diagnostics of wind reanalyses and gcm-rcm results at 10 m...
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Diagnostics of wind reanalyses and GCM-RCM results at 10 m above sea level in Eastern Canada
Corina Rosu Jean-Pierre Savard
May 2012, Montreal
Outline
1 . I nt r o d u c t i o n √ Necessity of studying the impact of data wind on
wave and storm surge modelling projects 2 . Re a n a l y s i s & E nv i r o n m e nt C a n a d a s ta t i o n s
√ Coast stations vs marine stations 3 . I nte r p o l a t i o n e f fe c t s
√ Temporal and spatial interpolation √ Reanalysis vs climat model
4 . C o m p a r i s o n b e t w e e n s e v e ra l - l e v e l w i n d s p e e d √ Surface and multi-level wind speed
5 . S u m m a r y
Environnement Illimité
1. Introduction
1. Introduction
Significant wave height near Sept-Iles from October 2010 to January 2011
Source: Adrien Lambert, UQAR-ISMER Source: Zhigang Xu, ISMER
Non tidal water level in Umiujaq from October 2009 to October2010
1. Introduction
Wind speed above to sea surface (typically at 10 m) is one the most important variables for oceanic applications. Wave height and storm surge are greatly influenced by wind speed.
North East
Sable Island Sable Island
NAR
R_10
m
NAR
R_10
m
2. Reanalysis & EC stations
SableIS NARR_10m
Comp_North Pearson R Linear regression
DJF 0.94 1.08x + 0.04
MAM 0.94 1.06x + 0.03
JJA 0.91 1.08x - 0.27
SON 0.94 1.09x - 0.10
Comp_East Pearson R Linear regression
DJF 0.94 1.01x - 0.66
MAM 0.94 1.03x - 0.53
JJA 0.90 1.07x - 0.80
SON 0.93 1.05x - 0.57
2. Reanalysis & EC stations
35 Km
NAR
R_10
m
NAR
R_10
m
North East
Kuujjuarapik Kuujjuarapik
2. Reanalysis & EC stations
Kuujjuarapik NARR_10m
Comp_North Pearson R Linear regression
DJF 0.79 0.79x + 0.52
MAM 0.78 0.70x + 0.43
JJA 0.73 0.73x - 0.10
SON 0.82 0.97x - 0.02
Comp_East Pearson R Linear regression
DJF 0.80 0.67x - 1.48
MAM 0.83 0.68x - 0.32
JJA 0.77 0.74x - 0.20
SON 0.85 1.00x - 1.04
2. Reanalysis & EC stations
3h 0h 3h 6h 9h 12h 15h 18h 21h
6h 0h 3h 6h 9h 12h 15h 18h 21h
12h 0h 3h 6h 9h 12h 15h 18h 21h
24h 0h 3h 6h 9h 12h 15h 18h 21h
Black - initial data Red - interpolated data
Spatial interpolation Temporal interpolation
Hudson Bay Labrador Sea 1 Labrador Sea 2 Sable Island Magdalen Islands
C0_03h: C4_03h 0.989 0.95x + 0.04 0.978 0.93x + 0.12 0.985 0.95x + 0.03 0.986 0.93x + 0.07 0.97 0.87x + 0.18 3h 288 km
C0_03h: C4_06h 0.976 0.94x + 0.09 0.967 0.91x + 0.20 0.970 0.92x + 0.17 0.960 0.90x + 0.25 0.95 0.85x + 0.31 6h 288 km
C0_03h: C4_12h 0.947 0.89x + 0.23 0.939 0.87x + 0.40 0.933 0.87x + 0.46 0.909 0.82x + 0.57 0.912 0.79x + 0.46 12 h 288 km
C0_03h: C4_24h 0.872 0.78x + 0.61 0.846 0.76x + 0.94 0.825 0.74x + 1.18 0.768 0.65x + 1.43 0.793 0.64x + 0.93 24 h 288 km
3. Interpolation effects
HB_MRCC_CGCM3 HB_MRCC_ECHAM5
HB_NARR HB_NCEP HB_ERAinterim
3. Reanalysis & GCM-RCM
HB_ECHAM5 HB_CGCM3
SAB_MRCC_CGCM3 SAB_MRCC_ECHAM5
SAB_NARR SAB_NCEP SAB_ERAinterim
3. Reanalysis & GCM-RCM
SAB_CGCM3 SAB_ECHAM5
4. Level wind speed
5. Summary
Over large bodies of seawater, reanalysis wind data at 10 m above sea level correlate well with
observed data (R>0,9) but the slope of the regression is variable (better with NARR than NCEP2,
NCEP and ERA-Interim. Correlations on coastal stations are lower than those of open waters (R<
0,75) due to topographic influences.
For atmospheric levels higher than 10 m (up to 850 HPa), wind data of most re-analysis models is
strongly correlated and the slopes of the regressions are generally near 1. This suggests that
discrepancy between models at 10 m above SL is due to boundary layer algorithm rather than
models themselves.
Wind roses and other statistical comparisons of data from reanalysis and CRCM model show good
agreement.
Model Grid size affects mildly the wind value but temporal linear interpolation can induce major
flattening of variability when the time interval exceeds 6 h (up to 25% for 24h interval).
Thank you/Merci Corina Rosu
Jean-Pierre Savard