# Presentation: 707641

## Assessing Forecast Uncertainty in the National Digital Forecast Database

- AMS Weather Analysis and Forecasting
- Matt Peroutka, Greg Zylstra, John Wagner
- NOAA/NWS Meteorological Development Lab
- August 1, 2005

## National Digital Forecast Database (NDFD)

- Contains a mosaic of NWS digital forecasts
- Is available to all users and partners – public and private
- Allows users and partners to create wide range of text, graphic, and image products

## How is NDFD Produced?

**User-Generated Products**

- Detailed
- Interactive
- Collaborative

**NWS Automated Products**

**Text**

**Graphic**

**Digital**

**Voice**

**National Digital ****Forecast**** Database**

**Local Digital ****Forecast**** Database**

**Field ****Offices**

**National ****Centers**

**Collaborate**

***Data and Science Focus***

**National Centers**** Model Guidance**

**Grids**

- TODAY...RAIN LIKELY. SNOW LIKELY ABOVE 2500 FEET. SNOW ACCUMULATION BY LATE AFTERNOON 1 TO 2 INCHES ABOVE 2500 FEET. COLDER WITH HIGHS 35 TO 40. SOUTHEAST WIND 5 TO 10 MPH SHIFTING TO THE SOUTHWESTEARLY THIS AFTERNOON. CHANCE OF PRECIPITATION 70%.

## Single-valued Forecasts

- Most NDFD weather elements are single-valued
- Can be viewed as a limitation
- Forecast uncertainty information can enhance customer decision processes

## Numeric Uncertainty Assessment of NDFD via Climatology and Ensembles (NUANCE)

## Development and Implementation

- Development
- Amass observation *(x)* and forecast *(f) *pairs
- Model joint distribution, *p(f,x)*
- Refine with diagnostic data *(d)* to form *p(f,x,d)*
- Implementation
- Use current values of *f* and *d*, and *p(f,x,d)*
- Infer conditional distribution of observations, *p(x|f,d)*

## Data Sources

- Ideally, NDFD grids and Analysis of Record
- Prototype with NDFD point forecasts and METAR observations
- Ensemble MOS (ENSMOS) archives
- One set of forecasts from control run
- Five sets of forecasts created from runs with positive perturbations
- Five sets of forecasts created from runs with negative perturbations

## Diagnostic Data

- Standard Deviation (SD) of 11 ENSMOS forecasts.
- “Ensemble Deviation” (ED)
- Difference each perturbed forecast with control forecast
- Compute root mean square

## Transformation to Percentiles

- Useful to transform both *f* and *x* from native values to climatological percentiles
- Data from U. S. Historical Climatology Network (USHCN)
- Addresses lack of extreme cases in development data
- Encourages combining of data
- NDFD has a short history
- Expect *p(f,x)* to vary by region and by forecast

## Development Phase

## Implementation Phase

## Results:  Transformation to Percentiles

- Obtained daily maximum temperature (MaxT) observations for 168 stations from USHCN
- Computed percentile function at 5-day intervals throughout the year
- Used Generalized Lambda Distribution to model percentile function
- Percentile function fitted to observations.
- Fit parameters expressed as cosine series over day of the year.
- Quality of fit judged subjectively.
- Additional terms added to cosine series, if needed.

## Results of Percentile Transform Technique for Blythe, California

## Baudette, Minnesota (KBDE; blue)

**Baudette, Minnesota (KBDE; ****blue****)**
**Fort Lauderdale, Florida (KFLL; ****purple****)**
**Blythe, California (KBLH; ****red****)**

**Comparison of MaxT Percentiles**

## Results:  Modeling Joint Distribution

## Slide 16

## Slide 17

## Day 1 vs. Day 7

- Day 1 forecasts verify better
- Day 7 points cluster around the 0.50 forecast value more
- Fewer extreme forecasts on Day 7

## Results:  Diagnostic Data

- Day 7 forecasts, stratified by Ensemble Deviation.
- ED < 6° F (above) vs.
- 	ED ≥ 6° F (below).
- Suggests more skillful forecasts when ED < 6° F, but relationship is not obvious.

## Future Plans

- Quantitatively assess uncertainty
- Expand to include minimum temperature
- Work with grids
- Prototype experimental guidance products