based on different data assimilation models. The MEPS ensemble is composed of seven physics‐based data assimilation models for the ionosphere, ionosphere‐plasmasphere, thermosphere, high‐latitude ionosphere‐electrodynamics, and middle to low latitude ionosphere‐electrodynamics. Hence, multiple data assimilation models can be used to describe each region. A selected storm event that was reconstructed with four different data assimilation models covering the middle and low latitude ionosphere is presented and discussed. In addition, the effect of different data types on the reconstructions is shown.

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