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