The Effects of Marine Winds from Scatterometer Data on Weather Analysis and Forecasting (original) (raw)
Related papers
Impact of seawinds scatterometer data on ocean surface analysis and weather prediction
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.
Journal of Geophysical Research, 1982
On the SEASAT-A satellite, a microwave scatterometer was used to determine the vector wind over the world's oceans. The technique is based on the sensitivity of microwave radar backscatter to the centimeter length ocean waves created by the action of the surface wind. This paper describes the algorithm used to convert the scatterometer' s measurements of ocean normalized radar cross section, •, to the neutral stability vector wind at 19.5 m height and the comparison of these winds with high quality surface observations. The wind vector algorithm used an empirical • model function to describe the dependence of the ocean • on the 19.5-m neutral stability wind vector. Two model functions, developed from a limited base of aircraft and satellite o • measurements, were evaluated by using an independent set of in situ surface wind observations from the Joint Air Sea Interaction Experiment (JASIN). Although these model functions were found to have some weaknesses, the results of these comparisons produced better results than the SEASAT specifications of wind speed accuracy of +-2 m/s and wind direction accuracy of +-20 ø over the 0-16 m/s range of winds observed during JASIN. An improved model function was later developed by 'tuning' to these JASIN data so that the remaining biases between the observed surface winds and the scatterometer-derived winds were minimized. Results are presented for this model function compared against other surface wind observations from the Gulf of Alaska SEASAT Experiment and the SEASAT Storms (Hurricane) Experiment. INTRODUCTION On June 28, 1978, •he National Aeronautics and Space Administration (NASA) launched SEASAT, the first satellite dedicated to establishing the utility of microwave sensors for remote sensing of the earth's oceans [Born et al., 1981]. This concept had its beginning in the mid-1960's when a conference called 'On the Feasibility of Conducting Oceanographic Explorations from Aircraft, Manned Orbital and Lunar Laboratories' was held at Woods Hole Oceanographic Institute, Woods Hole, Mass., in August 1964 [Ewing, 1965]. At this conference, the rudiments of many of the remote sensing systems for measuring oceanographic parameters were described that eventually were orbited on Skylab, Geos-3, and SEASAT. A few years later, a second conference sponsored by the National Academy of Sciences at Woods Hole made a broader study of potential areas of activity for NASA, including the study of the oceans. The concepts of high precision radar altimetry and of using radar backscatter to measure the winds both received considerable attention [National Research Council, 1970]. A third conference at Williamstown, Mass. [Kaula, 1970] also investigated the general subject; and ocean and atmospheric scientists postulated that satellite technology could provide the mecha
Large-scale analysis and forecast experiments with wind data from the Seasat A scatterometer
Journal of Geophysical Research, 1984
A series of data assimilation experiments is performed to assess the impact of Seasat A satellite scatterometer (SASS) wind data on Goddard Laboratory for Atmospheric Sciences (GLAS) model forecasts. The SASS data are dealiased as part of an objective analysis system utilizing a three-pass procedure. The impact of the SASS data is evaluated with and without temperature soundings from the NOAA 4 Vertical Temperature Profile Radiometer (VTPR) instrument in order to study possible redundancy between surface wind data and upper air temperature data. In the northern hemisphere the SASS data are generally found to have a negligible effect on the forecasts. In the southern hemisphere the forecast impact from SASS data is somewhat larger and primarily beneficial in the absence of VTPR data. However, the inclusion of VTPR data effectively eliminates the positive impact over Australia and South America. This indicates that SASS data can be beneficial for numerical weather prediction in regions with large data gaps, but in the presence of satellite soundings the usefulness of SASS data is significantly reduced.
Ocean model validation of the NASA scatterometer winds
Journal of Geophysical Research, 1999
The suitability of basin-scale, satellite-based scatterometer winds for forcing of numerical ocean models is examined using a reduced gravity, primitive equation model of the tropical Pacific Ocean. Three. surface forcing fields are validated in a comparison of upper layer thickness (ULT) from the ocean model with observed sea level data. The forcing fields are the Florida State University observed winds, winds derived from the NASA Scatterometer (NSCAT), and stresses derived directly from NSCAT. The sea level data sets are the World Ocean Circulation Experiment "fast" sea level data set from island measurements and sea level anomalies from TOPEX/POSEIDON. Results of this comparison demonstrate that while the three model results are qualitatively similar, the results are quantitatively better when forcing with the NSCAT derived stresses. This is particularly true in the eastern tropical Pacific and in convergent zones where forcing with the NSCAT stresses can lead to large differences in ULT (> 40 m) compared with results from the other two wind products. 1. Introduction One of the first applications envisioned for the NASA Scatterometer (NSCAT) was to record high-resolution fields of oceanic surface winds that could provide more detailed and accurate forcing for ocean models. The accuracy of NSCAT winds has been assessed in several comparisons to point observations: research vessels [Bourassa et al., 1997], fixed buoys (H. C. Graber and N. Ebuchi, University of Miami, personal communication, 1996; M. H. Freilich and R. S. Dunbar, Oregon State University, personal communication, 1997; M. J. Caruso et al., Evaluation of scatterometer winds using bquatorial Pacific buoy observations, manuscript in preparation, 1997), and drifting buoys (P. P. Niiler and R. F. Milliif, University of California, San Diego, personal communication, 1996). Accuracy in both sped and direction is much better than the engineering requirements developed for such applications [Bourassa Paper number 1998JC900105. 0148-0227/99/1998JC900105509.00 et al., 1997]. NSCAT observations have also been used to derive surface stresses [ Weissman et al., 1994; Weissmatt, 1998], which are more closely related to surface forcing than winds. In this study, the utility of NSCAT winds and stresses in forcing ocean models is examined with an upper ocean model. This is one of the first validations of NSCAT winds with an ocean model, as well as the first comparison of NSCAT stresses and winds in forcing an ocean model. Ocean models have often been used to compare various wind forcing functions. Before the availability of scatterometer winds, comparisons were being made between both observation and model-derived wind fields. For example, Busalacchi et al. [1990] used a linear model of the tropical Pacific Ocean to compare the Florida State University (FSU) subjective analysis winds, the University of Hawaii subjective analysis winds, and the Fleet Numerical Oceanography Center (FNOC) operational analysis winds. Rienecker et al. [1996] used a quasi-geostrophic model of the north Atlantic to compare wind fields from the Comprehensive Ocean-Atmosphere Data Set (COADS), the Goddard Earth Observing System (GEOS), m•d the Special Sensor Microwave/Imager (SSM/I). Many other researchers have also used numerical models to compare different forcing 11,359 11,360 VERSCHELL ET AL.: OCEAN MODEL VALIDATION OF NSCAT fields [e.g., Landsteiner et al.cal models was greatly anticipated, and a number of researchers used numerical models to examine the impact of scatterometer-based forcing fields. Milliff et al. [1996] used a high-resolution quasi-geostrophic model of the North Atlantic Ocean to compare model solutions when one of the forcing fields was an ideal synthetic scatterometer wind field. Other studies looked specifically at determining the effects of using European Remote Sensing 1 (ERS-1) scatterometer and NSCAT forcing fields prior to the availability of these fields /Barnlet et al., 1991; Boukthir et al., 1992; Barnlet et al., 1994]. The effect of scatterometer winds on atmospheric models and analysis has also been looked at [e.g., Ingleby and Bromley, 1991; $toffelen and Cats, 1991; Thepaut et al., 1993; Alpers et al., 1998; Liu et al., 1998].
Journal of Geophysical Research, 1982
About 10 years ago, the advanced application flight experiment radiometer scatterometer (AAFE RADSCAT) made its first successful measurements of ocean radar scattering cross section from a NASA C-130 aircraft. This instrument was developed as a research tool to evaluate the use of microwave frequency remote sensors (particularly radars) to provide wind-speed information at the ocean's surface. The AAFE RADSCAT flight missions and analyses helped establish the feasibility of the satellite scatterometer for measuring both wind speed and direction. Probably the most important function of the AAFE RADSCAT was to provide a data base of ocean normalized radar crosssection (NRCS) measurements as a function of the surface wind vector at 13.9 GHz. NRCS measurements over a wide parametric range of incidence angles, azimuth angles, and winds were obtained in a series of RADSCAT aircraft missions from 1973 to 1977. Presented herein are analyses of data from the 26 RADSCAT flights during which the quality of the sensor and the surface wind measurements were felt to be understood. Subsets of this data base were used to model the relationship between the Ku-band radar signature and the oceansurface wind vector. The models developed partly from portions of this data base, supplemented with data from the Seasat (JASIN Report
Effects of rain-rate and wind magnitude on Sea Winds scatterometer wind speed errors
2001
Rain within the footprint of the Sea Wmds scatterometer on the QuikSCAT satellite causes more significant errors than existed with its predecessor, the NASA scatterometer (NSCAT) on Advanced Earth Obsenling Satellite-I (ADEOS-I). Empirical relations are developed that showhow-dle rain..indnced errors in the scatterometer wind magnitude depend on both the rain rate and on the wind magnitude. These relations are developed with collocated National Data Buoy Center (NDBC) buoy measurements (to provide accurate sea surface winds) and simultaneous Next Generation Weather Radar (NEXRAD) observations of rain reflectivity. An analysis, based on electromagnetic scattering theory, interprets the dependence of the scatterometer wind errors on volumetric rain rate over a range of wind and rain conditions. These results demonstrate that the satellite scatterometer responds to rain in a manner similar to that of meteorological radars, with a Z-R relationship. These observations and results indicate that the combined (wind and rain) normalized radar cross section will lead to erroneously large wind estimates when the rain-related radar cross section exceeds a particular level that depends on the rain rate and surface wind speed.
On the Use of Scatterometer Winds in NWP
2010
Over the last years the processing of ERS scatterometer winds has been refined. Subsequently, High Resolution Limited Area Model, HIRLAM, and ECMWF model data assimilation experiments have been carried out to assess the impact of one scatterometer, ERS-1 and of two scatterometers, ERS-1 and ERS-2, on the analyses and forecasts. We found that scatterometer winds have a clear and beneficial impact in the data assimilation cycle and on the forecasts. Furthermore, ECMWF has shown that ERS scatterometer data improve the prediction of tropical cyclones in 4Dvar, where unprecedented skillful medium-range forecasts result of potential large social-economic value. Nevertheless, scatterometer winds contain much sub-synoptic scale information where the smallest scales resolved are difficult to assimilate into a Numerical Weather Prediction, NWP, model. This is mainly due to the otherwise general sparsity of the observing system over the ocean. In line with this it is found that scatterometer d...
Challenges to Satellite Sensors of Ocean Winds: Addressing Precipitation Effects
2012
Measurements of global ocean surface winds made by orbiting satellite radars have provided valuable information to the oceanographic and meteorological communities since the launch of the Seasat in 1978, by the National Aeronautics and Space Administration (NASA). When Quick Scatterometer (QuikSCAT) was launched in 1999, it ushered in a new era of dual-polarized, pencil-beam, higher-resolution scatterometers for measuring the global ocean surface winds from space. A constant limitation on the full utilization of scatterometer-derived winds is the presence of isolated rain events, which affect about 7% of the observations. The vector wind sensors, the Ku-band scatterometers [NASA's SeaWinds on the QuikSCAT and Midori-II platforms and Indian Space Research Organisation's (ISRO's) Ocean Satellite (Oceansat)-2], and the current C-band scatterometer [Advanced Wind Scatterometer (ASCAT), on the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)'s Meteorological Operation (MetOp) platform] all experience rain interference, but with different characteristics. Over this past decade, broadbased research studies have sought to better understand the physics of the rain interference problem, to search for methods to bypass the problem (using rain detection, flagging, and avoidance of affected areas), and to develop techniques to improve the quality of the derived wind vectors that are adversely affected by rain. This paper reviews the state of the art in rain flagging and rain correction and describes many of these approaches, methodologies, and summarizes the results.