Quantifying variance due to temporal and spatial difference between ship and satellite winds

Abstract

[1] Ocean vector winds measured by SeaWinds can be validated with comparison in situ data that are within a certain time and space range to the satellite overpass. The total amount of random observational error is composed of two primary components, which are quantified in this study: the uncertainty associated with the data sets and the uncertainty associated with the temporal and/or spatial difference between two observations. The variance associated with a temporal difference, which can be translated into a spatial difference using Taylor's hypothesis, between two observations is initially examined in an idealized case that includes only Shipboard Automated Meteorological and Oceanographic System (SAMOS) 1 min data. The results show that the amount of variance in wind speed and direction increases as the time difference increases, while the amount of variance in wind speed increases and direction decreases with larger wind speeds. Collocated SeaWinds and SAMOS observations are used to determine the total amount of variance associated with a temporal (equivalent) difference from 0 to 60 min. For combined differences less than 25 min (equivalent) and the selected wind speed bins, the variance associated with the temporal and spatial difference is dominated by small changes in the wind speed distribution, and the sum of the observational errors is approximately 1.0 m 2 s −2 (12 deg 2 ) and 1.5 m 2 s −2 (10 deg 2 ) for wind speeds between 4 and 7 m s −1 and 7-12 m s −1 . For larger combined differences, the observational error variance is no longer the dominant term; therefore, the total variance is seen to gradually increase with increasing time differences.Citation: May, J. C., and M. A. Bourassa (2011), Quantifying variance due to temporal and spatial difference between ship and satellite winds,

ASCAT wind quality under high subcell wind variability conditions

“…The variance associated with buoy wind time series is translated into spatial wind variability using Taylor's hypothesis [Taylor, 1938], which allows for a temporal dimension to be converted into a spatial dimension, and vice versa. The time window (centered on the buoy measurement collocated with ASCAT acquisition) used for calculating the mean buoy winds and the subcell spatial variability is defined by May and Bourassa [2011],…”

“…The variance associated with buoy wind time series is translated into spatial wind variability using Taylor's hypothesis [ Taylor , ], which allows for a temporal dimension to be converted into a spatial dimension, and vice versa. The time window (centered on the buoy measurement collocated with ASCAT acquisition) used for calculating the mean buoy winds and the subcell spatial variability is defined by May and Bourassa [], where l footprint is the ASCAT footprint size, and is the mean buoy wind speed within the time‐averaging window. As already mentioned, the spatial resolution of the averaged σ 0 in ASCAT 12.5 km L2 data is about 25 km and l footprint is set equal to 25 km.…”

Comparing in situ and satellite‐based parameterizations of oceanic whitecaps

“…Most of the parameterizations depend on U 10 and follow some form of a power law, but they were not developed for use with satellite-based winds. Satellite-based winds, which are reported as equivalent neutral 10 m winds U 10EN , differ from U 10 winds [Kara et al, 2008;May and Bourassa, 2011;Plagge et al, 2012]. Satellite winds are tuned to produce the correct stress when combined with a neutral drag coefficient.…”

Directional Bias of TAO Daily Buoy Wind Vectors in the Central Equatorial Pacific Ocean from November 2008 to January 2010

“…For example, the TAO buoy data have long been utilized in calibrating model functions for deriving satellite wind speeds or wind vectors (e.g., Dunbar, Hsiao, & Lambrigtsen, 1991;Wentz, 1997;Stoffelen, 1998). In addition to being assimilated in model predictions and reanalyses, the buoy measurements are also valuable in evaluating products from models and satellites and providing error or uncertainty estimates for those products (e.g., Freilich & Dunbar, 1999;Mears, Smith, & Wentz, 2001;Ebuchi, Craber, & Caruso, 2002;Abdalla, Janssen, & Bidlot, 2011;May & Bourassa, 2011;Peng, Zhang, Frank, Bidlot, Higaki, Stevens, et al, 2013). This process is beneficial to both buoy array operators and data users -instrumental bias revealed by satellite data have led to TAO instrument improvement (Dickinson, Kelly, Caruso, & McPhaden, 2001;Freitag, O'Haleck, Thomas, & McPhaden, 2001 Figure 1, the WMO 51010 site is noted in the figure).…”