Method for Object-based Diagnostic Evaluation (MODE) Fake forecasts.

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Presentation transcript:

Method for Object-based Diagnostic Evaluation (MODE) Fake forecasts

Test case results for MODE geometric cases  mode quantity A  quantity B perturbed fake cases  mode quantity A  quantity B  percentile of intensity  median of max MET – mode_analysis results  centroid distance for matched pairs  percentile intensity for forecast & observed objects

Geometric THE WINNER area ratio THE WINNER

Geometric THE WINNER Angle difference THE WINNER

Geometric THE WINNER Total interest

Perturbed fake cases 1.3 pts right, 5 pts down 2.6 pts right, 10 pts down 3.12 pts right, 20 pts down 4.24 pts right, 40 pts down 5.48 pts right, 80 pts down 6.12 pts right, 20 pts down, times pts right, 20 pts down, minus 0.05”

MODE objects

Perturbed Fake casePosition error and bias Max interest for observed objects 1-8 1(+ 3, - 5) (+ 6, -10) 3(+12, -20) 4(+24, -40) 5(+48, -80) 6(+12,-20) x1.5 7(+12,-20) -0.05”

Perturbed Fake casePosition error and bias Max interest for observed objects 1-8 1(+ 3, - 5) (+ 6, -10) (+12, -20) (+24, -40) (+48, -80) (+12,-20) x (+12,-20) -0.05”

Perturbed Fake cases casePosition error/bias Median of max interest for observed objects 1-8 1(+ 3, - 5) 1 2(+ 6, -10).975 3(+12, -20).88 4(+24, -40).805 5(+48, -80).555 6(+12,-20) x (+12,-20) -0.05”.84

Perturbed Fake cases casePosition error/bias Mean centroid distance for (un)matched objects 1(+ 3, - 5)(92) 17 grid pts 2(+ 6, -10)(96) 22 3(+12, -20)(102) 28 4(+24, -40)(110) 39 5(+48, -80)(107) 60 6(+12,-20) x1.5(91) 24 7(+12,-20) -0.05”(104) 30

would like to have… rainfall total for each object area ratio defined as fcst area/obs area  not just smaller area/larger area