The Precipitation Product error structure

The Precipitation Product error structureThe Precipitation Product error structure

Silvia Puca, Emanuela Campione, Corrado DeRosaIn collaboration with RMI (Belgium), BFG (Germany), OMSZ

(Hungary), UniFe and DPC (Italy), IMWG (Poland), SHMI (Slovakia), ITU

TMS (Turkey)

Dipartimento della Protezione Civile ItalianaDipartimento della Protezione Civile Italiana

outlinesoutlines



TMS (Turkey)


• PP Validation group;

• data used;

• validation approach (Common and Institute Specific Validation);

• precipitation classes;

• statistical scores;

•Common Validation results;

•Validation Results publication (web-page);

• Next steps;

‘‘Calibration and validation is a difficult activity in the case of Calibration and validation is a difficult activity in the case of precipitation, due to the natural space-time variability of the precipitation, due to the natural space-time variability of the precipitation field and the problematic error structure of the ground precipitation field and the problematic error structure of the ground truth measurements. ‘truth measurements. ‘

Developer need: Any product has to be related to information on Developer need: Any product has to be related to information on its error structure, necessary for its correct use in the applicationits error structure, necessary for its correct use in the application

Aims:• To improve the accuracy and the applicability of the products

delivered during the Development phase: – supporting the calibration and algorithm tuning,– generate the information on error structure to accompany the data,– quantify improvements stemming from the progressive

implementation of new developments.

• To monitor data quality and provide feedback for progressive quality improvement during the Operational phase.

validation

Product development

calibrationTo assess the accuracy:

Difference from the measured value and the “ground truth”

Tuning of the algorithm to maximase the accuracy

The calibration and validation activity will accompany The calibration and validation activity will accompany all steps of the all steps of the Development PhaseDevelopment Phase and also will be and also will be routinely carried out during the routinely carried out during the Operational Phase:Operational Phase:

outlinesoutlines



TMS (Turkey)


• PP Validation group;

• data used;

• validation approach (Common and Institute Specific Validation);

• precipitation classes;

• statistical scores;

•Common Validation results;

•Validation Results publication (web-page);

• Next steps;

WP-2300Precipitation validation

Italy (DPC)

WP-2310Philosophy

DPC

WP-2320in Belgium

IRM

WP-2330in Germany

BfG

WP-2340in

HungaryOMSZ

WP-2350in Italy

UniFerrara

WP-2360in ItalyDPC

WP-2370in Poland

IMWM

WP-2380in Slovakia

SHMÚ

WP-2390in Turkey

ITU

WP-2300 Silvia Puca (Team leader) [email protected]

WP-2310 Silvia Puca [email protected]

WP-2320 Emmanuel Roulin (+ Angelo Rinollo) [email protected] (+ [email protected])

WP-2330 Peer Helmke [email protected]

WP-2340 Eszter Lábó [email protected]

WP-2350 Federico Porcù [email protected] (+ [email protected])

WP-2360 Silvia Puca [email protected]

WP-2370 Bozena Lapeta [email protected]

WP-2380 Ján Kaňák [email protected]

WP-2390 Ibrhaim Sonmez + Ahmet Öztopal [email protected] (+ [email protected])

PP Validation groupPP Validation group

PPV Raingauge networkPPV Raingauge network is composed by is composed by 41004100 stations: stations:

Data Sources raingauges

Instrument characteristics

Telemetric and mechanic

time domain (near real time/ case studies)

Near real time, case studies

time resolution (15 min, 30

min)

10 – 30 min (telemetric),

3 – 24 h (mechanic)

spatial distribution

(whole national territory/

limited area)

Whole national territory

number of station (please attach a map)

~390 mechanic (RMI) + 12

telemetric (RMI) + 4160 telemetric

(SETHY)

operational/ for research only

Operational (RMI) + research (other

networks)

data quality check

Telemetric: automatically

checked / mechanic: autom.

+ manually checked

PPV Radar networkPPV Radar network is composed by is composed by 40 C-band 40 C-band andand 1 Ka-band: 1 Ka-band:We have now radars in Turkey

Data Sources radars

Instrument characteristics

Beam width ~1°, max range ~150

Km, 250m, C-band, single polarization,

Dopplerpolarimetric

time domainnear real time/ case studies

time resolution

5 min, 15 min, 30 min, 1h, 24h

spatial distribution

Whole national territory

number of station

33 C band +1 Ka band

operational/ for research

onlyOperational

data quality check

Permanent ground clutter removed;

monitoring of electronic calibration

validation approachvalidation approach

1) For the Common Validation activity all Institutes:

- use rain gauges and/or radar data,

- comparisons (sat vs obs) are evaluated on Satellite native grid: same up-scaling techniques ;

- evaluate the same monthly statistical scores (Multi-categorical and Continuous statistics) for the defined precipitation classes;

2) In addition to the common validation each Institute has developed an Institute Specific Validation activity based on its own knowledge and experience:

- case studies;

- also lightning data, numerical weather prediction and nowcasting products;

• Continuous verification statisticsContinuous verification statistics: calculating Mean : calculating Mean absolute error, root mean square error, correlation absolute error, root mean square error, correlation coefficient, standard deviation.coefficient, standard deviation.

• Multi-Categorical statisticsMulti-Categorical statistics: : calculating the calculating the contingency table (which allows for evaluation of false contingency table (which allows for evaluation of false alarm rate, probability of detection, equitable threat alarm rate, probability of detection, equitable threat score, Heidke skill score, etc ).score, Heidke skill score, etc ).

The The Common ValidationCommon Validation is based on is based on

ContinuousContinuous This means that the statistics are calculated using the numeric value

of the satellite precipitation estimation (SPE) and observation at each point.

CategoricalCategorical This means that the statistics are calculated from a contingency

table, where each SPE-observation pair is tabulated in the appropriate precipitation classes. This results in a contingency table. Because most of the categorical scores are actually computed for "threshold" intervals (wherein an event occurrence means observed or SPE was equal to or greater than the threshold value), entries in the table are appropriately combined to form a 2x2 table for each threshold.

scores evaluated for multi-categorical and scores evaluated for multi-categorical and continuous statistics:continuous statistics:

CS statistic: - Mean error - Multiplicative bias - Mean absolute error - Root mean square error - correlation coefficient - Standard deviation

MC statistic:– ACCURACY– POD – FAR – BIAS– ETS

Plots: - Scatter plot - Probability density function

Continuous ScoreContinuous Score

Mean Absolute Error (MAE)

This score is the mean of the absolute differences between the observations and PSE in the interval. The score provides a good measure of the accuracy. The closer the MAE is to zero the better the accuracy.

Root Mean Square Error (RMSE)

This score is the square root of the mean of the squared differences between the observations and SPE in the interval. The score provides a good measure of the accuracy while giving a greater weight to the larger differences than the MAE does. The closer the RMSE is to zero the better the accuracy.

Mean Error (ME) (bias)

This score is the mean of the arithmetic differences between the observations and SPE in the interval. The score is a measure of SPE bias, where positive values denote overforecasting, negative values denote underforecasting, and zero indicates no bias.

Standard Deviation (StD)

This score shows how much variation there is from the "average" (mean). It may be thought of as the average difference of the scores from the mean of distribution, how far they are away from the mean. A low standard deviation indicates that the data points tend to be very close to the mean, whereas high standard deviation indicates that the data are spread out over a large range of values.

Correlation Coefficient

This score is a good measure of linear association or phase error. Visually, the correlation measures how close the points of a scatter plot are to a straight line. Does not take SPE bias into account -- it is possible for a SPE with large errors to still have a good correlation coefficient with the observations. Sensitive to outliers.

Categorical ScoresCategorical Scores

Equitable Threat Score (ETS) This score measures the fraction of observed and/or forecast events that were correctly predicted, adjusted for hits associated with random chance (for example, it is easier to correctly forecast rain occurrence in a wet climate than in a dry climate). Sensitive to hits. Because it penalises both misses and false alarms in the same way, it does not distinguish the source of SPE error.

Probability of Detection (POD) This score is the fraction of the observed area of a threshold precipitation amount that was correctly forecast. A Satellite product with a perfect POD have a value of one, and forecast with the worst possible POD have a value of zero.

False Alarm Rate (FAR) This score is the fraction of the forecast of a threshold precipitation amount that were incorrect. The worst is one the best is zero. Sensitive to false alarms, but ignores misses. Very sensitive to the climatological frequency of the event. Should be used in conjunction with the probability of detection

Bias (Bias)This score is the ratio of the number of forecasts to the number of observations given the threshold amount. Forecast with perfect bias have a value of one, overforecasting results in bias greater than one, and underforecasting results in bias less than one.

Accuracy (Acc)Simple, intuitive. Can be misleading since it is heavily influenced by the most common category, usually "no event" in the case of rare weather.

PlotsPlots

• Scatter plot - Plots the SPE values against the observed values.

• This score is good first look at correspondence between SPE and observations. An accurate SPEt will have points on or near the diagonal.

• Probability Density Function plot

The radar and rain gauge data were up-scaled taking into account that the product follows the scanning geometry and IFOV resolution of AMSU-B scan and SSMI. Radar and rain gauge instruments provide many measurements within a single AMSU-B pixel, those measurements were averaged following the AMSU-B antenna pattern shown and SSMI.

All institutes involved in PP validation activity uses the same up-scaling technique which was indicated by CNR-ISAC. The codes were developed by University of Ferrara and RMI.

The comparisons (Sat vs obs) on Satellite native The comparisons (Sat vs obs) on Satellite native grid:grid:Up-scaling techniquesUp-scaling techniques

PRECIPITATION CLASSES for PR-OBS1, PR-OBS2 and PR-OBS3:

Precipitation classesPrecipitation classes

Validation Prec. Classes

Class # PR [mm/h]

1 PR<0.25

2 0.25≤PR<0.50

3 0.50≤PR<1.00

4 1.00≤PR<2.00

5 2.00≤PR<4.00

6 4.00≤PR< 8.00

7 10.00≤PR<16.00

8 16.00≤PR<32.00

9 32.00≤PR<64.00

10 64.00≤PR

URD Prec. Classes

Class # PR [mm/h]

1 PR<1

2 1≤PR<10

3 10≤PR

PR-OBS1, PR-OBS2 and PR-OBS3:

PR= PRECIPITATION RATE0,25 mm/h is the threshold for precipitation/no-precipitation.

Class # AP [mm]

1 AP<1

2 1≤AP<2

3 2≤AP<4

4 4≤AP<8

5 8≤AP<16

6 16≤AP<32

7 32≤AP<64

8 64≤AP<128

9 128≤AP<256

10 256≤AP

PR-OBS5: 3, 6, 12 and 24 hours accumulated precipitation:

AP= ACCUMULATED PRECIPITATION 1.00 mm is the threshold for precipitation/no-precipitation.

H01 continuous statistic: radar data and rain gauge H01 continuous statistic: radar data and rain gauge LANDLANDPeriod: September 2008 – June 2009Period: September 2008 – June 2009

H01 Land: RMSE (radar, rain gauge)

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H01 Land: ST DEV (radar, rain gauge)

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H01 Land: MEAN ERROR (radar, rain gauge)

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H01 Land: MEAN ABSOLUTE ERROR (radar, rain gauge)

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H02 New version

H01 continuous statistic: radar and rain H01 continuous statistic: radar and rain gaugegauge

INDEX:

H01 (mm/h)

MEAN ERROR (mm/h) 0,04

MULTI BIAS 3,39

MEAN ABS. ERR (mm/h) 0,13

ROOT MEAN SQUARE ERROR (mm/h) 0,30

CORRELATION COEF. 0,25

STANDARD DEVIATION ERROR (mm/h) 0,34

N RAD[mm/h]

ME [mm/h]

StD[mm/h]

MAE[mm/h]

RMSE[mm/h]

URDrmse[%]

N RG[mm/h]

ME [mm/h]

StD[mm/h]

MAE[mm/h]

RMSE[mm/h]

URDrmse[%]

0.25≤PR<1 163116 -0.04 1.16 0.73 1.24 251.73 112635 0.05 1.60 0.86 1.62 289.68

1≤PR<10 69283 -0.87 2.05 1.84 2.39 117.34 45549 -0.42 3.01 2.16 3.22 145.89

10.00≤PR 602 -10.94 6.15 12.14 13.03 77.05 261 -12.16 5.68 12.86 13.70 74.47

MEAN 233001 -0.31 1.44 1.09 1.61 211.32 158445 -0.12 2.03 1.27 2.13 246.37

N RAD[mm/h]

ME [mm/h]

StD[mm/h]

MAE[mm/h]

RMSE[mm/h]

URDrmse[%]

N RG[mm/h]

ME [mm/h]

StD[mm/h]

MAE[mm/h]

RMSE[mm/h]

URDrmse[%]

0.25≤PR<1 8797 -0.41 0.23 0.48 0.51 105.09 17501 -0.56 0.02 0.67 0.66 109.83

1≤PR<10 3222 -2.43 0.74 2.48 2.66 98.90 6138 -7.22 0.09 7.32 3.03 147.13

10.00≤PR 215 -15.55 2.25 15.55 12.75 100.00 1047 -19.45 1.05 19.45 14.26 104.95

MEAN 12234 -1.20 0.40 1.27 1.29 103.37 24686 -2.65 0.06 2.75 1.52 119.57

Period: September 2008 – December 2008

Period: Jenuary 2009 – June 2009

•There is an evident increase of the errors in the higher precipitation class;

ME= Mean Error, SD=Standard Deviation, MAE =Mean Aboslute Error, RMSE= Root Mean Square Error; URD RMSE= Root Mean Square Rrror defined in URD doc.

POD(rain/Norain) 0,95

FAR(rainNorain) 0,04

H01: Multi-category statisticH01: Multi-category statistic

•Good value of POD and FAR for rain/no-rain;•Clear underestimation of the precipitation.

PR<0.25 0.25<PR tot RD

PR<0.25 0,96 0,04 139695

0.25<PR 0,82 0,18 7383

tot SAT 140049 7029 147078

PR<0.25 0.25<PR<1 1<PR<10 10<PR tot RD

PR<0.25 0,960 0,023 0,017 0,000 139695

0.25<PR<1 0,879 0,052 0,066 0,003 5713

1<PR<10 0,614 0,073 0,306 0,007 1668

10<PR 1,000 0,000 0,000 0,000 2

tot SAT 140049 3601 3334 94 147078

coast/land analysis H01

University of Ferrara: F. Porcù





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m/h

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INDEX:

H01 (mm/h)


MULTI BIAS 3,39









N RAD[mm/h]

ME [mm/h]

StD[mm/h]

MAE[mm/h]

RMSE[mm/h]

URDrmse[%]

N RAD[mm/h]

ME [mm/h]

StD[mm/h]

MAE[mm/h]

RMSE[mm/h]

URDrmse[%]

0.25≤PR<1 134299 -0.15 0.82 0.58 1.00 185.89 69358 -0.22 0.97 0.60 0.98 217.35

1≤PR<10 63441 -0.88 1.73 1.75 2.33 111.99 27808 -1.10 2.24 1.90 3.41 149.45

10.00≤PR 624 -8.72 6.59 11.42 12.54 69.59 167 -10.49 5.58 11.40 13.11 81.68

MEDIA 198364 -0.41 1.13 0.99 1.46 161.89 97333 -0.54 1.39 1.05 1.62 195.21

N RAD[mm/h]

ME [mm/h]

StD[mm/h]

MAE[mm/h]

RMSE[mm/h]

URDrmse[%]

N RAD[mm/h]

ME [mm/h]

StD[mm/h]

MAE[mm/h]

RMSE[mm/h]

URDrmse[%]

0.25≤PR<1 31172 -0.21 0.48 0.51 0.68 135.33 39683 -0.44 0.85 0.59 0.95 208.09

1≤PR<10 8440 -0.57 1.12 1.32 1.63 95.24 43260 -2.38 2.05 2.90 3.28 106.03

10.00≤PR 26 -7.14 1.20 7.20 7.32 62.50 5250 -9.90 6.29 13.72 14.11 91.50

MEDIA 39638 -0.29 0.62 0.69 0.89 126.74 88193 -0.86 1.11 1.09 1.45 186.28


H02: Validation Precipitation classes for LAND areas. Data used: RADAR. Period: 10 2008

PR<0.25 0.25<PR<1 1<PR<10 10<PR tot RD

PR<0.25 0,989 0,006 0,003 0 292709

0.25<PR<1 0,818 0,093 0,086 0,001 9097

1<PR<10 0,626 0,131 0,237 0,004 7601

10<PR 0,437 0,158 0,379 0,025 709

tot SAT 302293 3821 3863 139 310116

H02: Validation Precipitation classes for LAND areas. Data used: RADAR. Period: 10 2008


PR<0.25 0,989 0,009 292709

0.25<PR<1 0,719 0,281 17407

tot SAT 302293 7823 310116

POD(rain/Norain)= 0,958

FAR(rainNorain)= 0,003

•Good value of POD and FAR for rain/no-rain;•Clear underestimation of the precipitation but more capacity to discriminate the precipitation than H01.



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INDEX:

H01 (mm/h)


MULTI BIAS 3,39





N RAD[mm/h]

ME [mm/h]

StD[mm/h]

MAE[mm/h]

RMSE[mm/h]

URDrmse[%]

N RAD[mm/h]

ME [mm/h]

StD[mm/h]

MAE[mm/h]

RMSE[mm/h]

URDrmse[%]

0.25≤PR<1 31172 -0.21 0.48 0.51 0.68 135.33 39683 -0.44 0.85 0.59 0.95 208.09

1≤PR<10 8440 -0.57 1.12 1.32 1.63 95.24 43260 -2.38 2.05 2.90 3.28 106.03

10.00≤PR 26 -7.14 1.20 7.20 7.32 62.50 5250 -9.90 6.29 13.72 14.11 91.50

MEDIA 39638 -0.29 0.62 0.69 0.89 126.74 88193 -0.86 1.11 1.09 1.45 186.28

N RAD[mm/h]

ME [mm/h]

StD[mm/h]

MAE[mm/h]

RMSE[mm/h]

URDrmse[%]

N RAD[mm/h]

ME [mm/h]

StD[mm/h]

MAE[mm/h]

RMSE[mm/h]

URDrmse[%]

0.25≤PR<1 134299 -0.15 0.82 0.58 1.00 185.89 69358 -0.22 0.97 0.60 0.98 217.35

1≤PR<10 63441 -0.88 1.73 1.75 2.33 111.99 27808 -1.10 2.24 1.90 3.41 149.45

10.00≤PR 624 -8.72 6.59 11.42 12.54 69.59 167 -10.49 5.58 11.40 13.11 81.68

MEDIA 198364 -0.41 1.13 0.99 1.46 161.89 97333 -0.54 1.39 1.05 1.62 195.21





N RAD[mm/h]

ME [mm/h]

StD[mm/h]

MAE[mm/h]

RMSE[mm/h]

URDrmse[%]

N RAD[mm/h]

ME [mm/h]

StD[mm/h]

MAE[mm/h]

RMSE[mm/h]

URDrmse[%]

0.25≤PR<1 8374045 -0.31 0.53 0.52 0.74 177.65 803197 -1.18 0.01 1.33 0.94 189.18

1≤PR<10 2547035 -1.65 0.94 1.88 2.07 105.93 391313 -13.22 0.09 13.46 5.54 204.57

10.00≤PR 19063 -15.94 3.91 15.96 16.59 94.47 1147818 -14.73 2.10 14.76 14.06 97.49

MEAN 10940143 -0.65 0.63 0.87 1.08 160.81 2342328 -4.01 0.03 4.18 2.03 192.61

N RAD[mm/h]

ME [mm/h]

StD[mm/h]

MAE[mm/

h]

RMSE[mm/h]

URDrmse[%]

N RAD[mm/h]

ME [mm/h]

StD[mm/h]

MAE[mm/h]

RMSE[mm/h]

URDrmse[%]

0.25≤PR<1 14767131 -0.44 0.29 0.50 0.58 108.48 69358 -0.43 0.35 0.53 0.60 131.65

1≤PR<10 4178286 -1.70 0.58 1.75 1.85 96.41 27808 -1.56 0.68 1.68 1.76 110.80

10.00≤PR 15838 -14.51 3.94 17.20 18.05 95.05 167 -14.26 2.69 14.69 15.21 83.15

MEAN 18961255 -0.73 0.36 0.79 0.87 105.81 97333 -0.69 0.42 0.79 0.87 127.02



PR<0.25 0,957 0,040 51880730

0.25<PR<1 0,723 0,276 2324380

tot SAT 51298933 2906177 54205110

POD(rain/Norain) 0,967

FAR(rainNorain) 0,040

PR<0.25 0.25<PR<1 1<PR<10 10<PR tot RD

PR<0.25 0,957 0,026 0,013 0,001 51880730

0.25<PR<1 0,740 0,167 0,088 0,003 1727431

1<PR<10 0,673 0,153 0,162 0,010 588126

10<PR 0,647 0,065 0,273 0,028 8823

tot SAT 51298933 1871293 997318 37566 54205110

•Good value of POD and FAR for rain/no-rain;•Clear underestimation of the precipitation but several cases of overestimation of precipitation area;





H05: Continuous statisticH05: Continuous statistic

[mm] # RADAR ME[mm] SD[mm] MAE[mm] RMSE[mm] URDrmse

AP< 8 13920871 -0,360 1,363 1,063 1,609 127%

8 ≤AP<32 3562429 -1,418 3,006 3,680 4,396 86%

32 ≤AP< 64 162626 -5,133 3,465 6,041 6,546 86%

64 ≤AP< 128 170027 -7,948 4,326 8,408 9,257 92%

128 ≤AP 25758 -14,42 4,997 14,421 15,308 87%

MEAN 17841711 -0,707 1,744 1,721 2,303 107%

[mm]# RAIN

GAUGE ME[mm] SD[mm] MAE[mm] RMSE[mm] URDrmse

AP< 8 4943648 489652,3 -5,927 0,413 0,011 469%

8 ≤AP<32 3029460149317,7 -48,705 6,286 0,033 2410%

32 ≤AP< 64 601979 37553,23 -159,432 56,892 0,014 6882%

64 ≤AP< 128 17009812010,45 -356,61 238,898 0,061 11699%

128 ≤AP 29611 7381,739 -298,066 52,913 0,129 23309%

MEAN 8774796 838385,3 -19,631 4,449 0,016 10546%


It is necessary a verification of rain gauge validation results!

Some conclusionsSome conclusions

• All the PP were validated by comparison with both radar and rain All the PP were validated by comparison with both radar and rain gauge data by 7 countries,gauge data by 7 countries,

• Multi category and continuous statistical scores were evaluated;Multi category and continuous statistical scores were evaluated;

• All the statistical scores evaluated and the case studies analysed are All the statistical scores evaluated and the case studies analysed are available in AM ftp server;available in AM ftp server;

*H01: -the majority of the precipitation is estimated less than 0.25 mm/h by H01; -there is a general under-estimation of the precipitation estimation;-No strong seasonal component is present;-there is an evident increase of the errors in the lower classes respect the previus version.

Some conclusionsSome conclusions

*H02:-There is a general underestimation but more capacity to discriminate precipitation greater than 0.25 mm/h;- Seasonal component is present;-there is an evident increase of the errors in the higher precipitation class;-problem with Noaa16:replacment of a channel. (noise effect)

*H03: -There is a general underestimation of precipitation rate and an overestimation of precipitation area;-Seasonal component is present;-There is an evident increase of the errors in the higher precipitation class;-heavy heavy convective precipitation events were underestimatedconvective precipitation events were underestimated;; -Moderate and light convective precipitation eventModerate and light convective precipitation eventss were often were often overestimated;overestimated;

*H05: -Same not realistic value of precipitation; -Not enough results;

Validation Results publication



TMS (Turkey)


• Rep 3: collects all the results of the PP Validation activity: It is a rolling document.

• User Requirement Documents: summarise the PP validation results;

• Web Page: all the results are in the H-saf web-page in the validation section.

Next steps



TMS (Turkey)


• Rep 3: collects all the results of the PP Validation activity: It is a rolling document.

• User Requirement Documents: summarise the PP validation results;

• Web Page: all the results are in the H-saf web-page in the validation section.

THANK YOU!