# Presentation: 396652

## Operational Use of Bias Corrected Ensemble Data

- Richard H. Grumm
- And
- Yuejian Zhu
- United States Weather Service

## OVERVIEW

- Why bias correct the data
- How to bias correct the data
- NCEP bias correction variables
- Examples of bias corrected data

## Why bias correct the data

**Models have systematic errors**
- Based on physics and parameterizations
- Based on the larger scale pattern
**Thus a model might**
- Have a cold bias in a region or areas
- And a warm bias in another region.
- There are many bias which may be present....

## Bias problems

- Too dry or too wet
- Too cold or too warm
- Winds to strong or too weak
- Lee side troughs too strong or too weak
- To much QPF in a location too little in another
- The list is nearly endless

## GEFS

- Is based on the NCEP GFS model
- Thus the GEFS members
- Have all the bias problems inherit in the GFS system
- The bias can be identified and mitigated
- Bias correction
- Can improve the range of predictability
- Gain hours to a few days more accuracy

## Key Point for Operational users

- Bias is based on Forecast (F) minus Analysis (observed)
**B = F-A**
- Example with 850 hPa temps
**B < 0 ******** F < A cold bias**
**B > 0 ******** F > A warm bias**
- Rapid use simple verification to improve the forecast of the EPS

## Bias Correction Method & Application

- Adaptive algorithm
- Kalman Filter type
***b ****and ****f ****represent the mean difference, raw forecast and (****a****) analysis at each grid point (****i,j)**** and each lead-time (****t****).*
* **B is decaying average bias, ****w**** is the weight for the decaying average.*
* **F is bias corrected forecast.*
* **w equals 0.02 (2%) has been used for operations, which is approximately using 50-100 days information**** ****use most recent data more heavily in process.***

## Bias Corrected GEFS/CMC/NAEFS Variables

## About the table

**Bias correction now used all 21 GEFS members**
*Studies show some improvement in skill scores*
*Varies by variable and geographic location*
*But bias correction adds value*
**Precipitation is not on the list**
**Precipitable water is not on the list**
-  *a critical parameter in rainfall events.*
*We need to get this variable added in the near future.*
**Several variables are also used to compute anomalies**
*There is PDF data of anomalies for the bias corrected data*
*Great potential to identify significant weather events.*

## Interim Summarynext topic

**Why bias correct the data**
*Remove systematic errors extend length of skill*
**How to bias correct the data**
***Use past forecasts and verifications***
***Based on verification F-A***
**NCEP bias correction variables**
**We have a table of variables that are used**
_***Examples of bias corrected data***_

## Examples and applications

- What can the bias data do for you is what it is all about.
- Limited to 120 hours at this time
- Provides similar products as the EPS
- But slightly more skillful with reduced systematic bias
- Same look and feel

## January 2007-Bias corrected data

## Jan 2007 Bias corrected winds

## Bias Corrected Data

- Okay those images do not show too much
- We cannot see what the bias correction is doing for us
- Lets look at some bias correction impact
- Display M-BC where M=ensemble mean and BC is bias corrected ensemble mean
- Will use contours of BC data too

## 850 hPa Temps 30 hours and 120 hours

- Bias correction is insignificant at short ranges! Has impact at longer ranges.

## 1000 hPa temperaturesimpact stronger at lower levels

## 700 hPa Temps 30 hours and 120 hours

- Bias correction is insignificant at short ranges! Has impact at longer ranges.

## Bias Corrected Temperatures

**Impacts of the correction is stronger at lower levels**
**Showed 1000, 850 and 700 hPa**
- Impact larger at longer ranges
- 850 and 700 hPa used in KI and TTI computations
- Can impact and incrementally improve products
- 850 hPa will be more impacted than 700 hPa.
**We see the **
- The impact of the BC application
- We see the value of verification in the forecast process

## Other variables

- Lets look at a few Bias corrected variables
*SLP*
*500 hPa*
*Winds *
- I bet one could figure out those which are more sensitive to the bias correction process. Lets see

## SLP increased impact with increased forecast range

- Mean-Bias
- Mean too low over most of Africa

## 500 hPa Heights values in m 

## 1000 hPa Heights similar to pressure 

**1000 hPa Heights *****similar to pressure***** **

## 850 hPa vgrdgrsagain we see the time dependency 

**850 hPa vgrdgrs*****again we see the time dependency***** **

## 250 hPa vgrdprsnot a big contibution at short range

## 2m Tempssame general trend and not a big contribution

## Bias Corrected Data

**Some general findings**
**Impacts of the correction is stronger at lower levels**
**Impacts evolve with time and show greater impacts at longer ranges**
**In first 6-48 hours impacts are generally quite small******** models more skillful at shorter time periods so smaller errors/bias.**
**Some concepts**
_***We can and do use these data like EPS data***_
**They should have slightly better skill most of the time at longer ranges**
**Bias correct fields such as 850 hPa and 700 hPa temperatures**
**Will impact our derived variables such as KI and TTI**
**These data should help**
- In the 3-5 day range
- With several critical variable
- We need PW for the rainfall threat problem!

## Bias Corrected Data850 hPa temps look and feel like EPS data

## REVIEW

**Why bias correct the data**
- Leverage verification and model performance knowledge
- Extend range and skill a bit.
**How to bias correct the data**
- Get rid of systematic errors at each forecast time step
**NCEP bias correction variables**
**They have a great set and it might grow**
**Only to 120 hours at this time**
**Examples of bias corrected data**
**Showed growing impact with time**
**Impacts on various variables and levels**
**Do not forget impact potential on derived variables.**

## Links

- http://www.cpc.ncep.noaa.gov/products/african_desk/swdfp/day0/00/