Transcript Nowcasting Flash Floods and Heavy Precipitation --

Satellite QPE
RFC/HPC Hydromet 02-1
COMET/Boulder, CO
28 November 2001
Bob Kuligowski
NOAA/NESDIS/Office of Research and Applications
Camp Springs, MD
[email protected]
(301) 763 -8251 x 192
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Outline
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
Why Use Satellite QPE?
Algorithm Description
•
•
•
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GOES IR-Based QPE
Microwave-Based QPE
Blended Algorithm
Algorithm Validation
Where to Find the Data
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Why Use Satellite QPE?
• Superior spatial coverage
– Offshore coverage (tropical systems, Pacific storms)
– Coverage outside CONUS
– No beam block problems
• Consistency
– Differences in calibration from radar to radar
– Radar range effects
– Beam overshoot (especially stratiform precip)
Not a replacement, but a companion to radar
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Comparison of WGRFC Stage III and satellite coverage for the
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24h ended 1200 UTC 24 October 2000.
GOES-Based QPE: Theory
• Basis
– Assumes that cloud-top temperature  cloud-top height 
cloud-top thickness  rainfall rate
• Strengths
– 24/7 coverage every 15 minutes throughout North America
– High spatial resolution (~4 km)
• Weaknesses
– Relationship between cloud-top properties and rain rate
often does not hold, esp. for non-convective precipitation
– Cold cirrus can be mistaken for cumulonimbus
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GOES-Based Algorithms
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The Interactive Flash Flood
Analyzer (IFFA) and Its Progeny:
•
•
•

IFFA
Auto-Estimator (AE)
Hydro-Estimator (HE)
The GOES Multi-Spectral Rainfall
Algorithm (GMSRA)
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Interactive Flash Flood
Analyzer (IFFA)
• Manually-produced QPE based on features in
GOES imagery (e.g. cold cloud tops,
temperature changes, cloud mergers, etc.)
• Produced as needed for areas of significant
rainfall
• Accompanying SPENES text messages
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IFFA (continued)
• Adjustment according to moisture availability
(PWRH)
• Equilibrium level adjustment for relatively
warm cloud tops
• Details in Scofield (MWR, 1987)
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Sample Interactive Flash Flood Analyzer (IFFA) Estimate
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Accompanying SPENES text bulletin
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Auto-Estimator (AE)
• Automated QPE algorithm:
– Instantaneous rate estimates every 15 minutes
– 1-h, 3-h, and 6-h totals updated hourly
– 24-h totals at 1200 UTC
• Calibrated against radar for convective rainfall
(power law fit to 10.7-mm Tb)
• Moisture adjustment (PWxRH)
• Dynamic (growth) adjustment
• Details in Vicente et al. (BAMS, 1997)
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AE Improvements
• Equilibrium level correction
– Uses Eta/AVN temperature/moisture profiles
• Radar screen of nonraining pixels
– If no precip in radar, no precip in AE
• Orographic correction
– Uses Eta/AVN 850 winds + digital topography
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Original
Operational
Operational w/o Radar Mask
Raingauge Observations
AE rainfall estimates for the 72 h ended 1200 UTC 4 November 2000
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Validation for June-August 1999
Cold-Top Convection (2-4 hours)
Algorithm
Bias (mm)
POD
FAR
CC
Original AE
22.3
0.93
0.33
0.34
Operational AE
9.4
0.92
0.24
0.50
 Significant reduction in false alarms, and thus in
overall wet bias, in the operational AE.
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Tips on Using the AE
(courtesy of Rich Borneman, SAB)
• Best for convective events of significant duration/intensity
• Watch for overestimates for very cold tops with significant
cirrus debris
• Most reliable totals are in the 1-6 h range; 24-h totals tend
to be too high
• Location may be off by one or two counties with strong
vertical wind shear—check against radar for location
• Despite EL adjustment, warm tops often underestimated
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Hydro-Estimator (HE)
•
Cloud texture adjustment to rain rate curve—
cloud “peaks” assigned heavier rain, while cloud
“valleys” assigned no rain.
 Significant improvement in distinguishing cirrus from
cumulonimbus—eliminates dependence on radar
•
Split PW and RH
– PW used to adjust rain rate curve
– RH used for linear subtraction from rain rate
 Significant improvement in estimates for lowPW/high-RH regions (e.g. cold-season precip)
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Operational AE (Current)
HE w/ Radar Mask
HE w/o Radar Mask
Raingauge Observations
AE rainfall estimates for the 72 h ended 1200 UTC 4 November 2000
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Operational (Current)
HE w/ Split PW
HE w/o Split PW
Radar
AE rainfall estimates for the 24 h ended 1200 UTC 9 November 2000
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Future AE/HE Work
• Rain burst factor
• Correction for shearing cloud tops
• Cloud model experiments to quantify and
calibrate AE/HE corrections
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GMSRA
• GOES Multi-Spectral Rainfall Algorithm
– Instantaneous rate estimates every 30 minutes
– 1-h, 3-h, and 6-h totals updated hourly
– 24-h totals at 1200 UTC
• Calibrated against radar (fit of 10.7-mm
brightness temperature to rain rate)
• Uses all 5 GOES imager channels:
–
–
–
–
Visible for cirrus identification (daytime only)
3.9- 10.7-, 12.0-mm for particle size (daytime only)
6.7- mm to distinguish overshooting tops from cirrus
10.7-mm for texture and cloud growth screens
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GMSRA (continued)
• Moisture adjustment (PWxRH)
• Details in Ba and Gruber (JAM, 2001)
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GMSRA rainfall estimates for the 24 h ended 1200 UTC 18 August 2000
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GMSRA Continuing Work
• Experimental nighttime warm-rain screen
using 3.9-mm and 10.7-mm differences
• Improvement of calibration—longer
calibration period and more varied
meteorological situations
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GMSRA
GMSRA--Nighttime Warm-Rain Screen
Radar
Rainfall estimates for the 6 h ended 1100 UTC 8 September 2000
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Microwave QPE: Theory
• Basis
– Scattering: ice in clouds scatters terrestrial radiation back
downward, resulting in cold areas in MW imagery
– Emission: water in clouds emits radiation, can be seen
against a radiatively cold background (i.e. oceans)
• Strength
– Amount of cloud water/ice much more strongly related to
rain rate than cloud-top temperature
• Weaknesses
– Only available on polar-orbiting platforms, limiting
availability
– Coarser spatial resolution than IR (15-48 km vs. 4 km)
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Microwave QPE Algorithms
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Special Sensor Microwave/Imager
(SSM/I)—available since 1987
Advanced Microwave Sounding
Unit-A (AMSU-A)—available since
1999
Advanced Microwave Sounding
Unit-B (AMSU-B)—available since
2000
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SSM/I Algorithms
• Scattering: TB at 19V, 22V, and 85V GHz
regressed against radar data separately for land
and ocean
• Emission: TB at 19V, 22V, and 37V GHz over
water in regions of weak scattering
• Maximum rain rate of 35 mm/h
• Approximately 25-km horizontal resolution
• Available 6x/day (~0600, 0915, 1100, 1800,
2115, 2300 LST)
• Details in Appendix A of Ferraro (JGR, 1997)
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SSM/I Rainfall Estimates for 2115 LST 3 November 2000
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AMSU-A Algorithms
• Scattering: TB at 23, 50, and 89 GHz regressed
against radar data over land
• Emission: TB at 23 and 50 GHz over water
• Maximum rain rate of 30 mm/h
• Approximately 48-km horizontal resolution
• Available 4x/day (~0130, 0730, 1330, 1930
LST)
• Details in Ferraro et al. (GRL, 2000)
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Comparison of SSM/I and AMSU-A Rain Rates, 8 November 2000
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AMSU-B Algorithm
• Scattering: TB and 89 and 150 GHz regressed
against radar data over both land and ocean
• Maximum rain rate of 35 mm/h
• Approximately 16-km horizontal resolution
• Available 4x/day (~0130, 0730, 1330, 1930
LST)
• Details in Ferraro et al. (GRL, 2000)
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AMSU-A
AMSU-B
Radar (Stage IV)
Rainfall estimates at 0730 LST 8 November 2000
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False signature due to
snow on ground
NEXRAD
40 N
120 W
5 10 15 20 25 30 35 40 45
mm/hr
DBZ
Comparison of AMSU Algorithms at 0300 UTC 21 February 2000
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Microwave-IR Blended
Algorithm
• Relationship between 10.7- mm Tb and rain rate
calibrated using microwave rain rate estimates
– “Best of both worlds”—combine robustness of MW
estimates with availability of GOES data
• Calibration updated every few hours for a 5x5degree region
• Uses all operational Auto-Estimator
adjustments (PWxRH, equilibrium level, etc.)
• Developed by F. J. Turk of NRL
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Blended rainfall estimate for the 24 h ended 1200 UTC 18 August 2000 36
Satellite QPE Validation
• Initiated 1 April 2001
• Six algorithms presently evaluated:
–
–
–
–
Auto-Estimator
Hydro-Estimator (with and without radar)
GMSRA (with and without nighttime cirrus screen)
GOES-Microwave blended algorithm
• Validation against Stage III (6-h totals) and
gauges (24-h totals)
• Limited validation region at present (West
Coast and southern Plains)
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2001 Validation for Southern Plains
Cold-Top Convection (6-h amounts)
AE
HE
HErad
GMSRA1
GMSRA2
Blend
RMSE (mm)
Bias Ratio
CC
Spring Summer Spring Summer Spring Summer
5.2
5.8
1.25
1.09
0.63
0.58
4.9
5.9
1.02
1.22
0.51
0.52
4.6
5.5
0.86
1.02
0.55
0.54
6.8
5.9
1.95
1.20
0.42
0.46
7.0
6.0
2.05
1.21
0.44
0.46
6.2
5.9
1.13
1.00
0.50
0.54
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Southern Plains Cold-Top Convection (6-h amounts)
Spring 2001
Summer 2001
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Apr-May 2001 Validation for West
Coast Stratiform Events
AE
HE
HErad
GMSRA1
GMSRA2
Blend
RMSE (mm)
6-h
Daily
2.4
6.3
3.4
9.0
3.0
8.3
1.6
4.2
1.6
4.3
1.5
4.1
Bias Ratio
6-h
Daily
1.44
1.49
1.92
1.89
1.21
1.14
0.18
0.17
0.19
0.20
0.18
0.17
CC
6-h
0.24
0.23
0.21
0.12
0.12
0.24
Daily
0.22
0.33
0.17
0.14
0.12
0.22
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Validation Summary
• AE performs slightly better than HE for coldtop convection, but HE does not need radar and
does not have systematic wet bias like AE does
• HE (without radar) is a significant improvement
over AE for West-coast stratiform precipitation
(though it’s too wet)
• Blend is an improvement over AE in some
situations but not others—need to investigate
why
• GMSRA needs better calibration
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Summary
• Satellite QPE represents a companion to radar
to compensate for radar limitations:
– Covers offshore, non-CONUS, and mountainous
regions where beam block presents problems
– Satellite estimates are spatially consistent: no
calibration differences, range effects, overshoot
• GOES IR-based QPE provides continuous,
high-resolution coverage, but physics a problem
• Microwave-based QPE more physically robust,
but available only intermittently
• Combination of the two offers promise
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Where to Find the Data
• IFFA: http://www.ssd.noaa.gov/SSD/ML/pcpn-ndx.html
• Auto-Estimator:
http://orbit-net.nesdis.noaa.gov/arad/ht/ff/auto.html
• GMSRA:
http://orbit-net.nesdis.noaa.gov/arad/ht/ff/gmsra.html
• SSM/I: http://orbit-net.nesdis.noaa.gov/arad2/
• AMSU:
http://orbit-net.nesdis.noaa.gov/arad2/MSPPS/index.html
• Blended Algorithm:
http://orbit-net.nesdis.noaa.gov/arad/ht/ff/blended.html
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