Joseph Ardizzone

A Cross-calibrated, Multiplatform Ocean Surface Wind Velocity Product for Meteorological and Oceanographic Applications

Abstract The ocean surface wind mediates exchanges between the ocean and the atmosphere. These air–sea exchange processes are critical for understanding and predicting atmosphere, ocean, and wave phenomena on many time and space scales. A cross-calibrated multiplatform (CCMP) long-term data record of satellite ocean surface winds is available from 1987 to 2008 with planned extensions through 2012. A variational analysis method (VAM) is used to combine surface wind data derived from conventional and in situ sources and multiple satellites into a consistent nearglobal analysis at 25-km resolution, every 6 h. The input data are cross-calibrated wind speeds derived from the Special Sensor Microwave Imager (SSM/I; F08–F15), the Tropical Rainfall Measuring Mission Microwave Imager (TMI), and the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E), and wind vectors from SeaWinds on the NASA Quick Scatterometer (QuikSCAT) and on the second Japanese Advanced Earth Observing Satellite (ADEOS-...

A multiyear global surface wind velocity dataset using SSM/I wind observations

The Special Sensor Microwave Imagers (SSM/I) aboard three DMSP satellites have improved 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 cortex in the Tropics that can be tracked from year to year in annual mean SSM/I wind fields. 11 refs., 5 figs., 2 tabs.

The effects of marine winds from scatterometer data on weather analysis and forecasting

Abstract 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 plann...

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.

Space-based surface wind vectors to aid understanding of air-sea interactions

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 Nnio-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 Administrations Ocean Data System (NODS).

Application of satellite surface wind data to ocean wind analysis

A new set of cross-calibrated, multi-satellite ocean surface wind data is described. The principal data set covers the global ocean for the period beginning in 1987 with six-hour and 25-km resolution, and is produced by combining all ocean surface wind speed observations from SSM/I, AMSR-E, and TMI, and all ocean surface wind vector observations from QuikSCAT and SeaWinds. An enhanced variational analysis method (VAM) performs quality control and combines these data with available conventional ship and buoy data and ECMWF analyses. The VAM analyses fit the data used very closely and contain small-scale structures not present in operational analyses. Comparisons with withheld WindSat observations are also shown to be very good. These data sets should be extremely useful to atmospheric and oceanic research, and to air-sea interaction studies.

Processes controlling dimethylsulfide over the ocean: Case studies using a 3‐D model driven by assimilated meteorological fields

This study investigates the processes that influence dimethylsulfide (DMS) concentrations over the ocean using a global three-dimensional chemistry and transport model (CTM). The model is driven by assimilated meteorological data from the Goddard Earth Observing System Data Assimilation System (GEOS-I DAS). Results from the model are compared with DMS measurements from two marine sites, a ship cruise, and an aircraft campaign. When observed seawater DMS concentrations and meteorological conditions are used, the model reproduces the observed daily and diurnal variations of DMS concentrations at a tropical Pacific station. The model also predicts the observed changes of DMS concentrations across the Atlantic, although it overestimates the DMS level by a factor of 2. The calculated vertical DMS concentrations off Tasmania are more than 4 times higher than the measured data. The model simulates day-to-day fluctuations and interannual variations observed at Amsterdam Island but underpredicts the magnitude of the variations. Sensitivities for DMS concentrations to the parameters used in DMS emission, oxidation, boundary layer mixing, and cloud convective transport are tested. The limitations of the current model in interpreting the observations are due to (1) the uncertainties in parameterization of DMS emission from the ocean, (2) the simplistic boundary layer mixing scheme, (3) the inaccurate spatial distribution and intensity of deep convective clouds in the GEOS-I DAS, and (4) the uncertainties in DMS oxidation rates.

New Multiplatform Ocean Surface Wind Product Available

A new cross-calibrated, multiplatform (CCMP) ocean surface wind product with wide-ranging research applications in meteorology and oceanography became available at the Physical Oceanography Distributed Active Archive Center (PO.DACC) in May 2009. Data sets at three different levels of processing may be downloaded from http://podaac.jpl.nasa.gov/DATA_CATALOG/ccmpinfo.html. The principal data set, denoted as level 3.0, has global ocean coverage (except for the Arctic Ocean) with 25-kilometer resolution every 6 hours for more than 20 years, beginning in July 1987. Applying an enhanced variational analysis method (VAM) to multiple input data sources creates the level 3.0 data set. The VAM performs quality control and optimally combines wind observations from several individual satellite microwave radiometer and scatterometer sensors along with available conventional ship and buoy wind observations and European Centre for Medium-Range Weather Forecasts (ECMWF) analyses.

A comparison of a two‐dimensional variational analysis method and a median filter for NSCAT ambiguity removal

[1] The ocean surface vector wind can be measured from space by scatterometers. For a set of measurements observed from several viewing directions and collocated in space and time, there will usually exist two, three, or four consistent wind vectors. These multiple wind solutions are known as ambiguities. Ambiguity removal procedures select one ambiguity at each location. We compare results of two different ambiguity removal algorithms, the operational median filter (MF) used by the Jet Propulsion Laboratory (JPL) and a two-dimensional variational analysis method (2d-VAR). We applied 2d-VAR to the entire NASA Scatterometer (NSCAT) mission, orbit by orbit, using European Centre for Medium-Range Weather Forecasts (ECMWF) 10-m wind analyses as background fields. We also applied 2d-VAR to a 51-day subset of the NSCAT mission using National Centers for Environmental Prediction (NCEP) 1000-hPa wind analyses as background fields. This second data set uses the same background fields as the MF data set. When both methods use the same NCEP background fields as a starting point for ambiguity removal, agreement is very good: Approximately only 3% of the wind vector cells (WVCs) have different ambiguity selections; however, most of the WVCs with changes occur in coherent patches. Since at least one of the selections is in error, this implies that errors due to ambiguity selection are not isolated, but are horizontally correlated. When we examine ambiguity selection differences at synoptic scales, we often find that the 2d-VAR selections are more meteorologically reasonable and more consistent with cloud imagery.

Error Estimates for Ocean Surface Winds: Applying Desroziers Diagnostics to the Cross-Calibrated, Multiplatform Analysis of Wind Speed

AbstractThe Desroziers diagnostics (DD) are applied to the cross-calibrated, multiplatform (CCMP) ocean surface wind datasets to estimate wind speed errors of the ECMWF background, the microwave satellite observations, and the resulting CCMP analysis. The DD confirm that the ECMWF operational surface wind speed error standard deviations vary with latitude in the range 0.8–1.3 m s−1 and that the cross-calibrated Remote Sensing Systems (RSS) wind speed retrievals’ standard deviations are in the range 0.5–0.7 m s−1. Further, the estimated CCMP analysis wind speed standard deviations are in the range 0.2–0.3 m s−1. The results suggest the need to revise the parameterization of the errors of the first guess at appropriate time (FGAT) procedure. Errors for wind speeds <16 m s−1 are homogeneous; however, for the relatively rare but critical higher wind speed situations, errors are much larger.