Weather & Climate Weather & Climate Forecasts and its Forecasts and its
ApplicationsApplications
K K SinghK K SinghHead, Agromet ServicesHead, Agromet Services
Talk Outline
Observing Weather Predicting Weather & Climate Applications & Services Future Plan
Incremental use of Met InformationImproved Quality of Information
Scientific and technical advances Numerical modelling and data assimilation
Improved Capacity & Need of User Increased awareness of need to act Value of evidence-based decision making Greater capacity to access information Greater capacity to use information in decision
making Ongoing resource pressures and accountability
Global environmental change conciousness Response to climate and natural disasters
What IMD DoesProduce Weather and Climate
Forecasts and Warnings- To Protect Life and Property
- To Enhance the National Economy
Also provide Data and Products:
This problem has three parts . . .
Analyze: Current state of atmosphere
Forecast: What’s going to happen?
Communicate: Forecast/warning dissemination
Meteorological information is becoming more accurate for applications the economy and business decisions
Observing Weather
The skill improvement in weather forecasts has mainly come from…
Enhanced understanding of earth's atmosphere
Better observing systems Better communication systems Better analytical techniques Improved numerical models Faster computers Skilled manpower Better dissemination systems
Observe to Define Initial State• International cooperation in exchanging
meteorological observations• End to end connection across observing system,
Telecom system, data processing system and forecasting system
• Integration – space and in situ• Observe once
• use many times & forever• Use for many applications
Observing System Nos.
Doppler Weather Radars (DWRs) 16Automatic Weather Systems (AWS) 675Automatic Rain Gauges (ARG) 1206Surface Observatories 559Aviation Current Wx Observatories 71High Wind Speed Recording Stations 14Hydrometeorological Observatories 701Non-Departmental Rain Gauge 3540Upper Air Radiosonde Observatories 39Pilot Balloon Observatories 62
Type of Observatory Nos.Non-Departmental Glaciological Observatories - Snowgauges 21- Ordinary Raingauges 10- Seasonal Snow Poles 6Agrometeorological Observatories 219Evaporation Stations 222Soil Moisture Recording Stations 49Dew-fall Recording Stations 80Evapotranspiration Stations 39Surface Ozone Stations 10Column Ozone (Dobson Spectrophotometer) 2Aerosol Measurement (Skyradiometer) 12Verical Ozone Profile (Sonde) 3Radiation Stations 45Global Atmospheric Watch 11Indian Voluntary Observing Fleet 203
INSAT-3D
Spectral Band
Wave length (µm)
Ground Resolution
Visible 0.55-0.75 1 km
SWIR 1.55-1.70 1 km
MIR 3.80-4.00 4km
WV 6.50-7.10 8km
TIR1 10.2-11.3 4km
TIR2 11.5-12.5 4km
INSAT-3D Satellite Imager Channel Specification
Global Data Dissemination Centre
Predicting Weather
Satellite Observations
Sat. Met Division, IMD (Deployed by Department
of Space)
Aircraft Reports
Ship Reports
Ocean Buoys data(Deployed by Dept. of Ocean Development)
Global Data
Data-flow at Three Hourly Intervals on 24x7 Mode
Regional Telecom Hub (RTH)
DelhiNational Weather Forecasting Centre PuneWeather Central
IMD, Delhi
LAFS NWP forecasts
Regional Centres – 6
State LevelMet.
Centres
Airport Met.
Offices
Cen
tral / S
tate
Govt/ M
ed
ia/
Pub
licCommunication
Analyses & Forecasting
Dissemination
Observations
Data ported to NCMRWF for
medium range weather forecasts
Upper-Air Observations
(62 PB stations + 39 RS/RW)
Surface Observations
Manned (559) + AWS (725) +ARG (1046)
Operational NWP System Medium Range Forecast > GFS T-574/L64 with GDAS ( 00 & 12 UTC) > MME based District Level Forecasts Short Range Forecast > WRF (ARW) VAR at 27 km and 9 km > HWRF > MME based cyclone track prediction > Polar WRF for Antarctica Nowcast and Very Short Range Forecast > Hourly venue specific forecast- WRF (3 km) > ARPS with assimilation of DWR > Nowcast System with assimilation of DWR
Operational Weather Forecasts• Weather Bulletins: All India, Regional, State level• District Level Forecasts • Cyclone prediction• Aviation Forecast• Hydromet Forecast• Marine Forecast• City Forecast• Tourism Forecast• High Way Forecast• Forecast for Antarctica • Air Quality Forecast • Now-cast: Hourly venue specific forecast• Extended Range Forecast• Seasonal Forecast
DISTRICT LEVEL FORECAST
Generation of district level weather forecast (DLWF)
The same was started since June 2008
Parameters: Rainfall Max and Min temperatureTotal cloud cover Surface Relative humidity Surface Wind
New Imitative: GFS 07 DAYS (WEEKLY) CUMULATIVE SUB-DIV. RAINFALL FORECAST
Forecast Observed
GFS All India daily mean rainfall for monitoring active/weak spell
New Initiative:
Generating all India Mean rainfall along with observed normal for monitoring Weak and active spells during monsoon periods.
EXTENDED RANGE WEATHER FORECAST
(NATIONAL MONSOON MISSION)
ARPS at 9 km resolution with assimilation of data from multiple DWRs
http://202.54.31.51/fdp2/arps_form_s.php
Prediction Skills
CC: 7 DAY CUM RAIN: MONSOON 2011
0.3
0.4
0.5
0.6
0.7
0.8
0.9
CENTRALINDIA
NW INDIA NE INDIA EASTINDIA
SP INDIA WESTCOASTINDIA
ALLINDIA
CC
T382
T574
Domain mean correlation coefficient (CC) of weekly (seven days) cumulative observed and forecasts for day-1 to day-5 of rainfall for GFS T382 and T574 over different homogeneous regions of India during monsoon 2011
Domain mean correlation coefficient
Verification of local rainfall forecast (24 hours)
DAY WISE SKILL SCORE VALUE FOR OBSERVED VS VALUE ADDED R/F
74
76
78
80
82
84
86
88
90
92
CHB DJG MLD NAD E.MDP W.MDP
SK
ILL
SC
OR
E I
N P
ER
CE
NT
AG
E
day1
day2
day3
day4
day5
West BengalQualitative verification of Rainfall Forecast 2013
Scores 2008-10 2011-13% improvementIn 2011-13 from
2008-10
FAR 0.47 0.21 -56%MR 0.55 0.30 -45%
BIAS 0.82 0.95 Near NormalPOD 0.45 0.70 56%
C-NON 0.89 0.92 4%PC 0.76 0.87 14%
HSS 0.34 0.63 86%CSI 0.32 0.56 78%
Heavy rainfall warning improvement in last 3 year
Cyclone ForecastingLocation and intensity estimation Direction and speed of movementTrack forecastingIntensity forecastTime and point of landfall Maximum sustained Gale Force Wind forecastRainfall forecastStorm surge forecastDamage expected and action suggested
CyclogenesisPrediction
TrackPrediction
IntensityPrediction
Rapid Intensification
Decay after Landfall
Decay Model
RI-Index
SCIP Model
Multimodel Ensemble(MME)
Genesis Potential Parameter(GPP)
STEP-I
STEP-II
STEP-III
STEP-IV
STEP-V
NWP based Objective Cyclone Prediction System
Track forecast error (km)
Trend in improvement in track forecast (km/Year) during 2003-12
12 hr- 5.1 km per year24 hr- 7.2 km per year
Mean landfall point forecast error (km)
Mean landfall time forecast error (hr)
Average (2008-12)24 hr- 91 km, 48 hr- 96 km 72 hr- 135 km
Average (2008-12)24 hr- 5.5 hrs48 hr- 7.3 hrs 72 hr- 1.2 hrs
Trend in improvement in Landfall forecast during 2003-12
Landfall point12 hr- 16 km per year24 hr- 33 km per year
Landfall Time12 hr- 0.5hr per year24 hr-0.0hr per year
Forecast Performance during 2013
TRIGGERING FOR WATER MANAGEMENT
The full reservoir level of Hirakud Dam is 630 ft.
DGM IMD ADVISED ON 9TH October to Hirakud Dam authorities
through MHA to release water in view of potential threat due to
cyclone
Due to release water level came down to 621 ft. on 11th October.
As the released water was increasing over the plain area of coastal
Odisha, DGM, IMD further advised to stop releasing water on 11 th
October
As a result the reservoir was well managed
Due to extremely heavy rain over Mahandi catchment the reservoir
level increased from 621ft on 11th to 629ft after the cyclone
As it was within the full capacity, it did not worsen the flood
situation in Mahanadi
38
April
June
All India June – September Rainfall
Update for All India June – September Rainfall
All India Monthly(July & August) Rainfall
June – September Rainfall for Four Geographical Regions
In addition, Forecast for Date of Monsoon Onset over Kerala in May
Month of Forecast Issue
All India Second Half of SeasonAugust - September Rainfall
All India MonthlySeptember Rainfall
July
August
All India
Geographical Regions
Long Range Forecast
11-Sep-12
Probabilistic Forecast Based on 5- Parameter Ensemble Forecasting system
Category CategoryClimatol.
Probability %Forecast
Probability %
Deficient <90% 16 10
Below Normal 90-96% 17 27
Normal 96%-104% 33 46
Above Normal 104%-110% 16 14
Excess >110% 17 03
Performance of Operational Forecast (Empirical Model) for All India Seasonal Rainfall (1988-2012):
During 7 years error was ≥ 10% with highest during 2002 (20%) and 1994 (18%). Error during 2009 was 15%.
Average Abs Error of Op. forecasts (1988-2012) =7.95% (1993-2002)= 9.3% of LPA & 2003-2012=6.6% of LPA).
During 1993-2002, the forecast was within the ±8% of actual values during 6 years. with forecast within ±4% of actual values during 2 years.
During 2003-2012, the forecast was within the ±8% of actual values during 7 years. with forecast within ±4% of actual values during 5 years.
Apr 21, 2023
PCR model for the Forecasting date of Monsoon onset over Kerala
Model Model error= 4 error= 4 daysdays
No Name of Predictor Period C.C (1975- 2000)
1 Zonal Wind at 200hpa over Indonesian Region 16th -30th Apr 0.48
2 OLR Over South China Sea 16th- 30thApr 0.40
3 Pre-Monsoon Rainfall Peak DatePre-monsoon
April-May0.48
4 Minimum Surface air Tem. over NW India 1st -15th May -0.37
5 Zonal Wind at 925hpa over Equatorial South Indian Ocean 1st -15th May 0.52
6 OLR Over Southwest Pacific 1st -15th May -0.53
YearActual
Onset DateForecast
Onset Date
2005 7th June 10th June
2006 26th May 30th May
2007 28th May 24th May
2008 31st May 29th May
2009 23rd May 26th May
2010 31st May 30th May
2011 29th May 31st May
2012 5th Jun 1st Jun
2013 1st Jun 3rd Jun
Performance of the PCR Model for Monsoon Onset over Kerala
-12
-8
-4
0
4
8
12
19
97
19
98
19
99
20
00
20
01
20
02
20
03
20
04
20
05
20
06
20
07
20
08
20
09
20
10
20
11
Year
Mo
ns
oo
n O
ns
et
Ov
er
Ke
rala
(D
iffe
ren
ce
fro
m t
he
No
rma
l Da
te)
Actual MOK Forecast
Apr 21, 2023
Applications & Services
Major Services to different Sectors
In the Service of Nation since 1875
•Real time rainfall monitoring•Reservoir Management• Input or Flood Forecasting & Drought Monitoring•Design storm projects (hydraulic structures)•Rainfall statistics (rainfall normal & extremes)•Rainfall climatology
HYDROMETEOROLOGICAL SERVICES
Preparation of value added medium range
forecast at district level Tuesday
Friday
NWP productsState Met Centre (SAMC)
Issuing State Level Composite Bulletin
Value addition
Agromet Field Units(AMFUs)
Dissemination of Agromet Advisory through Multi-Channel Dissemination System
Parameters Rainfall, Wind speed and
direction, Maximum temperature, Relative humidity, Minimum temperature, Cloud cover
Agromet Advisory
11.4 million farmersReceiving SMS
• Conducting State Level Meeting• Completed meetings in 6 States
in 2012
Issuing District (635) AgroMet Bulletin in 13 languages (http://imdagrimet.gov.in)
• Conducting Farmer Awareness Programme
• Completed at 106 stations
Also Brochures for Awareness were completed for 14 languages
From Composite State Level Bulletin, Agrimet Division, IMD preparing National AAS bulletin
Organised different training programmes.Established feedback mechanism
Economic benefits from savings in farm inputs.Increased farm productivity
Farmer Portal
Dissemination of Agromet Advisory1. Mass Mode All India radio, Television, Print Media
2. Outreach at Village level Ministry of IT Internet based Village Connectivity Web Pages: IMD, SAUs, ICAR Web Pages Mobile Phones (SMS & IVRS) through Public & private agencies “Kisan SMS”, a portal for farmers under www.farmer.gov.in 8.9 million farmers Kisan Call Centres
3. Human face for advisory dissemination KVK (ICAR): Training + interaction DAO (SDA): Coordinate Farm inputs with Line Dept. in
rhythm of weather forecast NGOs & other intermediary groups, Awareness Programme
Automation of AAS bulletin preparation
IMD in collaboration with Department of Agriculture & Cooperation (DAC) automated the process of AAS bulletin preparation by 130 AMFUs through Kisan portal (farmer.gov.in).
In addition, Regional Meteorological Centres (RMCs) / Meteorological Centres (MCs) of IMD also upload the moderated forecast in the portal for their respective States.
Agromet Field Units (AMFUs) immediately access the forecast and use it for the preparation of Agromet Advisories.
Transfer of data from Disc to the server of Agriculture Ministry, New Delhi.
Other Proposed ICT Initiatives Interactive portals (in regional languages) Multimodel Support: Audio, Video, Image, Text Multi Platform Backend: Web based and frontend: Mobile interface
Domain: Agriculture, Horticulture, Livestock and Fisheries Multi Linkages: Agri Experts, Knowledge Institutions (SAU/ ICAR
Institutes), Service Providers (Financial / input etc.),Personalized Information in appropriate mode, Farm and Farmer Database including soil, water, nutrients, GIS parameters etc.
ICT based agricultural information dissemination models: esagu, e aqua, Deal, Agrosense, Integrated Agri Services Program (IASP)
Media Lab Asia : An Interactive Information Dissemination System (IIDS): Using Web, IVRS and Mobile technology: through Krishi Vigyan Kendras of ICAR and Common Service Centres of NeGP
AAS BULLETIN BASED ON
EXTENDED RANGE WEATHER
FORECAST
IMD in Collaboration with Indian Institute of Tropical Meteorology, Pune started on a pilot mode experimental Agromet Advisory bulletin.
The ERFS bulletin has mainly three components:
i.Realized rainfall for the preceding two weeks.ii.Rainfall forecast of 4 pentads (5 days each). iii.Broad Agromet Advisories based on the realized and forecasted rainfall along with crop status for six homogeneous regions viz. South India, West India, Central India, East India, North India and Northeast India
NATIONAL AGROMET ADVISORY BULLETIN FOR
PREPAREDNESS BASED ON MONTHLY
RAINFALL FORECAST FOR JUNE 2015
Prepared byEarth System Science Organisation
India Meteorological DepartmentIndian Institute of Tropical Meteorology,
PuneMonthly Rainfall Forecast for June 2015
Monthly Rainfall Forecast for June 2015
Agromet Advisories
for preparedness for land preparation and sowing
of kharif crops
AAS BULLETIN
BASED SEASONAL
WEATHER FORECAST
Agromet Advisories for Preparedness under Kharif
Crop Campaignbased on Seasonal Rainfall
Forecast prepared by
Earth System Science Organisation
India Meteorological Department
&Indian Institute of Tropical
Meteorology, PuneOperational Seasonal Rainfall Forecast for Monsoon 2015
Advisory Based on operational and experimental rainfall forecast
Status of Surface Soil Moisture from Passive Microwave Radiometer,
AMSR-2
Waterlogged
Lo
w
Mo
de
rate
Hig
h
Ve
ry h
igh
Status of Surface Soil Moisture from SMOS
Input data required by this model are •Rainfall•Actual evapotranspiration estimated from potential evapotranspiration and •Soil water content, and •Available water storage capacity.
Simple Bucket Model method
Sowing Suitability using INSAT and AMSR-2 data
Probably sown area
Conducive area for sowing
Permanent agricultural area
The maps shows the sown area and area conducive for sowing. Sowing Suitability of crops during kharif season is initiated using the satellite data (AMSR-2 Soil moisture content INSAT 3A CCD NDVI).
Initiatives with ISRO
Change in agricultural vigorfrom long-term mean
No
+ve
-ve
Status of Agricultural Vigor from INSAT 3A
The agriculture vigour over India is compared with its long term-mean using INSAT 3A CCD NDVI. Positive change in agricultural vigour as compared to long-term mean is observed in major agricultural patches of India till the end of the week of 1st July 2015.
Status of Agricultural Vigor from INSAT 3 A
Satellite based products for Operational Agrometeorology• In season monitoring of crop
area• LST PRODUCT FROM Kalpana
1 VHRR• Daily Surface Insolation product
from Kalpana 1 VHRR• Surface Soil wetness index
(SWI)• Evapotranspiration using
surface energy balance for irrigation scheduling
• Monthly estimates of PET from VHRR insolation and WRF forecast
• Delineation of alarm zones to alert farmers
Economic Assessment by NCAP on IAAS estimated 10-25% economic benefit obtained by the farmers.
Potential economic benefit estimated by NCAER, Rs.50,000 crores per year (used by 24% farmers).
Extrapolation can rise to Rs.211,000 crores if the entire farming community were to apply Agromet information to their agricultural activity.
Economic Impact of IAAS
Future Plans Improving Observations
Sattelite, Radars, AWS/ARG, SG, LDR, GPS-Sonde, MRR, MR, WP, Aircraft, Drop-sonde, UAV etc
Improving Data Assimilation and Models Integrated R&D
Monsoon Mission, Mountain Met, Severe Weather Improving Forecast
Block level Forecast System Extended Range Forecast Seasonal Forecast (RCC)
Better Service Delivery & information dissemination Aviation Services Agro-Meteorological Services HydroMet etc. etc.
Thank youThank you
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