Development and application of Extended Range Forecast
System for Climate Risk Management in Agriculture
Centre for Atmospheric Sciences
Indian Institute of Technology Delhi
Objectives
Development of a Climate forecast system (monthly to seasonal) at
met-subdivision level (higher spatial resolution) along with six
homogeneous regions and India as a whole.
Experimental real-time extended range prediction of rainfall and
temperature in monthly scale with seasonal outlook.
End to end application of these climate forecast products in
agriculture through 9AUs and feed back from end users (prospective
farmers)
Broad Scientific Approaches
Climate Prediction with AGCMs/AOGCMs
Dynamical
Regional climate model
CustomizationThroughSensitivity Experiment
Initial & Lateral BoundaryCondition from GCM
High resolution RCMproducts
Statistical
Bias Correction
Deterministic MME• EM • Super ensemble (M1)• Supervised PCR (M2)• CCA (M3)• Unified model (M4)
Probabilistic
Advisory preparation on the basis of Climate prediction
Development of CRM
Application of weather generatorTo downscale monthly forecast todaily basis
crop model integration
Application in Agriculture
Feedback from end-users
Evaluation & Bias Correction
Multi-Model Ensemble
1.Superensemble(M1)
2.Supervised PCR (M2)
3.Canonical Correlation Analysis (M3)
Combined Forecast(M4)
Validation in Hindcast
Final Forecast
Forecast From GCM/AOGCM
Deterministic
Probabilistic
Methodology
Sr.no Model Resolution Ensemble
Members
Type
1 CFSv1 (NCEP) T62 15 Fully coupled
2 CFSv2 (NCEP) T126 24 Fully coupled
3 SINTEX-F(JAMSTEC) T106 9 Fully coupled
4 ECHAM4.5 GML (IRI) T42 12 Semi-Coupled
5 ECHAM4.5 MOM3-DC2 (IRI) T42 24 Fully coupled
6 ECHAM4.5 MOM3-AC1 (IRI) T42 12 Anomaly
Coupled
7 ECHAM4.5 CASST (IRI) T42 24 2-tier
8 ECHAM4.5- CFS SST (IRI) T42 24 2-tier
GCMs & AOGCMs Products used in ERFS
Observed data: IMD 1-degree rainfall data ( Rajeevan et al.2006)
-60
-50
-40
-30
-20
-10
0
10
20
30
40
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
Dep
artu
re(%
)
IMD
CFS
Echam4.5
Echam5
Echam4.5-
GMLEcham4.5-
MOM3
Rainfall departure(%) for 1982-2004
Models fails to capture extreme !
Correlation between Individual GCMs and
observation for JJAS rainfall(1982-2008)
May start
April start
CFSv2MOM3DC2ECHcfssst GML MOM3AC1CFSv1
CFSv2MOM3DC2ECHcfssst GML MOM3AC1CFSv1
-1
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
Signal to Noise Ratio
0
0.10.2
0.30.40.5
0.6
CF
S
GM
L
AC
1
DC
2
EC
Hcass
t
EC
Hcfs
sst
Correlation
-0.2-0.1
00.10.20.30.40.50.6
CF
S
GM
L
AC
1
DC
2
EC
Hca
sst
EC
Hcfs
sst
Root Mean Square Error
0
0.5
1
1.5
2
2.5
CF
S
GM
L
AC
1
DC
2
EC
Hcass
t
EC
Hcfs
sst
Skill at All India level for JJAS rainfall(1982-2008)
i
ii
i
ii
OOOf
Of
d2
2
1
Index of Agreement (d)
Willmott (1982)
Index of agreement
00.10.20.30.40.50.60.7
CF
S
GM
L
AC
1
DC
2
EC
Hca
sst
EC
Hcfs
sst
Skill at All India level for JJAS rainfall(1982-2008)
Remote Response from SST
Observed
GCMs
Real Time SST prediction by GCMs for 2009 monsoon
Real Time Large scale prediction by GCMs for 2009 monsoon
Real Time Large scale prediction by GCMs for 2009 monsoon
Webster-Yang Index
0
0.1
0.2
0.3
0.4
0.5
0.6
CF
S
GM
L
AC
1
DC
2
EC
Hcass
t
EC
Hcfs
sst
India Monsoon Index
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
0.4
0.5
0.6
CF
S
GM
L
AC
1
DC
2
EC
Hcass
t
EC
Hcfs
sst
Correlation between observed and GCM
predicted Monsoon Index
(U850 at 5°-15°N, 40°-80°E)-(U850 at 20°-30°N, 70°-90°E)
(Wang et al., 2001).[U850-U200] (0-20N, 40-110E) (Wang and Yang (1992))
Bias correction methods
Mean Bias-remove technique (U).
Multiplicative shift technique (M).
Standardized-reconstruction technique (Z).
Regression technique (R).
Quantile Mapping Method (Q).
Principal Component Regression (PCR)
Skill of each techniques
Statistic Obs Raw U M Z R Q PCR
Mean(mm day-1) 7.63 5.68 7.63 7.63 7.63 7.63 7.61 7.61
SD(mm day-1) 0.75 0.25 0.25 0.34 0.78 0.34 0.76 0.72
RMSE(mm day-1) 2.06 0.69 0.44 0.83 0.72 0.80 0.83
Index of
agreement (d)
0.37 0.45 0.58 0.65 0.48 0.66 0.60
Raw GCMs
Bias corrected GCMs
At All India level for monsoon
• M1: For carrying out weighted multi-model ensemble mean, multiple regression method
has been employed. Singular value decomposition (SVD) has been employed for the
computation of the regression coefficients (referred to as SVD scheme in the following text).
The advantage of SVD method is it removes the singular matrix problem while calculating
covariance among models which can’t be entirely solved with the Gauss-Jordan elimination
method.
iti
N
i
it FFaOS
,
1
Regression coefficient obtained by a minimization procedure during the training period.
Superensemble(M1)
Supervised PCR(M2)
In this methodology, different models are considered as predictors
(independent variables). These predictors are screened according to their
correlation with the observation. After screening, the pool of predictors are
gone through the principal component analysis procedure where these
variables are made orthogonal to each other. First three principal
components are selected on the basis of their correlation with observation.
These selected principal components will finally enter the regression
model stepwise.The PCs (Z) are obtained as
Z = X * V
Where V is matrix of eigenvectors of X (predictors selected after screening).
Then Step wise regression procedure is followed to find the coefficients of following
equation
Y= Z*a
Where Y is the predictand.
INPUT
PREDICTORS (X)
(GCM outputs)PREDICTAND (Y) (IMD
rainfall or Temperature)
Interpolate to
Indian grid
FINAL PREDICTORS
Correlation (X &Y)
PCR
Z
OUTPUT
Stepwise Model
construction using
R.M.S.E
PCR in ERFS
Canonical correlation analysis(CCA) is a multivariate statistical technique developed
by Hotteling (1935). It basically identifies new set of variables having maximum
correlation between them.
The multivariate predictor’s patterns (rainfall from various GCMs) are linearly
related with multivariate predictand’s patterns (Observed rainfall). Each pattern is
called as a CCA mode/variate.
The final equation becomes like
Yit= Erj (V')AqqU'(Esi)'Xst
Xst Predicted at time t; Yit predictand value at time t;
Erj and Esi Eigen Vector for predictand and predictors respectively
U and V are Canonical variates of predictor and Predictand respectively
Aqq canonical correlation matrix
Steps followed :1. Individual model forecast over the selected domain [10S-50N, 50-120E] is used to estimate predictor
EOFs and IMD rainfall is used to estimate the predictand EOFs
2. PCs (computed from 70% variance explaining EOFs) are used for the calculation of canonical vectors
3. Forecast (for each model) are generated at each point using final eqn
4. The final product is obtained by averaging individual model estimated forecast
Canonical correlation analysis (M3)
Use of CCA in ERFS project
The predictand (observation data) and the predictor (GCM output) for rainfall is
extracted for the extended Indian domain which is from 50°N to 10°S in latitude
and 50°E to 120°E in longitude-
Correlation at Met-Subdivision level for JJAS
(Period-1982-2008; Lead-1)
M1 M2 M3 M4
Time M1 M2 M3 M4
June 0.33 0.50 0.41 0.44
July 0.53 0.33 0.30 0.49
August 0.39 0.25 0.25 0.39
September 0.37 0.18 0.47 0.36
JJAS 0.41 0.57 0.47 0.62
Correlation at All India level (Period-1982-2008; Lead-1)
2%2%
14%14%
2%
84%
Illustration of probabilistic forecast
Below
Normal
Normal
Above
Normal
Blue is climatology
Red is forecast
X
Probabilistic Forecasting method
where
X is the forecast to be given,
β is the potentially predictable signal
ε is the error part
0)( as)()( EEXE
0),(222 CovasX
This yields two more relationship
XNXba Fxxx 3/11
3/1
Forecast Category
aN
aN
ax
xF
xF
xXPANP
1
,|,|
Probability of Above Normal
bN
bx
xF
xXPBNP ,|,|
Probability of Below Normal
,|,|1,| APBPNNP xxx
Probability of Near Normal
Assumption: Normal distribution
Final Forecast
One has to estimate the two unknown parameter β and ε
????
Probabilistic
Forecast
Ensemble Spread
(ES)
Error Residual
(ER)
Correlation
(CR)
Uncertainty represents by
Ensemble spread which is
calculated as the variance
of ensemble members for
a particular year or
average of year to year
variance of ensemble
members.
Uncertainty represents
by Root Mean Square
Error (RMSE).
Uncertainty is consider
as the function of
correlation between
observation and signal
(β)
Skill of probabilistic forecast in Rank Probability Skill
Score
CLIRPS
RPSRPSS 1
k
i
ii OYRPS1
2)(
k
i
iiCLI OPRPS1
2)(
Where, Yi , Pi and Oi are the probabilities of forecasts, Climatology and
observations respectively falling in category i
Thus, RPSS is a way of comparing skill of forecasts with the
climatological forecasts.
RPSS<0 means Skill is worse than Climatological forecasts.
RPSS=0 means Skill is same as climatological forecasts
RPSS>0 means Skill is better than climatological forecasts.
Skill of probabilistic forecast in Rank Probability Skill
Score (M4)
CLIRPS
RPSRPSS 1
k
i
ii OYRPS1
2)(
k
i
iiCLI OPRPS1
2)(
Where, Yi , Pi and Oi are the probabilities of forecasts, Climatology and
observations respectively falling in category i
Thus, RPSS is a way of comparing skill of forecasts with the
climatological forecasts.
RPSS<0 means Skill is worse than Climatological forecasts.
RPSS=0 means Skill is same as climatological forecasts
RPSS>0 means Skill is better than climatological forecasts.
Forecast System of Monsoon
Start Month Seasonal outlook Monthly forecast
(lead 1)
April JJAS (lead 2)
May JJAS (lead 1) June
June JJAS (lead 0) and
JAS (lead 1)
July
July Aug
Aug Sept
Rai
nfa
ll (%
De
par
ture
)Summer monsoon seasonal mean rainfall (JJAS): real time experimental forecast
for 2009, 2010, 2011and 2012
Summer monsoon monthly mean rainfall : real time experimental forecast -2009,
2010 ,2011and 2012
Monsoon 2012
Monsoon 2010
Monsoon 2011
Monsoon 2009
2009 2010 2011 2012
April
Start
25 18
May
Start
08 17 18 19
June
Start
21 21
2009 2010 2011 2012
June 08 13 13 09
July 08 10 15 16
August 12 09 16 17
September 12 14 14 16
Seasonal Monthly
No. of subdivision match in IMD’s category out of 34 subdivision
Summary of experimental ERFS
Seasonal forecast for monsoon 2009, 2010, 2011and 2012
Experimental forecast for
monsoon 2013
April Start JJAS-2013 May Start JJAS-2013
Deterministic
Probabilistic
Deterministic
Probabilistic
All India (100% LPA)
All India (97% LPA)
Seasonal
June 2013( May start) July 2013( June start)
Deterministic Deterministic
Probabilistic Probabilistic
All India (96% LPA) All India (106% LPA)
Monthly
Issue of forecast for seasonal and
monthly scale on subdivision level
Application in
Agriculture
Users feedback at pilot
sites
Application part of ERFS
9 pilot sites AU
Making CRM
Making advisories
1. Himachal Pradesh
2. Uttarakhand
3. West Rajasthan
4. Gujarat
5. East Madhya Pradesh
6. Vidarbha
7. Odisha
8. Telangana
9. Tamil Nadu and Pondicherry
S.
No.
Organization Districts Rabi Crops Kharif Crops
1 CSK Himachal Pradesh Krishi Vishwa
Vidyalaya, Palampur- 176062 (HP)
Kangra
Kullu
Wheat Apple, Maize
2 G.B. Pant Univ. of Agri.& Tech.
Pantnagar- 263145 (Uttarakhand)
U.S. Nagar Wheat Rice
3 Anand Agricultural University
Anand - 388 110, Gujarat,
Anand
kheda
Tobacco and potato Rice and Castor
4 Central Arid Zone Research Institute, ICAR,
Jodhpur- 342003 (Rajasthan)
Jodhpur Wheat and Mustard Pearl millet, Cluster
bean and Cumin,
Livestock
5 Orissa University of Agri.& Tech.
Bhubaneshwar- 751003 (Orissa)
Angul
Khorda
Groundnut Rice and Groundnut
6 Acharya N.G. Ranga Agriculture Uni.,
Hyderabad-(A P)
Mahabubnagar Maize Maize and Cotton
7 Tamil Nadu Agricultural University,
Coimbatore - 641 003 (TN)
Coimbatore
Nagapattinum
Maize Maize and Cotton
8 Dr. Panjabrao Deshmukh Krishi Vidyapeeth,
Akola-444104, (Maharashtra)
Akola Sorghum (Kharif
crop in N india)
Cotton and Soybean
9 J.N. Krishi Vishwa Vidyalaya
Jabalpur- 482004 (MP)
Jabalpur Chickpea Rice
List of demonstration sites for pilot Study
Advisories issued to
farmers based on ERFS
Format of advisory issued to the farmers based on
ERFS test forecast (Bhubaneswar and Jodhpur)
Format of advisory issued to the farmers based on
ERFS test forecast (Akola and Jabalpur)
40 (Forty) No. of farmers have been selected for the study under ERFS project particularly
climate risk management on two major crop i.e. cotton and soybean. The farmers were
contacted for the information about the utilization of the ERFS advisories.
The following information is provided on the basis of these feedbacks.
1. How many farmers made use these
bulletins in planning their operation
: Most of the farmers are utilizing bulletins for
planning day to day field operations.
Especially, the sowing time of crops,
occurrence of pest and disease for taking
up of plant protection measures.
2. How many farmers receive the ERFS
bulletins in the time.
: All the contacted farmers received the
bulletins very regularly and timely.
3. How do they rate this information : Useful or very useful. Some farmers offer
comment that this is a ready reckoned for
them.
4. How many farmers think this is a useful
information and should continue
: All of them
Feedback from selected farmers (Akola)
Rating given by the Farmers on the basis of its utility
in Agriculture decision making (Akola)
Particular Selected Village with No. of Farmers Overall %
Ugwa Gorwa
Usable 16 18 85
Non Usable 3 2 12
Can not say 01 0 3
Total 20 20 100
Total no. of published paper: 24; International Journal: 22; National Journal: 2
Journal Name Country No. of publication Impact Factor
Journal of Geophysical Research U.S.A 1 3.021
International of Journal Climatology U.K 3 2.906
Theoretical and Applied Climatology U.S.A 5 1.942
Pure and applied Geophysics Switzerland 2 1.787
Comptes Rendus Geoscience France 3 1.725
Natural Hazard Netherlands 1 1.529
Meteorological Application U.K 4 1.411
Dynamics of Atmospheres and Oceans Netherlands 1 1.565
Acta Geophysica Poland 2 0.617
Current Science India 1 0.935
Journal of Earth System Science India 1 0.820
Scientific outcomes
Include more operational coupled GCMs
output.
More R&D to improve skill of monthly
forecast.
Forecast of extremes (drought/excess).
Future prospects
Thanks
• MME1: MME is a deterministic forecast scheme as a simple arithmetic mean of predictions
based on individual member models. There is an assumption in MME, that each model is
relatively independent. & to some extent, it has the capability to forecast the regional climate
well; therefore we can expect a well model forecast by simple composite of each model
prediction from different models. This forecast technique constructed with bias-corrected data
is given by
NOTE: This simple scheme contains the common advantage & limitation of the model
predictions, therefore, it could be a good benchmark used to evaluate other MME
schemes.
N
i
itit FFN
OS1
,
1
ith Model’s forecast at t
ith Model’s meanNo of
Model
Observed mean
Final forecast at t
MME1(EM)
Method wise spatial skill of forecast for Monsoon season 2006-10
M1 M2
M3M4
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