GEOPHYSICAL RESEARCH LETTERS, VOL. ???, XXXX, DOI:10.1029/,
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2
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Supporting Information for “Can we use surface wind fields from meteorological reanalyses for Sahelian dust emission simulations ?” 1
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Yann Largeron , Francoise Guichard , Dominique Bouniol , Fleur Couvreux , 2
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Laurent Kergoat and B´eatrice Marticorena
Corresponding author: Yann Largeron, CNRM/GMME/MOANA e-mail adress:
[email protected] tel: +33 561079845 1
Centre National de Recherches
Meteorologiques, CNRM-GAME, CNRS, UMR 3589, Toulouse, France 2
Geoscience Environnement Toulouse,
CNRS-UPS-IRD, Toulouse, France 3
LISA, Universit´es Paris Est-Paris
Diderot-Paris 7, UMR CNRS 7583, Cr´eteil, France
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Contents of this file
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• Appendix A: Additional dataset information
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• Appendix B: 10m wind speed extrapolation, assumptions and errors
7
• Appendix C: Annual cycle of 10 m wind speed at different sites
8
• Appendix D: Additional statistics : Mean Biases and Root Mean Square Errors
9
• Appendix E: Diurnal cycle of 10 m wind speed at different sites
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• Appendix F: Wind speed distribution at differents sites for all reanalyses
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• Appendix G: Interannual variability : impact on distributions and diurnal cycles of
12
the 10 m wind speed
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Appendix A: Additional dataset information 13
The ERA-interim (European Re-Analysis) 3-hourly sampled fields used in the present
14
study come from the daily forecast started at 0000 UTC (we found that the results are not
15
much sensitive to this particular choice; i.e. results remain similar when using the forecast
16
starting at 1200 UTC or a combination of analyses and forecast). The spatial resolution is
17
about 80 km × 80 km ([Dee et al., 2011]). The NCEP-CFSR fields come from the 6-hourly
18
analyses with forecasts providing outputs every hour. The spatial resolution is 38 km ×38
19
km ([Saha et al., 2010]). For MERRA, fields come from hourly outputs. MERRA uses
20
a three-dimensional variational data assimilation (3DVAR) analysis algorithm with a 6-h
21
update cycle and uses an incremental analysis update procedure in which the analysis
22
correction is gradually applied to the forecast, through an additional tendency term in
23
the model equations during the corrector segment ([Rienecker et al., 2011]). The spatial
24
resolution is 55 km ×70 km.
25
The four measurement sites are located in an area of about 1000 km ×400 km (see map
26
in Figure A.1): Cinzana (Mali) 13.28o N, 5.93o W is in the south-west of the area; Bani-
27
zoumbou (Niger), 13.54o N, 2.66o E in the south-east; Agoufou (Mali), 15.34o N, 1.48o W in
28
the center of the area and Bamba (Mali), 17.1o N, 1.4o W to the North.
29
Surface wind speed is measured with a conventional in-situ windsonic 2D at the SDT
30
sites (Banizoumbou and Cinzana). The measurements provide the wind speed as 5-min
31
average time series for the SDT sites.
32
AMMA-CATCH (Agoufou and Bamba) benefits from both standard automatic weather
33
stations (cup anemometer) and high-frequency sonic anemometers (Gill ans CSAT3,
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Campbell sensors). At these two sites, cup anemometers provide 15-min average time
35
series of the wind speed whereas the sonic anemometers give a 20 Hz sampled wind.
36
The height of measurements are 3 m for the site of Agoufou, 3 m for Bamba, 2.30 m
37
for Cinzana, 6.5 m for Banizoumbou. All those measurements are extrapolated to a 10
38
m wind speed by assuming a logarithmic profile (see in next section the error induced by
39
this assumption).
Appendix B: 10m wind speed extrapolation, assumptions and errors 40
In the manuscript, measurements are extrapolated to a 10 m wind speed by assuming a
41
classical logarithmic profile [Stull , 1987]. Here, we evaluate the influence of the stability at
42
Agoufou were the availability of turbulence data allows to compute the Monin-Obukhov
43
length (L). Different stability corrections for stable cases have been proposed in the liter-
44
ature [Businger et al., 1971; Garratt, 1994; Foken, 2006]. We compute the one proposed
45
by Foken [2006] which corresponds to the largest stability correction term :
φ = −6
(z − z0 ) L
(B1)
46
Figure B.1 illustrates the monthly-mean diurnal cycle for 3 months (May, August and
47
December) with and without the stability correction and shows that stability correction
48
can be responsible for a maximum nocturnal bias of 0.25 and 0.19 m.s−1 in May and
49
August and a higher value of 0.48 m.s−1 in December, during which nighttime surface layer
50
can be strongly stable. This maximal correction value has to be compared to the December
51
nighttime bias of 2.9 m.s−1 found for ERA-interim (similar for the other reanalyses) at the
52
site of Agoufou (cf section 3.2 in the manuscript). Stability correction can then accounts
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53
for a maximal fraction of 17% of the systematic bias of ERA-interim, during the nights
54
of the most stable months of the year.
55
Note that further analysis confirms that the systematic bias of the reanalyses during
56
the dry season can certainly not be entirely due to our extrapolation : A significant bias
57
of a few m.s−1 is clearly detected at the site of M’bour (not shown) where measurements
58
have been made at 10 m and are therefore directly comparable to the analyzed wind
59
field without any extrapolation. The same conclusions can be drawn with the analysis of
60
SYNOP stations observations at 10 m over the Sahel (not shown).
61
As the quantification of the stability correction term required turbulence data, this can
62
not be done at the sites of Banizoumbou and Cinzana. Therefore, for seek of homogeneity,
63
we choose to use a logarithmic extrapolation at the four sites in the manuscript.
Appendix C: Annual cycle of 10 m wind speed at different sites 64
Figure 1a of the manuscript is produced here for the three other observational sites
65
in Figure C.1 The annual cycle of the 10 m wind speed has a similar shape at the three
66
southern sites. At the northern site (Bamba), the shape remain similar (increase in spring,
67
maximum at the beginning of the monsoon, progressive decrease during the later phase
68
of the monsoon and fall, and minimum at the beginning of the winter). Nevertheless, the
69
wind speed is stronger in Bamba.
70
The statistical biases and annual cycle described in section 3.1 and 4.1 are similar at
71
the four sites, except during the dry season bias at the northern site : it is negative there
72
whereas it is positive elsewhere. This is due to the underestimation of the wind speed in
73
the reanalyses at this northern site.
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Appendix D: Additional statistics : Mean Biases and Root Mean Square Errors 74
Some additional statistics are computed for the two seasons at the four sites and for
75
the three reanalyses, at two different time-scales, using the daily-mean wind speeds (U )
76
and for 3-hourly fluctuations around the daily-mean (Ue = U − U ).
77
78
We compute mean Biases (B) and Root Mean Square Errors (RMSE) for these two variables (cf Figure D.1).
79
During the dry season, ERA-interim performs best, particularly at sub-daily time scale,
80
as shown its correlation with observations. NCEP-CFSR displays the smallest synoptic
81
bias. Finally, MERRA is characterized by a negative correlation at sub-daily time-scale
82
and a high positive synoptic bias (2 m.s−1 ) at the three southern sites.
83
During the monsoon season, the three reanalyses perform similarly with a correlation
84
coefficient around 0.6 at synoptic time-scale which falls down to very low value at sub-
85
daily time-scale. All reanalyses display a small negative bias, worst for NCEP-CFSR than
86
MERRA and ERA-interim.
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88
These statistics show a very poor capture of the diurnal cycle and a misrepresentation of meso-scale convective processes.
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Appendix E: Diurnal cycle of 10 m wind speed at different sites 89
90
In this appendix, we reproduce Figure 2 for the three reanalyses and the 12 months of the year 2006 at the four sites in Figures E.1, E.2, E.3 and E.4
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Appendix F: Wind speed distribution at differents sites for all reanalyses 91
92
In this appendix, Figure 4a of the manuscript is reproduced for the three reanalyses at the four sites in Figure F.1.
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Appendix G: Interannual variability : impact on distributions and diurnal cycles of the 10 m wind speed 93
Here, we illustrate how Fig. 2 and 4 of the manuscript are modified when considering
94
larger time series. We compare results for the entire 2006-2011 period to results for 2006
95
only and illustrate below that the same conclusions can be drawn by either focusing on
96
the entire period or only on 2006.
97
As the results from the satellite-tracking algorithm are not available for the entire period
98
(only for 2006), we can not produce the orange curve in Fig. 2 and the grey bars in Fig. 4
99
for the entire 2006-2011 period. We therefore focus on 2006 in the manuscript and extend
100
the discussion with the help of statistics for the 2006-2011 period.
101
Figure G.2 compares the results shown in Fig. 4 of the manuscript at Banizoumbou
102
for these two time periods (without curves corresponding to the satellite-tracking com-
103
putation). Figure R1 shows that the interannual variability of the 10m wind Probability
104
Density Functions (PDF) during the monsoon season does not at all affect the shapes of
105
theses PDF and that conclusions of section 4.2 remain valid.
106
Similarly, Fig. G.1 illustrates that conclusions drawn in section 3.2 remain valid for the
107
2006-2011 period. It shows that the monthly-mean diurnal cycles of the 10 m wind speed
108
computed with either 2006 or 2006-2011 are close; the main differences between the three
109
months (May, August and December) are the same, as well as the differences between
110
observations and reanalyses. The main difference is that the envelope of minima and
111
maxima (grey shade) is wider when using 2006-2011, as expected from the larger sample
112
size. Therefore, focusing on 2006 only provides meaningful results for our purpose, as
113
compared to using longer time series.
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References 114
Businger, J. A., J.C. Wyngaard, Y. Izumi, and E. F. Bradley (1971), Flux-profile
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relationships in the atmospheric surface layer, J. Atmos. Sci. 28 (2), 181–189, doi:
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http://dx.doi.org/10.1175/1520-0469(1971)0280181:FPRITA2.0.CO;2
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Dee, D., S. Uppala, A. Simmons, P. Berrisford, P. Poli, S. Kobayashi, U. Andrae, M. Bal-
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maseda, G. Balsamo, P. Bauer, et al. (2011), The ERA-interim reanalysis: Configura-
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tion and performance of the data assimilation system, Quart. J. Roy. Meteorol. Soc.,
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137 (656), 553–597, doi:10.1002/qj.828.
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Foken, T. (2006), 0 years of the Monin-Obukhov similarity theory, Bound.-Lay. Meteorol., 119 (3), 431–447, doi:10.1007/s10546-006-9048-6
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Garratt, J. R. (1994), The atmospheric boundary layer, Cambridge university press
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Miller, M. A., and A. Slingo (2007), The ARM mobile facility and its first international
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deployment: Measuring radiative flux divergence in West Africa, Bull. Amer. Meteorol.
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Soc., 88 (8), 1229–1244, doi:10.1175/BAMS-88-8-1229.
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Rienecker, M. M., M. J. Suarez, R. Gelaro, R. Todling, J. Bacmeister, E. Liu, M. G.
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Bosilovich, S. D. Schubert, L. Takacs, G.-K. Kim, et al. (2011), MERRA: NASA’s
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modern-era retrospective analysis for research and applications, J. Climate, 24 (14),
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3624–3648, doi: 10.1175/JCLI-D-11-00015.1.
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Saha, S., S. Moorthi, H.-L. Pan, X. Wu, J. Wang, S. Nadiga, P. Tripp, R. Kistler,
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J. Woollen, D. Behringer, et al. (2010), The NCEP climate forecast system reanaly-
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sis, Bull. Amer. Meteorol. Soc., 91 (8), 1015–1057, doi:10.1175/2010BAMS3001.1.
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135
Stull, R. B. (1987), An introduction to Boundary Layer Meteorology, vol. 666, Kluwer academic publishers.
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Figure A.1.
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Locations of the observational sites in West Africa.
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Wind speed (m.s−1)
5
4
3
2
Obs U4m
1
Extrapolated U10m (log) Extrapolated U10m (stability correction) 0 0
5
10
15
20
Hour August 2006 6
Obs U
4m
Extrapolated U
10m
Wind speed (m.s−1)
(log)
Extrapolated U10m (stability correction)
5
4
3
2
1
0 0
5
10
15
20
Hour
December 2006
6
Wind speed (m.s−1)
5
4
3
2 Obs U4m
1
Extrapolated U10m (log) Extrapolated U10m (stability correction)
0 0
3
6
9
12
15
18
21
24
Hour
Figure B.1.
Monthly-mean diurnal cycle of the surface wind speed (m/s) at Agoufou.
Black dots: observed at 4m. Grey dots: extrapolated at 10m with logarithmic profile. Red crosses: extrapolated at 10m with stability correction. a) May 2006. b) August 2006. c) December 2006. D R A F T
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LARGERON ET AL.: WIND FIELDS OVER SAHEL - SUPPORTING INFORMATION 8 BAMBA
(m.s−1)
6 4 2 0 0
30
60
90
120 150 180 210 240 270 300 330 360 Day of year
8 AGOUFOU
(m.s−1)
6 4 2 0 0
30
60
90
120 150 180 210 240 270 300 330 360 Day of year
8 BANIZOUMBOU
(m.s−1)
6 4 2 0 0
30
60
90
120 150 180 210 240 270 300 330 360 Day of year
8 CINZANA
(m.s−1)
6 4 2 0 0
Figure C.1.
30
60
90
120 150 180 210 240 270 300 330 360 Day of year
Annual time series of 10 m wind speed (5-day running-mean) in 2006
at four observational sites (black: observations, red: ERA-interim, blue: NCEP-CFSR, green: MERRA). The grey shading delineates the minimum and maximum observed values over 2006-2011.
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NDJF
0.5
0.5 COR
1
COR
1
0
0.5 0
0
1 2 RMSE (m/s)
0.5 1
3
0 B
1 2 (m/s)
B
0 (m/s)
3
1
1
0.8
0.8
0.6
0.6
0.4
0.4
0.2
0.2
0
0
0
Figure D.1.
COR
COR
MJJAS
1 2 RMSE (m/s)
3
2
2
Correlation coefficient (COR) as a function of Root Mean Square Errors
(RMSE) (left panels) and mean Biases (B) (right panels) for the dry season (top panels) or the monsoon season (bottom panels) at all sites (circles: Banizoumbou, stars: Cinzana, crosses: Agoufou, squares: Bamba) for the three reanalyses and the two time scales; ERA-interim daily means (red) and 3-hourly fluctuations (orange), MERRA daily means (dark green) and 3-hourly fluctuations (light green), NCEP-CFSR daily means (dark blue) and 3-hourly fluctuations (light blue). Black circle: Observations.
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10
5
0
0
3
6 9 12 15 18 21 24 Time of day (UTC)
10
5
0
0
3
6 9 12 15 18 21 24 Time of day (UTC)
10
5
0
0
3
6 9 12 15 18 21 24 Time of day (UTC)
Figure E.1.
15
15
15
0
0
3
6 9 12 15 18 21 24 Time of day (UTC)
10
5
0
0
3
6 9 12 15 18 21 24 Time of day (UTC)
10
5
0
0
3
6 9 12 15 18 21 24 Time of day (UTC)
10
5
0
0
3
6 9 12 15 18 21 24 Time of day (UTC)
Surface wind speed (m.s−1)
6 9 12 15 18 21 24 Time of day (UTC)
Surface wind speed (m.s−1)
3
5
15
Surface wind speed (m.s−1)
0
10
15
15
Surface wind speed (m.s−1)
0
Surface wind speed (m.s−1)
15
5
Surface wind speed (m.s−1)
Surface wind speed (m.s−1)
15
10
15
Surface wind speed (m.s−1)
Surface wind speed (m.s−1)
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Surface wind speed (m.s−1)
Surface wind speed (m.s−1)
15
Surface wind speed (m.s−1)
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15
10
5
0
0
3
6 9 12 15 18 21 24 Time of day (UTC)
0
3
6 9 12 15 18 21 24 Time of day (UTC)
0
3
6 9 12 15 18 21 24 Time of day (UTC)
0
3
6 9 12 15 18 21 24 Time of day (UTC)
10
5
0
10
5
0
10
5
0
Monthly-mean diurnal cycles and extremes of the 10 m wind speed at
Bamba for each month of 2006. Observational (δt: 5 min) mean (black) and extrema (grey shading). ERA-interim (red), MERRA (green) and NCEP-CFSR (blue) means (diamonds) and extrema (bars), and 3-h sampling of observed (yellow) mean (circles) and extrema (bars).
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5
0
0
3
6 9 12 15 18 21 24 Time of day (UTC)
10
5
0
0
3
6 9 12 15 18 21 24 Time of day (UTC)
10
5
0
0
3
6 9 12 15 18 21 24 Time of day (UTC)
Figure E.2.
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15
15
0
3
6 9 12 15 18 21 24 Time of day (UTC)
10
5
0
0
3
6 9 12 15 18 21 24 Time of day (UTC)
10
5
0
0
3
6 9 12 15 18 21 24 Time of day (UTC)
10
5
0
0
3
6 9 12 15 18 21 24 Time of day (UTC)
Surface wind speed (m.s−1)
10
15
0
Surface wind speed (m.s−1)
6 9 12 15 18 21 24 Time of day (UTC)
5
15
Surface wind speed (m.s−1)
3
10
15
15
Surface wind speed (m.s−1)
0
Surface wind speed (m.s−1)
15
0
Surface wind speed (m.s−1)
Surface wind speed (m.s−1)
15
5
Surface wind speed (m.s−1)
Surface wind speed (m.s−1)
15
10
15
Surface wind speed (m.s−1)
Surface wind speed (m.s−1)
15
Surface wind speed (m.s−1)
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10
5
0
0
3
6 9 12 15 18 21 24 Time of day (UTC)
0
3
6 9 12 15 18 21 24 Time of day (UTC)
0
3
6 9 12 15 18 21 24 Time of day (UTC)
0
3
6 9 12 15 18 21 24 Time of day (UTC)
10
5
0
10
5
0
10
5
0
Same as Figure E.1 at the site of Agoufou
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10
5
0
0
3
6 9 12 15 18 21 24 Time of day (UTC)
10
5
0
0
3
6 9 12 15 18 21 24 Time of day (UTC)
10
5
0
0
3
6 9 12 15 18 21 24 Time of day (UTC)
Figure E.3.
D R A F T
15
15
15
0
0
3
6 9 12 15 18 21 24 Time of day (UTC)
10
5
0
0
3
6 9 12 15 18 21 24 Time of day (UTC)
10
5
0
0
3
6 9 12 15 18 21 24 Time of day (UTC)
10
5
0
0
3
6 9 12 15 18 21 24 Time of day (UTC)
Surface wind speed (m.s−1)
6 9 12 15 18 21 24 Time of day (UTC)
Surface wind speed (m.s−1)
3
5
15
Surface wind speed (m.s−1)
0
10
15
15
Surface wind speed (m.s−1)
0
Surface wind speed (m.s−1)
15
5
Surface wind speed (m.s−1)
Surface wind speed (m.s−1)
15
10
15
Surface wind speed (m.s−1)
Surface wind speed (m.s−1)
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Surface wind speed (m.s−1)
Surface wind speed (m.s−1)
15
Surface wind speed (m.s−1)
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15
10
5
0
0
3
6 9 12 15 18 21 24 Time of day (UTC)
0
3
6 9 12 15 18 21 24 Time of day (UTC)
0
3
6 9 12 15 18 21 24 Time of day (UTC)
0
3
6 9 12 15 18 21 24 Time of day (UTC)
10
5
0
10
5
0
10
5
0
Same as Figure E.1 at the site of Banizoumbou.
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5
0
0
3
6 9 12 15 18 21 24 Time of day (UTC)
10
5
0
0
3
6 9 12 15 18 21 24 Time of day (UTC)
10
5
0
0
3
6 9 12 15 18 21 24 Time of day (UTC)
Figure E.4.
D R A F T
15
15
0
3
6 9 12 15 18 21 24 Time of day (UTC)
10
5
0
0
3
6 9 12 15 18 21 24 Time of day (UTC)
10
5
0
0
3
6 9 12 15 18 21 24 Time of day (UTC)
10
5
0
0
3
6 9 12 15 18 21 24 Time of day (UTC)
Surface wind speed (m.s−1)
10
15
0
Surface wind speed (m.s−1)
6 9 12 15 18 21 24 Time of day (UTC)
5
15
Surface wind speed (m.s−1)
3
10
15
15
Surface wind speed (m.s−1)
0
Surface wind speed (m.s−1)
15
0
Surface wind speed (m.s−1)
Surface wind speed (m.s−1)
15
5
Surface wind speed (m.s−1)
Surface wind speed (m.s−1)
15
10
15
Surface wind speed (m.s−1)
Surface wind speed (m.s−1)
15
Surface wind speed (m.s−1)
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15
10
5
0
0
3
6 9 12 15 18 21 24 Time of day (UTC)
0
3
6 9 12 15 18 21 24 Time of day (UTC)
0
3
6 9 12 15 18 21 24 Time of day (UTC)
0
3
6 9 12 15 18 21 24 Time of day (UTC)
10
5
0
10
5
0
10
5
0
Same as Figure E.1 at the site of Cinzana.
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0.20
0.20
0.15
0.15
%
%
X - 20
0.10
0.05
0.00 0
0.10
0.05
5 10 Surface wind speed (m.s−1)
0.00 0
15
5 10 Surface wind speed (m.s−1)
b)
0.20
0.20
0.15
0.15
%
%
a)
0.10
0.05
0.00 0
0.10
0.05
5 10 Surface wind speed (m.s−1)
15
0.00 0
5 10 Surface wind speed (m.s−1)
c) Figure F.1.
15
15
d)
Probability density functions (PDF) of 10 m wind speed at the four
sites (a: Bamba, b: Agoufou, c: Banizoumbou, d: Cinzana) during the monsoon season (MJJAS) of 2006, observations (black) and ERA-interim (red), MERRA (green), NCEPCFSR (blue); or during convective events only : observations (grey) and ERA-interim (orange).
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LARGERON ET AL.: WIND FIELDS OVER SAHEL - SUPPORTING INFORMATION
Figure G.1.
May 2006
May, from 2006 to 2011
August 2006
August, from 2006 to 2011
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December 2006 December, from 2006 to 2011 Monthly-mean diurnal cycles of mean and extremes 10m wind speed
at Banizoumbou for May (top panels), August (centered panels) and December (bottom panels) for 2006 only (left) and for the 2006-2011 period (right). These are shown for observational mean (black) and extremes (grey shading), ERA-interim (red) mean (diaD R A F T March 5, 2015, 12:42pm D R A F T monds) and extremes (bars), and for 3-h sampled observed (yellow) mean (circles) and extremes (bars). Wind maxima associated with convective events are indicated by the orange segments.
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LARGERON ET AL.: WIND FIELDS OVER SAHEL - SUPPORTING INFORMATION
Figure G.2. Probability density functions (PDF) of 10 m wind speed at Banizoumbou during the monsoon season (MJJAS) of 2006, observations (black) and ERA-interim (red) (top panel) and for the MJJAS 2006-2011 (bottom panel).
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