QUALITY INFORMATION DOCUMENT
For the Global Ocean Wind Products
WIND_GLO_WIND_L4_NRT_OBSERVATIONS_012_004
Issue: 1.2
Contributors: Abderrahim Bentamy
CMEMS version scope : April 2019 Release
Approval Date by Quality Assurance Review Group : 29/04/2019
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CHANGE RECORD
Issue Date § Description of Change Author Validated By
1.0
2018-04-27
All
Creation of the document, Wind QUID split up from the SIW QUID.
Abderrahim Bentamy
Jean François Piollé
Cedric Prevost
Abderrahim Bentamy
1.1 2018-08-24 Rebranded to Wind TAC M. Belmonte Mercator Ocean
1.2 2019-01-10 Adapted to April 2019 Release
M. Belmonte Mercator Ocean
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Table of contents
Executive summary ................................................................................................................................................ 4
Products covered by this document ................................................................................................................ 4
Summary of the results .................................................................................................................................... 4
Production Subsystem description ................................................................................. Erreur ! Signet non défini.
L4 Global blended ocean wind ........................................................................................................................ 7
ValidatIon framework .......................................................................................................................................... 10
Procedure ........................................................................................................................................................ 10
Result summary .............................................................................................................................................. 13
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I. EXECUTIVE SUMMARY
Products covered by this document
This document describes the quality of the newest global ocean near-real-time 6-hourly
L4 wind product (WIND_GLO_WIND_L4_NRT_OBSERVATIONS_012_004), which
contains 6-hourly blended wind fields estimated from scatterometer wind vector observations;
SSMIS wind speeds, and NWP wind forecasts. The wind variables include 6-hourly averaged
wind speed, zonal and meridional wind components and wind stress amplitude, curl and
divergence of both wind and wind stress vectors. The input (scatterometer and retrievals) L2
scatterometer observations are made available by the operational Ocean and Sea Ice Satellite
Application Facility (OSI SAF) of EUMETSAT, and SSMIS are from Remote Sensing
System (RSS). In this version NWP data are equivalent wind stress winds calculated by
KNMI. The latter are available for IFREMER. The new L4 product involves the following
new variables: wind vector curl and divergence, and wind stress curl and divergence. The new
L4 product is produced by IFREMER and will be distributed by CMEMS.
Table 1: Wind TAC Wind products and partner roles.
Product Product description Production unit, PU Dissemination unit DU
WIND_GLO_WIND_L4_NRT_OBSERVATIONS
_012_004
Near-real-time global ocean L4 blended
winds with ECMWF operational forecast winds as background
OSI-IFREMER-BREST-FR
CNR-ISAC-GOS (Roma)
Summary of the results
The investigations of the quality of the objective method, used to estimate the gridded
wind fields, the resulting blended wind estimates, and of the operational procedure are
checked as follows:
Characterization of the error relied on the objective method used to estimate global
gridded wind fields from swath observations. It is performed using synthetic data
derived from numerical model (ERA Interim) interpolated onto satellite swaths. No
systematic departure is found between ERA Interim analysis and 6-hourly averaged
winds derived from the synthetic data based on the use of the objective method. The
correlations between the two sources exceed 0.97.
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Determination of L4 blended wind product accuracy through comprehensive
comparisons with 6-hourly winds from available moored buoy data. The latter are
derived from various buoy networks: NDBC/NOAA (Atlantic, Pacific oceans), UK
Met Office and Météo-France (Atlantic and Mediterranean Sea), TAO (Tropical
Pacific), PIRATA (Tropical Atlantic), and RAMA (Indian Ocean). Comparisons are
performed for April 2017 – April 2018. For instance, results drawn from NDBC
comparisons lead to quite low biases for wind speed as well as for wind direction. The
associated standard deviations do not exceed 1.30m/s and 20°, respectively. The
scatter and vector correlation coefficients for wind speed and direction are higher than
0.90 and 1.83 (vector correlation), respectively.
Assessment of L4 blended wind product quality based on comparisons with spatially
and temporally collocated ASCAT retrievals. The former are performed over global
ocean to characterize L4 and ASCAT wind speed and direction agreements. The
comparisons indicate that blended wind speed and wind components are in good
agreement with the remotely sensed ones. The main aim of such comparisons is to
highlight how the blended analysis retrieves the remotely sensed wind observations
derived mainly from ASCAT. Collocated data are used for comparisons purposes. The
overall statistics characterizing ASCAT and blended collocated data comparisons
indicate that the biases are close to zero and the standard deviation (std) values are less
than 1 m/s. The correlation coefficients exceed 0.95. It is noticeable that zonal and
meridional components have similar behaviours. Furthermore, ASCAT and blended
exhibit better agreements than those drawn from ASCAT and ECMWF equivalent
stress wind comparisons.
Quality control of each netcdf file geophysical content is performed based on the
calculation of the statistical parameters characterizing the difference between ASCAT
retrievals and L4 wind speed and direction data, and between ECMWF equivalent
stress and L4 winds. Only files such as differences indicate significant discrepancies
are checked. The statistical parameter time variabilities drawn from ASCAT and L4
comparisons are quite steady. For instance, wind speed bias, standard deviation, and
correlation are of 0.02 m/s, 1.0 m/s, and 0.97, respectively, along the study period.
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Estimated Accuracy Numbers
Table 2: Statistical parameters characterizing differences between 6-hourly averaged NDBC buoy and L4 blended wind speed and direction estimates for April 2017 - April 2018. Length for number of samples, Std and Cor stand for standard deviation and correlation, respectively.
Wind Speed Wind Direction
Length Bias
(m/s)
Std
(m/s)
Cor
(scalar)
Bias
(deg)
Std
(deg)
Cor (Vector)
OffShore(>=50km) L4 58962 0.07 1.28 0.92 -2 18 1.80
Coastal(<50km) L4 35638 0.85 2.11 0.85 -4 25 1.57
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II. PRODUCTION SUBSYSTEM DESCRIPTION
L4 Global blended ocean wind
The regular (in space and time) wind fields are estimated from near real time
scatterometer and radiometer data in combination with equivalent stress wind estimated from
ECMWF forecasts. The remotely sensed winds are derived from available ASCAT
scatterometers onboard Metop-A and Metop-B satellites. The radiometer winds are from the
special sensor microwave imager sensor (SSMIS) onboard defense meteorological polar
satellites (DMSP) F16, F17, F18 and F19. Wind speed and direction from WindSat
radiometer onboard the Department of Defense Coriolis satellite are also used. The
scatterometers as well as radiometers are provided in near real time by SAF OSI (KNMI) and
RSS, respectively. They are extracted based on ftp procedures. The scatterometer data are
provided as L2b products including backscatter coefficients measurements and the associated
radar parameters as well as wind retrievals. SSMIS and WindSat winds are provided as L2b
products. Both L2b products are quality controlled prior any analysis. L4 winds are calculated
from L2b products in combination with equivalent stress wind from ECMWF forecasts using
the objective method. The latter are processed and provided by KNMI. The analysis is
performed for each synoptic time (00h:00; 06h:00; 12h:00; 18h:00 UTC) and with a spatial
resolution of 0.25° in longitude and latitude over global ocean. The objective method details
may be found in (Bentamy and Croizé-Fillon, 2012)1. Figure 1 shows zooms of L4 blended
wind products estimated over the Northwest Atlantic Ocean and the Western Mediterranean
area and occurring during October 21st
2017.
1Bentamy A., D. Croizé. Fillon, 2012: Gridded Surface Wind Fields from Metop/ASCAT
Measurements. Inter. Journal of Remote Sensing, 33, pp 1729-1754.
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Table 3 L4 6-hourly blended wind Product Specification
Product Line WIND_GLO_WIND_L4_NRT_OBSERVATIONS_012_004 Geographical coverage Global
Variables wind speed
wind zonal component
wind meridional component
wind stress amplitude
wind stress zonal component
wind stress meridional component
wind vector curl
wind vector divergence
wind stress curl
wind stress divergence
root mean square (rms)
sampling length
Analysis yes
Available time series from Jan 2018 to present
Temporal resolution 6-hourly (00h:00, 06h:00, 12h:00, 18h:00 UTC) averaged field
Target delivery time daily
Delivery mechanism CMEMS Information System: SUBSETTER, FTP
Horizontal resolution 1/4°
Number of vertical levels 1 (surface)
Format Netcdf4
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Figure 1: Example of the four synoptic wind fields (CMEMS L4 product) estimated from
remotely sensed data occurring on 21 October 2017.
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III. VALIDATION FRAMEWORK
Validation is a continuous on going activity to characterize accuracy and quality of the
delivered sea ice and wind products. It is mainly be based on operational data, but can be
supported by campaign data
Each PU is responsible for validation of their products. The Wind TAC Validation activities
are for the most based on what is already implemented at the partners’ institutes and has
shown to be useful.
Description of validation data and procedures and link to validation results for each product
are given in the next sections.
Procedure
The subsystem performance and associated product quality are scientifically assessed in
the following way:
Determination of L4 blended wind products accuracy through comprehensive
comparisons with 6-hourly winds from available and valid moored buoy data. The
latter are derived from various buoy networks: NDBC/NOAA (Atlantic, Pacific
oceans), UK Met Office and Météo-France (Atlantic and Mediterranean Sea), TAO
(Tropical Pacific), PIRATA (Tropical Atlantic), and RAMA (Indian Ocean). Figure 2
shows buoy locations.. More than 196 buoy raw data are routinely collected,
investigated, and collocated in space and time with monthly satellite estimates. The
main statistical parameters, including the first four conventional moments and the
linear regression parameters, are estimated and provided (Table1). The differences
between buoy and blended wind products are investigated according to geographical
locations (e.g. off-shore, coastal, high-latitudes, mid-latitudes and tropical areas).
Scatterplots and the related statistical parameters (bias, rms, correlation, linear
regression coefficients) illustrating the comparison between L4 blended global ocean
wind and available and validated buoy wind speeds and directions (Figure 3)..
At global scale the quality of each L4 blended wind product is monthly assessed based
on comparisons with spatially and temporally collocated ASCAT retrievals (when
available). The former are performed over global ocean to characterize L4 blended and
scatterometer wind speed and direction agreements.
Determination of global maps of differences between 6-hourly satellite wind product
and the associated ECMWF analysis. Maps illustrate the bias, rms difference and
correlation coefficient spatial patterns for wind speed, zonal, and meridional
components.
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Time series of differences between 6-hourly satellite wind product and the associated
ECMWF analysis. They deal with bias, rms difference and correlation coefficient for
wind speed, zonal, and meridional components.
Comparison of the results characterising L4 blended wind product (wind speed, zonal
and meridional components) and ECMWF forecasts (equivalent stress winds) (see
Figures 6, 7, and 8)
Quality control of each netcdf file geophysical content is performed based on the
calculation of the statistical parameters characterizing the difference between L4
blended wind speed and direction data and the related ECMWF forecasts2. Only files
such as the difference frequency exceeding three times of standard deviations is higher
than 10% will be checked. According to the finding, the blended wind field could be
reprocessed.
2 MYO-SIWTAC-Cal/val Plan
https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=11&cad=rja&uact=8&ved=2ahUKEwi78Iq3q_XhAhUmxoUKHcF3BUAQFjAKegQIAhAC&url=http%3A%2F%2Fmyocean.met.no%2FSIW-TAC%2Fdoc%2Fcalibration-report-Ifremer_Wind_Clim.doc&usg=AOvVaw3BYMRzNCXW3Tcpmnbs6CdN
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Figure 2: Locations of buoys used for the determination of the L4 wind product.
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IV. RESULT SUMMARY
L4 blended and NDBC buoy wind Comparisons
The quality of the resulting wind fields is investigated trough comprehensive
comparisons with 6-hourly averaged wind estimated derived from buoy measurements.
Comparisons are performed for coastal buoy (located less than 50 km from coastline) and for
offshore buoys. Figure 3 shows an example of comparison results. The latter are illustrated
through scatterplots between buoy and satellite wind speeds (left panels), and between buoy
and satellite wind directions (right panels). and Table 2 illustrate the statistical results
obtained on one hand from collocated offshore buoy and L4 blended wind data, and on other
hand from collocated coastal (buoy moored less than 50 km off coastlines), occurring during
January – May 2017 period.
Table 2 indicates that for offshore comparisons, no systematic departures are depicted
and for most wind variable bins, the collocated data are close to the perfect line. The
associated errors are lower than 1 m/s and 20° for wind speed and direction, respectively. The
significant bias is found for buoy wind speeds less than 4 m/s. Indeed, L4 blended wind
speeds tend to be slightly overestimated compared to in-situ data due to differences in spatial
representation and binning. Table 2 summarizes the results characterizing the comparisons
between NDBC and L4 blended 6-hourly wind speeds and directions. The related statistical
parameters are mean (Bias) and standard deviation (Std) of buoy minus blended data, scalar
and vector correlation coefficients (Cor) for wind speed and direction, respectively. The
overall statistics indicate that the 6-hourly satellite wind fields compare well to averaged buoy
data. The rms differences do not exceed the scatterometer specifications, for wind speed and
direction, respectively. For in-situ and scatterometer winds higher than 3 m/s no significant
bias trend is found. The wind direction bias is relatively small. The results obtained from
comparisons performed based on the use of collocated coastal buoys are lower than those
found for offshore buoys. Therefore, L4 blended winds should be used with caution in near
shore areas.
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L4 blended and ASCAT L2b wind Comparisons
Surface winds retrieved from scatterometer measurements represent the state of art in
global wind vector estimations. Therefore, ASCAT retrievals are used to assess and evaluate
the blended wind vector estimates at global scales. The main aim of such comparisons is to
highlight how the blended analysis retrieves the remotely sensed wind observations derived
mainly from ASCAT. Collocated data are used for comparisons purposes. The bias and rms
differences between collocated ASCAT wind observations and the blended wind analyses of
wind speed, zonal and meridional components, estimated during May 2017, are shown in
Figure 4. The overall statistics characterizing ASCAT and blended collocated data
comparisons indicate that the biases are close to zero and the standard deviation (std) values
are less than 1 m/s. The correlation coefficients exceed 0.95. It is noticeable that zonal and
meridional components have similar behaviours. More specifically, the std differences of the
three variables (wind speed, zonal, and meridional components) exhibit low values (lower
than 1m/s) over the Atlantic, Pacific and Indian trade wind regions, where the wind is quite
Figure 3: Comparisons of NDBC and L4 wind speed (left panels) and wind
direction (right panels)
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steady. The highest std values are found at high latitude and especially north and south 40°
where surface wind is more variable. At these mid and high latitudes the std values are about
1-2m/s for wind speed, while for zonal and meridional components they are about 1.5–3 m/s.
Through the three std patterns, we can notice a band of high rms values located north of the
equator in the Atlantic and Pacific oceans and in the Gulf of Guinea. This may be related to a
misplacement of the intertropical convergence zone (ITCZ) in the blended wind fields.
However, the sampling length of collocated data from ASCAT and blended winds is
minimum over this band. Indeed, between the equator and 10°N, the sampling length is about
30 whereas is more than 70 elsewhere. Some high std value regions are depicted too and
especially for both wind components. For instance, over the northwest Atlantic Ocean (Gulf
Stream current) the std values may exceed 2m/s for both wind components. This is mainly
related to storm tracks characterising surface wind condition during north hemisphere winter.
To further assess the quality of the new L4 product, statistical parameters characterizing
ASCAT wind retrievals and ECMWF equivalent stress winds are calculated from spatial and
temporal collocated data. The spatial distributions of the resulting parameters are shown in
Figure 5. The latter indicates that L4 winds exhibit better results (Figure 4) that those obtained
for ECMWF.
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Figure 4: Mean (left) and standard deviation (right) wind differences between collocated
ASCAT retrievals and L4 blended estimates during May 2017. The left panels indicate the
wind speed (a), zonal wind component (b), and meridional wind component (c) biases. The
corresponding std distributions are shown in d), e), and f) panels, respectively.
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L4 blended and ECMWF wind Comparisons (Long term quality control)
Figure 5: Mean (left) and standard deviation (right) wind differences between collocated
ASCAT retrievals and ECMWF equivalent stress wind estimates during May 2017. The left
panels indicate the wind speed (a), zonal wind component (b), and meridional wind
component (c) biases. The corresponding std distributions are shown in d), e), and f) panels,
respectively.
The quality control of geophysical content of each L4 blended wind file is performed
based on the calculation of the statistical parameters characterizing the difference between L4
and ECMWF (equivalent stress wind) wind speed, zonal, and meridional wind components.
Figures 6-8 show the time series of mean differences (biases) (top) and the related standard
deviations (middle), and of correlation coefficients (bottom), of wind speed, zonal, and
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meridional wind components, respectively. Times series are shown for the period September,
15, 2017 through May, 05 2018. The time variabilities of the statistical parameters do not lead
any significant trend in difference between L4 and ECMWF winds.
To assess the quality control of geophysical content of each L4 blended wind estimate, the
statistical parameters characterizing the difference between on one hand L4 and ASCAT
retrievals (L2b), and L4 and ECMWF (equivalent stress wind) on other hand are calculated.
Figures 9-11 show time series of mean differences (biases) (top) and the related standard
deviations (middle), and of correlation coefficients (bottom), respectively. The time
variabilities of the statistical parameters drawn from ASCAT and L4 comparisons are quite
steady. For instance, wind speed bias, standard deviation, and correlation are of 0.02 m/s,
1.0 m/s, and 0.96, respectively, along the study period (January 2018).
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Figure 6: Time series (September 2017 – May 2018) of statistical parameters characterizing
the comparisons between 6-houly L4 blended and ECMWF analyses estimated from global
data. Top through bottom panels show time series of biases (L4 minus ECMWF), standard
deviations (m/s), and correlation coefficients, respectively.
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Figure 7: As Figure 4 for zonal wind component.
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Figure 8: As Figure 4 for meridional wind component.
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Figure 9: Time series (January 2018) of statistical parameters (Bias (top panel), STD
(middle), and correlation (bottom)) characterizing the wind speed comparisons between on
one hand 6-hourly L4 blended and ASCAT L2b (in red color), and on other hand 6-houly L4
blended and ECMWF analysis (in blue color) estimated over free ice and land global ocean
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Figure 10: As Figure 7 for zonal wind component
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Figure 11: As Figure 7 for meridional component
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V SYSTEM’S NOTICEABLE EVENTS, OUTAGES OR CHANGES
Please include in section "SYSTEM’S NOTICEABLE EVENTS, OUTAGES OR CHANGES" any time where you know there was/is lack in satellite observation.
Date Change/Event description System version other
11/2018 The background used in this version is the
stress-equivalent wind vector (U10S)
estimated from ECMWF 10m wind vector
operational forecasts. The latter are provided
by KNMI. Moreover, SSMIS F18 and F19
are used, in addition to SSMIS F16 and F17,
as ancillary data aiming at the enhancement
of the remotely sensed wind observation
spatial and temporal sampling. The third
main change is the introduction of the new
variables wind vector curl and divergence,
and wind stress curl and divergence.
V5
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VI QUALITY CHANGES SINCE PREVIOUS VERSION
Please include a paragraph regarding quality changes since the previous version. It can be as paragraph or in the form of a table.
As expected, using new observations derived mainly from radiometers, improves the quality of V5 product compared to V4. The improvement is mostly depicted for high variable (in space and/or time) wind conditions.