Satellite Wind Speed Research Articles

Construction of surface wind stress fields with high temporal resolution using ERS-1 scatterometer data

Wind stress fields with high temporal resolution over the North Pacific have been constructured by using ERS-1 scatterometer data. A simple objective analysis, a successive correction method, was used to construct the fields. Several necessary parameters used in the method are examined by a simulation based on the climatological data. The meridional decorrelation scale of the wind stress depends strongly on the season, while the zonal decorrelation scale is highly stable. We determined the decorrelation scale depending on the location and the time and applied to the successive correction method. The monthly mean field constructed by averaging the daily mean data is free from an aliasing error, which is a serious problem if a simple monthly averaging is applied. The daily wind stress data obtained in the present study represent small time- and spatial-scale variation and large amplitudes compared with data interpolated from simple monthly mean data. The satellite-derived data are also compared with in situ data obtained by meteorological buoys. The satellite wind speeds are lower than in situ wind speeds for every buoy. This underestimation is not due to the present objective analysis, but due to the original data, the ERS-1 Scatterometer Value-Added Product.

Comparison of ECMWF and satellite ocean wind speeds from 1985 to 1992

Abstract We compare wind speeds derived from analyses of the European Centre for Medium-Range Weather Forecasts (ECMWF) from 1986 to 1992 with wind speeds retrieved from three space borne instruments: the Geosat altimeter, the Special Sensor Microwave/Imager (SSM/I) microwave radiometer and the European Remote Sensing (ERS-I) scatterometer. The comparison reveals imperfections in both ECMWF and SSM/I wind speeds. The ECMWF wind speeds are systematically underestimated with respect to the satellite wind speeds in the tropical Pacific and the Atlantic Ocean before May 1989. After the changes to the ECMWF model in May 1989, the differences vanish in the Atlantic and are reduced by about 50 per cent in the Pacific Ocean. However, the differences between ECMWF and satellite wind speeds are greater than those observed between the satellite wind speeds themselves: yearly differences between the satellite and the ECMWF wind speeds are greater than 2 m s−1 in tropical regions whereas intercomparison of satellite w...

Seasonal variation of the CO2 exchange coefficient over the global ocean using satellite wind speed measurements

Seasonal variation of the CO2 exchange coefficient over the global ocean using satellite wind speed measurements

Intrinsic error in the air-sea CO2 exchange coefficient resulting from the use of satellite wind speeds

Intrinsic error in the air-sea CO2 exchange coefficient resulting from the use of satellite wind speeds

Intrinsic error in the air-sea CO<sub>2</sub> exchange coefficient resulting from the use of satellite wind speeds

This paper is aimed at justifying the use of satellite wind speed measurements for the determination of the carbon dioxide exchange coefficient at the air–sea interface. We relate it to the wind speed using a relationship determined by Liss and Merlivat, which enables us to monitor the spatio-temporal variations of the CO 2 exchange coefficient on a global scale, using satellite measurements of the wind speed. Yet these measurements integrate the wind speed over areas having a diameter ranging from 25 to 100 km. Moreover, a satellite has a global coverage only after a few days and samples a given place every few days (usually 3 days). We simulate the spatial integration and temporal sampling of spacecraft measurements from in-situ wind speed measurements to infer the errors induced. While the space integration results in a negligible error (a few tenths of cm h -1 at most), a caveat should be given about the sampling error. It is of the order of the statistical error of a normal distribution, depending on the standard deviation and number of measurements. An example of the statistical error resulting from the use of 1 month of SEASAT scatterometer data is shown. Provided this caution about the statistical error is kept in mind and the space and time integration is properly chosen, satellite data, after careful validation of the wind speed retrieval, are well suited for long-term global survey of the CO 2 exchange coefficient space and time variations. The use of a relationship between the exchange coefficient and the wind speed, which accuracy is presently discussed, is the only way to access these variations. DOI: 10.1034/j.1600-0889.1991.00016.x

Seasonal variation of the CO<sub>2</sub> exchange coefficient over the global ocean using satellite wind speed measurements

We used one year (July 1987 to June 1988) of wind speed measurements made by the microwave radiometer SSM/I to determine the CO 2 exchange coefficient between air and sea on a global scale through the Liss and Merlivat relationship. We determined the seasonal variation in every region of the world ocean. It can be as high as a factor of 4 in some areas. An estimate of the accuracy of the retrieved wind speeds showed that this variation is likely to be underestimated. The global averaged CO 2 exchange coefficient obtained is 3.34. 10 -2 mol m -2 yr -1 µ atm -1 , close to previous estimates. A study of the errors on the retrieved wind speed showed that the exchange coefficient is likely to be underestimated and could be as high as 4 10 -2 mol m -2 yr -1 µ atm -1 . We combined these results with various estimates of the annual CO 2 partial pressure gradient at the air sea surface and get a net flux absorbed by the ocean. The flux so determined is not meaningful, however, since the covariant variability of the exchange coefficient and of the CO 2 partial pressure gradient is not taken into account. DOI: 10.1034/j.1600-0889.1991.00017.x

Seasat scatterometer versus scanning multichannel microwave radiometer wind speeds: A comparison on a global scale

The wind speeds obtained from the Seasat A scatterometer system (SASS) and scanning multichannel microwave radiometer (SMMR) using two different algorithms were compared on a global scale. The temperature dependence of the sea surface emissivity was shown to be incorrectly modelled. After correcting this effect, regional differences up to ± 3 m s−1 are still observed between both instruments, even though they balance in global averaging, resulting in no bias between the global data sets. Validation experiments of satellite wind speeds should take into account this possibility of regional biases and insure the validity of the measurements everywhere in the global ocean.

Coherence between low‐level cloud motion vectors and surface wind measurements Near 0°, 152°W from April 1979 to February 1980

Abstract Low‐level cloud motion vectors determined by the National Oceanic and Atmospheric Administration (NOAA) and the University of Wisconsin in a 5‐degree square centred at 0°, 152°30’ W were intercompared with moored buoy wind measurements made at 0°, 152°W during April 1979‐February 1980. At the site of the intercomparison test the prevailing wind direction was easterly; monthly mean values of the meridional wind speed were less than 20% of the zonal component. The surface winds measured during the observational period were similar to the climatological‐mean wind conditions. Although the satellite wind speeds were larger than buoy wind speeds, as a priori expected, because of the vertical separation between the measurements, the comparison indicated that in the case of the zonal component there was a maximum usable frequency (muf) below which cloud drifts and surface wind vectors were 95% significantly correlated and the correlation was greater than the 50% noise level. The muf were 0.17 and 0.3 cpd...

Sea surface winds derived from cloud motion vectors over the tropical Atlantic

The relationship between satellite-derived low-level cloud motion, surface wind and geostrophic wind vectors is examined using GATE data. In the trades, surface wind speeds can be derived from cloud motion vectors by the linear relation: V = 0.62 V s + 1.9 m s−1 with a mean scatter of ±1.3 m s−1. The correlation coefficient between surface and satellite wind speed is 0.25. Considering baroclinicity, i.e., the influence of the thermal wind, the correlation coefficient does not increase, because of the uncertainty of the thermal wind vectors. The ratios of surface to geostrophic wind speed and surface to satellite wind speed are 0.7 and 0.8, respectively, with a statistical uncertainty of ±0.3. Calculations of the ratio of surface to geostrophic wind speed on the basis of the resistance law yield V/V g = 0.8 ± 0.2, in agreement with experimental results. The mean angle difference between the surface and the satellite wind vectors amounts to α ∼- 18 °, taking into account baroclinicity. This value is in good agreement with the mean ageostrophic angle α ∼- 25 °.

Comparison of Low-Level Cloud Motion Vectors and Moored Buoy Winds

Moored buoy wind measurements made at 3 m height at three sites (8°27′N, 150°45′W; 0°, 150°W; 0°, 125°W) in the central equatorial Pacific during 1976 and 1977 were compared with satellite-derived low-level (∼1 km) cloud motion vectors within a 5° square centered on each buoy site. The satellite winds were determined on a routine basis by NOAA's National Environmental Satellite Service and the buoy wind measurements were made by NOAA's Pacific Marine Environmental Laboratory. To compare satellite and buoy wind measurements, 3 h vector averages of the buoy data were computed at times of satellite wind observations. The number of pairs of buoy winds and winds derived from infrared satellite imagery was 265. Directions of the low-level cloud motion vectors were similar to the buoy wind directions. The average satellite wind speed was 8.0 m s−1, which was 3.3 m s−1 or 70% greater than the average buoy wind speed. At the 0°, 125°W site where 190 satellite-buoy wind pairs were obtained, the average wind speed difference was 3.8 m s−1 and 30% of the deviations were greater than 5 m s−1. The standard deviation of the satellite winds was nearly 80% larger than the wind recorder data. At the 0°, 125°W site the correlation between the satellite and buoy wind speeds was 0.25. Our results indicated that the mean and fluctuations of low-level cloud motion vectors were not in satisfactory enough agreement with the buoy wind data to be considered representative of the near-surface wind field. Although our results were presumably a priori expected because of the vertical separation between the measurements, this note provides an estimate of the difference between the two types of measurements.