Current Status and Prospects . of Drought Forecasting in South Asia . L. S. Rathore, Akhilesh Gupta and J.V. Singh. National Centre for Medium Range Weather Forecasting Department of Science & Technology Government of India. Website: www.ncmrwf.gov.in. PREDICTING DROUGHT.
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of Drought Forecasting in South Asia
L. S. Rathore, Akhilesh Gupta and J.V. Singh
National Centre for Medium Range Weather Forecasting
Department of Science & Technology
Government of India
Predicting drought means predicting Precip. & Temp, the prime causatives, in all temporal ranges i.e. medium, extended and seasonal.
Data of past 50 years show that number of Break days are more
in July as compared to August
Since 1991, number of years (11 out of 14 years) with less than LPA of
July rainfall was more as compared to previous decades. 2002 was
the worst year with All India rainfall 54% below normal.
Large inter-annual variability can be noticed for June-July rainfall.
Years with above normal and below normal rainfall are nearly
Range Weather Forecasting System.
using Global Data Assimilation and Forecast Systems
at T80L18 and T170L28 resolutions.
using MM5 and Eta Models.
* Weather based farm advisory service
# Operational Model
DATA RECEPTION AT NCMRWF
DATA PROCESSING & QUALITY CONTROL
6 HR FCST
6 HR FCST
6 HR FCST
GLOBAL DATA ASSIMILATION
GLOBAL SPECTRAL MODEL
7 DAYS FORECASTS
FORECAST DISSEMINATION TO USERS
Cray SV1: 24-Processors- 1.2 GFlops per processor,
8 GB Main Memory, 800 GB Disk
DEC-ALPHA:Parallel Processing System
2- Servers AS4100 @600 MHz, Memory– 1GB each
9-Work Stations @600 MHz, Memory– 512MB each
Switch: Gigabit Ethernet Smart Switch Router
ORIGIN 200:Parallel Processing System
2- Servers: 4 CPU each @225 MHz, Memory– 1GB each
ORIGIN 200:Single CPU Servers
3- Servers @270 MHz, Memory– 512 MB each
1- Server @180 MHz, Memory– 512 MB
4- O2 WORK STATIONS:@200 MHz, Memory– 512MB each
PARAM 10000:Parallel Processing System
2- SUN Ultrasparc-II Servers (4 CPU each) @300 MHz,
Memory– 1GB each, ( Switch: MYRINET)
LOCAL AREA NETWORK:on Fast Ethernet.
4- LINUX SERVERS: (WEB, FTP, PROXY, PRINT)
Internet: 2 MBPS Leased Line
SERVICE OF NCMRWF
PREPARATION OF LOCATION SPECIFIC FORECAST
PREPARATION OF AGROMET ADVISORY BULLETIN
SERVICE (AAS) UNITS OF NCMRWF
All India rainfall was 2% below normal.
19/36 sub-divisions reported normal rainfall
9/36 subdivisions reported excess rainfall
61% of district received normal to excess rains.
Only 19 subdivisions received normal to excess rainfall. 45% districts received normal to excess rainfall.
Growing Concern: Expansion of
R/F Deficient Area with time
1 June-14 July
(One week in advance) issued to Min. of Agri.
A typical bulletin of NCMRWF on MRWF of Monsoon situation issued for the week 19-25 July
Forecast anomalies for each zone from each ensemble member runs are examined and given a category >
Excess: > 20%
Normal: between –20% to 20%
Deficient: less than -20%
Probability of prediction is computed by counting how
many runs have predicted which category of rainfall
anomaly for a zone (e.g. if 10 runs are made, and for North-East zone, 7 runs Predict excess rainfall, 2 predict normal and 1 deficient, probability of forecast is given in % as (70,20,10) or (7,2,1) as number.
If the probability of forecast for a zone exceeds 80% for
any category, sufficient confidence exists, and on the
Anomaly Map, the zone is shaded.
No Shading denotes areas where category could not be determined as the spread is quite large
Performance of NCMRWF ERP Was generally GOOD for JUNE
The ERP prepared at 1.4x1.4 degrees.
To generate forecast for smaller domains:
Employing Eta Model (32x32km Grid) using the
Forecasts of the Global Model
Perfect Prog Method at monthly scale
Use Weather Generator for temporal downscaling
from monthly to daily values
MRWF skillful and can play a role in indicating synthesis of drought over small spatial domains.
Outreach system (AAS) existing at 83 zones.
ERP System is under development. Evaluation of the Predictions in progress.
Generally Rainfall Prediction over Peninsula,
eastern and NE Regions good.
Role of Initial Data, SST and other predictands on seasonal forecast need to be examined further.