Scatterometer-Based Assessment of 10-m Wind Analyses from the Operational ECMWF and NCEP Numerical Weather Prediction Models

Wind measurements by the National Aeronautics and Space Administration (NASA) scatterometer (NSCAT) and the SeaWinds scatterometer on the NASA QuikSCAT satellite are compared with buoy observations to establish that the accuracies of both scatterometers are essentially the same. The scatterometer measurement errors are best characterized in terms of random component errors, which are about 0.75 and 1.5 m s⁻¹ for the along-wind and crosswind components, respectively. The NSCAT and QuikSCAT datasets provide a consistent baseline from which recent changes in the accuracies of 10-m wind analyses from the European Centre for Medium-Range Weather Forecasts (ECMWF) and the U.S. National Centers for Environmental Prediction (NCEP) operational numerical weather prediction (NWP) models are assessed from consideration of three time periods: September 1996–June 1997, August 1999–July 2000, and February 2002–January 2003. These correspond, respectively, to the 9.5-month duration of the NSCAT mission, the first 12 months of the QuikSCAT mission, and the first year after both ECMWF and NCEP began assimilating QuikSCAT observations. There were large improvements in the accuracies of both NWP models between the 1997 and 2000 time periods. Though modest in comparison, there were further improvements in 2002, at least partly attributable to the assimilation of QuikSCAT observations in both models. There is no evidence of bias in the 10-m wind speeds in the NCEP model. The 10-m wind speeds in the ECMWF model, however, are shown to be biased low by about 0.4 m s⁻¹. While it is difficult to eliminate systematic errors this small, a bias of 0.4 m s⁻¹ corresponds to a typical wind stress bias of more than 10%. This wind stress bias increases to nearly 20% if atmospheric stability effects are not taken into account. Biases of these magnitudes will result in significant systematic errors in ocean general circulation models that are forced by ECMWF winds

Sampling Errors in Wind Fields Constructed from Single and Tandem Scatterometer Datasets

Sampling patterns and sampling errors from various scatterometer datasets are examined. Four single and two tandem scatterometer mission scenarios are considered. The single scatterometer missions are ERS (with a single, narrow swath), NSCAT and ASCAT (dual swaths), and QuikSCAT (a single, broad swath obtained from the SeaWinds instrument). The two tandem scenarios are combinations of the broad-swath SeaWinds scatterometer with ASCAT and QuikSCAT. The dense, nearly uniform distribution of measurements within swaths, combined with the relatively sparse, nonuniform placement of the swaths themselves create complicated space–time sampling patterns. The temporal sampling of all of the missions is characterized by bursts of closely spaced samples separated by longer gaps and is highly variable in both latitude and longitude. Sampling errors are quantified by the expected squared bias of particular linear estimates of component winds. Modifications to a previous method that allow more efficient expected squared bias calculations are presented and applied. Sampling errors depend strongly on both the details of the temporal sampling of each mission and the assumed temporal scales of variability in the wind field but are relatively insensitive to different spatial scales of variability. With the exception of ERS, all of the scatterometer scenarios can be used to make low-resolution (3° and 12 days) wind component maps with errors at or below the 1 m s⁻¹ level. Only datasets from the broad-swath and tandem mission scenarios can be used for higher-resolution maps with similar levels of error, emphasizing the importance of the improved spatial and temporal coverage of those missions. A brief discussion of measurement errors concludes that sampling error is generally the dominant term in the overall error budget for maps constructed from scatterometer dataset

Satellite Observations of the Wind Jets off the Pacific Coast of Central America. Part I: Case Studies and Statistical Characteristics

Measurements of near-surface winds by the NASA scatterometer (NSCAT) from October 1996 through June 1997 are analyzed to investigate the three major wind jets along the Pacific coast of Central America that blow over the Gulfs of Tehuantepec, Papagayo, and Panama. Each jet is easily identifiable as locally intense offshore winds in the lee of low-elevation gaps through the Sierra Madre mountain range. The jets have relatively narrow cross-stream width but often extend several hundred kilometers or more into the Pacific. The Tehuantepec and Papagayo jets sometimes merge with the northeast trade winds of the Pacific. The Tehuantepec jet was highly energetic with characteristic timescales of about 2 days. Events were triggered by high pressures associated with cold surges into the Gulf of Mexico that originated over the Great Plains of North America. The Papagayo and Panama jets were much more persistent than the Tehuantepec jets. The winds at both of these lower-latitude locations exhibited a strong seasonal variation with almost exclusively offshore flow from late November 1996 through late May 1997 and periods of onshore flow in October and November during the late stages of the 1996 Central American monsoon season. Superimposed on this low-frequency seasonal variation were events with characteristic timescales of a few days. Based on NSCAT data, the spatial and temporal evolution of major wind events is described in detail for three representative case studies. In December 1996, the jets developed sequentially from north to south, consistent with the notion that wind events in the two lower-latitude jets are associated with cold-air outbreaks that trigger the Tehuantepec jet a day or so earlier. In November 1996 and March 1997, the Papagayo and Panama jets were strongly influenced by tropical phenomena that had little apparent association with the Tehuantepec jet. These latter two case studies, together with the distinction between the statistical characteristics of the three jets, suggest that the Papagayo and Panama jets are predominantly controlled by a mechanism that is very different from the across-gap pressure gradients associated with high pressure systems of midlatitude origin that control the Tehuantepec jet

Satellite Observations of the Wind Jets off the Pacific Coast of Central America. Part II: Regional Relationships and Dynamical Considerations

Satellite estimates of winds at 10 m above the sea surface by the NASA scatterometer (NSCAT) during the 9-month period October 1996–June 1997 are analyzed to investigate the correlations between the three major wind jets along the Pacific coast of Central America and their relationships to the wind and pressure fields in the Inter-American Seas and eastern tropical Pacific. Comparisons with sea level pressure confirm the conventional view that Tehuantepec wind variations are driven by pressure variations in the Gulf of Mexico associated with North American cold-air outbreaks. The three jets sometimes developed sequentially from north to south. Statistically, however, the Papagayo and Panama jets were poorly correlated with variations of the Tehuantepec jet over the NSCAT observational period. The Papagayo and Panama jets were significantly correlated with each other and were coupled to coherent variations of the trade winds extending from the Caribbean Sea to the eastern tropical Pacific. The detailed structures of the wind fields within the three jets are examined to infer dynamical balances within the jets. After leaving the coast, the northerly Tehuantepec and Panama jets turn anticyclonically toward the west in manners that are consistent with jets that are inertially balanced at the coast and become progressively more geostrophically balanced with increasing distance from the coast. There is no evidence of anticyclonic turning of the easterly Papagayo jet, suggesting that the winds may remain in approximate geostrophic balance through the gap over the Nicaraguan lake district. NSCAT observations are compared with operational analyses by ECMWF to investigate the detailed structures of the wind fields over the Gulfs of Tehuantepec, Papagayo, and Panama. Systematic differences between the NSCAT observations and the ECMWF analyses of the divergent off-axis fanning of all three jets suggest that there may be systematic errors in parameterizations of boundary layer processes in the ECMWF “first-guess” fields in these data-sparse regions

On the Use of QuikSCAT Scatterometer Measurements of Surface Winds for Marine Weather Prediction

The value of Quick Scatterometer (QuikSCAT) measurements of 10-m ocean vector winds for marine weather prediction is investigated from two Northern Hemisphere case studies. The first of these focuses on an intense cyclone with hurricane-force winds that occurred over the extratropical western North Pacific on 10 January 2005. The second is a 17 February 2005 example that is typical of sea surface temperature influence on low-level winds in moderate wind conditions in the vicinity of the Gulf Stream in the western North Atlantic. In both cases, the analyses of 10-m winds from the NCEP and ECMWF global numerical weather prediction models considerably underestimated the spatial variability of the wind field on scales smaller than 1000 km compared with the structure determined from QuikSCAT observations. The NCEP and ECMWF models both assimilate QuikSCAT observations. While the accuracies of the 10-m wind analyses from these models measurably improved after implementation of the QuikSCAT data assimilation, the information content in the QuikSCAT data is underutilized by the numerical models. QuikSCAT data are available in near–real time in the NOAA/NCEP Advanced Weather Interactive Processing System (N-AWIPS) and are used extensively in manual analyses of surface winds. The high resolution of the QuikSCAT data is routinely utilized by forecasters at the NOAA/NCEP Ocean Prediction Center, Tropical Prediction Center, and other NOAA weather forecast offices to improve the accuracies of wind warnings in marine forecasts