Moving toward Multispectral, Multiplatform Operational

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Transcript Moving toward Multispectral, Multiplatform Operational

Moving toward Multispectral,
Multiplatform Operational
Satellite Precipitation Estimates
at NESDIS
Robert J. Kuligowski
Roderick A. Scofield
NOAA/NESDIS Office of Research and
Applications
Outline
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Brief History of Precipitation Work at
ORA
Future Directions
• Multi-Satellite Blending
• Lightning
• Multiple-Channel Algorithms
• Nowcasting
History: GOES Algorithms
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Emphasis on operational forecast
support (Satellite Analysis Branch)
Progression from manual techniques
(Interactive Flash Flood Analyzer—
IFFA) to automated (AutoEstimator/Hydro-Estimator)
Exploration of multi-channel
techniques (GOES Multi-Spectral
Rainfall Algorithm—GMSRA)
History: Microwave Algorithms
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Emphasis on climate applications
Progression from statistical
algorithms to physical algorithms
(Goddard PROFiling algorithm—
GPROF)
Development of some forecasting
applications (TRopical Rainfall
Potential—TRaP)
History: Blended Algorithms
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Resolution and latency favor GOES IR
estimates; accuracy favors polar-orbiter
MW estimates.
Efforts by many researchers to obtain the
accuracy of MW with the resolution of IR.
Some ORA collaboration with F. Joseph
Turk on Naval Research Lab algorithm.
Development at ORA of Self-Calibrating
Multivariate Precipitation Retrieval
(SCaMPR).
History: SCaMPR
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Flexible framework for automaticallycalibrated precipitation estimation:
• Calibrates against SSM/I and AMSU
• Discriminant analysis selects and
calibrates best rain/no rain predictors
• Stepwise forward regression selects and
calibrates the best rain rate predictors
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Predictors AND calibration updated
regularly
SCaMPR continued:
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SCaMPR is being transitioned into
real-time applications
Initial version uses basic predictors:
T6.9, T10.7, T13.2, temperature
differences, T10.7 texture information
Eta model PW, RH will be added soon
SCaMPR can use ANY gridded field as
a predictor
Preliminary SCaMPR Performance
• LIMITED sample; comparisons
of 6-h estimates to Stage IV
during the Oct. 7-15 test
period.
• Fewer false alarms than H-E,
but also fewer correct
detections, especially for
lighter precipitation.
• Less bias than the H-E, but
bias increases with amount;
GMSRA is least biased of the
three.
• Overall, SCaMPR performs
slightly worse than H-E and
GMSRA for low amounts (<10
mm/6h) but slightly better for
high amounts (>20 mm/6h).
Blended Algorithms and GPM
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Blended algorithms are not intended
to compete with GPM
No IR algorithm is a perfect substitute
for MW!
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Enhanced timeliness and latency in
GPM era will enhance combination
IR/MW algorithms
Ultimate solution is Geo MW, but that
remains at least a decade away
SCaMPR and Lightning
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Receiving National Lightning
Detection Network (NLDN) data in
real time
Working to design and test lightningbased SCaMPR predictors
Wider applications anticipated with
increase in number of spaceborne
lightning platforms
Multiple-Channel Algorithms
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GMSRA laid the groundwork, incorporating
a number of research techniques into a
real-time algorithm:
Visible: daytime thin cloud identification
3.9 µm: retrieving cloud particle size
during the daytime (after Rosenfeld and
Gutman 1994)
6.9 µm-10.7 µm: identifying overshooting
cloud tops (after Tjemkes et al. 1997)
10.7 µm – 12.0 µm: identifying thin
clouds during day or night (after Inoue
1987)
Multiple-Channel Algorithms
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Increased channel selection on current
and planned geostationary imagers (e.g.,
12 on SEVIRI, 16 on ABI)
Research needs to be transitioned into
operations as the data become available,
including:
• Cloud phase using 8.5, 11, 12 µm (Ackerman
et al.)
• Vertical profiles of cloud water/ice particle size
(Chang and Li)
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Research is being conducted at ORA using
MODIS data for rain/no rain discrimination
The Hydro-Nowcaster
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Nowcasts enhance the utility of satellite
precipitation estimates by increasing the
lead time of precipitation information.
The H-N produces 0-3 hour nowcasts of
rainfall (based on estimates by the HydroEstimator) and updates every 15 min.
Two components:
• Extrapolation: identifies cloud clusters, tracks
and extrapolates motions out to 3 hours
• Growth/decay: changes in cluster size and
temperature are used to determine time
change of rain intensity during the nowcast
period
Example: Hurricane Isabel on
18-19 September 2003
1 h nowcast:
2100 UTC –
2200 UTC
3-h nowcast:
2100 UTC –
0000 UTC (19)
Hurricane Isabel on 18-19
September 2003
1-h Nowcasts
RMSE
(mm)
Bias
Ratio
3-h Nowcasts
Correl RMSE
ation (mm)
8.1
Bias
Ratio
Correl
ation
H-E
2.6
1.48 0.48
1.48 0.43
H-N
2.8
1.17 0.38 10.0 1.28 0.34
Statistics for 2100 UTC 18 September to 0000 UTC 19 September 2003
Summary
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Many opportunities for progress in
precipitation estimation/nowcasting:
• Blending of IR/MW data
• New instruments and channels
• Space-based lightning (and someday
MW?) sensors
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International cooperation—
development, data sharing, and
education—are essential for
maximum impact