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Title: Satellite Meteorology: Indian Initiatives


1
Satellite Meteorology Indian Initiatives
Rangnath R. Navalgund
Space Applications Centre (ISRO) Ahmedabad-380015
International Monsoon Conference Celebrating the
Monsoon Indian Institute of Science July 24-28,
2007
2
INDIAN EO PROGRAMME
  • Space Infrastructure
  • Launch vehicles (PSLV, GSLV)
  • Spacecrafts (LEO, GEO and beyond)
  • Sensors (optical/microwave)
  • Ground Segment
  • Data Acquisition and Processing
  • International Ground stations
  • Cal-Val Programme
  • TTC Network
  • Capacity Building
  • Formal education through CSSTE-AP, IIRS, IITs.
  • On-the job training
  • International Cooperation
  • Bilateral and multilateral cooperation with
    various countries and international Organisations

3
Beginning of Satellite Meteorology ..
  • Though satellites with meteorological instruments
    were first launched in late 1950s ..
  • The first satellite totally dedicated to
    satellite meteorology was TIROS (Television and
    IR Observational Satellite ), launched on
    1-April-1960, initiating an era of satellite
    meteorology

Based on composite of 450 images from TIROS
4
Need for Satellite Data
  • Conventional observations of temperature,
    wind, and moisture profiles (by radiosondes)
    are concentrated over land areas and primarily
    in the Northern Hemisphere
  • Over oceans, conventional observations are
    primarily limited to single level data provided
    by aircraft, ships, and buoys.
  • The coverage of these and other ground based
    observing systems is not sufficient for global
    atmospheric and oceanographic research or
    weather prediction.

5
Advantage of Space Observations
  • Synoptic view
  • Frequent observations
  • Spatial information
  • Simultaneous multi parameter observations
  • Large inaccessible and remote areas
  • Uniformity of global Observation from same
    platform

6
Observational Requirements for weather Climate
Forecast
Parameters Accuracy Hor. Res. (km) Vert. Res. (km)
Temp. Profile 0.5 K 50 2
Humidity Profile Lower Troposphere Upper Troposphere 0.5 gm/kg 0.05 gm/kg 50 50 2 2
Sea Surface Temperature 0.25K 50 -
Sea Ice 5 50 -
Vegetation Cover 5 50 -
Snow Cover 2 50 -
Sea Level 5 cm 100 -
Sea surface winds 1-2 m/s 20 deg 50 -
Aerosol Load 10 100 -
Soil Moisture 5 100 -
Source WMO(1998)
7
Hierarchy of SAT-MET Sensors Optical Sensors
OPTICAL
Atmospheric Sounders
Multi-spectral Imager
Active Sensors (LIDAR)
Clouds
Aerosols/ Cloud Microphysics
Cyclones
AMV
HIRS, AIRS, AMSU
CALIPSO, GLAS/ICESAT
VHRR, AVHRR, MODIS
8
Hierarchy of SAT-MET Sensors Microwave Sensors
MICROWAVE
Multi-spectral Imager
Active Sensors
Atmospheric Sounders
100 mb
200 mb
350 mb
500 mb
Soil Moisture
AMSU
QUIKSCAT, SAR, Altimeter.
SSM/I, TMI, MSMR, AMSR
9
MAJOR MILESTONES IN GLOBAL SAT-MET SENSOR
DEVELOPMENT
2000..
1980-2000
Hyper-spectral Imagers/Sounder (GIFTS)
1970-80
EOS-Era Multi-spectral, Super-resolution
Imagers (MODIS) Superior sounders Based on
spectroscopy (AIRS)
1960-70
NOAA Series QuikScat DMSP,
TRMM AVHRR HIRS AMSU
Nimbus-4-7 HIRS, ESMR,SMMR TOMS
Sounding Formed a legacy for NOAA, DMSP and TRMM
satellited
1960
Nimbus-1,2 0.5-0.7 3.4-3.9 3-Axis, Sun-Synchron
TIROS
10
MAJOR MILESTONES IN INDIAN SAT-MET SENSOR
DEVELOPMENT
2007
2001-2005
1996-2000
Oceansat-2 (OCM, Scatterometer, ROSA) RISAT
(SAR) INSAT-3D(Imager, Sounder) Megha Tropique
(Madras, ScaRab, SAPHIR, GPS
Occ.) SARAL
1991-95
KALPANA (VHRR) INSAT-3A (VHRR,CCD)
1982-90
IRS-P3 (MOS) INSAT-2D (VHRR) INSAT-2E
(VHRR,CCD) IRS-P4 (OCM,MSMR)
1979-81
INSAT (2A/2B/2C) (VHRR)
INSAT (1A/1B/1C/1D) (VHRR)
BHASKARA-1 BHASKARA-2 (SAMIR)
11
Bhaskara (SAMIR)
Microwave Radiometer Bhaskara- 1 SAMIR
(19.24, 22.235 GHZ) Bhaskara- 2 SAMIR
(19.24, 22.235, 31.4 GHz)
SAMIR 19 GHz BT (July 28 August 6
1979)
Bhaskara-1/2 1979/1981
12
INSAT-1D VHRR
Spectral Channels Spectral Band (?m) Spatial Resolution (km)
Visible (Vis) 0.55 0.75 2.75
Infrared (IR) 10.0 12.0 11
INSAT-1D 1990
13
INSAT-2E (VHRR CCD)
INSAT 2E - VHRR
INSAT 2E - CCD

INSAT-2E 1999
14
INSAT-2E (VHRR)
15
INSAT - CCD
Red
SWIR
NIR
FCC
NDVI
16
IRS-P3 P4 Atmospheric Ocean Observations
Satellite (Year) Sensor Spectral Bands (micron) Spatial Resolution (m) Swath (km)
IRS-P3 (1996) MOS-A MOS-B MOS-C WiFS 0.408-1.6 (18 bands) 0.62-0.68 0.77-0.86 1.55-1.70 500 188 200 810
IRS-P4 (1999) OCM MSMR 0.402-1.6 (8 bands) 6.6, 10.65, 18, 21 GHz 360x 236 150, 75, 50, 50 1420
OCM
MSMR
WiFS
IRS-P3/P4 1996/1999
17
Geophysical Products from IRS-P4
WV
SST
SSW
Chlorophyll-a
18
Current Indian Geostationary Meteorological
Satellites
93.5o
74o
83o
INSAT-3A
Kalpana-1
INSAT-2E
19
INSAT 3A Kalpana-1
Payload (i) VHRR CCD camera in INSAT-2E,
3A (ii) VHRR in Kalpana-1
  • VHRR Bands (µm)
  • Visible 0.55 0.75
  • Water vapour 5.70 7.10
  • Thermal Infra Red 10.5 12.5
  • Resolution (km) 2 X 2 for Visible
  • 8 X 8 for WV TIR
  • CCD Camera Bands (µm)
  • Visible 0.62 0.68
  • Near Infra Red 0.77 0.86
  • Short Wave Infra Red 1.55 1.69
  • Resolution (km) 1 X 1 for all bands

Kalpana/INSAT-3A 2002/2003
20
Continuous Monitoring of Weather from INSAT
Channel
VIS (0.5-0.7?m)
IR (10.5-12.5 ?m)
WV (6.7-7.3 ?m)
  • Cloud Detection
  • Cloud Tracking
  • Aerosol
  • Vegetation
  • Upper Tropospheric Humidity
  • Cloud Water Vapor Tracking
  • Height Assignment to
  • semitransparent Clouds
  • Cloud Top Temp.
  • Cloud Tracking
  • Earth-surface Temp
  • Cloud Height

USE
There are 3 INSAT systems at present covering
most of the Indian Ocean. These Systems generate
images and products at frequent intervals day and
night using one (VIS) responsive to daylight,
infrared (IR) channels responsive to the
temperatures of clouds and the surface, another
IR channel to measure atmospheric water vapour .

21
Oceansat-2Payloads
Pencil Beam Scanning Scatterometer
Ocean Color Monitor -2
  • Changed 765 nm channel into 740 nm to avoid O2
    absorption
  • Replacement of 670 nm channel into 620 nm
    channel for better quantification of suspended
    sediments
  • Ku-band 13.515 GHz
  • Spatial resolution, 50 km
  • Ocean surface wind speed from 4 m/sec to 24
    m/sec

ROSA
22
RISAT
Space borne SAR in C-band at 5.35 GHz
  • Stripmap FRS-1 / FRS-2 (Range Doppler/ Chirp
    Scaling)
  • ScanSAR MRS CRS (Range Doppler/Specan)
  • Spotlight (modified sub-aperture) modes.

100 km (UNQUALIFIED)
SINGLE / DUAL / QUAD Polarisation imaging with 3-
50 m Resolution 10 - 240 km Swath
23
INSAT - 3D
Improved Understanding of Mesoscale Systems
19 Channel SOUNDER
6 Channel IMAGER
  • Spectral Bands (µm)
  • Short Wave Infra Red Six bands
    (3.98,4.13,4.45,4.52,4.57 ?m)
  • Mid Wave Infra Red Five Bands (6.51,
    7.02,7.43, 9.71, 11.03 ?m)
  • Long Wave Infra Red Seven Bands
    (12.02,12.66, 13.37, 13.64, 14.06, 14.37,
    14.71?m)
  • Visible One Band
  • Resolution (km) 10 X 10 for all bands
  • No of simultaneous Four sounding per band
  • Spectral Bands (µm)
  • Visible 0.55 - 0.75
  • Short Wave Infra Red 1.55 - 1.70
  • Mid Wave Infra Red 3.80 - 4.00
  • Water Vapour 6.50 - 7.00
  • Thermal Infra Red 1 10.2 - 11.3
  • Thermal Infra Red 2 11.5 - 12.5
  • Resolution 1 km for Vis, SWIR
  • 4 km for MIR, TIR
  • 8 km for WV

CO2 bands
Water Vapor bands
24
Atmospheric Sounding
25
INSAT - 3D Geophysical Products
Geophysical Products from Sounder Data
Product Resolution Expected Accuracy
Temperature profile 50 km x 50 km ( 5 x 5 pixels) 40-vertical pressure levels 1-2 0C
Water vapour profile 50 km x 50 km ( 5 x 5 pixels) 21-vertical pressure levels up to 100 hPa 30
Ozone profile 50 km x 50 km ( 5 x 5 pixels) 40-vertical pressure levels -
Total Column Ozone 50 km x 50 km ( 5 x 5 pixels) 5-10 Dobson unit
Surface skin temperature 50 km x 50 km ( 5 x 5 pixels) 0.5 1 0C
Other Geophysical Products
Outgoing longwave radiation (OLR), Quantitative
Precipitation Estimation (QPE), Sea Surface
Temperature (SST), Snow cover, Snow depth, Fire,
Smoke, Aerosol, Cloud Motion Vector, Upper
Tropospheric Humidity (UTH), Value Added
parameters from sounder products)
26
Megha Tropiques
SAPHIR
  • Frequency 183.31? 0.2, 1.1, 2.7, 4.2, 6.6, 11.0
    GHz
  • Water vapour profile
  • Six atmospheric layers upto 12 km height
  • 10 km Horizontal Resolution

For studying water cycle and energy exchanges in
the tropical belt
SCARAB
  • Low inclination (20º) for frequent simultaneous
    observations of tropics
  • Water vapour
  • Clouds
  • Cloud condensed water
  • Precipitation
  • evaporation
  • Outgoing fluxes at TOA
  • 40 km Horizontal Resolution

MADRAS
  • Precipitation and cloud properties
  • 89 157 GHz ice particles in cloud tops
  • 18 37 GHz cloud liquid water and precipitation
  • 23 GHz Integrated water vapour

GPS Occultation
Contributing to Global Precipitation Mission (GPM)
27
GPS Radio Occultation An innovative global
sounding technique for Temperature Humidity
(Low Earth Orbit GPS Receiver)
28
Derived Meteorological Products from INSAT Data
OLR (W/m2)
Quantitative Precipitation (mm/day)
29
Kalpana 20 July 2007 0000 Z
CIMSS 20 July 2007 0000 GMT
30
Atmospheric Circulation from INSAT
INSAT-AMV
INSAT-CMV
Water Vapor Winds
Cloud Motion Winds
In order to forecast the weather, knowledge of
the current state of the atmosphere is needed.
Conventional surface-based observations are
sparse in both space and time, whereas
satellite-based observations provide near-global
coverage at regular time intervals. The
derivation of wind motion vectors from
consecutive geostationary satellite images is an
important source of global wind information.
These data are regularly input into the large
computer models used for weather forecasting.
They are especially useful over the world's
oceans and the more remote continental areas.
31
Tracking Tropical Cyclones
One of the more important justifications of
meteorological satellites is their ability to
detect, monitor and track severe tropical
cyclones that can cause Immense loss of life and
property. Satellites play a vital role in
providing information about hurricanes, enabling
forecasters to track them for days before they
make landfall, and helping to provide essential
warnings of huge economic and social importance.

32
Real Time Cyclone Track Prediction (
2-cyclones in Year-2006)
Landfall Position Error 19 Km Landfall Timing
Error None
Mean Position Error 87 Km (4-positions
predicted)
33
FORECAST VERIFICATION
GONU
AKASH
(May-07)
(June-07)
FCST ACCURACY FOR LANDFALL45 KM
MEAN FCST ACCURACY (05,06,07JUN-07)
48 KM
34
Regional Modelling
Radiosonde locations
  • Assimilation of Quikscat winds and MODIS profiles
    made positive impact on weather system
    simulations.
  • Forecast For GSLV-F01 Launch
  • September 09 20th, 2004
  • Impact of local observed radiosonde data over
    Indian region on forecasting

35
Regional Climate Simulation
  • MM5 used for regional climate studies.
  • Preliminary simulations made including
    satellite-derived land surface parameter
    generated at SAC for July 1998.
  • Significant improvement in the simulated
    rainfall was detected using SAC generated
    vegetation fraction data to initialized the model.

Rainfall (cm)
Vegetation Fraction
36
THE OPERATIONAL MET-DATA PROCESSING AT SAC
(INSAT-3A and KALPANA)
AWS-UP
AWS-DN
SAC
AWS
Data Reception Processing Product Generation
BES
AWS Data (SAC)

C-Band
MOSDAC
Ext C-Band
37
Network of Weather Radars Present Future
Scenario
Existing Network of Cyclone Detection Radars (IMD)
Proposed Network of DWR (No57)
Indigenous DWR at SHAR
38
Trends Challenges in Satellite Meteorology
  • Hyper spectral sounding (Imaging FT
    Spectrometer)
  • Synthetic Aperture Radiometers (L
    band) for Ocean Salinity and Soil Moisture.
  • Sensors for atmospheric Chemistry
  • Constellation of EO Systems

39
The Use of Satellite Observations in NWP
The impact of satellite measurements on NWP began
in the early 70s with the operational use of
global satellite cloud imagery. Nowadays the
impact is from active and passive radiances
measurements from a multitude of satellite
instruments together with Atmospheric Motion
Vectors. Up to the mid 90s satellite the
satellite impact was mostly in the Southern
Hemisphere and Tropics but today the Northern
Hemispheric skill very dependent on satellite
measurements.
- Graeme Kelly ECMWF
Fact-1 Today, about 85 of the data used by
operational models is derived from
satellites. Fact-2 Only 14 of total satellite
data available to NWP community is actually used.
40
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