ABSTRACT:The accurate estimation of marine wind speed is important for climate and air-sea interaction applications. There are many datasets of monthly mean wind speeds available based either on in situ measurements, satellite retrievals, atmospheric reanalysis assimilating both in situ and satellite data and blended datasets combining some or all of these other data sources. Twelve different monthly mean wind speed datasets are compared for the period from 1987 to 2009. The results suggest that we cannot presently be confident that the mean wind speed over the ocean is known to the accuracy required for the calculation of air-sea heat fluxes. Comparisons are complicated by different representations of wind speed being presented in different datasets. The in situ and reanalysis datasets present stability-dependent, earth-relative, wind speeds adjusted to a reference level of 10 m. The satellite and blended datasets present neutral equivalent, surface-relative, speeds adjusted to a reference level of 10 m. Differences between these estimates depend on atmospheric stability and ocean currents and can be greater than the required accuracy target. The adjustment for stability is itself uncertain but it is shown that these uncertainties are likely to be smaller than biases caused when the effects of stability are neglected. Further differences among the datasets are identified. Biases are caused by unidentified rain in K u -band scatterometer-derived wind speeds and by atmospheric effects on passive microwave wind retrievals. When satellite observations affected by rain are removed a fair-weather bias remains. Some datasets are biased low in coastal regions by the effects of lower wind speeds over land in atmospheric models affecting wind speeds near the coast. All these uncertainties combine to give a wide range of estimates of monthly mean wind speed for the chosen datasets with uncertainty in mean values, spatial patterns and changes over time.
Satellite-based microwave sensors have, since the 1980s, provided a means to retrieve near-surface marine specific humidity (q a ), accurate estimation of which is necessary for climate and air-sea interaction applications. Seven satellite measurement-derived monthly mean humidity datasets are compared with one another and with a dataset constructed from in situ measurements. The means, spatial and temporal structures of the datasets are shown to be markedly different, with a range of yearly, global mean q a of ∼1 g kg -1 . Comparison of the datasets derived using the same satellite measurements of brightness temperature reveals differences in q a that depend on the source of satellite data; the processing and quality control applied to the data; and the algorithm used to derive q a from the satellite measurements of brightness temperature. Regional differences between satellite-derived q a due to the choice of input data, quality control and retrieval algorithm can all exceed the accuracy requirements for surface flux calculation of ∼0.3 g kg -1 and in some cases can be several g kg -1 in monthly means for some periods and regions.
[1] Global air-sea flux of carbon dioxide (CO 2 ) is calculated from wind data acquired by the satellite scatterometer QuikSCAT, the passive microwave radiometer AMSR-E, and the model reanalysis ERA-40 using four of the most commonly used wind speed dependent parameterizations of gas transfer velocity. Assuming QuikSCAT as reference, the results are compared to obtain an estimate of that relative uncertainty in the flux calculations which results solely from the origin of the input wind data. We illustrate the discrepancies between these data sets and quantify the uncertainty in the computed air-sea CO 2 flux that arises from data processing such as temporal and spatial averaging using AMSR-E as an example data set. The impact of temporal variability of wind speed is shown to be significantly greater than that of spatial variability. However, simple parameterizations of temporal variability are found to be sensor-specific and cannot be applied in a straightforward way to data sets with lower temporal resolutions from other sensors. We show a simple methodology to correct monthly mean data in such a way that seasonally and zonally varying parameterizations of temporal variability derived from QuikSCAT data can be applied to data from AMSR-E and ERA-40. This allows us to produce a global 44-year time series of gas transfer velocity and to present a more coherent estimate of air-sea transfer of carbon dioxide from the three most commonly available types of wind data.
Differences between Quick Scatterometer (QuikSCAT) level-2b wind vectors from the Jet Propulsion Laboratory [JPL; the Direction Interval Retrieval with Thresholded Nudging (DIRTH) product] and from the Remote Sensing Systems Co. (RSS; smoothed versions 3.0 and 4.0) for one sample month are presented. Each dataset is derived from the same observations, but processing methods result in differences between wind vectors. These differences originate from 1) uncertainty in the geophysical model functions that relate backscatter to wind, 2) noise in the backscatter measurements, and 3) spatial filtering. Statistics of wind vector differences from RSS and JPL are used as an indication of structural uncertainty in QuikSCAT wind retrievals. When grouped by 1 m s 21 bins, systematic differences are largest beyond 20 m s 21 , where wind speeds from version 3.0 (version 4.0) of RSS can be more than 15 (10) m s 21 higher (lower) than JPL wind speeds. Below 20 m s 21 , systematic differences on the order of tenths of a meter per second are attributed to differences in the retrieval methods, rain and ice contamination, and cross-swath position. Even once the recommended data flags are applied, differences in individual wind speed retrievals exceed 10 m s 21 in a few cases but are much smaller in regions of the swath for which the viewing geometry allows more reliable retrievals. In all parts of the swath, the standard deviations of the differences are smaller than 1.0 m s 21 . The analyses provide a measure of the structural uncertainty in QuikSCAT wind velocity that is due to the retrieval process, although such comparisons are not able to determine which dataset is closest to the actual wind.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.