# Presentation: 686560

## CLIMATE PREDICTION CENTER ENSEMBLE R, D & Application

- Edward O’Lenic*, Muthu Chelliah, Dan Collins,
- Jonathan Gottschalck, Kingtse Mo, David Unger, Wanru Wu, Qin Zhang
- * Presenter
- 4TH ENSEMBLE USER WORKSHOP
- MAY 13-15
- Holiday Inn Laurel West 15101 Sweitzer Ln Laurel, MD 20707

## OUTLINE & MOTIVATION FOR NAEFS APPLICATIONS

- I.  MJO DIAGNOSIS & PREDICTION
- II.  OBJECTIVE MME GUIDANCE
- III.  DROUGHT FORECASTING

## I.  MJO DIAGNOSIS AND FORECASTING

-  GEFS data has improved CPC subseasonal forecasting applications
-  MJO weekly update, US and global tropics hazard assessments

## Example Use of GEFS Forecast Data

- _Example Use of GEFS Forecast Data_

-  MJO Index Phase Diagram Wheeler and Hendon (2004)
-  Multivariate EOF analysis using OLR, 850 hPa / 200 hPa zonal wind
-  Index is first two PCs (RMM1, RMM2) taken together

**RMM1 / RMM2 values for the most recent 40 days (CDAS) and forecasts from the GEFS for the next 15 days**
_**light gray shading**_**: 90% of members                     **
_**dark gray shading**_**: 50% of members**

_**Yellow Lines**_**  – 20 Individual Members             **_**Green Line**_** – Ensemble Mean**

## Recent Verification of the GEFS MJO index

- _Recent Verification of the GEFS MJO index_

- Useful skill out to approximately 12-14 days

## Review of the Winter 2007-2008 MJO Activity

## Ensemble Regression (ER) on Canadian and NCEP T2mRegression of ensemble mean to observations applied to ‘best’ member

***The best member correlation is a simple function of the mean and ensemble member correlations***

_**Assumptions**_**:****Each individual model forecast is a ****potential future atmospheric state.****Distribution of ensemble members ****describes the likelihood of future states.**

_**A normal distribution “kernel” is placed **__**around each ensemble member**__ _**The Kernel width is related to the ****“best-member” correlation to observations**

- _II.  OBJECTIVE MME GUIDANCE_

**Notes:**
‘Best’ member is the closest member to the observation. However, we do not explicitly identify the ‘best’.
Error term implies uncertainty in which member is best. The hypothesis is: Minimizing error in mean also minimizes error in ‘best’ member.

## NAEFS Use in CPC Operational ER Forecasting 

**D+8**
**NAEFS**

**D+11**
**NAEFS**

**D+11**
**Official**
**ERF**

**D+8**
**Official**
**ERF**

## Uncalibrated Smooth probabilities with kernel normal distributionsClimatology Climatology distribution as a forecastCalibrated Calculates ensemble correlations usingEnsemble Regression

- _RELIABILITY: DJF 2007-08_

- Observed frequency

- Forecast Probability

**Warm bias**

**Cold bias**

## Most of the skill is gained from assuming an imperfect

_**Continuous Rank Probability Skill Score**_
- Skill gain of probability compared to climatology

- model and smoothing probabilities.
- Some additional skill from calibration relative to true skill.

- Percent Skill Increase

## III.  DROUGHT DIAGNOSIS AND PREDICTION

- Explore the possibility to apply the North American Ensemble Forecast System (NAEFS) for short-range drought outlook operationally

- Data, Methods

**NAEFS:** _http://www.emc.noaa.gov/gmb/ens/NAEFS.html_).
**Data:** 23 May 2006 to present. Means and spreads for week1 (1-7 days) and week2 (8-14 days) forecasts were calculated from 60 ensemble members, with 1x1 degree spatial resolutions and 6-hour temporal intervals.
**Verification:** focused on JAS 2006, with NARR taken as the estimated truth.

## Week 2 F-O Differences Averaged over July 2006 are large, and appear to be related to large systematic errors in the IC, especially for soil moisture.  We improve NAEFS skill by correcting these systematic errors with a simple regression.

- FORECAST-MINUS-OBSERVATION

- INITIAL CONDITION

**Notes:**
F minus O, where O is the regional reanalysis (model-assimilated data). Systematic errors mostly come from the IC, as is evident by the resemblance of the forecast “errors” (F-RR) to the IC (F24-RR).  In the case of SM, the IC are not updated, but are inherited estimates of SM based on rainfall observations.

## NAEFS SM minus RR Difference >> Noah, Mosaic, VIC SM Ensemble Spread

- JAS 2007 Noah, Mosaic & VIC Ensemble Anomaly & Spread (TOP)
- Compared with NAEFS Forecast Errors (BOTTOM)

- NAEFS – NARR Difference Averaged over JAS 2007

**Notes:**
This shows the 3-model SM ensemble mean forecast (a), the 3-model SM spread, in mm (b), and the F-O in mm (c).  The fact that the forecast error is larger than the spread is the point here.

## Black – no correction 

**Red**** ****– after correction**

- Anomaly Correlation over 70o-125oW 20o-50oN

| P | 0.38 0.45 | 0.14 0.22 |
| --- | --- | --- |
| SM | 0.20 0.79 | 0.12 0.66 |
| E | 0.34 0.61 | 0.26 0.47 |
| T2m | 0.57 0.81 | 0.36 0.55 |

**Week1**

**Week2**

- SM and E differences are mostly systematic, T2m and P are less so.  Dashed line is 0.5 AC.

- A Simple Regression Correction, based on 45 days of observations and forecasts greatly improves the forecast skill, especially for SM and E

**Notes:**
Effects of correcting systematic errors.

## NAEFS Experimental Bias-Corrected Forecast Guidance Temperature (oC)            Precipitation (mm)           Soil Moisture (mm)

- Made 05Jan2008

- Made 29Dec2007

- “observed” SM (CPC leaky-bucket)

- observed T (CPC analysis)

- CPC real time precipitation analysis 7-day accumulation 12Z06Jan2008–12Z13Jan2008

- Made 29Dec2007

- Made 29Dec2007

- Made 05Jan2008

- Made 05Jan2008

**Notes:**
How we use the corrected forecasts for temperature.

## Future Plans

- Large systematic biases exist in the verified land-surface quantities (P, E, SM, T2m), especially for soil moisture and evaporation.
- A large portion of the mean biases is inherited from the initial conditions, because NAEFS does not initialize the land-surface conditions,
- The systematic bias correction significantly improves the forecast and is not sensitive to the training period.

- Add more hydro-meteorological variables: snow water equivalent, runoff ...
- Apply multi-model ensemble for systematic bias correction.
- Leave no county behind: NAEFS forecasts forcing  NLDAS models to produce ensemble forecast products at 1/8 degree.
- Use CFS to extend forecasts beyond 2 weeks.

## NAEFS Land-Surface Forecasts: Verification & Application

- Wanru Wu, Kingtse Mo and Muthuvel Chelliah
- Climate Prediction Center/NCEP/NOAA

**COLLABORATORS:**
**NCEP/CPC:** Kingtse Mo, Wanru Wu, Muthuvel Chelliah, Wesley Ebisuzaki, Yun Fan, Huug van den Dool, Wei Shi
**NCEP/EMC -** NLDAS Team: Ken Mitchell, Youlong Xia, Jesse Meng, Helin Wei
**NCEP/EMC -** NAEFS Team: Zoltan Toth, Yuejian Zhu
**NASA/GSFC:** Brian Cosgrove,         Chuck Alonge
**Princeton U:** Eric Wood, Lifeng Luo, Justin Scheffield
**U Washington:** Andy Wood, Dennis Lettenmaier
**Web Masters:** Viviane Silva, Joe Harrison
**Project Funded by NOAA CPPA**