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Surface Analysis (I)

M. Drusch

Room TT 063, Phone 2759

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Outline

1. Sea Surface Temperature (SST)2. Sea Ice3. Snow

1. 2 m Relative Humidity and Temperature2. Soil Moisture

Part 1:

Part 2:

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Overview

1. Sea Surface Temperature (SST)- Reynolds 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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SST (1)The 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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SST (2)

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

Future changes to the Integrated Forecast System: •NCEP / MMAB high resolution analysis (1/12 degree)•GODAE High Resolution SST data sets

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Lake SST (1)

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 (2)

SST Lake(t) = T2m(t-1)

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Sea Ice (1)

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.

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Sea Ice (2)Sea 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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Sea Ice (3)

(hhtp://polar.wwb.noaa.gov/seaice)

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Snow Analysis

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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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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• sun-synchronous, circular, near polar orbit

• snow detection based on:Bands 1 (620-670 nm) and 2 (841-876 nm) for NDVI and Bands 4 (545-565 nm) and 6 (1628-1652 nm) for NDSI

• snow present if NDSI > 0.4 and reflectance Band 2 > 11%

• forested areas: canopy reflectance model is used to create NDVI – NDSIpolygon

• mapped to 500 m resolution with 40 % minimum snow cover

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MODIS Fractional Snow Cover (II)

Daily L3 Global 0.05 Deg CMG Products: Frac. Snow Cover := snow covered pixels / visible land pixels Confidence Index := visible land pixels / total land cover Cloud mask

Regridding to 0.5 Deg: - SC = FSC × CI for pixels labeled ‚snow covered‘ - SC = FSC + (1. – CI) for pixels labeled ,snow free‘ - SC = 1/N SC

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Motivation for a Revised Analysis (I)

MODIS snow extent 17.-24.1.2002

by NSIDCby NSIDC

MODIS vis image 27.10.2002

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Motivation (II)

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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Motivation for a Revised Analysis (III)

March 2002 May 2002

December 2002

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General Comments on the Revision

NOAA NESDIS satellite data contain no information on snow depth. The parameters for the spatial interpolation and quality checks were developed for T106, they are not ideal for higher resolutions.

Satellite data outnumber conventional observations at the snow edges.

It is difficult to obtain independent observations and to compare observations with the analyses.

Satellite derived snow extent has been available for ~ 20 years. It has not beenintegrated in any operational analysis ( & there are no papers on combining satellite data, ground based observations and modelled snow depth).

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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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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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SWE [cm]

SWE [cm]

NESDIS Snow Cover [%]

MODIS

operational

revised

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MODIS ComparisonMarch 2002 May 2002

operational

revised

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Metarstation meteorological obs

Rapid Update Cycle (RUC2)NWP analyses / forecasts

NCEP stage IV radarprecipitation analysis

NESDIS GOESsolar radiation

NOAA GOES AVHRRcloud cover albedo

physicaldownscaling

automatic quality control

snowmodel

static geophysicaldata

NRCS SNOTELsnow water equivalent

CADWR & BC Hydrosnow water equivalent

NWS / Cooperative Observersnow water equivalent, snow depth

automaticquality control

NOHRSCAirborne Gamma

SWE

NOHRSCGOES / AVHRR

snow cover

[after Carrol et al., 2001]

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NOHRSC (II): Technical implementation and analysis scheme

1. 24 hour model run at 1 hour time steps

2. Snow observations are sampled during the last 18 hours.

3. Satellite data is used to identify snow boundaries.

4. Differences between modelled values and observations are computed andspatially interpolated.

5. Difference fields are analyzed MANUALLY to identify regions to update.

6. Difference fields are divided by 6 to provide hourly increments for the final6 hourly model run.

7. The model is re-run for 6 hours, at the the end of each time step estimated state variables (snow depth, SWE, and snow pack temperature) are nudged.

[after Carrol et al. 2001]

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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 intercomparison

40°N

120°W

120°W

100°W

100°W

80°W

80°W

ECMWF Analy sis VT:Sunday 30 Nov ember 2003 06UTC Surf ace: snow depth

0.0001 0.01 0.02 0.05 0.1 0.2 0.4 0.8 10.00

40°N

120°W

120°W

100°W

100°W

80°W

80°W

ECMWF Analy sis VT:Sunday 30 Nov ember 2003 06UTC Surf ace: snow depth

0.0001 0.01 0.02 0.05 0.1 0.2 0.4 0.8 10.00

40°N

120°W

120°W

100°W

100°W

80°W

80°W

ECMWF Analy sis VT:Sunday 30 Nov ember 2003 06UTC Surf ace: snow depth

0.0001 0.01 0.02 0.05 0.1 0.2 0.4 0.8 10.00

40°N

120°W

120°W

100°W

100°W

80°W

80°W

ECMWF Analy sis VT:Sunday 30 Nov ember 2003 12UTC Surf ace: snow depth

0.0001 0.01 0.02 0.05 0.1 0.2 0.4 0.8 10.00

SNODAS 30/11/04, 6 UTC CTRL 30/11/04, 6 UTC

EXP 30/11/04, 12 UTCEXP 30/11/04, 06 UTC

SWE [m]

SWE [m]

SWE [m]

SWE [m]

0.0001 0.01 0.02 0.05 0.1 0.2 0.4 0.8

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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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SNODAS mean SWE

01/11/03 01/12/03 01/01/04 01/02/040

10

20

30

40

50m

ean

SW

E [m

m]

US

CTRLEXPSNODAS

01/11/03 01/12/03 01/01/04 01/02/040

10

20

30

40

50

mea

n S

WE

[mm

]

West

01/11/03 01/12/03 01/01/04 01/02/040

10

20

30

40

50

mea

n S

WE

[mm

]

Central

01/11/03 01/12/03 01/01/04 01/02/040

10

20

30

40

50

mea

n S

WE

[mm

]

East

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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

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Natural hazard / severe storm application

‘A surprise storm buried sections of Colorado with as much as 61 centimetersof snow on April 10, 2005. According to the Associated Press, the spring stormcancelled flights and closed a major interstate highway, stranding travellers. TheMODIS on NASA’s Terra satellite captured this view of the fresh snow onApril 12, 2005. … Denver, the capital of Colorado, forms a dark circle in the snownear the base of the mountains. The city reported 30 cm of snow.’

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Natural hazard / severe storm application

09.04.2005 12.04.200511.04.200510.04.2005

38°N38°N

39°N 39°N

40°N40°N

108°W

108°W 107°W

107°W 106°W

106°W 105°W

105°W 104°W

104°W 103°W

103°W

ECMWF Analysis VT:Monday 11 April 2005 12UTC Surface: snow depth

0.1

0.5

1

2

5

10

20

40

75

125

200

10000

38°N38°N

39°N 39°N

40°N40°N

108°W

108°W 107°W

107°W 106°W

106°W 105°W

105°W 104°W

104°W 103°W

103°W

ECMWF Analysis VT:Tuesday 12 April 2005 12UTC Surface: snow depth

0.1

0.5

1

2

5

10

20

40

75

125

200

10000

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Summary

• Climatology can be omitted.

• The revised analysis using the satellite product results in an improved snow extent compared to MODIS.

• Higher skill scores for March, December and the first half of May compared to CMS observations (lower skill for the end of May). [not shown]

• Improved snow extent compared to SNODAS (US domain).

• In general, ECMWF analyses systematically ‘underestimate’ SWE in the western part of the US.

• The impact on the forecast is neutral with respect to 1000 and 850 hPa temperatures.

• The satellite product is not free of errors and can deteriorate the analyses on regional scales.