Rainfall Estimation from Satellite Data

1
Rainfall Estimation fromSatellite Data
APSATS 2002
2
Outline

• Rain measurement systems
• Geostationary (VIS/IR) satellite rain estimation
• Passive and active microwave rain estimation
• Estimates using combined sensors
• Verification results for satellite rainfall
  estimates
• Tropical Rain Potential
• Future directions

3
User requirements

• Short-term needs
• Nowcasting of severe storms
• Weather forecasting
• Initialisation of NWP
• River management
• Flood control
• Medium-term needs
• Intraseasonal variability
• Crop forecasting
• Long-term needs
• Climate change
• Hydrological planning

4
Rain measurement systems - rain gauges

• Advantages
• True measurement of rain
• Disadvantages
• No coverage over oceans or remote regions
• Point measurement not representative of area
• Wind ? underestimates of rain
• Different gauge designs

5
Rain measurement systems - radar

• Advantages
• Excellent space and time resolution
• Observations in real time
• Disadvantages
• Little coverage over oceans or remote regions
• Signal calibration
• Corrections required for beam filling, bright
  band, anomalous propagation, attenuation, etc.
• Z-R relationship
• Expensive to operate

6
Rain measurement systems - geostationary
satellite (VIS/IR)

• Advantages
• Good space and time resolution
• Observations in near real time
• Samples oceans and remote regions
• Consistent measurement system
• Disadvantages
• Measures cloud-top properties instead of rain
• Spatial and/or temporal resolution may be too
  coarse
• Large data volume

7
VIS/IR rainfall estimates Principle Rainfall
at the surface is related to cloud properties
observed from space VIS reflectivity Brighte
r (thicker clouds) ? heavier rainfall Dark
? no rain IR brightness temperature Colde
r (deeper clouds) ? heavier rainfall Warm
? no rain NIR brightness
temperature TNIR-TIR0 (large drops or
ice) ? rain more likely TNIR-TIRgt0 (small
water drops) ? no rain
8
GOES Precipitation Index (GPI)
Simple threshold method R 3.0 mm/hr
(fraction of pixels with TB ? 235K)

• Works better over large areas and long times
  (i.e., monthly)
• Better suited to convective rainfall

9
Global annual rainfall from GPI
Bob Joyce, NCEP
http//tao.atmos.washington.edu/data_sets/gpi/
10
Power law technique
IR image
Rain rate
R a (T0-TB)b - R0 ( TB ? 253K)
11
Auto-Estimator
Based on Scofields NESDIS Operational Convective
Precipitation Estimation Technique R Rfit RH
correction factor growth correction factor
12
Auto-Estimator
http//orbit-net.nesdis.noaa.gov/arad/ht/ff/auto.h
tml
13
GOES Multispectral Rainfall Algorithm (GMSRA)
VIS a ? 0.40 NIR re (effective radius) ? 15
mm OR T11-T6.7 Negative for deep convective
cores (T11lt 230K) R probability of rain(T11)
mean rain rate (T11) RH
correction factor growth correction factor
14
GOES Multispectral Rainfall Algorithm (GMSRA)
http//orbit-net.nesdis.noaa.gov/arad/ht/ff/gmsra.
html
15
Rain measurement systems - passive microwave from
polar orbiting satellite

• Advantages
• Samples remote regions
• Consistent measurement system
• More physically based, more accurate than VIS/IR
  estimates
• Disadvantages
• Poorer time and space resolution (6 hr, 5-25
  km)
• Not a direct measurement of rain
• Beam filling

16
Rain measurement systems - passive microwave from
polar orbiting satellite Principle Rainfall at
the surface is related to microwave emission from
rain drops (low frequency channels) and microwave
scattering from ice (high frequency
channels) Low frequency (emission) channels -
ocean only Warm ? many raindrops, heavy
rain Cool ? no rain HIgh frequency
(scattering) channels Cold ? scattering from
large ice particles, heavy rain Warm ?
no rain
Excellent reference http//kauai.nrlmry.navy.mil/
training-bin/training
17
Special Sensor Microwave Imager (SSM/I)
Spatial resolution 25 km 25 km 25
km 12.5 km
18
Special Sensor Microwave Imager (SSM/I)
OCEAN
LAND
Ferriday and Avery, 1994
19
Special Sensor Microwave Imager (SSM/I)

NOAA algorithm
SI a0 a1T19V a2T22V a3T22V2 - T85V R a
SIb
http//orbit-net.nesdis.noaa.gov/arad2/
PRODUCT RAIN RATE (mm/hr) DATA FOR JULIAN DATE
2002145 SATELLITE F15 IN ASCENDING NODE
20
Special Sensor Microwave Imager (SSM/I)

• Profiling algorithms
• Iteratively match 7-channel TB observations to
  theoretical values computed from radiative
  transfer calculations and mesoscale cloud model
  (table look-up).
• Use cloud model to estimate rain rate
• Basis for TRMM algorithm

Kummerow et al., 1994
21
Advanced Microwave Sounding Unit (AMSU)
AMSU-A (50 km spatial resolution) 1 23.8
GHz 2 31.4 GHz 3-14 50.3-57.29
GHz 15 89 GHz AMSU-B (17 km spatial
resolution) 1 89 GHz 2 150 GHz 3-5 183.3
GHz (water vapour line)
22
Advanced Microwave Sounding Unit (AMSU)
Rain rate is based on an IWP and rain rate
relation derived from the MM5 cloud model
data. RR a0 a1 IWP a2 IWP2
http//amsu.cira.colostate.edu/ (browse
images-rain)
23
Tropical Rain Measuring Mission (TRMM)

• TRMM Microwave Imager (TMI), 780 km swath
• Band Frequency Polarization
  Horiz. Resol.
• (GHz) (km)
• 1 10.7 V, H 38.3
• 2 19.4 V, H 18.4
• 3 21.3 H 16.5
• 4 37.0 V, H 9.7
• 5 85.5 V, H 4.4
• Precipitation Radar, 220 km swath
• Horizontal resolution of 4 km
• Profile of rain and snow from surface to 20
  km altitude
• Use radar to tune TMI rain

24
Tropical Rain Measuring Mission (TRMM)
Instantaneous rain rate
http//trmm.gsfc.nasa.gov/trmmreal/
25

• Combined geostationary / passive microwave
  rainfall estimates
• Combines the best features of both approaches
• Good space/time resolution of geostationary
  estimates
• Better accuracy of microwave estimates
• How to do the combination?
• 1. Blend rain estimates using weighted averages
• 2. Use matched VIS/IR and microwave image set to
  
• (a) get a field of multiplicative correction
  factors
• (b) recalibrate VIS/IR algorithm coefficients
• (c) map IR TB onto microwave rainrates

26
Global Precipitation Climatology Project (GPCP)
http//orbit-net.nesdis.noaa.gov/arad/gpcp/
Weighted average of rain estimates from IR
(GPI), SSM/I, TOVS, rain gauges
Products available from GPCP _at_ 2.5 monthly
resolution monthly average rain rate
4-, 8-hour lag correlations of rain rate
standard deviation of instantaneous rain
rate frequency of rain sampling error for the
monthly rain rate estimate fractional rainy
area algorithm error for the monthly rain rate
estimate number of available samples
27
Global Precipitation Climatology Project (GPCP)
28
Combined sensors
Monthly mean rainfall
Weighted average of TRMM, SSM/I, IR, rain gauges
http//trmm.gsfc.nasa.gov/images_dir/avg_rainrate.
html
29
Near real time SSM/ITRMM3-hourly rainfall
George Huffman, GSFC, experimental product only!
30
http//kauai.nrlmry.navy.mil/sat-bin/rain.cgi
US Naval Research Laboratory
31
http//kauai.nrlmry.navy.mil/sat-bin/rain.cgi?GEO
aus
US Naval Research Laboratory
32
http//kauai.nrlmry.navy.mil/sat-bin/rain.cgi?GEO
aus
US Naval Research Laboratory
33
http//kauai.nrlmry.navy.mil/sat-bin/rain.cgi?GEO
aus
US Naval Research Laboratory
34
http//kauai.nrlmry.navy.mil/sat-bin/rain.cgi?GEO
aus
US Naval Research Laboratory
35
NRL 24 hr rain - 3 Dec 2001GEO estimate
36
NRL 24 hr rain - 3 Dec 2001SSM/I estimate
37
NRL 24 hr rain - 13 June 2001GEO estimate
38
NRL 24 hr rain - 13 June 2001SSM/I estimate
39
NRL monthly rain - December 2001
GEO
SSM/I
TRMM
Observed
40
NRL monthly rain - June 2001
GEO
SSM/I
TRMM
Observed
41
NRL monthly rain - Apr 2001 - Feb 2002Australian
Midlatitudes
42
NRL monthly rain - Apr 2001-Feb 2002Australian
Tropics
43
GPCP Algorithm Intercomparison Project results

• Skill greater over tropics than over higher
  latitudes
• Passive microwave algorithms give most accurate
  instantaneous rain rate, esp. outside tropics
• Geostationary algorithms give best monthly
  estimates due to better sampling

Ebert et al., 1996
44
Tropical Rainfall Potential (TRaP)
45
SSM/I Rain Rate
46
Tropical Rain Potential (TRaP)
http//www.ssd.noaa.gov/PS/TROP/trap-img.html
47
(No Transcript)
48
Satellites used to perform TRaP
DMSP SSMI NOAA AMSU NASA TRMM
NESDIS AE
Resolution 15km 16km
5km 4km Frequency
1-2 per 12hrs 1 per 6-12hrs 1 per 24hrs
1 per 30min Satellites 3
2 1
2 Max RR 35mm/hr
20mm/hr 60mm/hr
50mm/hr Priority 1 2
3 4
Slides courtesy of Sheldon Kusselson, NOAA/NESDIS
Satellite Services Division
49
Future TRaP Initiatives
Continued validation Automated operational TRaP
products for ALL STORMS...ALL THE
TIME...WORLDWIDE
AMSU-b Rain Rates
SSM/I Rain Rates
TRMM Rain Rates
AE Rain Rates
Chantal
Barry
Humberto
Helene

AE TRaP
AMSU-b TRaP
SSM/I TRaP
TRMM TRaP
50
Satellite rainfall estimation -where to next?
51
http//www.isao.bo.cnr.it/eurainsat/main.shtml
1. Microphysical characterisation of
precipitating clouds with VIS/IR sensors 2.
Creation of microphysical and radiative databases
on cloud systems using cloud model outputs and
aircraft penetrations 3. Tuning of microwave
algorithms on the different cloud systems
(convective, stratiform,...) 4. Combination of
data from the different algorithms and
application to a rapid update cycle that makes
use of the different sensors at the geostationary
scale.
52
(No Transcript)
53
International Precipitation Working Group (IPWG)

• Under the auspices of the Co-ordination Group for
  Meteorological Satellites (CGMS)
• 1st meeting in Boulder, June 2001
• 2nd meeting in Madrid, September 2002

54
Global Precipitation Measurement Mission (GPM)
Radar passive microwave radiometer
Passive microwave radiometers
In this configuration the "core" spacecraft
serves as a high quality reference platform for
training and calibrating the passive microwave
rain retrieval algorithms used with the
"constellation" radiometers.