Transcript South Asian Regional Reanalysis (SARR) Ashish Routray

South Asian Regional Reanalysis
(SARR)
Ashish Routray
National Centre for Medium Range Weather
Forecasting (NCMRWF)
Ministry of Earth Sciences
Government of India
Motivation for
Reanalysis
South
Asian
Regional
Due to the direct societal impacts, interest
in Regional Hydroclimate (precipitation,
surface temperature, soil moisture, stream
flow, drought indices, etc.) is intense and
growing.
National Action Plan on Climate Change
Government of India
Prime Minister’s Council on Climate Change
3.8.2 ……. Regional data reanalysis
projects should be encouraged. ……..
South Asian Regional Reanalysis (SARR)
A Collaborative Project between
Ministry of Earth Sciences,
Government of India
and
National Oceanic and Atmospheric Administration,
Department of Commerce,
United States of America
Specific SARR Goals
Refinement in methods of precipitation and radiances
assimilation.
Conduct a 5-year pilot-phase reanalysis
(to test and optimize data stream organization and the geographic domain
and assimilating model choices)
Develop high-resolution SST analysis for the Indian
ocean from satellite and in-situ observations, including moorings,
drifters and Argo floats
Design techniques for assimilation of aerosols
Generate a high spatio-temporal resolution (≤25 Km, ≤3
hours) climate data set for the 1979-2009 period over the
South Asian land-ocean region.
Responsibilities of the Parties
NOAA agrees to:
Provide MoES full access to the archived observations used in
the global reanalysis projects.
Provide technical help, training, and guidance in organization of
data streams and in the implementation of the regional reanalysis
model.
Provide training to MoES scientists in regional reanalysis
techniques and procedures during 6-8 week annual visits to
US institutions and NOAA laboratories.
Share the NCEP data processing and quality control procedures
during reanalysis project with MoES scientists.
Support travel of NOAA and US university scientists to India in
connection with SARR project activities.
MoES agrees to:
• Provide NOAA full access to all historical and current
meteorological observations as per requirement of the project over the
Indian subcontinent and Indian Ocean, including those from Indian
satellites.
•Execute the South Asian Regional Reanalysis project through
NCMRWF.
•Provide full-time modeling scientists to develop, implement, and test
numerical codes.
•Provide 4-6 full time Ph.D. scientists to design, test, and implement
various assimilation schemes in the numerical model.
•Provide high-speed mainframe computer resources for execution
of this computationally intensive project.
•Provide storage devices and skilled manpower (data management
specialists) to organize data streams, data archival, data
dissemination, and webpage design and maintenance.
•Provide continuous high-speed internet access to project scientists,
including visiting ones.
•Provide lodging and boarding for visiting US project scientists.
Exchange visits
• NOAA will provide training to 2-3 MoES scientists in regional
reanalysis techniques and procedures during 6-8 week annual
visits to the University of Maryland and NOAA's National
Centers for Environmental Prediction (NCEP).
• NCEP will seek resources and assistance from NOAA's
International Activities office in meeting its responsibilities.
• NOAA and MoES scientists will meet yearly to discuss the
project's progress, and to strategize on how to best accomplish
the project goals.
• NOAA and MoES will separately cover travel costs associated
with exchange visits for their respective technical and scientific
personnel.
Milestones
SARR IA signed in September 2008 in New Delhi
1st Annual Review by JEM held in October 2009 in New Delhi
Functional Group created at NCMRWF for SARR in November 2009
SARR Scoping Workshop held in New Delhi in February 2010
2nd Annual Review by JEM held in October 2010 in Washington DC
The SARR Project is being carried out with an objective
that the SARR Products shall be useful for
Climate Diagnostics,
Climate Variability,
Climate Change,
Model Verification/Tuning
It is expected that
The SARR project will provide an Atmosphere-LandOcean surface state description where consistency
between circulation and hydroclimate components is
assured.
To achieve the goal, assimilation of rainfall, radiance,
and aerosol observations in numerical weather
prediction models shall be carried out
SARR Project Team at NCMRWF
Sarat C. Kar
Project Management
Ashish Routray
Assimilation- Lead
Prashant Mali
Modeling- Lead
Jaganabdhu Panda
Modeling (worked for about 3 months and
left in September 2010)
K. Sowjanya
Assimilation (worked for about 1 year and
left in September 2011)
Sapna Rana
Diagnostics (worked for about 1 year and
left in November 2011)
Domain chosen for SARR
Lat: 150S-450N (286 pts)
Lon: 400E-1200E (332 pts)
Res.: 25 km (pilot phase)
18 km (final SARR)
Cen-lat: 17.50N
Cen-lon: 80.00E
SARR
OBSERVATION
DATA BANK
NCEP
NCMRWF
INCOIS
IMD
Countries in
SARR domain
ISRO
Field
Experiments
DATA from FIELD EXPERIMENTS
25
Paradip
20
latitude (N)
DS4
TS2 (SK)
15
Chennai
DS3, TS1 (SD)
10
BOBMEX
5
0
70
75
80
85
90
longitude (E)
Figure 1. Cruise track and time series (TS) observation positions.
Period: 16 July - 30 August 1999. TS1 - 13N,87E; TS2 - 17.5N, 89E.
SK - ORV Sagar Kanya, SD - INS Sagardhwani, DS3 & DS4 - met ocean b uoys
95
C
T
C
Z
Land Surface Processes
Experiment (LASPEX)
ARMEX
C
PROWNM
B
STORM Programme
SARR Scoping Workshop
held in New Delhi, India (February 10-11, 2010)
9 scientists from USA and about 20 scientists from
India participated.
Analysis method and the model as
well as domain of analysis finalized.
WRF model (3.1 version) and
WRF-3DVar shall be used to carry
out SARR Pilot phase.
The Workshop recommended an
implementation strategy for
success of the SARR project.
Work plan at NCEP
•
Training on methodology for assimilation of the
radiance data (mainly the older period radiance
data) using the GSI system so that a similar
technique can be developed later for the WRF3DVAR analysis system.
•
As part of the training, experiments using radiance
data assimilation for Indian summer monsoon
seasons (mainly for older period) using the NCEP
GSI system and document impact assessment.
•
Familiarization with the available
diagnosis
package for monitoring and for calculation of
statistics of the radiance data utilized in the
assimilation cycle.
SARR Pilot Phase Experiments
(1999-2003)
Analysis Scheme & Model for SARR Pilot Phase
WRF 3.1 and WRF-VAR (3.1) has been chosen for SARR
Pilot phase experiments
Several modeling and assimilation experiments have
been carried out using past data.
Most of the experiments are for July 1999 using NCEP &
NCMRWF observation datasets
Challenging regions for obs. data
Sound
Av. Number of TEMP observation per day
reaching particular height in July 1999
Average Number of Observations per
day in July 1999
300
300
250
250
00Z
200
00Z
06Z
150
12Z
18Z
100
12Z
200
150
100
50
50
0
0
Total
TEMP
WIND
PILOT
Blocks- 42 and 43
>800hPa
800-450hPa
450-100hPa
<100hPa
Mean RMSE of wind components from different observations at
model initial time
Mean RMSE of O-B and O-A for U (m/s)
O-B
U (m/s)
4
O-A
3.5
3
2.5
sound
pilot
geoamv
airep
synop
ship
Types of Obs.
Mean RMSE of O-B and O-A for V (m/s)
O-B
5
O-A
V (m/s)
4
3
2
1
0
sound
pilot
geoamv
airep
Types of Obs.
synop
ship
SARR Test runs with NCEP & NCMRWF data
Mean of RMSE of O-B for U (m/s)
6
NCMRWF
5
4
3
1
pilot
airep
synop
ship
3
0.5
sound
pilot
synop
sound
ship
airep
NCMRWF
PrepBufr
1.5
PrepBufr
4
NCMRWF
3
2
PrepBufr
1.1
t (k)
V (m/s)
4
ship
Mean RMSE of O-A for t (k)
1.3
5
synop
Types of Obs.
Mean RMSE of O-A for V (m/s)
5
U (m/s)
airep
Types of Obs.
Mean RMSE of O-A for U (m/s)
NCMRWF
1.5
1
Types of Obs.
6
PrepBufr
2
1
sound
NCMRWF
2.5
2
2
Mean
RMSE of
OBS-ANA
PrepBufr
4
V(m/s
U (m/s)
NCMRWF
5
PrepBufr
t (k)
Mean
RMSE of
OBS-FG
Mean RMSE of O-B for t (k)
Mean of RMSE of O-B for V (m/s)
3
0.9
0.7
0.5
2
0.3
1
sound
pilot
airep
Types of Obs.
synop
ship
1
0.1
sound
pilot
airep
Types of Obs.
synop
ship
sound
airep
synop
Types Obs.
ship
SARR Pilot Phase Experiments
(i) with various Physics Options
Dynamic Downscaling using WRF
(ii) with various Physics Options
Assimilation using WRF & WRF-VAR
Most of the experiments are for July 1999 using NCEP &
NCMRWF observation datasets
SARR Pilot Phase Sensitivity Experiments
All Experiments were done for July 01- 31 1999.
With Assimilation- Cyclic, Four times a day (6-hourly)
No Assimilation- only Model run Four-times a day (6-hourly).
(Similar to downscaling experiments)
Precipitation in July 1999 CMAP, TRMM (3B42) and IMD Observed Rain
Precipitation from Global Reanalysis datasets for July 1999
As can be seen, the global reanalysis has failed to bring out details of
rainfall distribution over India and higher rainfall amounts are placed at
incorrect locations
EXPERIMENTAL DESIGN
CU schemes
PBL Schemes
SFC Schemes
Kain-Fritsch (KF)
Betts-Miller-Janjic
(BMJ)
KF-YSU-Noah
Yonsei University
(YSU)
BMJ-YSU-Noah
Noah Land surface
Grell Devenyi (GD)
KF
BMJ
GD
Mellor-YamadaJanjic (MYJ)
BMJ-YSU-Noah
GD-YSU-Noah
KF-YSU-TD
YSU
BMJ-YSU-TD
GD
GD-YSU-TD
Thermal Diffusion
(TD)
KF
BMJ
GD
GD-YSU-Noah
KF-YSU-Noah
KF
BMJ
Expt. Names
MYJ
KF-MYJ-TD
BMJ-MYJ-TD
GD-MYJ-TD
SARR Pilot phase Sensitivity Experiments
No Assimilation
With Assimilation
It has been shown that
just downscaling of coarse resolution
global reanalysis (No Assimilation runs) is
not sufficient for accurate representation of
the Indian monsoon hydroclimate.
When regional assimilation is carried out,
such representation is improved.
SARR Pilot Phase Sensitivity Experiments
Experiments
have
been
carried out using ISRO
derived
vegetation
data
instead
of
USGS
climatological
vegetation
available with the WRF
model.
Results
indicate
that
hydroclimate representation
over India is sensitive to
such specifications.
Impact of Field phase Experiments- BOBMEX data
25
Paradip
20
DS4
latitude (N)
Bay of Bengal
Monsoon Experiment
(BOBMEX)
TS2 (SK)
15
Chennai
July-August 1999
DS3, TS1 (SD)
10
5
0
70
75
80
85
90
longitude (E)
Figure 1. Cruise track and time series (TS) observation positions.
Period: 16 July - 30 August 1999. TS1 - 13N,87E; TS2 - 17.5N, 89E.
SK - ORV Sagar Kanya, SD - INS Sagardhwani, DS3 & DS4 - met ocean b uoys
95
Impact of Field phase Experiments- BOBMEX data
(00Z 12 August 1999)
Assim- Control
Assim- with BOBMEX
Difference
Parallel Assimilation from May 2001 to Sept 2001.
Need of Overlapping period
Pilot phase Assimilation
with conventional data has
been completed from 19992003.
U at 850hPa
Assimilation with Radiance
data and conventional data
is being carried out for the
same period.
T at 850hPa
Parallel run period is also
being extended.
Comparison between CFSR, SARR and Observation
(1-31 July 2000)
OBS
CFSR
SARR
SARR Production Runs
Five simultaneous Streams





Jan. 1979 - Dec. 1985
Apr. 1985 - Dec. 1991
Apr. 1991 - Dec. 1997
Apr. 1997 - Dec. 2003
Apr. 2003 - Dec. 2009
7 years
7 years
7 years
7 years
7 years
9-month overlap for each stream
Total 35 years of Reanalysis Computation
SARR Products
Archival and Distribution
Archival Format (Reanalysis):
IEEE (suitable for GrADS)
NetCDF
GRIB2
Archival Format (Observed data):
ASCII (GTS)
PrepBUFR
little-R
Original format of data
Archival online/nearline disk, Tapes
Available to Partner Organizations: Immediately
Tasks
Pilot phase reanalysis
production (1999-2003)
Evaluation of pilot phase
reanalysis data
Refinement of assimilation
techniques
Collection of data from
countries in SARR domain
Level-I SARR Production
for 1979-2009 period
Evaluation of Level-I
reanalysis data
Final SARR Production for
1979-2009 period
Reanalysis data- public
Aug
2010
Dec
2010
Apr
2011
Aug
2011
Dec
2011
Apr
2012
Aug
2012
Dec
2012
Apr
2013
Aug
2013
SARR – What next?
SARR -II
After the successful completion of SARR’s
present project, We propose to carryout SARR-60
SARR-60 From 1950 to 2009 at 9 km resolution
Regional Ocean-Atmosphere coupling
- shall be the comprehensive dataset for
climate studies in South Asia.
IMPACT of BACKGROUND ERRORS (BE)
& ASSIMILATION
Numerical Experiments
•
The objective of the study is to evaluate the impact of the different
back ground errors (Global and Regional) towards simulation of
four Monsoon Depressions (MDs) over Indian region during SARR
pilot phase period.
•
•
•
•
•
•
27-29 July 1999 (Case-1)
17-18 June 1999 (Case-2)
11-12 June 1999 (Case-3)
6-8 August 1999 (Case-4)
For this purpose three numerical experiments are carried with
WRF-3DVAR as follows:
1)
CNTL:
Without data assimilation using NCEP reanalyses as IC and BC.
2)
BG-3DV:
Data assimilation using NCEP global
Background Error (BE).
3)
BR-3DV:
Data assimilation using own calculated BE over
SARR region.
The additional observations viz. SYNOP, SHIP, TEMP, BUOYS,
PILOT, GEOMV, AIREP etc. are used to improve the model initial
condition derived from coarse resolution large scale global
analysis.
a)
b)
Mean RMSE of O-A for U (m/s)
6
BG-3DV
5
BR-3DV
5
V (m/s))
4
3
2
BG-3DV
BR-3DV
4
3
2
1
1
0
Sound
Synop
Geoamv
Airep
Pilot
Metar
Ships
0
Sound
Synop
Types of OBS
Geoamv
Airep
Pilot
Metar
Ships
Types of OBS
c)
Mean RMSE of O-A for Temperature (k)
2
Temperature (k)))
U (m/s))
Mean RMSE of O-A for V (m/s)
6
BG-3DV
BR-3DV
1.5
1
0.5
0
Sound
Synop
Airep
Metar
Ships
Types of OBS
Mean RMSE from BR-3DV and BG-3DV of O-A for a) U (m/s), b) V
(m/s) and c) T (K).
Case-1
NCEP ANA
BG-3DV ANA
BR-3DV ANA
OBS: 21.0/89.0
CNTL:21.8/89.8
BG-3DV:21.6/88.8
BR-3DV:20.8/89.5
Case-2
NCEP ANA
BG-3DV ANA
BR-3DV ANA
OBS:18.5/86.0
CNTL:18.5/87.0
BG-3DV:18.9/87.1
BR-3DV:19.2/86.5
Model Initial time wind fields at 850 hPa and MSLP
Case-1
Case-2
Track Errors
900
800
700
600
500
400
300
200
100
0
CNTL
BG-3DV
BR-3DV
Errors (kms)
Errors (km)
Track Error
700
CNTL
600
BG-3DV
500
BR-3DV
400
300
200
100
0
0
12
24
Forecast hours
36
48
0
12
Forecast hours
24
Case-3
Case-4
250
CNTL
BG-3DV
200
BR-3DV
Track Errors (km)
CNTL
400
Errors (km)
Errors (km)
Track Eorrors (km)
150
100
50
0
BG-3DV
300
BR-3DV
200
100
0
0
12
Forecast hrs
24
0
12
24
Forecast hrs
36
48
Spatial RMSE (mm) and Correlation Co-efficient (CC) of rainfall over the
area (Lat=150-250N; Lon=750-900E) for all cases.
Cases
RMSE
CNTL
CC
BG-3DV
BR-3DV
CNTL
BG-3DV
BR-3DV
Case-1
29. 62
(27-29 July 99)
26. 24
22.15
0.23
0.36
0.52
Case-2
24. 32
(17-18 June 99)
22. 31
18. 38
0.21
0.33
0.46
Case-3
(11-12 Jun 99)
26. 93
22. 54
18. 68
0.26
0.35
0.46
Case-4
(6-8 Aug 99)
40. 59
34. 25
29. 41
0.33
0.46
0.52
Mean
30. 37
26. 34
22. 16
0.26
0.38
0.49
BG-3DV
BR-3DV
CNTL vs. BG
CNTL vs. BR
BG vs BR
60
300
40
200
20
100
0
0
00
12
24
Forecast hours (UTC)
Mean VDEs (km) and gain skill of experiments
Skill of Expts.(%)
Mean VDEs(kms)
400
CNTL
Impact of Radiance data
Temperature (oC) at
850 hPa
GTS
Diff. (Rad-GTS)
GTS+Rad
Wind (m/s) at 850 hPa
GTS
Diff. (Rad-GTS)
GTS+Rad
Wind (m/s) at 500 hPa
GTS
Diff. (Rad-GTS)
GTS+Rad
Rainfall Climatology
These are accumulated 6-hrly Rainfall from the
models used for Reanalysis. Every 6-hour,
observed data are inserted into the data
Assimilation systems, and analyses are carried
out. Assumption is that models are good enough
for at least 6 hour.
These studies show there are large uncertainties in the
Global Reanalysis data over our Region.
Model Resolution? Data Quality/Quantity?
We need to carry out a Systematic Regional Reanalysis
for our Region to have a consistent Hydro-climate
dataset.
The Global reanalysis data are utilized for studying
climate change and to develop several Application
models.
Therefore, we should provide the users with a good
quality data set for our Region.
Response of the Analysis Increments to
a single Temperature observation 10K
• A large part of tropical forecast
errors can be represented by
equatorial waves.
Global •
BE
These modes effectively reduce
the mass/wind coupling at the
equator.
Reg.
BE
a single u-wind observation 1 m/s
• Daley (1996) has noted that
equatorial error covariance is
weaker than higher latitude and
similar to that obtained by
equatorial beta plane theory.
• By suppressing the erroneous
tropical wind-height coupling,
Global Daley did not find the covariance
BE
pattern to the south of central
latitude in the tropical domain.
Reg.
BE
•In our study, we find that for the
BE statistics, the effect of a
single wind observation is
consistent with theoretically
derived wind correlations for
non-divergent flow.