probabilistic forecasts of extreme precipitation events for the u.s. hazards assessment kenneth...
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![Page 1: Probabilistic Forecasts of Extreme Precipitation Events for the U.S. Hazards Assessment Kenneth Pelman 32 nd Climate Diagnostics Workshop Tallahassee,](https://reader036.vdocuments.mx/reader036/viewer/2022081603/5697c0301a28abf838cdaaf0/html5/thumbnails/1.jpg)
Probabilistic Forecasts of Extreme Precipitation Events for the U.S. Hazards Assessment
Kenneth Pelman
32nd Climate Diagnostics Workshop
Tallahassee, Florida
![Page 2: Probabilistic Forecasts of Extreme Precipitation Events for the U.S. Hazards Assessment Kenneth Pelman 32 nd Climate Diagnostics Workshop Tallahassee,](https://reader036.vdocuments.mx/reader036/viewer/2022081603/5697c0301a28abf838cdaaf0/html5/thumbnails/2.jpg)
Outline
• Current Hazards Assessment
• Motivation for Probabilistic Forecast
• Details of Objective Probabilistic Tool
• Verification Results
• Conclusions
• Future Work
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Current CPC Hazards Assessment
• Made each Monday-Friday and covers Days 3-14
• Designed to take current state of climate and predict hazardous weather conditions in support of CPC’s mission
• Hazards include heavy rain (a proxy for flooding), severe weather, extreme heat and cold, and severe drought
• Hazard forecasts are subjective and deterministic
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Motivation for a Probabilistic Hazards Assessment
• Looking to improve on subjective scores
• Probabilistic forecasts provide more information about uncertainty to users
• Puts Hazards Assessment in same format as other popular CPC products, such as 6-10/8-14 day, monthly, and seasonal forecasts
![Page 5: Probabilistic Forecasts of Extreme Precipitation Events for the U.S. Hazards Assessment Kenneth Pelman 32 nd Climate Diagnostics Workshop Tallahassee,](https://reader036.vdocuments.mx/reader036/viewer/2022081603/5697c0301a28abf838cdaaf0/html5/thumbnails/5.jpg)
Objective Probabilistic Heavy Precipitation Tool
• Uses 0z, 6z, and 12z GFS ensemble members to forecast for 881 grid points across the CONUS
• Rainfall totals not bias corrected or calibrated
• 1,2,and 3-day Hazards are forecast out to 384 hours
•A Hazard is defined as the greater of 1 inch/day or the 95th percentile value
•Climatology (1971-2000) derived from U.S. precipitation dataset (Higgins et. al. 2000)Forecast probability contours in solid green, hazard
thresholds in dashed black (in mm)
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Reliability Diagrams (Using Total Precipitation in Period)
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Reliability Diagrams (Using only 1 Day in Period)
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1-Day Event Contingency Table Scores (10% threshold)
POD
a/(a+c)
Threat Score
a/(a+b+c)
FAR
b/(a+b)
Bias
a+b/a+c
Day 3 0.32 0.14 0.80 1.62
Day 4 0.26 0.12 0.81 1.45
Day 5 0.21 0.10 0.85 1.38
Day 6 0.14 0.07 0.87 1.10
Day 7 0.09 0.05 0.88 0.83
Day 8 0.05 0.04 0.90 0.55
Assessment 0.12 0.08 0.79 0.57
a b
c d
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2- Day Event Contingency Table Scores (10% threshold)
1 Day in Period POD Threat Score FAR Bias
Days 2+3 0.13 0.09 0.76 0.55
Days 3+4 0.12 0.08 0.77 0.52
Days 4+5 0.10 0.07 0.78 0.48
Days 5+6 0.06 0.05 0.81 0.35
Assessment 0.12 0.08 0.79 0.57
Total Precipitation in Period
POD
a/(a+c)
Threat Score
a/(a+b+c)
FAR
b/(a+b)
Bias
(a+b)/(a+c)
Days 2+3 0.21 0.10 0.85 1.38
Days 3+4 0.17 0.08 0.86 1.18
Days 4+5 0.16 0.08 0.85 1.19
Days 5+6 0.15 0.08 0.86 1.09
Assessment 0.12 0.08 0.79 0.57
a b
c d
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3-Day Event Contingency Table Scores (10 % threshold)
Total Precipitation in Period
POD
a/(a+c)
Threat Score
a/(a+b+c)
FAR
b/(a+b)
Bias
(a+b)/(a+c)
Days 1+2+3 0.08 0.05 0.91 0.95
Days 2+3+4 0.09 0.05 0.90 0.91
Days 3+4+5 0.08 0.05 0.88 0.67
Assessment 0.12 0.08 0.79 0.57
1 Day in Period POD Threat Score FAR Bias
Days 1+2+3 0.05 0.04 0.80 0.24
Days 2+3+4 0.04 0.04 0.81 0.22
Days 3+4+5 0.03 0.03 0.82 0.17
Assessment 0.12 0.08 0.79 0.57
a b
c d
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ROC Diagram
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Conclusions
• Limited usefullness of this tool in a strict probabilistic sense
• 1-Day Hazard forecasts show the best improvement over human-made Assessments
• Tool can immediately be used by forecasters as a first guess
• There is validity in converting the Hazards Assessments into a probabilistic forecast
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Future Work
• Determine best definition of a hazard
• Generate contingency tables using different thresholds
• Use calibrated precipitation forecasts and calibrated probabilities.
• Use more ensembles in forecast (e.g. CAN, ECMWF, CFS)
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Reference
• Higgins, R.W., W. Shi, E. Yarosh and R. Joyce, 2000: Improved United States Precipitation Quality Control System and Analysis. NCEP/Climate Prediction Center Atlas No. 7.