Northern tools and data for model comparisons

Northern tools and data for model comparisons

Northern tools and data for model comparisons
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Laura Rontu, FMI laura rontu@fmi.fi
Zhenya Atlaskin, RSHU atlaskin@rshu.ru
Timo Vihma, FMI timo.vihma@fmi.fi
Kalle Eerola, FMI kalle.eerola@fmi.fi
Nastya Senkova, RSHU senkova@rshu.ru
Markku Kangas, FMI markku.kangas@fmi.fi
December 10, 2005
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How to validate a new scheme?
Standard station verification
Details of bias
Introduction to Sodankylä
Sounding comparison
Mast comparison
Further possibilities
How to validate a new scheme?
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How to validate a new scheme?
Northern winter problem
In the model, too warm predicted near-surface temperatures in a stable
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arctic boundary layer. Differences between observation and forecast of
the order of ten degrees are common. In reality, clear sky, no
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significant SW radiation, shallow surface layer with strong surface
temperature inversion over snow covered surface. Relative humidity
may be large but not close to saturation. Observed latent and sensible
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heat fluxes are small, in the model generally somewhat larger. Extra
clouds/fog may form in HIRLAM.
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SFS: a possible element of the solution
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SFS: a possible element of the solution
• Snow - Forest - Scheme by Stefan Gollvik et al.
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• Detailed handling of interactions
in (snow covered) forest and open land
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• ISBA framework with diffusion equation in soil
partly based on the old HIRLAM surface scheme
developed for the Rossby Centre Climate Model
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• references: Norrköping SRNWP workshop, Sodankylä summer school
SFS: a possible element of the solution
• Snow - Forest - Scheme by Stefan Gollvik et al.
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• Detailed handling of interactions
in (snow covered) forest and open land
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• ISBA framework with diffusion equation in soil
partly based on the old HIRLAM surface scheme
developed for the Rossby Centre Climate Model
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• references: Norrköping SRNWP workshop, Sodankylä summer school
The scheme is ready for one-dimensional and three-dimensional comparisons
Page 4 of 19
How to know if it solves our problem?
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Standard station verification
Verification statistics
against Scn observations
40E (left) OMA (right)
Period: 20050101 - 20050131
Surface pressure
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2
1
0
-1
0
6
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3
2
1
0
-1
-2
-3
12 18 24 30 36 42 48
BIAS
RMS
0
7
6
5
4
3
2
1
0
-1
-2
-3
-4
BIAS
RMS
0
6
6
BIAS
RMS
12 18 24 30 36 42 48
Forecast length (hours)
Ten metre wind
Mean speed and RMS vector error (m/s)
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5
4
Forecast length (hours)
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BIAS
RMS
3
-2
I
BIAS
RMS
Two metre relative humidity
BIAS
RMS
12 18 24 30 36 42 48
Forecast length (hours)
Mean and RMS error (%)
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Mean and RMS error (hPa)
5
4
Two metre temperature
Mean and RMS error (K)
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35
30
25
20
15
10
5
0
-5
-10
-15
BIAS
RMS
0
6
BIAS
RMS
12 18 24 30 36 42 48
Forecast length (hours)
ZOBS or details of the bias (1)
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ZOBS (2)
Verification statistics
Verification statistics
observations
WMO022221
Verification
statistics
experiment
40E
observations WMO022221
Period + fc length:
200501+024
experiment
40E
Period + fc length: 200501+024
Mean sea level pressure
Mean
Two metre
sea level
temperature
pressure
0
4000
2
3000
1
2000
0
1000
-1
4000
3000
2000
1000
0
-2
0
960 970 980 990 1000 1010 1020 1030
-1
Observed pressure (hPa)
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960 970 980 990 1000 1010 1020 1030
8
8000
lassified and accumulated bias (m/s)
8
6
4
Close
2
0
4
4000
2
2000
0
0
8000
-2
-4
6000
-6
4000
class
accum
-8
-10
0
3
6
2000
9
12 15 18
21 024 27
Observed wind speed (m/s)
-2
Quit
-4
-6
-8
6000
class
accum
Classified and accumulated bias (%)
10
Ten6 metre wind
Full Screen
10
88
77
66
5
54
43
32
1
2
0
1
-1
0-2
class
-3
accum
-1
-4
-2-24 -21 -18 -15 -12 -9 -6 -3 0 3
class
Observed temperature (C)
-3
accum
-4
-24 -21 -18 -15 -12 -9 -6 -3
Two
metre relative
Ten
metre
humidity
wind(C)
Observed
temperature
Observed
pressure
(hPa)
lassified and accumulated bias (m/s)
lassified and accumulated bias (%)
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Classified and accumulated bias (m/s)
Ten metre
wind
Observed pressure
(hPa)
Classified and accumulated bias (C)
I
3
5000
Classified and accumulated bias (C)
1
4
5000
6000
88
8000
8
8000
class
77 class 7
7000 7000
accum
accum
6000
6
6000
66
6000
6
6000
5
5
5000
54
4000
5
5000
4000
4
4000
43
4
3
20004000 3000
32
1
2000
2
3
3000 2000
2
0
0
1000
1
1
-1
2000
2
0
0
0-2
0
class
1000
1
-1
-3
accum
-1
-4
-2
0
0
-15 -12
970 -9980-6 990
-3 1000
0 3 1010
6 1020 1030
-2 -24 -21 -18960
class
-1 -3
Observed temperature
(C) (hPa)
Observed pressure
accum
-2 -4
960 -24
970-21980
-18 -15
990 -12
1000
-9 1010
-6 -3
10200 1030
3 6
8
7
10
40
35
25
4020
15
8
35
6
30 5
4
250
2
20-5
0
10
8
TwoTen
metre
relative
wind humidity
9000
6 metre
30
10
-10
15 30
-2
10
-4
5
-6
0
-8
-5
6000
2
6000
8000
2000
0
3000
-2
6000 9000
-4
0
-6
class
-8
accum
8000
4000
4
0
class
accum
-10
40 50
0 3 60 6 709
4000
2000 6000
80
12 1590 1810021 24
0 27
Observed relative
Observed
humidity
wind speed
(%) (m/s)
3000
0
class class
accumaccum
8000
8000
6000
6000
4000
4000
2000
2000
0
0
6
0
3
6
Two Observed
metre relative
humidity (C)
temperature
Classified and accumulated bias (%)
2
6000
5
Two metre temperature
Classified and accumulated bias (hPa)
3
6
7000
Classified and accumulated bias (m/s)
II
7000
Classified and accumulated bias (C)
4
class
accum
Classified and accumulated bias (C)
J
5
8
class 7
accum
Two metre temperature
Two metre Mean
temperature
sea level pressure
Classified and accumulated bias (hPa)
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Classified and accumulated bias (hPa)
Classified and accumulated bias (hPa)
Mean sea level pressure
7
Period + fc length: 200501+024
lassified and accumulated bias (%)
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observations
WMO022221
Verification
statistics
experiment
OMA
observations WMO022221
Period + fc length:
200501+024
experiment
OMA
40
35
Two metre relative humidity
9000
30
25
20
40
6000
15
35
10
3000
305
250
20-5
-10
15 30
10
9000
0
class
accum
40
50
6000
60
70
80
90
Observed relative humidity (%)
100
3000
5
0
-5
0
class
accum
Details of the bias (3)
Verification statistics
Verification statistics
observations
WMO022122
Verification
statistics
experiment
40E
observations WMO022122
Period + fc length:
200501+000
experiment
40E
Period + fc length: 200501+000
Mean sea level pressure
Mean
Two metre
sea level
temperature
pressure
0
4000
2
3000
1
2000
0
1000
-1
4000
3000
2000
1000
0
-2
0
960 970 980 990 1000 1010 1020 1030
-1
Observed pressure (hPa)
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960 970 980 990 1000 1010 1020 1030
8
8000
lassified and accumulated bias (m/s)
8
6
4
Close
2
0
4000
2
2000
0
0
8000
-2
-4
6000
-6
4000
class
accum
-8
-10
0
3
6
2000
9
12 15 18
21 024 27
Observed wind speed (m/s)
-2
Quit
-4
10
8
6
40
4020
10
30
4
2
0
-2
10
8
TwoTen
metre
relative
wind humidity
12000
6 metre
30
0
20
-10
30
10
8000
2
6000
8000
2000
0
4000
-2
600012000
-4
0
-6
class
-8
accum
8000
4000
4
0
class
accum
-10
40 50
0 3 60 6 709
-6
class
accum
-8
4000
2000 8000
80
12 1590 1810021 24
0 27
Observed relative
Observed
humidity
wind speed
(%) (m/s)
0
4000
0
class class
accumaccum
8000
8000
6000
6000
4000
4000
2000
2000
0
0
6
0
3
6
Two Observed
metre relative
humidity (C)
temperature
-4
-6
-8
6000
4
Classified and accumulated bias (%)
10
Ten6 metre wind
Full Screen
10
88
77
66
5
54
43
32
1
2
0
1
-1
0-2
class
-3
accum
-1
-4
-2-24 -21 -18 -15 -12 -9 -6 -3 0 3
class
Observed temperature (C)
-3
accum
-4
-24 -21 -18 -15 -12 -9 -6 -3
Two
metre relative
Ten
metre
humidity
wind(C)
Observed
temperature
Observed
pressure
(hPa)
lassified and accumulated bias (m/s)
lassified and accumulated bias (%)
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Classified and accumulated bias (m/s)
Ten metre
wind
Observed pressure
(hPa)
Classified and accumulated bias (C)
I
3
5000
Classified and accumulated bias (C)
1
4
5000
6000
88
8000
8
8000
class
77 class 7
7000 7000
accum
accum
6000
6
6000
66
6000
6
6000
5
5
5000
54
4000
5
5000
4000
4
4000
43
4
3
20004000 3000
32
1
2000
2
3
3000 2000
2
0
0
1000
1
1
-1
2000
2
0
0
0-2
0
class
1000
1
-1
-3
accum
-1
-4
-2
0
0
-15 -12
970 -9980-6 990
-3 1000
0 3 1010
6 1020 1030
-2 -24 -21 -18960
class
-1 -3
Observed temperature
(C) (hPa)
Observed pressure
accum
-2 -4
960 -24
970-21980
-18 -15
990 -12
1000
-9 1010
-6 -3
10200 1030
3 6
8
7
Classified and accumulated bias (%)
2
6000
5
Two metre temperature
Classified and accumulated bias (hPa)
3
6
7000
Classified and accumulated bias (m/s)
II
7000
Classified and accumulated bias (C)
4
class
accum
Classified and accumulated bias (C)
J
5
8
class 7
accum
Two metre temperature
Two metre Mean
temperature
sea level pressure
Classified and accumulated bias (hPa)
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Classified and accumulated bias (hPa)
Classified and accumulated bias (hPa)
Mean sea level pressure
7
Period + fc length: 200501+000
lassified and accumulated bias (%)
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observations
WMO022122
Verification
statistics
experiment
OMA
observations WMO022122
Period + fc length:
200501+000
experiment
OMA
40
Two metre relative humidity
12000
30
20
40
8000
10
4000
30
0
20
-10
30
10
12000
0
class
accum
40
50
8000
60
70
80
90
Observed relative humidity (%)
0
100
4000
0
class
accum
Introduction to Sodankylä - FMI ARC
What makes Sodankylä observations interesting?
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Introduction to Sodankylä - FMI ARC
What makes Sodankylä observations interesting?
Boreal - subarctic enviroment
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• (long-living) Stable boundary layer, cold Ts
• Boreal forest, snow, low solar elevations
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Introduction to Sodankylä - FMI ARC
What makes Sodankylä observations interesting?
Boreal - subarctic enviroment
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• (long-living) Stable boundary layer, cold Ts
• Boreal forest, snow, low solar elevations
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Unique combination of measurements
• Sounding data
• Mast measurements: profiles and fluxes
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• AWS - SYNOP data + ceilometer
• Soil and snow temperature measurements
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• Radar measurements within 40 km
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Introduction to Sodankylä - FMI ARC
What makes Sodankylä observations interesting?
Boreal - subarctic enviroment
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• (long-living) Stable boundary layer, cold Ts
• Boreal forest, snow, low solar elevations
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Unique combination of measurements
• Sounding data
• Mast measurements: profiles and fluxes
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• AWS - SYNOP data + ceilometer
• Soil and snow temperature measurements
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• Radar measurements within 40 km
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Operational and continuous observations
• Used for operational monitoring
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• Quality control: + and Close
• Data/Tools availability: + and • Possibility for special observation periods
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Sounding comparisons
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Sounding v.s. model as a function of temperature
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Data for SCM
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Data for SCM
• Sounding data as initial profile
• HIRLAM experiment data as initial profile
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• Observed fluxes etc as lower boundary condition
Data for SCM
• Sounding data as initial profile
• HIRLAM experiment data as initial profile
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• Observed fluxes etc as lower boundary condition
Tools to pick HIRLAM profile in ASCII
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Tools to write ASCII input based on HIRLAM profile
Tools to handle sounding data
Mast comparisons
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How to make the sounding and mast comparison pictures?
Raw material
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How to make the sounding and mast comparison pictures?
Raw material
• Sodankylä soundings - ASCII tables
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• Mast profiles: T, RH, wind
- raw observation ASCII files
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• Mast fluxes: senf, latf, momf, SWdn, SWup, LWup
- raw observation ASCII files
• (LWdown separate measurements
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- raw observation ASCII files)
• HIRLAM analyses and forecast
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- GRIB files, possibly subareas/ASCII profiles
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Tools
• Main shell scripts RunSounding, RunMast
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• Fortran programmes interpol.f90 and sodaconv.f90
• ( Tools to pick subareas and profiles from HIRLAM )
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• Grads scripts for drawing
Tools
• Main shell scripts RunSounding, RunMast
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• Fortran programmes interpol.f90 and sodaconv.f90
• ( Tools to pick subareas and profiles from HIRLAM )
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• Grads scripts for drawing
Output
• Readable converted and interpolated ASCII data
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- for drawing, reading, calculations
• Grads pictures
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Further possibilities
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Further possibilities
• Surface energy balance studies
• Radiation balance and clouds
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• Comparison of snow and soil temperatures
• High temporal resolution AWS data
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• Mast data for surface layer studies, e.g.:
stability dependency of roughness
definition of PBL height based on fluxes
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• High resolution sounding data for PBL studies
• Combination of FMI ARC data with Luosto radar
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Further possibilities
• Surface energy balance studies
• Radiation balance and clouds
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• Comparison of snow and soil temperatures
• High temporal resolution AWS data
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• Mast data for surface layer studies, e.g.:
stability dependency of roughness
definition of PBL height based on fluxes
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• High resolution sounding data for PBL studies
• Combination of FMI ARC data with Luosto radar
Page 19 of 19
Need for a Sodankylä Data and Tool Base !
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