Joseph Terry

Geophysical validation of NSCAT winds using atmospheric data and analyses

A detailed geophysical evaluation of the initial NASA scatterometer (NSCAT) wind data sets was performed in order to determine the error characteristics of these data and their applicability to ocean surface analysis and numerical prediction. The first component of this evaluation consisted of collocations of NSCAT data to ship and buoy wind reports, special sensor microwave imager wind observations, and National Centers for Environmental Prediction and Goddard Earth Observing System (GEOS) model wind analyses. This was followed by data assimilation experiments to determine the impact of NSCAT data on analysis and forecasting. The collocation comparisons showed the NSCAT wind velocity data to be of higher accuracy than operational ERS 2 wind data. The impact experiments showed that NSCAT has the ability to correct major errors in analyses over the oceans and also to improve numerical weather prediction. NSCAT data typically show the precise locations of both synoptic-scale and smaller-scale cyclones and fronts over the oceans. This often results in significant improvements to analyses. Forecast experiments using the GEOS model show approximately a 1-day extension of useful forecast skill in the southern hemisphere, in good agreement with the results of Observing System Simulation Experiments conducted prior to launch.

Geophysical validation of WINDSAT surface wind data and its impact on numerical weather prediction

A detailed evaluation of the latest version of WINDSAT surface wind data has recently been performed to determine the quality of these data and their usefulness for ocean surface wind analysis and numerical weather prediction. The first component of this evaluation consisted of both subjective and objective comparisons of WINDSAT wind vectors to other sources of ocean surface winds (eg. ship and buoy observations, Quikscat satellite winds, or model derived wind analyses). This was followed by data impact experiments using a variational surface wind analysis, as well as an operational four-dimensional data assimilation system. The results of this evaluation demonstrate the usefulness of WINDSAT data, but also show deficiencies relative to current scatterometer measurements.

Potential impact of space-based lidar wind profiles on weather predicition

Observing system simulation experiments (OSSEs) provide an effective means to evaluate the potential impact of a proposed observing system, as well as to determine tradeoffs in their design, and to evaluate data assimilation methodology. Great care must be taken to ensure realism of the OSSEs, and in the interpretation of OSSE results. All of the OSSEs that have been conducted to date have demonstrated tremendous potential for space-based wind profile data to improve atmospheric analyses, forecasts, and research. This has been true for different data assimilation systems, analysis methodology, and model resolutions. OSSEs clearly show much greater potential for observations of the complete wind profile than for single-level wind data or observations of the boundary layer alone.

OSSEs to determine the requirements for space-based lidar winds for weather prediction

Observing system simulation experiments (OSSEs) provide an effective means to evaluate the potential impact of a proposed observing system, as well as to determine tradeoffs in their design, and to evaluate data assimilation methodology. Great care must be taken to ensure realism of the OSSEs, and in the interpretation of OSSE results. All of the OSSEs that have been conducted to date have demonstrated tremendous potential for space-based wind profile data to improve atmospheric analyses, forecasts, and research. This has been true for differing data assimilation systems, analysis methodology, and model resolutions. OSSEs clearly show much greater potential for observations of the complete wind profile than for single-level wind data or observations of the boundary layer alone.

Automated detection of frontal systems from numerical model-generated data

Fronts are significant meteorological phenomena of interest. The extraction of frontal systems from observations and model data can greatly benefit many kinds of research and applications in atmospheric sciences. Due to the huge amount of observational and model data available nowadays, automated extraction of front systems is necessary. This paper presents an automated method to detect frontal systems from numerical model-generated data. In this method, a frontal system is characterized by a vector of features, comprised of parameters derived from the model wind field. K-means clustering is applied to the generated sample set of the feature vectors to partition the feature space and to identify clusters representing the fronts. The probability that a model grid belongs to a front is estimated based on its feature vector. The probability image is generated corresponding to the model grids. A hierarchical thresholding technique is applied to the probability image to identify the frontal systems and a Gaussian Bayes classifier is trained to determine the proper threshold value. This is followed by post processing to filter out false signatures. Experiment results from this method are in good agreement with the ones identified by the domain experts.

Impact of Quikscat Data on Numerical Weather Prediction

The SeaWinds scatterometer (like NSCAT and ERS) is able to detect unequivocal signatures of meteorological features including cyclones, fronts, anticyclones, easterly waves and other precursors of hurricanes and typhoons. Through collaborative efforts between NASA and NOAA, National Weather Service marine forecasters are using SeaWinds data to improve analyses, forecasts and significant weather warnings for maritime interests. This results in substantial economic savings as well as the reduction of weather related loss of life at sea. The impact of SeaWinds on Numerical Weather Prediction models is on average modest but occasionally results in significant forecast improvements.