September 29, 2008 1
QPEQPE
Bob KuligowskiBob KuligowskiNOAA/NESDIS/STARNOAA/NESDIS/STAR
Walt PetersenWalt PetersenNASA-MSFCNASA-MSFC
GOES-R Science Meeting
September 29, 2008 2
OutlineOutline
SCaMPR Overview (Bob)SCaMPR Overview (Bob) SCaMPR and Lightning: Previous Work (Bob)SCaMPR and Lightning: Previous Work (Bob) SCaMPR and Lightning: Ongoing and SCaMPR and Lightning: Ongoing and
proposed Work (Walt)proposed Work (Walt) Questions / Discussion (all)Questions / Discussion (all)
September 29, 2008 3
SCaMPR OverviewSCaMPR Overview
SSelf-elf-CaCalibrating librating MMultivariate ultivariate PPrecipitation recipitation RRetrievaletrieval Calibrates IR predictors to MW rain ratesCalibrates IR predictors to MW rain rates Updates calibration whenever new target data (MW Updates calibration whenever new target data (MW
rain rates) become availablerain rates) become available Selects both best predictors and calibration Selects both best predictors and calibration
coefficients; thus, predictors used can change in coefficients; thus, predictors used can change in time or spacetime or space
Flexible; can use any inputs or target dataFlexible; can use any inputs or target data
September 29, 2008 4
SCaMPR Overview SCaMPR Overview (cont.)(cont.)
Two calibration steps:Two calibration steps:» Rain / no-rain discrimination using discriminant Rain / no-rain discrimination using discriminant
analysisanalysis» Rain rate using stepwise multiple linear regressionRain rate using stepwise multiple linear regression
Classification scheme based on BTD’s (ABI only):Classification scheme based on BTD’s (ABI only):» Type 1 (water cloud): TType 1 (water cloud): T7.347.34<T<T11.211.2 and T and T8.58.5-T-T11.211.2<-0.3<-0.3
» Type 2 (ice cloud): TType 2 (ice cloud): T7.347.34<T<T11.211.2 and T and T8.58.5-T-T11.211.2>-0.3>-0.3
» Type 3 (cold-top convective cloud): TType 3 (cold-top convective cloud): T7.347.34>T>T11.211.2
September 29, 2008 5
Predictors from ABI / GLM
Target rain ratesAggregate to spatial resolution of target data
Match raining target pixels with corresponding predictors in
space and time
Match non-raining raining target pixels with corresponding
predictors in space and time
Calibrate rain detection algorithm using discriminant
analysis
Calibrate rain intensity algorithm using regression
Create nonlinear transforms of predictor values
Apply calibration coefficients to independent data
Subsequent ABI / GLM data
Matched non-raining predictors
and targets
Matched raining predictors and
targets
START
END
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September 29, 2008 6
SCaMPR and Lightning: Previous SCaMPR and Lightning: Previous WorkWork
Back in 2005, performed some experiments using Back in 2005, performed some experiments using National Lightning Detection Network (NLDN) data as National Lightning Detection Network (NLDN) data as predictors to SCaMPRpredictors to SCaMPR
Basic predictor was number of lightning flashes in a Basic predictor was number of lightning flashes in a GOES pixel in previous 15 minGOES pixel in previous 15 min
Two uses of the predictor:Two uses of the predictor:» Classification: separate convective (lightning present) Classification: separate convective (lightning present)
and stratiform (no lightning) calibrationsand stratiform (no lightning) calibrations» Lightning flash rate as a predictor for the convective Lightning flash rate as a predictor for the convective
classclass Positive impact; hoping to incorporate into the real-Positive impact; hoping to incorporate into the real-
time version of SCaMPR by year’s endtime version of SCaMPR by year’s end
September 29, 2008 7
Impact of Lightning on SCaMPR Impact of Lightning on SCaMPR CorrelationCorrelation
Time Series of SCaMPR Correlation Coefficient vs. MW Rain Rates
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
0.45
0.50
0.55
0.60
0.65
0.70
239.01 240.01 241.01 242.01 243.01 244.01 245.01
JJJ.HH
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Control Lightning
September 29, 2008 8
Impact of Lightning on Impact of Lightning on SCaMPR BiasSCaMPR Bias
Time Series ofSCaMPR Volume Bias Ratio vs. MW Rain Rates
0.1
1.0
10.0
239.01 240.01 241.01 242.01 243.01 244.01 245.01
JJJ.HH
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Control Lightning
Prediction_SEVIRI_000
No
Loop through pixels
Compute solar zenith angle [POSSOL]
Assign inputs
Adjust reflectances for solar zenith angle
Yes
Determine rain class [Rain_type]
Derive output from inputs and coefficients [gforecast]
GLM Information Content for SCaMPR• Rain Presence• Rain Class/Type (C/S/Other)• Rain Rate (Gross PDF constraints)• IWP (not a requirement)
Rain_type
End Rain_type
Determine pixel latitude band (1-4)
T7.34 < T11.2?
T8.5 - T11.2<-0.3?
Initial Class=1 (water cloud)
Initial Class=2 (ice cloud)
Initial Class=3 (cold-top
convective)Yes
Yes
Determine pixel longitude band (1-4)
Combine pixel latitude and longitude bands with initial class to get final rain class
No
No
SUB
Construct predictor from input variables and coefficients
Loop through predictors
Rain rate predictor?
Use to build rain rateUse to build rain / no
rain discriminator
Rain / no rain discriminator >= threshold?
Rain rate=0
No
Yes
No
No
gforecastSUB
Yes
GOES-R GLM QPE ALGORITHM: Conceptual…………….
Petersen, 9/29/2008
Lightning
Yes
SCaMPR Rain Type SCaMPR GForecast
Lightning-Producing Entity/Weather Type
Location, Cluster, Extent, Characteristics (Flash Rate [FR], D(FR)/Dt, location etc.
Volume Rain Character Ice Volume Rain Character Ice
Convective Fraction
Growing Mature Decaying
Stratiform Fraction
Growing Mature Decaying
GLM, TBD DATA (Boldi, SCaMPR
No SCaMPR
Convective and Stratiform (C/S) Precipitation
Recognized in the precipitation community as a fundamental characteristic of convective system rainfall process since the 70’s-80s
Implemented in most state of the art ground-based radar estimation algorithms (historically problematic for satellite algorithms Vis/IR and MW alike (both fundamental to SCaMPR)
For satellite QPE- this application of lightning information is “low-hanging fruit”: Lightning occurs predominantly in precipitating convection and when lightning does occur, it identifies/pin points convective areas of precipitation.
When combined with other observations- can enhance the classic “presence or absence” problem and facilitate better C/S partitioning (e.g., Grecu and Anagnostou; convective/stratiform fractions and revised rain volume calculation).
Relative to applications in C/S partitioning we can ask (and suspect):
Fundamentally, between raining convective systems of similar area coverage does the presence/absence/amount of lightning identify a systematic difference (e.g., constraint) in system-wide C/S precipitation behavior?
September 29, 2008 12
Turn to TRMM and Testbeds: Feature C/S behavior as f(lightning)Turn to TRMM and Testbeds: Feature C/S behavior as f(lightning)Establishing Establishing broadbroad constraints in behavior constraints in behavior
For each TRMM Orbit and precipitation feature:
1. 2A25/2A12 Convective/Stratiform rain fractions (within 1Z99)
2. C/S partitioned Avg./Max rain rate, volume rate, IWP, LWP, Scattering Index
3. Lightning flash count
Evaluate over TN Valley (first)1. C/S feature fractions as f(flash, no flash)2. C/S feature area fain volume rates f(flash,
no flash)3. C/S Rain volume ratios f(flash, no flash)4. Feature C/S Avg. rain rates f(flash, no
flash)5. C/S IWP/LWP f(flash, no flash)
PR LIS
7-Year TN Valley Feature Sample number and C/S Fraction7-Year TN Valley Feature Sample number and C/S Fraction
September 29, 2008 13
21,788 Total Features (20 dBZ / 250 K 85 GHz)Feature area > 500 km2
•82 % Had convection•33 % Had lightningAll features (including area <500 km2 )•33% were “convective” (artifacts present)•Only 5% Had lightning
With lightning present there is a clear increase in convective fraction regardless of feature area
This is most pronounced for smallest features (PR/TMI partitioning artifact?)
Average Feature Rain Rate (C/S)Average Feature Rain Rate (C/S)
September 29, 2008 14
• Average convective RR ~ 10%-larger for features with lightning regardless of feature area.
• No difference for stratiform. Asymptotic 2 mm/hr stratiform rain rate is similar to Adler and Negri (1988) value.
Convective
Stratiform
C/S Rain VolumeC/S Rain Volume
September 29, 2008 15
Total Convective rain volume for features w/lightning is markedly larger regardless of area
Volume rain rate behavior (e.g., slope of line) different for convective feature areas with and without lightning
About the same for stratiform
Future/Ongoing WorkFuture/Ongoing Work
September 29, 2008 16
Bias = -10% (-0.99 mm)
Error = 12%
• Calibrated/QC’d dual-pol rain map C/S partitioning with LMA for TN Valley (Proxy dataset?)
• Relate statistics of C/S and flash behavior over lifecycles, compared to TRMM “snapshots”.
• IWP retrieval validation (Not requirement, yet…….).
• Process ASCII features and C/S file for the globe (almost done). Freely available to those who abhor…hate…HDF.
• Extend TRMM features C/S study to global domain including African SEVIRI and other SCaMPR/GLM testbeds.
• Add C/S partition/identifier to Boldi cell ID/tracking algorithm.
• ISOLATE BEST SCaMPR APPLICATION
N. Alabama Testbed TRMM Features/C-S/LIS
September 29, 2008 17
Questions / Questions / Discussion?Discussion?
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