# Presentation: 669542

## Probabilistic Forecasts of Extreme Precipitation Events for the U.S. Hazards Assessment 

- Kenneth Pelman
- 32nd Climate Diagnostics Workshop
- Tallahassee, Florida

## Outline

- Current Hazards Assessment
- Motivation for Probabilistic Forecast
- Details of Objective Probabilistic Tool
- Verification Results
- Conclusions
- Future Work

## 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

**Notes:**
Extreme event forecasting is part of CPC mission.

## 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

**Notes:**
Hazards Assessments are usually the 4th or 5th most popular product each month based upon web hit statistics.
As Louis said, ensembles are being used for a range of applications, and extreme event forecasting is one area of numerical weather prediction that has a lot of interest.

## 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)

**Notes:**
Hazard definition subjective and designed to best capture Hazard conditions.

## Reliability Diagrams (Using Total Precipitation in Period)

## Reliability Diagrams (Using only 1 Day in Period)

## 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 |

## 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 |

## 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 |

## ROC Diagram

## 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

## 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)

## 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.