Publication_Draft_09Aug

Schmidt et al. [1]
Comparison between satellite wind products and in situ Wave Glider
wind observations in the Southern Ocean
Kevin M. Schmidta
, Sebastiaan Swartb,c
, Chris Reasonc
, Sarah Nicholsonb,c
a
University of Cape Town, Marine Research Institute
b
Southern Ocean Carbon & Climate Observatory, Council for Scientific
and Industrial Research (CSIR), Rosebank, Cape Town
c
University of Cape Town, Department of Oceanography
Author’s emails:
Kevin Schmidt: kmschmdt@gmail.com
Sebastiaan Swart: seb.swart@gmail.com
Chris Reason: chris.reason@uct.ac.za
Sarah-Anne Nicholson: sarahanne.n@gmail.com
Keywords: Sea surface winds, reanalysis, intercomparison, wave gliders, wind speed,
scatterometer, wind products, Southern Ocean
Abstract:
Surface wind datasets are required to be of high spatial and temporal resolution
and high precision in order to accurately force coupled atmosphere-ocean numerical
models and understand ocean-atmospheric processes. In situ observed sea surface
winds from the Southern Ocean are scarce and consequently the validity of simulation
models is often in question. Multiple wind data products were compared to high
resolution in situ measurements of wind speed from Wave Glider deployments in the
Subantarctic Southern Ocean. The intent was to determine which blended satellite or
reanalysis product best represents the magnitude and variability of the observed wind
field. Results show that the ECMWF reanalysis wind product is more accurate in
representing the temporal variability of winds, exhibiting consistently higher correlation
coefficients with in situ data across all wind speed categories. However, the
NCEP/NCAR Reanalysis II product matches in situ trends of deviation from the mean
and performs best in depicting the mean wind state, especially during high mean wind
states. The high-resolution ECMWF product is best used during periods of lower mean
wind state, and exhibits lower biases in wind speed across all speed categories. It can
be concluded that the ECMWF product leads to smaller differences in wind speeds from
the in situ data due to a combination of higher temporal and spatial sampling.

Schmidt et al. [2]
1. Introduction:
Mid- to high-latitude regions in the Southern Ocean are host to the strongest
wind fields at the ocean surface. These strong winds (e.g. speeds >20 m.s-1; Yuan
(2004)) significantly impact upper ocean properties and processes, such as mixed layer
dynamics, Ekman processes and air-sea exchange. Exchanges in heat, moisture, and
momentum at the air-sea interface are facilitated by sea surface winds. In addition to
driving physical processes at the sea surface, these winds also have implications for
biological processes which extend below the surface. The flux in carbon dioxide (CO2)
between the atmosphere and ocean is closely related to wind speed (Wanninkhof,
2009), and as such sea surface winds are of interest to many scientists studying the
biogeochemical cycle within the ocean.
The Southern Ocean, owing to its limited access to sources of iron (e.g.
landmasses), is known as an iron limited High Nitrate Low Chlorophyll (HNLC) region. It
remains an integral player in the regulation of the global climate by sequestering
atmospheric CO2 (Takahashi et al., 2012). Many recent studies (Thomalla et al., 2011;
Fauchereau et al., 2011; Swart et al., 2014; Domingues et al., 2014; Carranza and Gille,
2015; and Nicholson et al., submitted 2016) have been conducted in this region to
investigate the physical mechanisms which drive the abundance of primary production.
During times of iron limitation, transient deepening events of the surface mixed-layers is
hypothesized to be driven by variability in sea surface winds which consequently
creates a supply of iron via entrainment. However, generally these studies base their
assumptions on coarse satellite derived data, which may represent this process poorly.
A better understanding of Southern Ocean wind field characteristics, such as variability
and strength, may help to explain its impacts on mixing and the response of physical-
biological processes in the Southern Ocean.
Surface winds are measured using in situ techniques or remote sensing
instruments. It has been observed by several scientists (Stoffelen and Anderson, 1997;
Andrews and Bell, 1998; Atlas, 1999) that numerical weather prediction (NWP) models
benefit from scatterometer wind observations. However due to the varying methods of
collecting and synthesizing wind measurements, the uniformity of sea surface wind data
is often questioned. Satellite instruments use wave scatter to infer wind vectors

Schmidt et al. [3]
generally at a height of 10 meters above sea level, while in situ measurements are
generally taken at varying heights above sea level dependent on the platform (e.g. ship,
mooring, or glider). As a result, many studies (Yuan et al., 2009), involving wind data
require a validation prior to the start of their analysis. In order to determine the ability of
satellite datasets to accurately represent wind fields at the air-sea interface, and to see
if post processing is required to reduce errors once compared to in situ measurements.
There is a great need to reduce or eliminate this time costly step in wind data analysis in
the Southern Ocean.
A commonly used global wind product is the QuikSCAT dataset. QuikSCAT was
a remote sensing satellite, which provided global ocean surface wind vector
measurements from 1999 to 2009. In an evaluation of extreme wind events in the
Southern Ocean, Yuan (2004) validated QuikSCAT wind data with reanalysis wind data
from the National Center for Environmental Prediction (NCEP), National Center for
Atmospheric Research (NCAR) and European Centre of Medium Weather Forecasts
(ECMWF). It was found that both weather station and QuikSCAT winds were stronger
than reanalysis data, albeit only during high wind events (Yuan, 2004).
The approach of Yuan (2004) required the input of many different wind products
to investigate the spatial and seasonal variability of high wind characteristics. The
occurrence and intensity of extreme events was examined along with a cross
examination of the QuikSCAT scatterometer data against other wind products,
specifically reanalysis wind products. Reanalysis wind products are created by
assimilating in-situ observations into numerical weather prediction models. This
produces datasets with greater spatial and temporal resolutions. The reanalysis
products that Yuan (2004) used were limited with regard to in-situ data input. The
majority of the input came from weather station observations from Macquarie Island,
due to the remote area of the study. Yuan (2004) showed that monthly mean wind
observations from satellite averages over the Southern Ocean were in agreement with
model simulations except for the period from May to October (winter months), when
scatterometer winds were stronger than simulated winds, thereby suggesting a strong
winter bias in data. Similar to Hilburn et al. (2003), Yuan (2004) attributed this

Schmidt et al. [4]
discrepancy to the NCEP/NCAR reanalysis missing some storms entirely, along with a
lack of observational data assimilated into the reanalyses.
Patoux et al. (2009) modified scatterometer derived pressure fields with
reanalysis wind data. QuikSCAT data was blended with ECMWF forecast data to
produce modified pressure swaths. A wavelet-based method was used to blend the
swaths from the scatterometer and surface pressure model. The resulting blends were
successful in extending the tracks of more than 1100 cyclones by at least one synoptic
period. The modified cyclones, however, were observed to be deeper than the ECMWF
cyclones with a maximum mean difference of 0.3 hPa observed in the Southern Ocean
between 45⁰S - 55⁰S (Patoux et al., 2009). This difference was initially attributed to the
partial blending methodology, but was further investigated by Yuan et al. (2009) who
assessed the impacts of the differences in momentum and heat fluxes at the air-sea
interface in the Southern Ocean. Yuan et al. (2009) claim that even small mesoscale
cyclones contribute significantly to heat fluxes at the air-sea interface due to the high
frequency of their occurrence. As such, a correct representation of mesoscale cyclones
in NWP models of the Southern Ocean is therefore critical (Yuan et al. 2009). Although
these studies have aided in improving estimates of wind stress and understanding time
and space variability of Southern Ocean wind events, significant gaps in our
understanding of wind stress accuracy and uncertainty exist.
The aim of this paper is to determine which wind data product (satellite blended,
reanalysis and modelled) best represents the wind field variability of the Southern
Ocean. This is done by comparing wind products with in situ sea surface wind data
gathered by Wave Glider technology deployed in a series of experiments in the
Subantarctic Ocean. The paper is organized as follows: an introduction to the surface
wind datasets, their sources, and the transformations performed to collocate them in
space and time. In this work, both in-situ real time winds as well as satellite blended
near surface derived wind fields are considered. In the following section, a detailed
description of the statistical analysis performed in this study is provided. In Section 3,
remotely measured winds are compared with in-situ winds at a fixed point in the
Southern Ocean. The final section presents a discussion of the results and their
implications for biological studies.

Schmidt et al. [5]
2. Data and Methodology
2.1 Satellite Wind Data
The QuikSCAT satellite launched by the U.S. National Aeronautics and Space
Administration (NASA) in 1999 was fitted with a SeaWinds scatterometer. This active
microwave radar sampled 25 kilometer measurements across a 1600 kilometer swath
every 24 hours (Chelton et al. 2004). The SeaWind (SW) gridded and blended sea
surface vector winds were generated from the multiple satellite observations of DOD,
NOAA and NASA along with the input of satellite wind retrievals from Remote Sensing
Systems, Inc. Surface wind fields are available for download at https://www.ncdc.noaa.
gov/data-access/marineocean-data/blended-global/blended-sea-winds. The methods
used to generate such data include objective analysis and simple spatiotemporally
weighted interpolation. This product provides global ocean coverage with a spatial
resolution of 0.25⁰ x 0.25⁰ and a 6 hourly temporal resolution. The data are provided at
a height of 10m above sea level.
NOAA uses a global NWP model called the Climate Forecast System which
represents the interaction between the air-sea interfaces throughout the world’s oceans.
The CFS version II (CFSv2) operational near real time wind vector dataset provides
time series at a period of record from 01 January 2011 – 01 April 2016 at a 6 hourly
temporal resolution. CFS time series products are available at 0.2, 0.5, 1.0, and 2.5
degree horizontal resolutions and for this study the highest resolution was selected for
use; a spatial scale of 0.205⁰ x 0.204⁰. This product is available for download at
http://rda.ucar.edu/datasets/ds094.1/. The data are provided at a height of 10m above
sea level.
European Centre of Medium Weather Forecasts (ECMWF) ERA-Interim Global
Reanalysis Sea Surface Winds are available for download at http://apps.ecmwf.int/
datasets/data/ interim-full-daily/. The spatial resolution of the data set is T255 spectral,
specifically 0.125⁰ x 0.125⁰. The project is in near real-time production and the data are
calculated at a height of 10m above sea level and are available at a 6 hourly temporal
resolution.

Schmidt et al. [6]
The NCEP/NCAR Reanalysis project has created a sea surface wind dataset
which covers the period from 01 January 1979 to the present. The development of the
NCEP–DOE Atmospheric Model Intercomparison Project (AMIP-II) Reanalysis R-2
(NCEPII) was initiated in the late 1990’s and the data are available for download at
http://rda.ucar.edu/datasets/ds091.0. It is important to note that NCEPII is of the same
spatial (1.9047⁰ x 1.8750⁰) and temporal resolution as NCEPI and incorporates similar
raw observational data. NCEPII is a global Gaussian Latitude/Longitude T62 resolution.
The data are calculated at a height of 10m above sea level and are available at a 6
hourly temporal resolution.
Table 1: Characteristics of the different wind products used in this study
2.2 In situ Wind Observations
2.2.1 Experimental Setup and Region
The in-situ data used in this study were collected via a Liquid Robotics Wave
Glider (WG) autonomous surface vehicle. The primary components of the WG include a
surface float, an underwater glider, and a tether cable which connects the two. Solar
panels and batteries power sensors and communication systems which enabled it to
remain at sea for an extended period of time (multiple months). At the ocean surface,
this vehicle derived its propulsion from wave energy and was set to navigate an octagon
track around a fixed location in the Southern Ocean (Figure 1). Two different gliders
were deployed at different locations during research cruises in 2013 and twice in 2015.
Using real-time Iridium burst communication, these gliders navigated to predetermined
Wind
Products
Time (h)
Resolution
Spatial
Resolution
Paper
Reference
Time Coverage
SW 6 0.25⁰ x 0.25⁰ Zhang 2006
09 July 1987 -
Present
ECMWF 6 0.125⁰ x 0.125⁰ Dee et al. 2011 1979 – 31 Jan 2016
CFSv2 6 0.205⁰ x 0.204⁰ Saha et al. 2014
01 April 2011 -
Present
NCEPII 6 1.875⁰ x 1.904⁰ Kanamitsu et al. 2002 1979 - Present

Schmidt et al. [7]
coordinates. Glider CSIR1, deployed in November of 2013 and 2015, was set to
navigate to 43°S, 8.5°E. Glider CSIR2 was deployed in July of 2015 and navigated a
large area, as shown in Figure 1. Shown in Figure 2, upon arrival at the study site a
pseudomooring circular sampling pattern with a diameter of 16 km was maintained
(Monteiro et al., 2015). This technology is designed with a cloud-based infrastructure
that manages the transfer of real-time data from the WG to the pilots and scientists.
Figure 1: Scope of the study area in the Subantarctic Southern
Ocean, with the majority of data points collected at 43°00'S
8°30'0E. A color map overlay shows the monthly averaged wind
speeds in meters per second (ECMWF) of the study area for
December 2015. A photo insert shows the surface setup of the
Wave Glider with Airmar meteorological station.
The WG is designed to augment existing marine technology by providing
autonomous real-time data collection of parameters at the air-sea interface. The

Schmidt et al. [8]
Southern Ocean Carbon & Climate Observatory (SOCCO) at CSIR uses this technology
in their integrated multi-platform approach for a series of the Southern Ocean Seasonal
Cycle Experiments (SOSCEx), which use research vessels, WGs, profiling gliders, bio-
optic floats, satellite data and numerical models to explore the climate sensitivity of
carbon and ecosystem dynamics. The WGs deployed during the research expeditions
were equipped with an Airmar WX-200 meteorological sensor mounted on a mast 0.70
m above sea level. Via ultrasonic transducers, this sensor detected instantaneous
changes in weather and measured wind speed and wind direction. The sensor
averaged wind speed and direction every 10 minutes.
Figure 2: Map of sampling region in the Subantarctic Southern Ocean, centered at 43°00'S
8°30'0E. The WG sampling locations are indicated with respect to spatial distribution of gridded
wind products.
2.2.2 Direct comparison approaches between in situ and satellite product winds
Satellite derived winds are remotely retrieved by microwave sensors which
measure wind in an equivalent neutrally stable state. This process makes them
sensitive to ocean surface roughness due to the stratification of the atmosphere above

Schmidt et al. [9]
sea level. These sensors are thus calibrated to an equivalent neutral wind at a
reference height of 10m above the sea surface (Liu and Tang, 1996; Bourassa et al.
1999a; Bourassa et al. 2003; Chelton et al. 2004). These equivalent neutral winds are
the winds that would exist if the atmospheric boundary layer was neutrally stratified
(Chelton et al. 2004; Carvalho et al. 2013). In order to compare real-time stability
dependent winds to equivalent neutral winds, a transformation must be performed to
shift the WG winds to the same reference level as satellite winds. Any extrapolation
method to shift measured wind speed to a different height is a function of atmospheric
turbulence. Wind shear and the buoyant forcing of the atmosphere must be taken into
account; ultimately the vertical density stratification of the atmosphere must be
accurately represented (Ruti et al., 2008).
Various methods can be used depending on the amount of input data available.
One commonly used method, proposed by Liu and Tang (1996) is based on the bulk
aerodynamic relation. This method takes into account differences in atmospheric
stability during extrapolation. A prior analysis by Liu (1990) on moisture induced
atmospheric instability indicated that along with wind speed, air and sea surface
temperatures, pressure, and relative humidity should be included in the computation of
equivalent neutral wind. The algorithm developed to do this transformation is based on
the following relation
𝑈 𝑛 − 𝑈𝑠 = (
𝑢∗
𝑘
)[ln(
𝑍
𝑍0
)] + (
𝑢∗
𝑘
)𝜑𝑗(𝑍, 𝑍0, 𝐿), (2)
where the term (Un – Us) denotes wind speed relative to the surface speed (Us) as a
function of height Z, while ln(Z/Z0) is an adjustment term for height (Singh et al., 2013).
This approach requires additional observational inputs of air and sea surface
temperature, relative humidity, and atmospheric pressure which are not available for
this study period and site. As in Satheesan et al. (2007), Singh et al. (2013), Sudha and
Rao (2013), and Yang et al. (2014), the present study required a method which does
not include inputs associated with atmospheric stability.
Bentamy et al. (2008) and Qing and Chen (2015) use the wind profile power law
in their validation tasks of OSCAT and ASCAT wind data in the Atlantic Ocean. This law

Schmidt et al. [10]
shows the relationship between wind speeds at one height and those at another and is
expressed as
𝑈
𝑈 𝑟
= (
𝑍
𝑍 𝑟
)
∝
, (3)
where U denotes wind speed at height Z (Qing and Chen, 2015). Ur is the known wind
speed at reference height Zr. With this method, neutrally equivalent winds are calculated
with the use of the coefficient (α) which varies with atmospheric stability. Qing and Chen
(2015) found that over open water, this coefficient should be assigned a value of 0.11.
However, no intercomparison has been performed to determine the difference between
a power expression method and the method proposed by Liu and Tang (1996). As such
the present study uses a mixing-length approach used by Herrara et al. (2005), Ruti et
al. (2008), Carvalho et al. (2013), Singh et al. (2013), Alvarez et al. (2014) and Yang et
al. (2014) in the vertical transformation of in situ surface wind data to a reference height
of 10 meters. With a lack of atmospheric stability input, a logarithmic method proposed
by Peixoto and Oort (1992) is used. This is expressed as
𝑈 𝑍 = 𝑈 𝑍 𝑚
ln
𝑍
𝑍0
ln
𝑍 𝑚
𝑍0
, (4)
where Uz is wind speed at height Z, Zm denotes measurement height, and Z0 represents
the roughness length. A typical oceanic value of 1.52 x 10-4 m was assumed for Z0
(Peixoto and Oort, 1992). This approach generates a logarithmically varying vertical
wind profile while assuming neutral stability conditions. An inter-comparison of the
correction methods proposed by Liu and Tang (1996) and by Peixoto and Oort (1992)
was performed by Mears et al. (2001). They conducted a comprehensive analysis to
determine if these laws can account for effects due to differences in atmospheric
stability. It was concluded that the Liu and Tang (1996) correction is typically on
average 0.12 m.s-1 stronger than a logarithmic correction (Mears et al., 2001; Pickett et
al., 2003; Ruti et al., 2008). Under stable conditions, Liu and Tang (1996) corrected
winds are greater than logarithmic corrected winds, and for stable conditions the
opposite is seen (Carvalho et al., 2013). Ruti et al. (2008) also compared these
correction methods and concluded that generally a difference in collocated wind speed

Schmidt et al. [11]
is only observed during extreme wind events, where wind speeds exceed 15 m.s-1.
Singh et al. (2013) conclude that during these extreme wind events, wind speed
transformation carried out by a logarithmic profile may lead to errors of 1–1.5 m.s-1.
However, it can be assumed that the use of a logarithmic extrapolation method will not
cause discernable error as there are very few instances in the WG data where the wind
speed is greater than 15 m.s-1. As such, the logarithmic method proposed by Peixoto
and Oort (1992) was chosen for this study.
Each WG dataset consisted of more than ten thousand wind measurements
averaged over 10 minute intervals. Initially, the WG data was difficult to interpret due to
the level of noise which accompanies high frequency data. In order to reduce the noise
of the data, a simple running mean filter was applied over the time series. Because we
are interested in a 6 hourly resolution, the running mean was set to average each data
point over a 6 hourly period to match the temporal resolution of the wind products used
in comparison. Once the noise was reduced, it was necessary to temporally sample the
WG data in an attempt to scale down the sample size to match the varying wind
products. A comparison of possible methods was performed at the start of this study. An
instantaneous sampling method at hourly intervals was applied, as well as 10 minute
bins were temporally averaged to estimate hourly WG parameters. Figure 2 shows the
results of the different binning methods at a 6 hourly resolution.
Figure 3: A comparison of binning methods applied to in situ data. WG data are gridded
temporally via averaging and instantaneous sampling on hourly and 6 hourly time scales.
An instantaneous sampling approach may be appropriate in cases where the in-
situ time series matches exactly with the satellite time series. On the other hand,
according to Ruti et al. (2008) an averaging method may require consideration of the
phase velocities of cyclones in the region of interest. By determining the typical phase

Schmidt et al. [12]
velocity for Mediterranean cyclones, Ruti et al. (2008) were able to determine a general
time frame of 30-60 minutes over which in-situ data from the Mediterranean should be
averaged when compared with scatterometer winds. Other studies, such as one by
Bourassa et al. (2003), state that the optimal averaging period varies with respect to
wind speed at the moment of observation. During high wind periods, a shorter
averaging period may result in a lower RMSE, whereas during low wind speed periods,
longer averaging periods result in lower RMSE (Bourassa et al., 2003). This variability in
optimum averaging time with respect to wind speed is anticipated from Taylor’s
hypothesis (Taylor, 1938). This study took an averaged sampling approach to binning
the time series. The data was binned twice, first to create an hourly product and then a
6 hourly product. Instead of sampling over the duration of each hour, data points before
and after each desired interval were averaged.
In order to compare blended wind fields from satellite data with the WG
observations, a collocation procedure was necessary to match the datasets spatially
and temporally. For all wind products, a linear interpolation procedure was performed to
produce wind pairs from both datasets. This method was possible due to the
aforementioned temporal gridding of the WG data. Because the WGs were set to circle
a fixed point, the majority of the collocations were performed on a coordinate points
close to 43°00’S 8°30'E (Figure 1 and 2).
2.3 Statistical Comparison
WG meteorological records are provided every 10 minutes, while the satellite
products and numerical weather products provide four data points per day
(corresponding to the hours 00H00, 06H00, 12H00, and 18H00 UTC). Initial statistical
analysis is intended to assess the quality of the simultaneous records with respect to
temporal and spatial accuracy. In order to determine if the error associated with the
satellite and reanalysis derived wind data is related to a particular wind speed, the wind
data were analysed as a whole as well as in low, medium, and high wind speed
categories. The thresholds for these wind speed categories were determined by the
lower and upper quartiles of the WG dataset. This varied for each time series; for
example, the first deployment of the CSIR1 WG in 2013 had a low speed thresholds of

Schmidt et al. [13]
3.6 m.s-1 and a high wind speed threshold of 6.1 m.s-1. However, the same WG in 2015
had a low speed threshold of 9.0 m.s-1, and a high speed threshold of 16 m.s-1. In all
analyses, the WG wind speeds fall within these categories. However, all valid data
collocated records of each wind product were considered. As a result, there will be
times during which the satellite and reanalysis derived wind speeds fall outside these
wind speed categories. The purpose of this approach was to show the error
dependence on wind speed as well as to determine which product best represented
each wind category.
The mean as well as the standard deviation of all wind products per wind speed
category are initially calculated with the intent to show the standard variance from the
mean. Statistical parameters such as the Root Mean Square Error (RMSE)
𝑅𝑀𝑆𝐸 = [
1
𝑁
∑ (𝑈𝑖 − 𝑈𝑗)
2𝑁
𝑖=1 ]1/2
(5)
and correlation coefficient (R2) are calculated to assess the ability of the wind products
to represent the observed winds. Ui and Uj are variables which represent the satellite
product wind speed and the observed wind speed by the WG, respectively. More
specifically, these parameters will determine the accuracy of the varying wind product’s
ability to represent the temporal variability of the wind. Bias is calculated
𝐵𝐼𝐴𝑆 =
1
𝑁
∑ (𝑈𝑖 − 𝑈𝑗)𝑁
𝑖=1 (6)
in order to evaluate the tendency of the data and is intended to estimate differences in
the mean state of the wind field. The intent of observing this parameter is to determine if
wind products have a tendency to over/underestimate the WG measured wind speed.
Lastly, Weibull Probability Density Functions (PDF’s) were used to evaluate the
various wind products ability to describe the WG measured wind regime. This method
was similarly used by Liu et al. (2008) and Carvalho et al. (2013) in their assessment of
QuikSCAT and CCMP’s ability to characterize buoy measured wind regimes closest to
reality.

Schmidt et al. [14]
3. Results
3.1 In-Situ Comparison
Wind speeds measured by the WG are compared against the corresponding
gridded 10m surface winds available from various products. Initially, comparison was
made between the entireties of all datasets. After hourly and 6 hourly binning methods
were applied, the raw data are reduced to 264 collocated data points of each product
with the WG series (Figure 4).
In order to understand the degree of variance from the mean of each product, the
mean and standard deviation of all wind products per wind speed category provided in
Table 1. The mean for the varying wind speed categories increases with respect to wind
speed. Additionally, the magnitude of increase in the mean is fairly uniform between low
and medium wind speeds (on average an increase of 2-3 m.s-1). However this
magnitude varies greatly between medium and high wind speed categories for all wind
products. For instance, between the medium and high wind speed categories the mean
of the SW product increased on the order of 1-2 m.s-1, while the other wind products
increased on the order of 5-7 m.s-1. During the the CSIR2 WG time series in 2015 the
SW, ECMWF, and CFSv2 products underestimated the mean state while the NCEPII
product overestimated the mean state. All wind products, including NCEPII
underestimated the mean state of the winds recorded by the CSIR1 WG in 2015-2016.
The variance of the mean of the WG data, as depicted by the standard deviation, is
consistent at low and medium wind speeds, and increases slightly for high wind speeds.
The only wind product which depicted this trend was NCEPII. For all three time series,
the NCEPII low wind speed category had the lowest variance of all wind speed
categories, and the high wind speed category had the highest variance from the mean.
The SW product exhibited low variance with low and high speeds, while the greatest
variance from the mean was at medium wind speeds. The ECMWF product consistently
exhibited a trend of decreasing variance with an increase in wind speed. The CFSv2
product for two time series depicted low wind speeds exhibiting the highest variance,
however the most recent time series (CSIR1 2015-2016) depicted the opposite; low
wind speeds having the least variance from the mean.

Schmidt et al. [15]
Figure 4: Comparison of wind products with in situ WG data in black and upper and lower limits
of each product dotted lines of their respective colours. (a) WG CSIR1 [2013-2014]. (b) WG
CSIR2 [2015]. (c) WG CSIR1 [2015-2016].

Schmidt et al. [16]
Table 1: The mean and standard deviation of all wind products and WG data per wind speed
category, as defined in Section 2.3. All bold products indicate the closest match to the WG
data.
Dec2013
Jan2014
ALL
(m.s-1
)
LOW
(WG ≤ 3.6 m.s-1
)
MEDIUM
(3.6 ≤ WG ≤ 6.0 m.s-1
)
HIGH
(WG ≥ 6.0 m.s-1
)
CSIR1 4.77 ± 1.76 2.46 ± 0.68 4.83 ± 0.68 6.98 ± 0.87
SW 8.41 ± 2.79 6.12 ± 3.52 9.01 ± 2.01 9.49 ± 1.92
CFSv2 9.19 ± 3.78 5.54 ± 3.52 9.22 ± 2.43 12.78 ± 2.55
ECMWF 8.99 ± 3.41 6.77 ± 3.88 9.62 ± 2.92 9.93 ± 2.84
NCEPII 10.25 ± 4.43 6.35 ± 3.57 10.68 ± 3.51 13.29 ± 4.07
July2015
Nov2015
ALL
(m.s-1
)
LOW
(WG ≤ 7.3 m.s-1
)
MEDIUM
(7.3 ≤ WG ≤ 12.4 m.s-1
)
HIGH
(WG ≥ 12.4 m.s-1
)
CSIR2 9.98 ± 3.76 5.21 ± 1.32 9.93 ± 1.46 14.83 ± 2.03
SW 9.63 ± 3.24 8.42 ± 3.06 9.51 ± 3.37 11.10 ± 2.49
CFSv2 9.80 ± 3.97 6.30 ± 3.04 9.51 ± 2.86 13.86 ± 2.93
ECMWF 9.58 ± 3.64 6.25 ± 2.92 9.39 ± 2.57 13.26 ± 2.57
NCEPII 11.11 ± 5.03 7.39 ± 3.81 10.52 ± 3.63 16.00 ± 4.70
Dec2015
Feb2016
ALL
(m.s-1
)
LOW
(WG ≤ 9.0 m.s-1
)
MEDIUM
(9.0 ≤ WG ≤ 16 m.s-1
)
HIGH
(WG ≥ 16 m.s-1
)
CSIR1 12.50 ± 4.79 6.76 ± 1.84 12.20 ± 2.06 18.82 ± 2.53
SW 8.57 ± 3.14 7.36 ± 2.93 8.67 ± 3.14 9.59 ± 2.99
CFSv2 9.39 ± 4.05 5.17 ± 2.16 8.96 ± 2.39 14.50 ± 2.32
ECMWF 9.21 ± 3.90 4.95 ± 2.28 8.88 ± 2.16 14.12 ± 2.03
NCEPII 11.15 ± 4.97 6.72 ± 2.70 10.67 ± 2.80 17.28 ± 3.41
Further statistical comparison was conducted using parameters such as root
mean square error, mean bias, and the correlation coefficient (Table 2). Globally, there
is a tendency for satellite product wind speed errors to be greater in the presence of low
and high winds (Carvalho et al., 2013). As shown in Table 2, when comparing
observations by WG CSIR1 with the other wind products, there is a clear increasing
trend with respect to increasing wind speed. Only the SW product exhibited a decrease

Schmidt et al. [17]
in error with respect to increasing wind speed. This would indicate that the ECMWF,
CFSv2, and NCEPII wind products are more effective at representing low – medium
wind speeds of the wind fields during the months of December – February. The
observations made by WG CSIR2 during the months of July – November indicate the
opposite trend. The ECMWF, CFSv2, and NCEPII products all exhibit a decreasing
error with increasing wind speed. Overall, the lowest error depicted varies, but is most
commonly exhibited by the NCEPII and ECMWF wind products.
The bias exhibited by all wind products with respect to the data collected by the
WG ranged from as low as 0.18 m.s-1 (CFSv2 vs CSIR2 2015 all wind speeds) to as
high as -9.23 m.s-1 (SW vs CSIR1 2015-2016 high wind speeds). The trends of the bias
are also depicted in Figure 5, which compares the wind speed residuals among the
wind products. For each time series (ranging seasonally), varying products best
represent the temporal variability of different wind speed categories. For the all wind
speed and medium wind speed categories, the ECMWF product had the highest
correlation coefficients with respect to the WG in situ data. ECMWF exhibited the
following correlation coefficients for the all wind speed category: 0.75, 0.76 and 0.93,
and the following coefficients for the medium wind speed category: 0.40, 0.40, and 0.81.
Low wind speeds are best represented by the CFSv2 product, with correlation
coefficients of 0.56, 0.21, and 0.58 respectively.
Table 2: Statistics of the comparison between in-situ WG and products wind speed error per
wind speed category. This includes the root mean square error, bias, and correlation
coefficients of the differences observed in the comparison of the in- situ measurements and the
wind products. The bold values highlight the product providing the closest match with the WG
data (i.e. the smallest value of the error or bias and the highest value of the correlation
coefficient). Correlation coefficients highlighted in green exhibit a confidence interval >99%,
yellow exhibit a confidence interval between 80% and 99%, and red exhibit a confidence interval
less than 80%.

Schmidt et al. [18]
Nov2013
Feb2014
CSIR1
vs.
ALL
(m.s-1
)
LOW
(WG ≤ 3.6 m.s-1
)
MEDIUM
(3.6 ≤ WG ≤ 6 m.s-1
)
HIGH
(WG ≥ 6 m.s-1
)
N
RMSE
SW 4.40 4.95 4.64 3.11 264
CFSv2 5.17 4.45 4.93 6.22 268
ECMWF 4.84 4.50 4.68 5.45 268
NCEPII 6.58 5.17 6.75 7.43 268
BIAS
SW +3.64 +3.66 +4.19 +2.51 264
CFSv2 +4.41 +3.07 +4.38 +5.82 268
ECMWF +4.20 +3.25 +4.19 +5.17 268
NCEPII +5.47 +3.87 +5.84 +6.32 268
R^2
SW 0.48 0.32 0.16 0.32 264
CFSv2 0.76 0.56 0.34 0.54 268
ECMWF 0.75 0.52 0.40 0.60 268
NCEPII 0.60 0.27 0.26 0.27 268
July2015
Nov2015
CSIR2
vs
ALL
(m.s-1
)
LOW
(WG ≤ 7 m.s-1
)
MEDIUM
(7 ≤ WG ≤ 12 m.s-1
)
HIGH
(WG ≥ 12 m.s-1
)
N
RMSE
SW 4.25 4.58 3.64 4.95 250
CFSv2 2.81 3.22 2.76 2.47 259
ECMWF 2.62 3.14 2.45 2.35 259
NCEPII 3.73 4.47 3.34 3.64 259
BIAS
SW -0.42 +3.14 -0.49 -3.80 250
CFSv2 -0.18 +1.09 -0.43 -0.97 259
ECMWF -0.40 +1.04 -0.54 -1.57 259
NCEPII +1.13 +2.18 -0.58 +1.17 259
R^2
SW 0.27 -0.03 0.05 0.01 250
CFSv2 0.74 0.21 0.34 0.63 259
ECMWF 0.76 0.17 0.40 0.73 259
NCEPII 0.71 0.07 0.42 0.74 259
Table 2 continued on next page.

Schmidt et al. [19]
Dec2015
Feb2016
CSIR1
vs
ALL
(m.s-1
)
LOW
(WG ≤ 9 m.s-1
)
MEDIUM
(9 ≤ WG ≤ 16 m.s-1
)
HIGH
(WG ≥ 16 m.s-1
)
N
RMSE
SW 6.37 3.23 5.24 9.84 243
CFSv2 3.58 2.39 3.51 4.57 245
ECMWF 3.70 2.43 3.42 5.06 217
NCEPII 2.52 2.75 2.40 2.50 245
BIAS
SW -3.92 +0.60 -3.53 -9.23 243
CFSv2 -3.06 -1.55 -3.19 -4.32 245
ECMWF -3.16 -1.55 -3.16 -4.78 217
NCEPII -1.31 -0.75 -1.47 -1.54 245
R^2
SW 0.25 0.16 -0.08 0.24 243
CFSv2 0.93 0.58 0.79 0.81 245
ECMWF 0.93 0.59 0.81 0.77 217
NCEPII 0.90 0.35 0.73 0.82 245
Table 2 Continued
Figure 5 shows the correlation of the wind products with the in situ data collected
by WG CSIR1 from December 2013 - February 2014. Even though the SW product
exhibited the lowest errors and bias, the ECMWF and CFSv2 products had the best
correlation with the in situ data. However the low wind speeds of all wind products are
overestimated significantly which produces a skewed overall correlation. The NCEPII
product exhibited a lower correlation yet was more consistent across all wind speed
categories. Overall, the ECMWF product best represented the temporal variability of the
wind field during this time series.
by WG CSIR2 from July 2015 to November 2015. The SW product performed poorly
across all wind speed categories. All products poorly represented low wind speeds,
which is also shown in Table 2. CFSv2 and ECMWF similarly represented the medium
wind speeds, while NCEPII outperformed all other products in this category while

Schmidt et al. [20]
Figure 5: Comparison of wind products with in situ [CSIR1 WG] data collected December 2013
– February 2014 (austral summer). Wind speed categories are determined using the lower and
upper quartiles of the in situ data. Relationships for each wind speed category are shown using
the colored lines.
significantly overestimating the magnitude of high winds. CFSv2 and ECMWF similarly
best represent the high wind speed category and in general best represent the temporal
variability of the wind field during this time series.
by WG CSIR1 from December 2015 to February 2016. Similar to the CSIR2 time series,
the SW product performed poorly across all wind speed categories. ECMWF and
CFSv2 best represented low wind speeds, while NCEPII best represented medium and

Schmidt et al. [21]
Figure 6: Comparison of wind products with in situ [CSIR2 WG] data collected July 2015 –
November 2015. Wind speed categories are determined using the lower and upper quartiles of
the in situ data. Trendlines for each wind speed category are shown using the colored lines.
high wind speeds. Overall, CFSv2 performed most consistently and as such, best
represents the temporal variability of the wind field during this time series.
The residuals (or anomalies) are the differences between the expected and
measured wind speeds. As depicted in Figure 8, all wind products except SW show an
increasing trend in residuals with respect to an increasing wind speed. This is indicative
of low bias with low wind speed, and high bias with high wind speed. The SW product
depicted the opposite trend, with high bias attributed to low wind speeds, and lower bias

Schmidt et al. [22]
to high wind speeds. Figure 9 shows the residuals of the varying wind products
compared to the in situ data [WG CSIR2] collected from July 2015 to November 2015.
Figure 7: Comparison of wind products with in situ [CSIR1 WG] data collected December 2015
– February 2016 (austral summer). Wind speed categories are determined using the lower and
upper quartiles of the in situ data. Trendlines for each wind speed category are shown using the
colored lines.
The SW product still depicted a strong negative trend in bias with respect to
increasing wind speed. However, all other wind products also depicted a negative trend.
Most interestingly, the CFSv2, ECMWF, and NCEPII show increased positive residuals
for low and high wind speeds, but reduced residuals for medium wind speeds. In this
time series [CSIR2 July2015- Nov2015], the ECMWF product had the lowest residuals

Schmidt et al. [23]
for medium and high wind speeds. Figure 10 shows the residuals of the varying wind
products compared to the in situ data [WG CSIR1] collected from December 2015 to
February 2016. All products display similar trends seen from Figure 9. Again, NCEPII
product showed positive residuals for low and high wind speeds, and negative residuals
for medium wind speeds. For this time series [CSIR1 Dec2015- Feb2016], the CFSv2
and ECMWF products similarly had the lowest residuals for all wind speed categories.
Figure 8: Wind speed residuals (wind product minus in situ data) for WG CSIR 1 time series
between December 2013 – February 2014. Linear fit is displayed in gray.
In order to assess which of the wind products offers a characterization of the WG
wind regime closest to reality, a Weibull PDF is used. The PDFs of each wind product
are shown in Figure 11. For the CSIR1 time series which spanned from Dec 2015 –
Feb2014, the Sea Winds product showed better performance for the wind speed

Schmidt et al. [24]
temporal variability (RMSE, R^2), along with low errors in the estimation of the wind
speed frequency distribution due to its lower biases for the wind speed. As such, the
SW PDF curve is closest to the WG PDF curve. All wind products significantly
overestimate the frequency of wind speeds greater than 6 m.s-1 and underestimate the
frequencies of wind speeds below 4-6 m.s-1. This behavior is portrayed by a shift in the
Weibull curves to the right side of the wind speed axis for all products with respect to
the WG CSIR1 [December 2014 – November 2014] distribution curve. For the second
time series [CSIR2 July 2015- November 2015], the ECMWF product is almost
between July 2015 – November 2015. Linear fit is displayed in gray.

Schmidt et al. [25]
between December 2015 – February 2016. Linear fit is displayed in gray.
identical to the ECMWF frequency distribution for wind speeds. Lastly, the wind speed
frequency distribution of the NCEPII product is almost identical to the of the third time
series [CSIR1 December 2015- February 2016]. The PDFs shown in Figure 11 show
interesting trends. While the in situ wind speed frequency distribution varies significantly
with respect to time, the wind products used in this comparison do not. When compared
to the other products, the SW product systematically represented the wind fields having
higher frequencies of low winds. The ECMWF and CFSv2 products had a more even
frequency distribution across all wind speed categories, while the NCEPII product
tended to introduce a lower variability in the wind speed frequency distribution. It is also
interesting to note that the higher the maximum wind speed of the wind product, the
greater the variability and the more even the distribution of wind speeds.

Schmidt et al. [26]
Figure 11: Wind speed frequency distribution (Weibull PDF) for all products (SW, ECMWF,
CFSv2, and NCEPII) from all time series of in situ wind collection by WGs CSIR1 and CSIR2.
5. Discussion
The intent of the study was to identify which product most accurately represents
the variability of the wind field in the Southern Ocean in comparison to in situ. Previous
studies have concluded that globally, satellite products, such as QuikSCAT, OSCAT,
and CCMP tend to overestimate wind speed exponentially with respect to wind intensity
(Carvalho et al., 2013), and have the highest margins of error during low and high wind
events (Sudha et al., 2013; Alvarez et al., 2014; Jayaram et al., 2014). However,
because the systems have not been rigorously inter-calibrated, any comparison
between satellite wind data, in situ data, or numerical weather predictions will only give
relative information on accuracy (Vogelzang et al., 2012).
5.1 Product Performance
Based on the results from Table 2 and as seen in Figures 5-11, performance of
each product varied with respect to time. For the first time series [December 2013-
February 2014], while the SW product best represented the mean wind state with the
lowest errors (Table 2), ECMWF had the highest and most consistent correlation
coefficients across all wind speed categories. Figure 5 and 8 show that the ECMWF

Schmidt et al. [27]
product, albeit with high residuals with respect to the SW product, best represents the
temporal variability of the wind field for the first time series. During the second time
series [July 2015 – November 2015], the ECMWF product was consistent across all
wind speeds at best representing the mean wind state. The CFSv2 product was a close
second and compared to the WG data, had the most similar overall mean wind speed
with respect to the in situ data. The WG all wind speed average was 9.98 m.s-1 while
the CFSv2 all wind speed average was 9.80 m.s-1 (Table 1). ECMWF all wind speed
average was slightly less (9.58 m.s-1). The SW product significantly underestimated all
wind speeds, which is why it appears to be a good representative of the all wind speed
average for the second time series. As shown in Figure 2, ECMWF had the lowest
errors and was a close second to CFSv2 in the lowest bias with respect to the WG
winds. Regarding the correlation of the products with the July 2015 – November 2015
WG series, ECMWF had the highest overall correlation and was marginally second to
the CFSv2 and NCEPII products for the other wind speed categories. It should be noted
that all products performed very poorly in their representation of the temporal variability
of low winds (Table 2, Figure 6). Additionally, as depicted in Figure 6, the NCEPII
product derives its high correlation from a gross overestimation of high wind speeds and
underestimation of low wind speeds. For these reasons as well as due to its low errors
and bias and consistently high correlation across all wind fields, the ECMWF product
best represented both the mean wind state and temporal variability of the wind field
sampled from July 2015 – Nov 2015. The final time series [December 2015 – February
2016] was best represented by two products; ECMWF and NCEPII. During this time
series, all wind products underestimated the wind fields observed by the WG CSIR1.
NCEPII performed best in representing the mean wind state. It also had the lowest
errors and biases in all categories except the low wind speed category. NCEPII was
only outperformed by ECMWF with regard to its correlation to the temporal variability of
the observed wind field. Even though ECMWF outperformed NCEPII in this category,
overall the NCEPII product performed best for the third time series [December 2015 –
February 2016].
Regarding the first time series, the overestimation of all wind products forces the
SW product to in some cases have the lowest residuals, even though it clearly does not

Schmidt et al. [28]
represent the wind fields as well as other wind products. For the following two time
series, the SW product grossly underestimated the wind fields and systematically had
poor correlation across all wind speeds; as such it is not considered to be a viable
product for use in fine-scale modelling. This can be attributed to the fact that while SW
data are available at a relatively high spatial resolution (0.25⁰ x 0.25⁰), it is a gridded
satellite product. This product uses a simple objective analysis method (spatial-
temporally weighted interpolation) to generate a gridded and blended product (Zhang,
2006). This method of blending satellite observations minimizes but does not completely
eliminate data gaps. As such, the high resolution of spatial and temporal sampling
conducted by WG technology may not be represented accurately. The high
performance of both the ECMWF and CFSv2 products can be attributed to the high
spatial resolution of the datasets (0.125⁰ x 0.125⁰, and 0.205⁰ x 0.204⁰ respectively).
Four of the ECMWF data grid locations fall either directly on or within a tenth of a
degree away from the circumference of the circular sampling pattern (Figure 2) of the
WGs for the three time series. The high bias of the NCEPII product can be attributed to
its low resolution, as the nearest point of reference to the majority of the in situ
measurements is roughly 100 kilometers away (see Figure 2). Overall, the ECMWF
product is best at consistently representing the temporal wind field variability.
For the first two time series, measures of error and bias for ECMWF and CFSv2
were lower than for NCEPII for all wind speed categories. However, for the third time
series the NCEPII product clearly outperforms all other products with regards to error
and bias. While the CFSv2 product is better than ECMWF at representing the mean
wind state of low and medium wind speeds, ECMWF is better at representing the
temporal variability of the wind field magnitude. Upon closer inspection of the
performance of the NCEPII product, it best represented mean wind state in that it
exhibited similar trends in standard deviation across wind speed categories. For
example, in the first time series NCEPII had similar deviation from the mean of the low
and medium wind speeds, and a higher deviation for the high wind speed category. A
similar trend is observed regarding the deviation from the mean of the in situ data
across wind speed categories. This similar trend observed between the WG and
NCEPII data is reflected across all three time series, while all other products exhibit

Schmidt et al. [29]
opposing trends in deviation from the mean. Due to these factors, the NCEPII product is
best at representing the mean wind state and deviation from the mean state, especially
during times of high mean winds. However this product must be used with caution, as
Figures 9 and 10 show that NCEPII derives its overall good correlation through its
overestimation of low and high wind speeds.
The ability of a product to best represent the wind speed frequency distribution
also varied over the different time series. Findings by Pickett et al. (2003) and Tang et
al. (2004) suggest that satellites measures higher frequencies of strong winds and lower
frequencies of weak winds. It is curious to see that the frequency distribution of the in
situ wind speeds varies among times series, while the frequency distribution of the wind
product wind speeds remains fairly constant across the three time series. As such, a
different product for each time series best represented the frequency distribution of
observed wind speeds. The SW product consistently showed higher frequencies of low
wind speeds, while ECMWF and CFSv2 similar showed a fairly uniform distribution of
wind speed frequencies. NCEPII consistently showed a more broad distribution of
speed frequencies. However, the similarity of the WG and SW PDFs during the time
frame of the first sampling period is not indicative of a better ability of the SW to
represent the wind speed frequency spectrum. In fact, it is indicative of a gross
overestimation of wind speed by all wind products, which forces the product with the
lowest bias to have the most similar wind speed frequency distribution. During the
second time series, the average mean wind state increased by more than 5 m.s-1, which
caused the product which best represents low and medium wind speeds to accurately
represent the speed frequency distribution – in this case, ECMWF. There is also an
increase in the mean wind speed of the third time series. This increase of more than 3
m.s-1 (with respect to the second time series) caused the NCEPII product to best
represent the frequency distribution of the wind speed.

Schmidt et al. [30]
5.2 Sampling Period Sensitivities
Satellite derived wind data as well as reanalysis and model products can be very
different from in-situ anemometer data in that they tend to represent synoptic winds. A
study by Atlas et al. (1999) showed that satellite products are able to detect mesoscale
features, however this representation is limited due to the fact that satellite and
numerical weather prediction products are of lower spatial and temporal resolution
(Carvalho et al., 2013). Anemometer wind data is closer to a temporal average of
instantaneous moment, while satellite winds represent a spatial average of
instantaneous moments (Pansieri et al., 2010). Studies have shown that in cases where
a limited amount of data existed, such as from a single satellite, any interpolations/
extrapolations were inaccurate in representing the true strength of mesoscale events
(Isaksen and Stoffelen, 2000). Bearing this in mind, over most SW grids the number of
observational input data points is over 40 (Zhang, 2006). As such a multiple satellite
blending scheme can be advantageous in regions of lower mean wind states in the
Southern Ocean. ECMWF is a reanalysis product, however and uses a sequential data
assimilation scheme, advancing forward in time using 12 hourly analysis cycles (Dee et
al., 2011). A temporally short-scale assimilation scheme could give ECMWF an
advantage over other reanalysis products such as NCEPII in representing the temporal
variability on shorter temporal scales, as is the scale of this study. CFSv2 is a fully
coupled ocean–land–atmosphere dynamical model using a global ocean data
assimilation system (GODAS) operational at NCEP (Saha et al., 2014). This product is
unique in that it makes retrospective forecasts to calibrate operational subsequent real
time sub-seasonal and seasonal predictions (Saha et al., 2014). This calibration
scheme could be why CFSv2 is a more rounded product and does well in representing
both the temporal variability and the mean wind state. The NCEPII product is a model
simulation as well (forecast/hindcast reanalysis) however the data assimilation scheme
is slightly different to ECMWF which is forced to conform to observational input. Using
4-D data assimilation methodology, observational input from ships, satellite, radiosonde,
aircraft, and meteorological station observations are incorporated into NCEPII (Kalnay
et al., 1999). An assimilation methodology with high input gives NCEPII ability in

Schmidt et al. [31]
representing regional climate dynamics (Kanamitsu et al., 2002), even though it is of the
coarsest spatial resolution compared to all other wind products.
5.3 Implications:
Ocean circulation modelling, climate change estimation, and numerical weather
prediction is heavily dependent on the accuracy of input. Upper ocean dynamics are
heavily linked to atmospheric variability in the Southern Ocean. As mentioned
previously, the mid-latitude regions of the Southern Ocean are host to the strongest
wind fields at the ocean surface. If these winds are not accurately represented, models
will incorrectly simulate sea surface exchanges of heat and moisture flux, lateral
advection, stratification and mixed layer dynamics (frontal formation and instabilities),
and Ekman pumping and transport for example. However, the present study among
others shows that in many cases the wind fields are not accurately represented.
Upper ocean models which include satellite-derived wind speed input will not
accurately represent variance in MLD and SST. Ocean circulation models have shown
the sensitivity of the MLD to the gustiness in the wind (Lee et al., 2008). Turbulent
mixing and subsequent buoyancy forcing caused by sea surface winds strongly
influences the MLD. As the MLD shallows, high wind events are observed to have a
greater impact on the deepening of the mixed layer (Carranza and Gille, 2015).
Carranza and Gille (2015) observed that during high wind events, water from below the
mixed layer is entrained in the upper ocean as the mixed layer drops. Swart et al.
(2014) claim that this variability of the MLD is driven by synoptic storms (4-9 days)
where turbulent mixing deepens the surface mixed layer, while quiescent wind episodes
draw the MLD up rapidly. This wind-driven mixing also has an effect on SST, where cold
water from below the MLD is entrained in the upper ocean. This in turn influences the
mixed layer heat budget (Bonekamp et al., 1999) and produces cold SSTs. The study
by Carranza and Gille (2015) found a statistically significant negative correlation
between wind speed and SST anomalies, and that wind speed alone can explain as
much as 80% of the variance observed in SSTs.
There are many important biological applications of wind data which require high
temporal and spatial quality. Knox (2007) observed the MLD to be the shallowest in the

Schmidt et al. [32]
summer at less than 90 meters. This is close to the bottom of the euphotic zone;
beyond which photosynthesis can no longer be supported by solar radiation. Deepening
of the MLD either facilitates phytoplankton growth via a greater input of nutrients, or
brings the chlorophyll (Chl-a) maximum up to a shallower depth. Since the mixed layer
is deepened by strong winds, high wind speeds and high Chl-a are generally positively
correlated (Carranza and Gille, 2015). Carranza and Gille (2015) observe on short time
scales (days to weeks), nutrient and chlorophyll entrainment to the mixed layer follows
wind variability with a time lag of just a few hours. Areas of the Southern Ocean which
showed positive correlations between wind speed and Chl-a tended to persist on
smaller, weekly timescales and were associated with synoptic storms. However, on
longer time scales the Southern Ocean does not exhibit a positive correlation between
high wind and high Chl-a. On monthly to seasonal timescales, correlations between
wind speed and Chl-a tend to be negative, especially over regions with deep winter
mixed layers (Carranza and Gille, 2015). Their rationale for this is that wind-induced
mixing action is strong enough to force the Chl-a out of the euphotic zone. In high
latitudes of the Southern Ocean, the seasonality of the mixed-layer depth is
fundamental for explaining seasonal chlorophyll bloom characteristics (such as its
initiation and decline) (Sverdrup, 1953). Thomalla et al. (2011) investigated the
seasonality of Chl-a in the Southern Ocean. Along with Fauchereau et al. (2011), this
study concluded that variability in Chl-a bloom events on a sub-seasonal scale is a
result of highly-variable wind stress in addition to buoyant forcing induced stabilization
of the upper water column via mesoscale to sub-mesoscale dynamics. A similar study
by Swart et al. (2014) attributes gaps in biogeochemistry/ phytoplankton distribution and
abundance to a lack of good understanding of the wind field in the Southern Ocean.
Swart et al. (2014) emphasize that poor knowledge of the wind field could potentially
inhibit an understanding of physical and biogeochemical interactions in the region.
Thus, quantifying the impact of wind on stratification is needed before the seasonal
scale variability of Chl-a can be modelled. The physical forcing mechanisms responsible
for enhanced Chl-a are still unclear (Thomalla et al., 2011).
Carranza and Gille (2015) further investigated the impacts of winds and
buoyancy forcing on Chl-a abundance in the Southern Ocean. With respect to their sea

Schmidt et al. [33]
surface wind analysis, Cross Calibrated Multi-Platform (CCMP) data provided gridded
surface wind fields at a 6-hour temporal resolution. CCMP wind data is different from
reanalysis data in that it falls under the class of blended scatterometer data, where
multiple datasets from different platforms are blended to fill in spatial and temporal gaps
(similar to SW). Global studies by Kahru et al. (2010) have shown that sea surface
winds, such as the ones represented by CCMP data, have a measurable influence on
Chl-a variability. Klein and Coste (1984) find that entrainment of chlorophyll at the mixed
layer is directly related to variability of wind speed. Carranza and Gille (2015) use the
following formula to calculate the entrainment rate at the base of the mixed layer
following the methods of De Szoeke (1980); Musgrave et al. (1988); and Close and
Goosse. (2013):
𝑊𝑒 =
𝜕ℎ
𝜕𝑡
+ 𝑊𝐸𝑘(ℎ) (7)
Turbulent (
𝜕ℎ
𝜕𝑡
) and non-turbulent ( 𝑊𝐸𝑘) processes which facilitate entrainment are
represented in this equation. The turbulent parameter is modified by wind induced
mixing and will vary with variability of the wind field (Carranza and Gille, 2015).
Additionally, the vertical displacement of the mixed layer is represented by the non-
turbulent parameter. This displacement is a result of divergences in the mixed layer
driven solely by wind stress curl (τ) (Carranza and Gille, 2015). This is shown in the
calculation of the non-turbulent parameter ( 𝑊𝐸𝑘 =
∇ × τ
𝜌𝑓
). If sea surface wind fields are
not correctly represented by satellite data (in this case CCMP data), both turbulent and
non- turbulent processes will consequently be misrepresented in the calculation of
entrainment.
A better representation of variability in the upper ocean dynamics such as winds,
SST, and MLD will yield a greater understanding of trends in primary productivity.
Findings by Nicholson et al. (submitted 2016) emphasize the need for high resolution
wind input in biogeochemical models. They created a model with idealized storm-driven
vertical mixing scenarios in an attempt to show how summer primary production can be
sustained over seasonal time scales. The premise of their study is that inertial (wind)

Schmidt et al. [34]
driven subsurface mixing beneath the surface-mixed layer makes significant
contributions to the refurbishment of the subsurface reservoir of iron which available to
be entrained by deepening mixed-layers. As such, Nicholson et al. (submitted 2016)
suggest that the intra-seasonal mixed layer perturbations linked to mid-latitude storms
may offer relief from iron limitation in the summer bloom period. If wind products are of
low resolution and miss synoptic storms, it is possible that correlations between wind
speed and Chl-a during summer blooms are inaccurate. Swart et al. (2014) use high
resolution glider observations (similar to those used in the present study) in the Sub-
Antarctic Zone (SAZ) and provide evidence to suggest that transient wind events drive
intra-seasonal variability in Chl-a. During sustained blooms, their observations of
collocated wind stress with MLD yielded a correlation coefficient of greater than 0.40
(Swart et al. 2014). Such observations from gliders emphasizes that the variability of
wind stress will drive the MLD variability, which has since been observed to correlate
with Chl-a. There is need to examine these relationships further whilst considering the
shortcomings of NWP models as well as blended satellite wind products ability to
represent mesoscale cyclones (Yuan et al. 2009).
A hemispheric scale process that has important climatic implications is the
Southern Annular Mode (SAM). The SAM reflects the north-south transfer of
atmospheric mass and pressure between the Southern Hemisphere mid- and high
latitudes and hence strongly influences the variability in the westerly winds over the
Southern Ocean. Thompson et al. (2011) show that changes in Antarctic ozone levels
has driven poleward displacement of the SAM, which results in summertime Southern
Hemisphere climate change. In the summer, over high latitudes there is an acceleration
of the prevailing eastward wind which drives eastward directional departures from the
long term mean (Thompson et al., 2011). Thompson et al. (2011) state that anomalous
eastward flow at high latitudes results in increased Ekman transport over much of the
Southern Ocean and upwelling south of 60⁰S. They observe the opposite at middle
latitudes in the Southern Ocean, where the southward movement of the SAM results in
a deceleration of the prevailing eastward wind. This deceleration results in westward
anomalies of surface flow, which consequently causes downwelling along with variability
in temperature and precipitation in the midlatitude region. Since the SAM exists on all

Schmidt et al. [35]
time scales from synoptic, intraseasonal to interannual and longer and is fundamental to
Southern Hemisphere climate, it is important to choose a wind product that represents
this mode accurately.
Variability in the air-sea interface not only affects the physical oceanography of a
region, but also the biogeochemical cycling of gaseous compounds such as CO2. A
number of studies (Wanninkhof 1992, 1999, 2009; Nightingale et al., 2000; McGillis et
al., 2001; Ho et al., 2006) have developed methodology to determine gas transfer rates,
processes which control them, and quantifying flux. These studies (Wanninkhof 1992;
Sweeney et al., 2007; Wanninkhof 2009) often find a squared or cubic relationship
between observed fluxes and wind speed. This means that an incorrect
parameterization of wind speeds could lead to significant errors in simulations of global
CO2 flux. In their study, Takahashi et al. (2009) state that due to the dependence of the
sea–air gas transfer piston velocity and the transfer coefficient on wind speed, with a
constant scaling factor a systematic increase in wind speed by 10% could consequently
increase the global CO2 flux by as much as 20%. Wanninkhof (2009) declare that as
such, much methodology to determining gaseous exchange is still in developmental
stages due to significant biases in different wind speed products.
5.4 Caveats and Future Work
Seasonal variability of the mean wind state was not a factor which was
considered in the statistical analysis, but could possibly explain why some wind
products performed better than others at different times of the year. However, the first
and third time series were measured over the same season (December – February) and
differences in the results of the statistical analysis are significant enough to debunk this
theory. Thus this discrepancy may be a result of poor sensor performance specific to
the mission. Either the sensor may not be calibrated properly, or salt from sea spray
could have plugged the vent on the Airmar sensor, preventing the mylar pressure from
stabilizing and thus impacting the measurements taken by the wind sensor. This theory
is under investigation and will be confirmed if pressure levels measured during the first
time series indicate there was in fact a blocked vent. Additional further study should
investigate the seasonal, annual and interannual variability of the wind field in the study

Schmidt et al. [36]
region prior to analysis. Seasons or years where a lower mean wind speed state (less
than 6 m.s-1) is prevalent could cause a product such as ECMWF or CFSv2 to be best
at representing the wind fields. Seasons or years where the mean wind state is high
(greater than 10 m.s-1) could allow a product such as NCEPII to be best at representing
wind fields. Furthermore, highly variable residuals were observed for most wind
products and for every wind speed category. Due to the small profile of the WG in the
water, it is possible that periods of the year with increased gustiness (extreme wind
events), high seas and wave sheltering could lead to inaccuracies in measurements.
The WG samples wind speed at an instantaneous location, while scatterometers
measure wind relative to a moving ocean surface (Sudha and Rao, 2013). Dickinson et
al. (2001) observed that during alignment (both in same and opposite direction) of both
wind and surface ocean current, scatterometer data tend to be under/overestimated.
This could partially explain the bias observed by all products. Furthermore, there
appears to be a signal - noise ratio. From Table 1, the highest standard deviation of the
wind products is exhibited with low wind speeds (except for NCEPII). This could be
indicative of poor scatterometer ability to detect weak wind speeds. It is possible that
the small detection limits of the sensor are ineffective in measuring backscatter from
weak wind speeds. Further study should be conducted to determine if there is a
relationship between weak wind variability and the backscatter detection sensitivity of
scatterometer sensors. Additionally, the stability of the atmospheric boundary layer
above the sea surface can potentially affect the variability of wind speeds of satellite-
derived products. At a fixed height of 10m above sea surface, the generation of
smallscale waves via wind stress varies with respect to atmospheric stability (Ebuchi et
al., 2002). This in turn affects radar backscatter which is used to derive wind speed. In
addition to this discrepancy in satellite product data, the vertical transformation of WG
data lacked atmospheric stability input. It is possible that data transformation could be of
0.7 m.s-1 greater magnitude (Singh et al., 2013). For future studies, atmospheric stability
information should be included when possible to reduce bias. This can come from the
use of WG technology, which provides a means to achieve continuous high resolution
observations of atmospheric stability in data sparse regions like the Southern Ocean.

Schmidt et al. [37]
6. Summary
Predictions of heat, moisture, and momentum exchanges between the ocean and
the atmosphere are inaccurate and the Southern Ocean is a region where there is large
uncertainty in many model estimates. Detailed observations such as ones from Wave
Glider technology are crucial for future work in that they provide valuable insight to the
sensitivity and variability of the ocean-atmosphere physical and biological response. In
this study it was found that the NCEPII product best represented the mean wind state,
while ECMWF most consistently represented the temporal wind field variability. There is
a great need to correctly parameterize transient, synoptic scale storm events to depict
decadal and century scale changes in ocean-atmosphere relationships. Studies suggest
that with changes in the SAM and subsequent higher winds in the higher latitudes,
primary productivity will increase and enhance atmospheric CO2 drawdown. Changes to
the biological pump will have a global impact on climate and it is imperative that wind
products which correctly parameterize the wind field are used in order to model and
predict climate variability and ecosystem functioning.

Schmidt et al. [38]
ACKNOWLEDGEMENTS
The Wave Glider wind data were provided by the Southern Ocean Carbon & Climate
Observatory, Council for Science and Industrial Research (CSIR) from a series of three
SOSCEx missions. The SeaWinds blended altimetry data, CFS climate forecast, and
NCEPII reanalysis wind product was downloaded from NOAA’s National Centers for
Environmental Information (NOAA/NCDC). The ECMWF reanalysis wind product was
downloaded from the European Centre for Medium-Range Weather Forecasts open
access database. I would like to thank University of Cape Town Applied Marine Science
course convener Dr. Cecile Reed for support throughout the duration of the program.
Thanks to PhD candidate Luke Gregor for his assistance with understanding the CO2
fluxes of the Southern Ocean.

Schmidt et al. [39]
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