The IAU (incremental analysis updating) process incorporates analysis increments into a model integration in a gradual manner. It does this by using analysis increments as constant forcings in a model's prognostic equations over a 6-h period centered on an analysis time. A linear analysis of the IAU procedure shows it to have the attractive properties of a low-pass time filter. The IAU process affects the response of the model to the analysis increments, and it leaves the model state unaffected where there were no data to assimilate. This result is contrasted with a simple dynamical relaxation (or "nudging") scheme, which is shown, in this linear analysis, to have less desirable response characteristics, both from the analysis increments and from the background state of the model. The behavior of IAU in the context of the Goddard Earth Observing System (GEOS) Data Assimilation System is examined using a combination of large-scale diagnostics from month-long assimilations and derailed diagnostics from short assimilations. These studies indicate that IAU assimilations have improved observed-minus-forecast statistics and improved globally averaged precipitation by removing spinup effects. The detailed diagnostics of the behavior of the GEOS system with IAU corroborate the results of the linear analysis of the response behavior of IAU.

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.

Scatterometer observations of the ocean surface wind speed and direction improve the depiction and prediction of storms at sea. These data are especially valuable where observations are otherwise sparse-mostly in the Southern Hemisphere and tropics, but also on occasion in the North Atlantic and North Pacific. The Sea Winds scatterometer on the QuikScat satellite was launched in June 1999 and it represents a dramatic departure in design from the other scatterometer instruments launched during the past decade (ERS-1,2 and NSCAT). This paper will be limited to results from the SeaWinds scatterometer on Quikscat. This presentation shows the influence of QuikScat data in data assimilation systems both from the NASA Data Assimilation Office (GEOS-3) and from NCEP (GDAS). The strategy for assessing the impact of SeaWinds in NWP was largely described and parallels the approach used for the geophysical validation of NSCAT data.

In this study, a two-dimensional variational analysis method (2DVAR) is applied to select a wind solution from NASA Scatterometer (NSCAT) ambiguous winds. A 2DVAR method determines a "best'' gridded surface wind analysis by minimizing a cost function. The cost function measures the misfit to the observations, the background, and the filtering and dynamical constraints. The ambiguity closest in direction to the minimizing analysis is selected. The 2DVAR method, sensitivity, and numerical behavior are described. 2DVAR is used with both NSCAT ambiguities and NSCAT backscatter values. Results are roughly comparable. When the background field is poor, 2DVAR ambiguity removal often selects low probability ambiguities. To avoid this behavior, an initial 2DVAR analysis, using only the two most likely ambiguities, provides the first guess for an analysis using all the ambiguities or the backscatter data. 2DVAR and median filter-selected ambiguities usually agree. Both methods require horizontal consistency, so disagreements occur in clumps, or as linear features. In these cases, 2DVAR ambiguities are often more meteorologically reasonable and more consistent with satellite imagery.

Scatterometer observations of the ocean surface wind speed and direction improve the depiction and prediction of storms at sea. These data are especially valuable where observations are otherwise sparse, mostly in the Southern Hemisphere and tropics, but also on occasion in the North Atlantic and North Pacific The SeaWinds scatterometer on the QuikScat satellite was launched in June 1999 and it represents a dramatic departure in design from the other scatterometer instruments launched during the past decade (ERS-1,2 and NSCAT). More details on the SeaWinds instrument can be found in Atlas et al. (2001) and Bloom et al. (1999). This presentation shows the influence of QuikScat data in data assimilation systems both from the NASA Data Assimilation Office (GEOS-3) and from NCEP (GDAS).

Satellite scatterometer observations of the ocean surface wind speed and direction improve the depiction of storms at sea. Over the ocean, scatterometer surface winds are deduced from multiple measurements of reflected radar power made from several directions. In the nominal situation, the scattering mechanism is Bragg scattering from centimeter-scale waves, which are in equilibrium with the local wind. These data are especially valuable where observations are otherwise sparse-mostly in the Southern Hemisphere extratropics and Tropics, but also on occasion in the North Atlantic and North Pacific. The history of scatterometer winds research and its application to weather analysis and forecasting is reviewed here. Two types of data impact studies have been conducted to evaluate the effect of satellite data, including satellite scatterometer data, for NWP. These are simulation experiments (or observing system simulation experiments or OSSEs) designed primarily to assess the potential impact of planned satellite observing systems, and real data impact experiments (or observing system experiments or OSEs) to evaluate the actual impact of available space-based data. Both types of experiments have been applied to the series of satellite scatterometers carried on the Seasat, European Remote Sensing-1 and -2, and the Advanced Earth Observing System-1 satellites, and the NASA Quick Scatterometer. Several trends are evident: The amount of scatterometer data has been increasing. The ability of data assimilation systems and marine forecasters to use the data has improved substantially. The ability of simulation experiments to predict the utility of new sensors has also improved significantly.

The first SeaWinds scatterometer was launched in to space aboard the Quikscat satellite on June 19, 1999 at 7:15 p.m. PDT. Flying in a near polar orbit 800 km above the earth's surface, SeaWinds uses an advanced scatterometer design to measure surface wind velocity over 90 percent of the ice free oceans ever 24 hours. This first SeaWinds mission is designed to replace the NASA Scatterometer (NSCAT) which ceased providing wind velocity data when the ADEOS I satellite failed. A second SeaWinds is scheduled to be launched late in 2000 aboard ADEOS II. Previous scatterometer assimilation experiments conducted by the NASA Data Assimilation Office, using both ERS and NSCAT wind observations, have demonstrated considerable potential for this type of data to improve both atmospheric analyses and forecasts, however much of the smaller scale information content of the scatterometer data could not be taken into account in the early coarse resolution versions of the Goddard (GEOS) Data Assimilation System (DAS) or in operational data assimilation systems. In this paper, we will describe data assimilation experiments in which the new higher resolution versions of the GOES DAS are used to assimilate SeaWinds scatterometer winds. Following a brief discussion of the SeaWinds design and the methodology used to assimilate scatterometer data in the GOES DAS, the quality of the SeaWinds data and the impact of SeaWinds on GOES analyses and forecasts at different resolutions will be presented.

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.

The Special Sensor Microwave Imagers (SSM/I) aboard three DMSP satellites have provided a large dataset of surface wind speeds over the global oceans from July 1987 to the present. These data are characterized by high resolution, coverage, and accuracy, but their application has been limited by the lack of directional information. In an effort to extend the applicability of these data, methodology has been developed to assign directions to the SSM/I wind speeds and to produce analyses using these data. Following extensive testing, this methodology has been used to generate a seven and one-half year dataset (from July 1987 through December 1994) of global SSM/I wind vectors. These data are currently being used in a variety of atmospheric and oceanic applications and are available to interested investigators. Recent results presented in this paper show the accuracy of the SSM/I wind velocities, the ability of these data to improve surface wind analyses, and the propagation of a synoptic-scale convergent vol tex in the Tropics that can be tracked from year to year in annual mean SSM/I wind fields.

This report describes the analysis component of the Goddard Earth Observing System, Data Assimilation System, Version 1 (GEOS-1 DAS). The general features of the data assimilation system are outlined, followed by a thorough description of the statistical interpolation algorithm, including specification of error covariances and quality control of observations. We conclude with a discussion of the current status of development of the GEOS data assimilation system. The main components of GEOS-1 DAS are an atmospheric general circulation model and an Optimal Interpolation algorithm. The system is cycled using the Incremental Analysis Update (IAU) technique in which analysis increments are introduced as time independent forcing terms in a forecast model integration. The system is capable of producing dynamically balanced states without the explicit use of initialization, as well as a time-continuous representation of non- observables such as precipitation and radiational fluxes. This version of the data assimilation system was used in the five-year reanalysis project completed in April 1994 by Goddard's Data Assimilation Office (DAO) Data from this reanalysis are available from the Goddard Distributed Active Center (DAAC), which is part of NASA's Earth Observing System Data and Information System (EOSDIS). For information on how to obtain these data sets, contact the Goddard DAAC at (301) 286-3209, EMAIL daac@gsfc.nasa.gov.

Our understanding and prediction of the large‐scale air‐sea interactions that are thought to significantly influence both the atmosphere and ocean can be improved by consistent oceanic surface wind data of high quality and high temporal and spatial resolution. Surface wind stress provides the most important forcing of the ocean circulation and the fluxes of heat, moisture, and momentum across the air‐sea boundary are important factors in theories of El Nñio‐Southern Oscillation (ENSO) and the 50‐day oscillation. Unfortunately, an adequate observational data base to perform such studies has been lacking.In this paper, we describe a new and unique ocean surface wind data set derived by combining the Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave Imager (SSM/I) data with other conventional data, presenting both the methodology and some examples of the results. We are currently using these data in several studies, as discussed in the conclusion, and are preparing a more detailed description of the development and testing of our algorithms. These data are available through the National Aeronautics and Space Administration's Ocean Data System (NODS).