Snow Analysis for Numerical Weather prediction at ECMWF

ECMWFIGARSS 2011, Vancouver, 25-29 July 2011 Slide 1

Snow Analysis Snow Analysis Snow Analysis Snow Analysis

for Numerical Weather prediction for Numerical Weather prediction for Numerical Weather prediction for Numerical Weather prediction

at ECMWFat ECMWFat ECMWFat ECMWF

European Centre for Medium-Range Weather Forecasts

Patricia de Rosnay, Gianpaolo Balsamo, Lars Isaksen


Land surface data assimilationLand surface data assimilationLand surface data assimilationLand surface data assimilation

1999 2004 2010/2011

OI screen level analysis

Douville et al. (2000)

Mahfouf et al. (2000)

Soil moisture 1D OI analysis

based on Temperature and

relative humidity analysis

SYNOP Data

Revised snow analysis

Drusch et al. (2004)

Cressman snow depth analysis

using SYNOP data improved

by using NOAA / NSEDIS Snow

cover extend data (24km)

NOAA/NESDIS IMS

Structure Surface Analysis

Optimum Interpolation (OI) snow analysis

Pre-processing NESDIS data

High resolution NESDIS data (4km)

SEKF Soil Moisture analysis

Simplified Extended Kalman Filter

Drusch et al. GRL (2009)

De Rosnay et al. ECMWF NewsLett. (2011)

METOP-ASCAT SMOS


Snow Analysis Snow Analysis Snow Analysis Snow Analysis

Snow Quantities:

- Snow depth SD (m)

- Snow water equivalent SWE (m) – ie mass per m2

- Snow Density ρs, between 100 and 400 kg/m3

Background variable used in the snow analysis:

- Snow depth Sb

computed from forecast SWE and density

model parameterization revised in 2009

(Dutra et al., J Hydromet. 2009)

Observation types:

- Conventional data: SYNOP snow depth (SO)

- Satellite: Snow cover extent (NOAA/NESDIS)

1000

SSD

SWEρ×

= [m]


NOAA/NESDIS Snow extent dataNOAA/NESDIS Snow extent dataNOAA/NESDIS Snow extent dataNOAA/NESDIS Snow extent dataInteractive Multisensor Snow and Ice Mapping System

- Time sequenced imagery from geostationary satellites

- AVHRR,

- SSM/I

- Station data

Northern Hemisphere product

- Daily

- Polar stereographic projection

Resolution- 24 km product (1024 × 1024)- 4 km product (6044 x 6044)

Information content: Snow/Snow freeFormat: - 24km product in Grib- 4 km product in Ascii

More information at: http://nsidc.org/data/g02156.html


NOAA/NESDIS Pre-Processing

- Pre-processing revised in 2010.

- NOAA/NESDIS data received daily at 23UTC.

- Pre-processing:

- Conversion to BUFR

- BUFR content: LSM, NESDIS snow extent (snow or snow free),

and orography, interpolated from the model orograpghy

on the NESDIS data points.

Orography (m) includedin the BUFR

→ used in the snow analysis


Snow AnalysisSnow AnalysisSnow AnalysisSnow Analysis

Snow depth analysis in two steps:1- NESDIS data (12UTC only):

- First Guess snow depth compared to NESDIS snow coverNOAA snow & First Guess snow free � put 0.1m snow in First Guess (First Guess snow free: < 0.01m of snow, ie SWE in [1; 4] mm;

Update: SD 0.1m, snow density=100kg/m3, SWE=0.01m)

- NESDIS snow free � used as a SYNOP snow free data 2- Snow depth analysis (00, 06, 12, 18 UTC):

- Cressman interpolation: 1987-2010 Still used in ERA-Interim

- Optimum Interpolation: Used in Operations since November 2010

SYNOP Pre-Processing: - SYNOP reports converted into BUFR files. - BUFR data is put into the ODB (Observation Data Base)


( )

∑

∑

=

=

−

+=N

n

n

N

n

bO

nn

ba

w

SSw

SS

1

1

'

Cressman Interpolation

- (Cressman 1959)

- 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 r and vertical difference h (model –

obs): w = H(r) v(h) with:

+

−= ,0

rr

rrmaxH(r)

22max

22max

2h2max

h

2h2max

h

+

−

1 if 0 < h Model above observing station

0 if h < - hmax

if – hmax < h < 0v(h) =

rmax = 250 km (influence radius)

hmax = 300 m (model no more than

300m below obs)

Obs point more than

300m higher than model

55°N55°N

60°N 60°N

65°N65°N

70°N 70°N

75°N75°N

85°E

85°E 90°E

90°E 95°E

95°E 100°E

100°E 105°E

105°E 110°E

110°E 115°E

115°E 120°E

120°E 125°E

125°E 130°E

130°E 135°E

135°E 140°E

140°E 145°E

145°E 150°E

150°E 155°E

155°E

SNOW Depth and SYNOP data in cm (1) 20070212 at 12UTC

1

2

5

10

15

20

50

100

150

4000

10001

1517 3

2

7145

119 14 17

319

44 285

6 18

40 4634

20

49

19

33

42

77

60

35

2

15

2310

12

29

8

940

37

34

20

63

67

18

55

5026

49

30

6 2

512515 26

411

2

12738

6072517146

2371

453

182

6

279

0

4252

35

36

43

5

26

20

36

35

68

7 7

42 17

2

55

24

12

14

45

2521

25 79

7

59

15

3218

26

3

3924

27

55

45

6

3

1615 5

4

73

12

6

37

2126 19

4

101

71

39

1

2

8

271

69

13

36

14

92

9

15

65

26

80

310

3

18535

10

53

1033

2

44 109

15

28

38

26

12

34

57

10

45°N45°N

50°N 50°N

55°N55°N

60°N 60°N

65°N65°N

70°N 70°N

75°N75°N

165°W

165°W 160°W

160°W 155°W

155°W 150°W

150°W 145°W

145°W 140°W

140°W 135°W

135°W 130°W

130°W 125°W

125°W 120°W

120°W 115°W

115°W 110°W

110°W 105°W

105°W 100°W

100°W 95°W

95°W 90°W

90°W 85°W

85°W 80°W

80°W 75°W

75°W 70°W

70°W 65°W

65°W


1

2

5

10

15

20

50

100

150

4000

10001

North America 2005

Siberia2007

“

Some issues in the Some issues in the Some issues in the Some issues in the CressmanCressmanCressmanCressman Snow analysisSnow analysisSnow analysisSnow analysis

“PacMan” Snow Patterns where observations are scarce

55°N55°N

60°N 60°N

65°N65°N

70°N 70°N

75°N75°N

85°E

85°E 90°E

90°E 95°E

95°E 100°E

100°E 105°E

105°E 110°E

110°E 115°E

115°E 120°E

120°E 125°E

125°E 130°E

130°E 135°E

135°E 140°E

140°E 145°E

145°E 150°E

150°E 155°E

155°E


1

2

5

10

15

20

50

100

150

4000

10001

55°N55°N

60°N 60°N

65°N65°N

70°N 70°N

75°N75°N

85°E

85°E 90°E

90°E 95°E

95°E 100°E

100°E 105°E

105°E 110°E

110°E 115°E

115°E 120°E

120°E 125°E

125°E 130°E

130°E 135°E

135°E 140°E

140°E 145°E

145°E 150°E

150°E 155°E

155°E


1

2

5

10

15

20

50

100

150

4000

10001

Snow Patterns, “PacMan” issues – example on 23 Feb 2010

ERA-Interim re-analysis, T255 (80km), IFS cycle 31r1

(cm)Deterministic Analysis, T1279 (16km)Integrated Forecasting System IFS cycle 36r1

Winter 2009-2010 highlighted several shortcomings of the snow analysis related to the Cressman analysis scheme as well as to a lack of satellite data in coastal areas, as well as issues in the NESDIS product pre-processing at ECMWF, fixed in flight in operations on 24 Feb 2010.

Revised snow analysis from Nov. 2010:

(from Integrated Forecasting System, IFS cycle 36r4)

� OI: New Optimum Interpolation Snow analysis, using weighting functions of Brasnett, J. Appl. Meteo. (1999). The OI makes a better use of the model background than Cressman.

� NESDIS: NOAA/NESDIS 4km ASCII snow cover product (substituting the 24 km GRIB product) implemented with fixes in geometry calculation. The new NESDIS product is of better quality with better coverage in coastal areas.

� QC: Introduction of blacklist file and rejection statistics. Also allows easier identification of stations related to MS queries.

Revised snow analysis


Snow depth Optimum InterpolationSnow depth Optimum InterpolationSnow depth Optimum InterpolationSnow depth Optimum Interpolation

1. Observed Increments from the interpolated background ∆Si are estimated at

each observation location i.

2. Analysis increments ∆Sja at each model grid point j are calculated from:

∑ =

∆×=∆N

i 1

ii

a

j SwS

3. The optimum weights wi are given for each grid point j by: (B + O) w = b

b : error covariance between observation i and model grid point j

(dimension of N observations) b(i) = σb . X µ(i,,j)

B : error covariance matrix of the background field (N × N observations)

B(i1,i2) = σ2b ×µ(i1,i2) with the horizontal correlation coefficients µ(i1,i2)

and σb = 3cm the standard deviation of background errors.

(Brasnett , 1999)

−

−+=)(

2

ii

2

iiii

21Lz

zexp.

Lx

rexp)

Lx

r1(i,iµ 212121

O : covariance matrix of the observation error (N × N observations):

O = σ2o × I with σo = 4cm the standard deviation of obs. Errors

Lz; vertical length scale: 800m, Lx: horizontal length scale: 55km

Quality Control: if ∆Si> Tol (σb2 + σo2 )1/2 ; Tol = 5

In both cases:

Cressman (1959): weights are function of

horizontal and vertical distances.

OI: The correlation coefficients of B and b follow a

second-order autoregressive horizontal structure

and a Gaussian for the vertical elevation

differences.

OI has longer tails than Cressman and considers

more observations. Model/observation information

optimally weighted by an error statistics.

OI vs Cressman

∑ =

∆×=∆N

i 1

ii

a

j SwS

NESDIS 4km productNESDIS 4km productNESDIS 4km productNESDIS 4km product

- Data thinning to 24 km -> same data quantity, improved quality- Data Orography interpolated from high res (T3999, ie 5km) IFS

Old:

Cressman

NESDIS

24 km

New:

OI

NESDIS

4km

Snow Depth Analysis

Snow Water Equivalent Analysis increments

Accumulated

January 2010

In mm of water

Old:

Cressman

NESDIS

24 km

New:

OI

NESDIS

4km

(18UTC) Accumulated for January 2010, in mm of water

Cressman, NESDIS 24 km OI, NESDIS 4km

Case study: 22 december 2009 – major snow fall in Europe- Removes snow free patches and overestimated snow patches- Better agreement with SYNOP data and NESDIS data

Snow Depth Analysis

New snow analysis

- Snow Optimum Interpolation using Brasnett 1999 structure functions- A new IMS 4km snow cover product to replace the 24km product- Improved QC and monitoring possibilities

Number of SYNOP data used in the Analysis in January 2010

� Identified a lack of SYNOP Snow depth data in Sweden

Use of Additional Snow depth data

Snow depth analysis using SYNOP data

Snow depth analysisusing SYNOP data + additional snow data

Implemented as a new report type, in flight from 29 March 2011

�Since December 2010, Sweden has been providing additional snow depthData, Near Real Time(06 UTC)through the GTS

Use of Additional Snow depth data

Snow Depth difference (cm)Due to using additional non-SYNOP data in Sweden

Impact on 500hPa Geopotential

Population: 18Confidence: 90%

500hPa Geopotential 00UTCDate: 20101221 00UTC to 20110107 00UTC

N.hem Lat 20.0 to 90.0 Lon -180.0 to 180.0Root mean square error forecast

control normalised fi28 minus fi29

0 1 2 3 4 5 6 7 8 9 10 11Forecast Day

-0.08

-0.06

-0.04

-0.02

0

0.02

0.04

0.06

0.08

Comparison against SYNOP data

Old analysis (Cressman and NESDIS 24km)New analysis (OI and NESDIS 4km)

RMSD between analysis and observations

Independent validation Sodankyla, Finland (67.368N, 26.633E)

ECMWF deterministic analysisSYNOP snow depths FMI-ARC snow pit and ultrasonic depth gauge

Figures produced by R. Essery, Univ Edinburgh)

Winter 2010-2011

RMSE Forecast 1000hPa

Geopotentail in Europe

Improved when above 0

New snow analysis New snow analysis New snow analysis New snow analysis Impact Impact Impact Impact

Impact of OI vs Cressman(both use NESDIS 24km)

Overall Impact ofOI NESDIS 4km vs Cressman NESDIS 24 km

New snow Analysis in Operations

OI Brasnett 1999 +4km NESDIS

- OI has longer tails than

Cressman and considers more

observations.

-- Model/observation information

optimally weighted by an error

statistics.

Cressman +24km NESDIS

Old: CressmanNESDIS 24km

New: OINESDIS 4km

FC impact (East Asia):

• New snow analysis implemented at ECMWF

• Based on a 2D Optimum Interpolation

• Uses Brasnett 1999 structure functions

• Uses SYNOP and high resolution snow cover data from NOAA/NESDIS,

• Flexible to use non-SYNOP reports (new report codetype)

• Improved QC (blacklisting and monitoring possibilities).

• OI has longer tails than Cressman and considers more observations.

• Model/observation information optimally weighted with error statistics.

• Positive impact on NWP. However extensive validation using independent data needs to be done

• In the future, use of combined EKF/OI system for NESDIS/conventional information data assimilation.

Summary and Future PlansSummary and Future PlansSummary and Future PlansSummary and Future Plans

Snow Analysis for Numerical Weather prediction at ECMWF

Technology

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