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Surface Analysis (I)
M. Drusch
Room 1007, Phone 2759
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The Current Surface Analysis System
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Outline
1. Sea Surface Temperature (SST)2. Sea Ice (CI)3. Snow
1. 2 m Relative Humidity and Temperature2. Soil Moisture
Part 1:
Part 2:
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Overview Part I
1. Sea Surface Temperature (SST)- NCEP / MMAB SST- lake SST
2. Sea Ice
3. Snow- observation types- operational Cressman analysis- revision based on satellite derived snow extent- analyses’ validation against independent satellite
and in-situ observations- impact on the forecast
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Sea Surface Temperature (SST) - AnalysisThe SST analysis is produced by NCEP / MMAB:- daily data set- two dimensional variational interpolation - buoy and ship observations, satellite retrieved SST
Analysis steps:1) Satellite retrieved SST values are averaged within 0.5º grid boxes2) Bias calculation and removal for satellite retrieved SST3) SST from ships and buoys are separately averaged4) The first guess is the prior analysis with one day’s climate adjustment added.5) Where fractional sea ice cover exceeds 50%, surface temperature is
calculated from Millero’s formula for the freezing point of water:
with s the salinity in psu.6) Empirical autocorrelation function has the form:
with d and l in km, grad T K/km
22
3
0002.00017.00575.0)( ssssSST
)/exp( 22 ld
))100,/25.2max(,450min( gradTl
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MMAB SST Data
80°S80°S
70°S 70°S
60°S60°S
50°S 50°S
40°S40°S
30°S 30°S
20°S20°S
10°S 10°S
0°0°
10°N 10°N
20°N20°N
30°N 30°N
40°N40°N
50°N 50°N
60°N60°N
70°N 70°N
80°N80°N
160°W
160°W 140°W
140°W 120°W
120°W 100°W
100°W 80°W
80°W 60°W
60°W 40°W
40°W 20°W
20°W 0°
0° 20°E
20°E 40°E
40°E 60°E
60°E 80°E
80°E 100°E
100°E 120°E
120°E 140°E
140°E 160°E
160°E
ECMWF Analysis VT:Thursday 1 January 2004 12UTC Surface:
270
275
280
285
290
295
300
305
310
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SST – OSTIA Data Sets
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OSTIA-NCEP
[K]
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Lake SST - Data
22 non analysed lakes at T319 resolution, Great Lakes and Caspian Seaare included in the NCEP analysis
Current analysis method was developed using:
• 18,000 observations of mean monthly surface air temperature compiled by Legates and Wilmott (1990)
• ERA15 monthly mean SST based on satellite and in-situ observations (NCEP data set) for the Great Lakes and the Caspian Sea
• Lake temperatures for 4 African Lakes from Spigel and Coulter (1996)
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Lake SST - Methodology
SST Lake(t) = T2m(t-1)
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Sea Ice – ‘Analysis’
Based on SSM/I (Special Sensor microwave / Imager) antenna temperatures.
1. Remapping from scan points to a polar stereographic grid (25km true at 60)2. Conversion to brightness temperatures (Hollinger et al., 1987).3. Weather filter following Gloersen and Cavalieri (1986).4. Sea ice concentration algorithm (Cavalieri et al., 1991).5. Polar gap filling.6. Quality check (100 % maximum ice cover).7. Final filtering based on Reynolds SST (no ice if SST > 2º C).
NCEP’s algorithm (Grumbine, 1996):
ECMWF post-processing:
1. Resampling to model grid using a spatial interpolation (Cressman Analysis).
2. Final quality check.3. Replace sea ice in the Baltic Sea with the high resolution
product from SMHI.
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Sea Ice - AlgorithmSea ice fraction algorithm (Cavalieri et al., 1991)
ocean tie point
multi year sea ice tie point
first year sea ice tie pointSSM/I
channelopenwater
first year sea ice
multi-year sea
ice
19 H 100.8 242.8 203.9
19 V 177.1 258.2 223.2
37 V 201.7 252.8 186.3
2. Northern Hemisphere tie points
HVVV
HVHV
TTTTGR
TTTTPR
37371937
19191919
/
/
1. Calculate polarization ratio &
spectral gradient ratio
MFT
M
F
CCC
GRPRcGRcPRccD
DGRPRbGRbPRbbC
DGRPRaGRaPRaaC
3210
3210
3210
/
/3. Calculate fractional sea ice coverage
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(hhtp://polar.wwb.noaa.gov/seaice)
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Sea Ice – Baltic Sea
Mean sea ice concentration for the period 5-24 January 2004:a) NCEP / ECMWF (CTRL) and b) SMHI / ECMWF (EXP)
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Sea Ice – Baltic Seasensible heat flux latent heat flux
CTRL
EXP - CTRL
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Sea Ice – Baltic Sea
GP1: thin lines, GP2: thick linesCTRL: solid, EXP: dashed grey
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Snow Analysis - Definitions
Definitions - snow extent (binary information 1/0)- fractional snow cover (0 – 100 %) - snow depth SD (m)
- snow water equivalent (SWE)
Observation types - in situ measurements (snow depth and SWE) - remote sensing microwaves (SWE)
- remote sensing visible & infrared (snow extent, aggregation gives fractional snow coverage)
1000SSD
SWE
[m]
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Cressman Analysis (I)
N
n
n
N
n
bOnn
ba
w
SSwSS
1
1
'
1. Cressman spatial interpolation:
with: - SO snow depth from synop reports, - Sb background field estimated from the short-range forecast of snow water equivalent,- Sb‘ background field at observation location, and - wn weight function, which is a function of horizontal distance r and vertical displacement h (model – obs): w = H(r) v(h) with:
,0rr
rrmaxH(r)
22max
22max
2h2maxh
2h2maxh
1 if 0 < h
0 if h < - hmax
if – hmax < h < 0v(h) =
rmax = 250 km
hmax = 300 m
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Cressman Analysis (II)
2. Quality check for every grid point
3. Final analysis using climatological values
cliaa SSS 1(
with: - Scli snow depth from climate data set (Foster and Davy 1988), - relaxation coefficient of 0.02
- If Tb2m < 8 C only snow depth observations below 140 cm are accepted.
- If Tb2m > 8 C only snow depth observations below 70 cm are accepted.
- Observations which differ by more than 50 cm from the background are rejected.
- When only one observation is available within rmax, the snow depth increments are set to 0.
- Snow-depth analysis is limited to 140 cm.
- Snow-depth increments are set to 0 when larger than (160-16Tb2m) mm, where Tb
sm is in C.
- Snow-depth analysis is set to 0 if below 0.04 cm- If there is no snow in the background and in more than half of the observations within a
circle of radius rmax, the snow depth increment is kept to 0.
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Analyses vs Satellite Data
MODIS snow extent 17.-24.1.2002
by NSIDCby NSIDC
MODIS vis image 27.10.2002
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Analyses vs Satellite Data
MODIS 16/02/2002
SWE [cm]
operational analysis
40°N
50°N
60°N
70°N
20°W
20°W
0°
0°
20°E
20°E 40°E
40°E
60°E
60°E
ECMWF Analysis VT:Saturday 16 February 2002 12UTC Surface: snow depth
0.1
0.5
1
2
5
10
20
40
75
125
200
10000
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NOAA / NESDIS Snow Extent
Interactive Multisensor Snow and Ice Mapping System:- time sequenced imagery from geostationary satellites,- AVHRR, - SSM/I, - station data, - previous day‘s analysis
Northern Hemisphere product- real time- polar stereographic projection- 1024 × 1024 elements
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Revision of the Global Snow Depth Analysis using NESDIS snow extent
1) Comparison between first guess and NESDIS: - NESDIS is interpolated to actual model resolution
- fractional snow cover is calculated - snow free f.g. boxes are updated with 10 cm of snow where the NESDIS product has 100% snow cover
2) Cressman Analysis - NESDIS snow free grid boxes are used as observations with
0 cm snow depth. - Observation height is calculated from high resolution ‚ECMWF‘
orography on the corresponding polarstereographic grid. - Climatology is switched off.
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Technical implementation
NOAA NESDIS snow extent: snow present
NOAA NESDIS snow extent: no snow
first guess updated with previous increments
00 UTC 12 UTC
6 hour forecast(first guess)
12 hour forecast(first guess)
SYNOP observations SYNOP observations
06 UTC
Cressman analysis /quality check
(& climatology)
Cressman analysis /quality check
(& climatology)
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6-h cycling in 12 hour 4DVarSWE [cm]
SWE [cm]
SWE [cm]
SWE [cm]
00 UTC 06 UTC
18 UTC12 UTC
first guess:12 hour fc
first guess:6 hour fcobservations
first guess:12 hour fc &update with previous analysisincrements &satellite data
first guess:6 hour fc
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Snow Depth Analyses for 1/3/2002
SWE [cm]
SWE [cm]
NESDIS Snow Cover [%]
MODIS
operational
revised
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Validation and intercomparison
Research Experiments• November 2003 to May 2004 (Cycle 28R1)• March, May and December 2002 (Cycle 26R3)• Satellite Data ingestion at 12:00 UTC / CTRL
National Operational Hydrologic Remote Sensing CenterAnalysis (SNODAS) • November 2003 to May 2004• 1km, re-sampled to T511 reduced Gaussian grid
MODIS snow extent• March, May, and December 2002• 0.05 deg CMG, re-sampled to 0.5 deg
Canadian Met Service daily observations• March, May, December 2002• Heidke Skill Score (2 class contingency table: snow / no snow)
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MODIS ComparisonMarch 2002 May 2002
operational
revised
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Snow Depth Analyses for 2/12/2002
NWS National Operational Hydrologic Remote Sensing Center
Operational Revised
NESDIS
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SNODAS snow extent
01/11/03 01/12/03 01/01/04 01/02/040.0
0.2
0.4
0.6
0.8
1.0
snow
ext
ent
US
CTRLEXPSNODAS
01/11/03 01/12/03 01/01/04 01/02/040.0
0.2
0.4
0.6
0.8
1.0
snow
ext
ent
West
01/11/03 01/12/03 01/01/04 01/02/040.0
0.2
0.4
0.6
0.8
1.0
snow
ext
ent
Central
01/11/03 01/12/03 01/01/04 01/02/040.0
0.2
0.4
0.6
0.8
1.0
snow
ext
ent
East
(-124° W to -105° W)
(-80° W to -60° W)(-105° W to -80° W)
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Impact on the forecast
0 1 2 3 4 5 6 7 8 9 10
Forecast Day
40
50
60
70
80
90
100%
DATE1=20031107/... DATE2=20031107/...
AREA=N.HEM TIME=12 MEAN OVER 144 CASES
ANOMALY CORRELATION FORECAST
500 hPa GEOPOTENTIAL
FORECAST VERIFICATION
CNTL
EICE
MAGICS 6.9 metis - dar Thu Oct 21 16:10:30 2004 Verify SCOCOM
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Fractional snow coverage
SWE [mm]
Fra
ctio
nal s
now
cov
er
SWE [mm]
Fra
ctio
nal s
now
cov
erSNODASat T511
30/11/03 31/1/04
Sellers et al. 1996: SiB2Marshall et al. 1994: CCM2 (NCAR)Yang et al. 1997
z0 : 2 cm; ρ = 300 kg m-3
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Problems and Limitations …
100
100 100
100
100
100
100
200
200
200
200
200
200
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1 0.1
0.1
0.1
2
2
2
2 2
2
22
10
10
10
10
10
10
25
25
25
25
25
50
50
5050
50
100
100
100
100
100
46°N
48°N
6°E
6°E
8°E
8°E
10°E
10°E
12°E
12°E
14°E
14°E 16°E16°E
00
0
0
00
00
0 0
0
0
0
0
0
0
0
0
0
0
0
00
0
0
0
0
0 0
0
0
0
0
0 0
0
00
0
0
0
0
0
0
00
33
59
81
79
230
310199
285
395
FC field from 2007040712, t + 42 VT: 2007040906 [ cm ]Observations from 2007040900 to 2007040909 [ cm ]
snow depth verification
2 - 10 cm 10 - 25 cm 25 - 50 cm 50 - 160.82 cm
MAGICS 6.11 bee06 - moa Tue Apr 10 05:02:54 2007
285
230395
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Problems and Limitations …
40°N 40°N
50°N50°N
20°E
20°E
0.1
0.5
1
2.5
5
10
25
50
100
250
500
40°N 40°N
50°N50°N
20°E
20°E
0.1
0.5
1
2.5
5
10
25
50
100
250
500
40°N 40°N
50°N50°N
20°E
20°E
0.001
0.1
0.5
1
2.5
5
10
20
30
40
40°N 40°N
50°N50°N
20°E
20°E
0.001
0.1
0.5
1
2.5
5
10
20
30
40
ERA Interim T255 (May 1991)ERA 40 T159 (May 1990-2000)
Operations scaled to T159 (May 2006)Operations T799 (May 2006)