A study of the feasibility of using sea and wind information from the ERS-1 satellite. Part 1: Wind scatterometer data

The use of scatterometer and altimeter data in wind and wave assimilation, and the benefits this offers for quality assurance and validation of ERS-1 data were examined. Real time use of ERS-1 data was simulated through assimilation of Seasat scatterometer data. The potential for quality assurance and validation is demonstrated by documenting a series of substantial problems with the scatterometer data, which are known but took years to establish, or are new. A data impact study, and an analysis of the performance of ambiguity removal algorithms on real and simulated data were conducted. The impact of the data on analyses and forecasts is large in the Southern Hemisphere, generally small in the Northern Hemisphere, and occasionally large in the Tropics. Tests with simulated data give more optimistic results than tests with real data. Errors in ambiguity removal results occur in clusters. The probabilities which can be calculated for the ambiguous wind directions on ERS-1 contain more information than is given by a simple ranking of the directions

QuikSCAT Satellite Comparisons with Nearshore Buoy Wind Data off the U.S. West Coast

To determine the accuracy of nearshore winds from the QuikSCAT satellite, winds from three satellite datasets (scientifically processed swath, gridded near-real-time, and gridded science datasets) were compared to those from 12 nearshore and 3 offshore U.S. West Coast buoys. Satellite observations from August 1999 to December 2000 that were within 25 km and 30 min of each buoy were used. Comparisons showed that satellite–buoy wind differences near shore were larger than those offshore. Editing the satellite data by discarding observations recorded in rain and those recorded in light winds improved the accuracy of all three datasets. After removing rain-flagged data and wind speeds less than 3 m s21, root-mean-squared differences (satellite minus buoy) for swath data, the best of the three datasets, were 1.4 m s21 and 378 based on 5741 nearshore comparisons. By removing winds less than 6 m s21, these differences were reduced to 1.3 m s21 and 268. At the three offshore buoys, the root-mean-squared differences for the swath data, with both rain and winds less than 6 m s21 removed, were 1.0 m s21 and 158 based on 1920 comparisons. Although the satellite’s scientifically processed swath data near shore do not match buoy observations as closely as those offshore, they are sufficiently accurate for many coastal studies

Systematic Scatterometer Wind Errors Near Coastal Mountains.

Satellite scatterometers provide the only regular observations of surface wind vectors over vast swaths of the world oceans, including coastal regions, which are of great scientific and societal interest but still present challenges for remote sensing. Here we demonstrate systematic scatterometer wind errors near Hawaii's Big Island: Two counter-rotating lee vortices, which are clear in the International Comprehensive Ocean-Atmosphere Data Set ship-based wind climatology and in aircraft observations, are absent in the Jet Propulsion Laboratory and Remote Sensing Systems scatterometer wind climatologies. We demonstrate similar errors in the representation of transient Catalina Eddy events in the Southern California Bight. These errors likely arise from the nonuniqueness of scatterometer wind observations, that is, an "ambiguity removal" is required during processing to select from multiple wind solutions to the geophysical model function. We discuss strategies to improve the ambiguity selection near coastal mountains, where small-scale wind reversals are common

NSCAT high-resolution surface wind measurements in Typhoon Violet

NASA scatterometer (NSCAT) measurements of the western Pacific Supertyphoon Violet are presented for revolutions 478 and 485 that occurred in September 1996. A tropical cyclone planetary boundary layer numerical, model, which uses conventional meteorological and geostationary cloud data, is used to estimate the winds at 10-m elevation in the cyclone. These model winds are then compared with the winds inferred from the NSCAT backscatter data by means of a novel approach that allows a wind speed to be recovered from each individual backscatter cell. This spatial adaptive (wind vector) retrieval algorithm employs several unique steps. The backscatter values are first regrouped in terms of closest neighbors in sets of four. The maximum likelihood estimates of speed and direction are then used to obtain speeds and directions for each group. Since the cyclonic flow around the tropical cyclone is known, NSCAT wind direction alias selection is easily accomplished. The selected wind directions are then used to convert each individual backscatter value to a wind speed. The results are compared to the winds obtained from the tropical cyclone boundary layer model. The NSCAT project baseline geophysical model function, NSCAT 1, was found to yield wind speeds that were systematically too low, even after editing for suspected rain areas of the cyclone. A new geophysical model function was developed using conventional NSCAT data and airborne Ku band scatterometer measurements in an Atlantic hurricane. This new model uses the neural network method and yields substantially better agreement with the winds obtained from the boundary layer model according to the statistical tests that were used

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

Assessment and Analysis of QuikSCAT and COAMPS Model Vector Wind Products for the Gulf of Mexico: A Long-Term and Hurricane Perspective

Global weather changes have become a matter of grave concern in hurricane prone areas as intensities of hurricanes are observed to be increasing every year, necessitating improved monitoring capabilities. NASA’s QuikSCAT satellite sensor has provided significant support in analyzing and forecasting winds for the past 8 years. In this study, the performance of QuikSCAT products, including JPL’s latest L2B 12.5km swath winds, was evaluated against buoy-measured winds in the Gulf of Mexico. The long-term study period was 1/2005 – 2/2007. The Coupled Ocean/Atmospheric Mesoscale Prediction System (COAMPS) was also assessed. The regression analyses showed very good results for QuikSCAT products, with the best results obtained from L2B winds. R2 values for moderate wind speeds were 0.75 and 0.89, 0.88 and 0.93, 0.66 and 0.77 for speed and direction and for L3, L2B and COAMPS respectively. The National Weather Product (NWP) model winds provided in the L2B dataset were also studied. Hurricanes that took place from 2002 to 2006 were studied individually to obtain regressions of QuikSCAT and COAMPS versus buoys for those events. The correlations were very high indicating that QuikSCAT is at par with buoys during hurricanes. These measurements were compared with the NHC best track analyses to determine the accuracy and found to be almost half those obtained by NHC, possibly due to rain contamination. Sea Surface Height Anomaly (SSHA) measurements by Jason-1 and sea surface temperature (SST) measurements by the Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua and GOES-12 (Geostationary) were compared with wind fields during hurricanes to study the effects of the Loop Current and Warm Core Rings on the intensification of the hurricanes. A preliminary study was conducted in which the regions of enhanced wind speeds were observed by studying the longitudinal and latitudinal transects across the hurricane for two hurricanes, namely Hurricanes Ivan and Katrina. This study would act as a precursor to further analysis of the radius of maximum wind and critical wind radii

Evaluation of NSCAT scatterometer winds using equatorial Pacific buoy observations

As part of the calibration/validation effort for NASA's Scatterometer (NSCAT) we compare the satellite data to winds measured at the sea surface with an array of buoys moored in the equatorial Pacific Ocean. The NSCAT data record runs from September, 1996 through the end of June, 1997. The raw NSCAT data, radar backscatter, is converted to wind vectors at 10 meters above the surface assuming a neutrally stratified atmosphere, using the NSCAT-1 and NSCAT-2 model functions. The surface winds were measured directly by the TAO (Tropical Atmosphere Ocean) buoy array which spans the width of the equatorial Pacific within about 8° of the equator. The buoy program and data archive are maintained by the Pacific Marine Environmental Laboratory, at the National Oceanic and Atmospheric Administration, in collaboration with institutions in Japan, France and Taiwan. We also use data from two buoys maintained by the Woods Hole Oceanographic Institution located along 125°W. Since the buoy winds are measured at various heights above the surface, they are adjusted for both height and atmospheric surface layer stratification before comparisons are made to the NSCAT data. Co-location requirements include measurements within 100 km and 60 minutes of each other. There was a total of 5580 comparisons for the NSCAT-1 model function and 6364 comparisons for the NSCAT-2 model function. The NSCAT wind speeds, using the NSCAT-1 model function, are lower than the buoy wind speeds by about 0.54 ms-1 and have a 9.8° directional bias. The NSCAT-2 winds speeds were lower than the TAO buoy winds by only 0.08 ms-1, but still have the same 9.8° directional bias. The wind retrieval algorithm selects the vector closest to the buoy approximately 88% of the time. However, in the relatively low wind speed regime of the TAO array, approximately 4% of the wind vectors are more than 120° from the buoy wind.Funding was provided by the National Aeronautics and Space Administration under Contract No. 957652

Simultaneous Wind and Rain Retrieval using Seawinds Data

The Sea Winds scatterometer is designed primarily to retrieve winds over the ocean. Since the deployment of Sea Winds on QuikSCAT in 1999, rain corruption in wind measurements has been recognized as one of the largest contributors to wind retrieval error. This paper presents a new estimation method that incorporates rain effects into Sea Winds wind retrieval. The new method simultaneously retrieves wind and rain, giving improved wind estimates in rain-corrupted areas and providing Sea Winds-derived estimates of the rain rate. The simultaneous wind/rain estimation method works especially well in the sweet spot of Sea Winds\u27 swath. On the outer-beam edges of the swath, rain estimation is not possible. This area, however, is only a small fraction of the total data. Wind speeds from simultaneous wind/rain retrieval are nearly unbiased, while the wind-only wind speeds become increasingly biased with rain rate. A synoptic example demonstrates that the new method has the capability of visually reducing the error due to rain while producing a consistent (yet somewhat noisy) estimate of the rain rate

Wind Field Retrieval from Satellite Radar Systems

Wind observations are essential for determining the atmospheric flow. In particular, sea-surface wind observations are very useful for many meteorological and oceanographic applications. In this respect, most of the satellite remote-sensing radar systems can provide sea-surface wind information. This thesis reviews the current wind retrieval procedures for such systems, identifies the most significant unresolved problems, and proposes new methods to overcome such problems.In order to invert the geophysical model function (GMF), which relates the radar backscatter measurement with the wind speed and direction (unknowns), two independent measurements over the same scene (wind cell) are at least needed. The degree of independence of such measurements is given by the azimuth (view) angle separation among them. This thesis is focused on improving the wind retrieval for determined systems (two or more measurements) with poor azimuth diversity and for underdetermined systems (one single measurement). For such purpose, observations from two different radar systems, i.e., SeaWinds and SAR (Synthetic Aperture Radar), are used.The wind retrieval methods proposed in this book for determined (Multiple Solution Scheme, denoted MSS) and underdetermined (SAR Wind Retrieval Algorithm, denoted SWRA) systems are based on Bayesian methodology, that is, on maximizing the probability of obtaining the "true" wind given the radar measurements and the a priori wind information (often provided by numerical weather prediction models), assuming that all wind information sources contain errors. In contrast with the standard procedure for determined systems, the MSS fully uses the information obtained from inversion, which turns out to positively impact the wind retrieval when poor azimuth diversity. On the other hand, in contrast with the various algorithms used nowadays to resolve the wind vector for underdetermined systems, the SWRA assumes not only that the system can not be solved without additional information (underdetermination assumption) but also that both the algorithms and the additional information (which are combined to retrieved the wind vector) contain errors and these should be well characterized. The MSS and the SWRA give promising results, improving the wind retrieval quality as compared to the methods used up to now.Finally, a generic quality control is proposed for determined systems. In general, high-quality retrieved wind fields can be obtained from scatterometer (determined systems) measurements. However, geophysical conditions other than wind (e.g., rain, confused sea state or sea ice) can distort the radar signal and, in turn, substantially decrease the wind retrieval quality. The quality control method uses the inversion residual (which is sensitive to inconsistencies between observations and the geophysical model function that are mainly produced when conditions other than wind dominate the radar backscatter signal) to detect and reject the poor-quality retrievals. The method gives good results, minimizing the rejection of good-quality data and maximizing the rejection of poor-quality data, including rain contamination