verifying satellite precipitation estimates for weather and hydrological applications
DESCRIPTION
Verifying Satellite Precipitation Estimates for Weather and Hydrological Applications. Beth Ebert Bureau of Meteorology Research Centre Melbourne, Australia. 1 st IPWG Workshop, 23-27 September 2002, Madrid. -e. -e. - PowerPoint PPT PresentationTRANSCRIPT
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Verifying Satellite Precipitation Estimates for Weather and Hydrological Applications
Beth EbertBureau of Meteorology Research Centre
Melbourne, Australia
1st IPWG Workshop, 23-27 September 2002, Madrid
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val.i.date ( ) tr.v. 1. To declare or make legally valid. 2. To mark with an indication of official sanction. 3. To substantiate; verify.
ver.i.fy ( ) tr.v. 1. To prove the truth of by the presentation of evidence or testimony; substantiate. 2. To determine or test the truth or accuracy of, as by comparison, investigation, or reference: "Findings are not accepted by scientists unless they can be verified" (Norman L. Munn)
-e 'tad lav '
-e 'if rev '
The American Heritage Dictionary of the English Language. William Morris, editor, Houghton Mifflin, Boston, 1969.
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Satellite precipitation estimates -- what do we especially want to get right?
Climatologists - mean bias
NWP data assimilation (physical initialization) - rain location and type
Hydrologists - rain volume
Forecasters and emergency managers - rain location and maximum intensity
Everyone needs error estimates!
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Short-term precipitation estimates• High spatial and temporal resolution desirable
• Dynamic range required
• Motion may be important for nowcasts
• Can live with some bias in the estimates if it's not too great
• Verification data need not be quite as accurate as for climate verification
• Land-based rainfall generally of greater interest than ocean-based
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Some truths about "truth" data
• No existing measurement system adequately captures the high spatial and temporal variability of rainfall.
• Errors in validation data artificially inflate errors in satellite precipitation estimates
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Rain gauge observations
Advantages DisadvantagesTrue rain measurements May be unrepresentative of
aerial valueVerification results biased
toward regions with high gauge density
Most obs made once daily
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Radar dataAdvantages DisadvantagesExcellent spatial and Beamfilling, attenuation,
temporal resolution overshoot, clutter, etc.Limited spatial extent
TRMM PR
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Rain gauge analysesAdvantages DisadvantagesGrid-scale quantities Smoothes actual rainfall Overcomes uneven values
distribution of raingauges
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Stream flow measurementsAdvantages DisadvantagesIntegrates rainfall over Depends on soil conditions,
a catchment hydrological modelMany accurate measure- Time delay between rain
ments available and outflowHydrologists want it Blurs spatial distribution
time
Discharge(m3/hr) estimated
observed
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Verification strategy for satellite precipitation estimates
Use (gauge-corrected) radar data for local instantaneous or very short-term estimates
Use gauge or radar-gauge analysis for larger spatial and/or temporal estimates
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Focus on methods, not results
• What scores and methods can we use to verify precipitation estimates?
• What do they tell us about the quality of precipitation estimates?
• What are some of the advantages and disadvantages of these methods?
• Will focus on spatial verification
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Does the satellite estimate look right?
• Is the rain in the correct place?• Does it have the correct mean value?• Does it have the correct maximum value?• Does it have the correct size?• Does it have the correct shape?• Does it have the correct spatial variability?
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Spatial verification methods• Visual ("eyeball") verification• Continuous statistics• Categorical statistics• Joint distributions
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
• Scale decomposition methods • Entity-based methods
"standard"
"scientific" or "diagnostic"
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Step 1: Visual ("eyeball") verificationVisually compare maps of satellite estimates and
observations
Advantage: "A picture tells a thousand words…"
Disadvantages: Labor intensive, not quantitative, subjective
Verifies this attribute?LocationSizeShapeMean valueMaximum valueSpatial variability
Rozumalski, 2000
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Continuous verification statisticsMeasure the correspondence between the values of
the estimates and observations
Examples:• mean error (bias)• mean absolute error• root mean squared error• skill score• linear error in probability
space (LEPS)• correlation coefficient
Advantages: Simple, familiar
Disadvantage: Not very revealing as to what's going wrong in the forecast
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Mean absolute error||1
1i
N
ii OF
NMAE
Measures: Average magnitude of forecast error
Root mean square error2
1
)(1i
N
ii OF
NRMSE
Measures: Error magnitude, with large errors having a greater impact than in the MAE
Verifies this attribute?LocationSizeShapeMean valueMaximum valueSpatial variability
Mean error (bias))(1
1i
N
ii OF
NMean Error
Measures: Average difference between forecast and observed values
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Time series of error statistics
24-hr rainfall from NRL Experimental Geostationary algorithm validated against Australian operational daily rain gauge analysis
0.25° grid boxes, tropics only
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Linear error in probability space (LEPS)
Measures: Probability error - does not penalise going out on a limb when it is justified.
|)()(|11
io
N
iio OCDFFCDF
NLEPS
Verifies this attribute?LocationSizeShapeMean valueMaximum valueSpatial variabilityOi Fi
Cumulativeprobability of observations
CDFo
Value
error {
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Correlation coefficient
22 )()(
)( )(
OOFF
OOFFr
Measures: Correspondence between estimated spatial distribution and observed spatial distribution, independent of mean bias
Verifies this attribute?LocationSizeShapeMean valueMaximum valueSpatial variability
Danger...
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Rozumalski, 2000
AutoEstimator validated against Stage III
8x8 km grid boxes
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Skill score
referenceperfect
referenceestimate
scorescorescorescore
scoreSkill
Measures: Improvement over a reference estimate. When MSE is the score used in the above expression then the resulting statistic is called the reduction of variance.
The reference estimate is usually one of the following(a) random chance(b) climatology(c) persistence
but it could be another estimation algorithm.Verifies this attribute?LocationSizeShapeMean valueMaximum valueSpatial variability
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Cross-validation - useful when observations are included in the estimates
),...,1 ,,( * NiOYscorescore ii
where Yi* is the estimate at point i computed with Oi excluded from the analysis
Measures: Expected accuracy at the scale of the observations. The score is usually bias, MAE, RMS, correlation, etc.
Verifies this attribute?LocationSizeShapeMean valueMaximum valueSpatial variability
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Categorical statisticsMeasure the correspondence between estimated and
observed occurrence of events
Examples:• bias score• probability of detection• false alarm ratio• threat score• equitable threat score• odds ratio• Hanssen and Kuipers score• Heidke skill score
Advantages: Simple, familiar
Disadvantage: Not very revealing
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Estimated yes no
yes hits misses
no false correctalarms negativesO
bser
ved
Estimated Observed
Falsealarms
Hits
Misses
Correct negatives
Categorical statistics
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Bias score
misseshitsalarmsfalsehitsBIAS
Measures: Ratio of estimated area (frequency) to observed area (frequency)
Verifies this attribute?LocationSizeShapeMean valueMaximum valueSpatial variability
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Probability of Detectionmisseshits
hitsPOD
Verifies this attribute?LocationSizeShapeMean valueMaximum valueSpatial variability
False Alarm Ratioalarmsfalsehits
alarmsfalseFAR
Threat score (critical success index)
alarmsfalsemisseshitshitsCSITS
Equitable threat score
random
random
hitsalarmsfalsemisseshitshitshitsETS
Odds ratio
alarmsfalsemissesnegativescorrecthitsOR
*
*
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Hanssen and Kuipers discriminant (true skill statistic)
Measures: Ability of the estimation method to separate the "yes" cases from the "no" cases.
negativescorrectalarmsfalsealarmsfalse
misseshitshitsHK
Verifies this attribute?LocationSizeShapeMean valueMaximum valueSpatial variability
Heidke skill score
Measures: Fraction of correct yes/no detections after eliminating those which would be correct due purely to random chance
random
random
correctNcorrectnegativescorrecthits
HSS
) (
)*()*(1nonoyesyesrandom EstObsEstObs
Ncorrect
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Categorical verification of daily satellite precipitation estimates from GPCP 1DD algorithm during summer 2000-01 over Australia
Rain threshold varies from light to heavy
North (tropics) Southeast (mid-latitudes)
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Real-time verification example24-hr rainfall from NRL Experimental Geostationary algorithm
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Real-time verification example24-hr rainfall from NRL Experimental blended microwave algorithm
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Distributions oriented view
Verifies this attribute?LocationSizeShapeMean valueMaximum valueSpatial variability
Advantage: Much more complete picture of forecast performance
Disadvantage: Lots of numbers
Estimated category
1 2 … K total
1 n11 n12 … n1K No1
2 n21 n22 … n2K No2
… … … … … …Observedcategory
K nK1 nK2 … nKK NoK
total Ne1 Ne2 … NeK N
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PREDICTED (mm/d) .0--.1--.2--.5---1---2---5--10--20--50--100--200 total 0.0 | 4134 130 267 136 111 83 28 23 18 6 0 4936 0.1 | 206 25 45 42 30 15 3 4 4 1 0 375 0.2 | 281 17 52 36 25 29 12 6 3 3 0 464 0.5 | 260 6 34 17 17 31 16 20 6 3 1 411 1 | 229 13 41 28 28 61 20 26 29 4 1 480 2 | 259 22 77 50 51 55 53 43 38 6 1 655 5 | 182 21 59 37 48 76 66 68 80 15 0 652 10 | 104 21 27 47 54 106 112 127 134 27 5 764 20 | 42 6 19 13 41 96 125 158 325 127 9 961 50 | 7 1 0 0 0 1 7 8 46 45 13 128 100 | 0 0 0 0 0 0 0 0 0 8 1 9 200
total 5704 262 621 406 405 553 442 483 683 245 31 9835
OBSERVED (mm/d)
24-hr rainfall from NRL Experimental Geostationary algorithm validated against Australian operational daily rain gauge analysis on 21 Jan 2002
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Scatterplot
Shows: Joint distribution of estimated and observed values
NRL geo 20020121
R=0.63
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Probability distribution function
Shows: Marginal distributions of estimated and observed values
geoanal
NRL geo 20020121
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Heidke skill score (K distinct categories)
Measures: Skill of the estimation method in predicting the correct category, relative to that of random chance
Verifies this attribute?LocationSizeShapeMean valueMaximum valueSpatial variability
K
1k
1 1
)()( 1
)()( ),(
kk
K
k
K
kkkkk
OPFP
OPFPOFP
HSS
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Scale decomposition methodsMeasure the correspondence between the estimates
and observations at different spatial scales
Examples:• 2D Fourier decomposition• wavelet decomposition• upscaling
Advantages: Scales on which largest errors occur can be isolated, can filter noisy data
Disadvantages: Less intuitive, can be mathematically tricky
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Discrete wavelet transforms
Verifies this attribute?LocationSizeShapeMean valueMaximum valueSpatial variability
Concept: Decompose fields into scales representing different detail levels. Test whether the forecast resembles the observations at each scale.
Measures, for each scale:• % of total MSE• linear correlation• RMSE• categorical verification scores• others...
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Casati and Stephenson (2002) technique
Step 1: "Recalibrate" forecast using histogram matching
errortotal = errorbias + errorrecalibrated
Step 2: Threshold the observations and recalibrated forecast to get binary images
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Step 3: Subtract to get error (difference) image
Step 4: Discrete wavelet decomposition of error to scales of resolution x 2n
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Odds ratio
Step 5: Compute verification statistics on error field at discrete scales. Repeat for different rain thresholds.
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Multiscale statistical organizationZepeda-Arce et al. (J. Geophys. Res., 2000)
Concept: Observed precipitation patterns have multi-scale spatial and spatio-temporal organization. Test whether the satellite estimate reproduces this organization.
Method: Start with fine scale, average to coarser scaleVerifies this attribute?LocationSizeShapeMean valueMaximum valueSpatial variability
Measures:• TS vs. scale• depth vs. area• spatial scaling parameter• dynamic scaling exponent
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obs fcst
Scale (km)
Thre
at sc
ore
+++
+
+
+
+
+
Area (km2)
Dep
th (m
m)
obs
fcst
Std.
dev
.Scale (km)
**
**
obs
fcst
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Upscaling verification of IR power law rainrate16 September 2002, Melbourne
IR
radar
IR
radar
mm hr-1
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GMSRA validated against rain gauge analyses at different spatial scales
(Ba and Gruber, 2001)
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Entity-based methodsUse pattern matching to associate forecast and observed
entities ("blobs"). Verify the properties of the entities.
Examples:• CRA (contiguous rain area) verification
Verifies this attribute?LocationSizeShapeMean valueMaximum valueSpatial variability
Advantages: Intuitive, quantifies "eyeball" verification
Disadvantage: May fail if forecast does not sufficiently resemble observations
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CRA (entity) verification
Ebert and McBride (J. Hydrology, Dec 2000)
Concept: Verify the properties of the forecast (estimated) entities against observed entities
Method: Pattern matching to determine location error, error decomposition, event verification
Verifies this attribute?LocationSizeShapeMean valueMaximum valueSpatial variability
Measures:• location error• size error• error in mean, max values• pattern error
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Determine the location error using pattern matching:
• Horizontally translate the estimated blob until the total squared error between the estimate and the observations is minimized in the shaded region. Other possibilities: maximum correlation, maximum overlap
• The displacement is the vector difference between the original and final locations of the estimate.
Observed Estimated
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CRA error decomposition
The total mean squared error (MSE) can be written as:
MSEtotal = MSEdisplacement + MSEvolume + MSEpattern
The difference between the mean square error before and after translation is the contribution to total error due to displacement,
MSEdisplacement = MSEtotal – MSEshifted
The error component due to volume represents the bias in mean intensity,
where and are the CRA mean estimated and observed values after the shift.
The pattern error accounts for differences in the fine structure of the estimated and observed fields,
MSEpattern = MSEshifted - MSEvolume
2)( XFMSEvolume
XF
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24-hr rainfall from NRL Experimental Geostationary algorithm validated against Australian operational daily rain gauge analysis
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Diagnosis of systematic errors
Displacement (km)
NRL Experimental Geostationary algorithm
289 CRAs
April 2001-March 2002
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Diagnosis of systematic errors
EstimateAnalyzed
NRL Experimental Geostationary algorithm
289 CRAs
April 2001-March 2002
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Tropical Rain Potential (TRaP) verification?
TRaP 24 h rain from 20001208_16
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Which methods verify which attributes?Visual
(“eyeball”)Contin-
uousstatistics
Cate-gorical
statistics
J ointdistri-bution
Scaledecom-position
Entity-based
Location
Size
Shape
Mean value
Maximumvalue
Spatialvariability
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Conclusions• The most effective diagnostic verification method is
still visual ("eyeball") verification.
• Categorical statistics based on yes-no discrimination are probably the least informative of all of the verification methods, although they remain very useful for quantitative algorithm intercomparison.
• The newer diagnostic verification methods (scale decomposition, entity-based) give a more complete and informative diagnosis of algorithm performance
• Need methods to deal with observational uncertainty
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