# Presentation: 912752

## Qin Zhang, Jon Gottschalck, Michelle L’Heureux, Peitao Peng, Song Yang, Arun Kumar, and Wayne Higgins

**Intra-seasonal Variability in the NCEP Global Ensemble Forecast System**

**Acknowledgments: Thanks Kyong-Hwan Seo, Zoltan Toth, Yuejian Zhu, Bo Cui for discussion and contribution.**

- http://www.cpc.ncep.noaa.gov/products/people/wd52qz/mjoindex/MJO_INDEX.html

## 1. Introduction

******  ****CPC is actively developing additional MJO-related forecast tools to aid its operational mission for the monitoring, assessment, and prediction of the MJO and its associated impacts **
****** ****We apply the Wheeler and Hendon (2004) MJO identification methodology. Although this approach for identification of the MJO is well-established, it has only been recently that it has been applied to dynamical model data at several global operational centers**
****** ****A description of our approach, modifications necessary for practical realtime operational considerations, and initial verification statistics from both the GEFS and CFS data are illustrated.**
****** ****We present products using data from the Global Ensemble Forecast System (GEFS) and Climate Forecast System (CFS) **
- 	Wheeler and Hendon, 2004: An All-Season Realtime Multivariate MJO Index: Development of an Index for Monitoring and Prediction. Monthly weather Review,132, 1917-1932.

## CLIVAR MJO Experimental Prediction Project 

**CLIVAR MJO Experimental Prediction Project**

**NCEP**

**UK Met**

**PSD/NCEP**

**CMC**

**http://www.cdc.noaa.gov/MJO/Forecasts/index_phase.html**

## 2. MJO Identification

**The following steps are used to filter variability to that associated with the MJO:**
**a.  Remove the first 3 harmonics of the seasonal cycle**
**b.  Remove ENSO by using the SST1 index (Bureau of Meteorology Research Centre, Australia) **
**c. Remove mean of last 120 days **
**d. Perform multivariate EOF analysis (U850, U200 and observed OLR) and determine EOFs using historical data from 1979-2005**
**e. Realtime OLR and NCEP GDAS/CDAS data (U850 and U200) to project to multivariate EOF patterns**

## Realtime MJO Monitoring and Forecast

**Notes:**
Fig. 1: The first (a) and second (b) modes of the multivariate Empirical Orthogonal Function (EOF) analysis of near-equatorially-averaged (15°S -15°N) satellite-observed outgoing longwave radiation (OLR), 850 hPa and 200 hPa zonal wind zonal wind data. 
Fig. 2:  Phase diagram illustrating the evolution of RMM1 and RMM2 after projection of the daily observed data onto above EOFs with the seasonal cycle and components of interannual variability removed. 
Fig. 3:  OLR composites based on the index for the eight phases shown in figure 2 for OLR during the boreal winter (NDJF) months. 

## 3. Application to Operational Forecasts

****** ****Calculated from the NCEP Global Ensemble Forecast System (GEFS) – 20 members**
****** ****Use bias-corrected GEFS U850 and U200 wind data and CDAS climatology for anomalies (Bo Cui et al. 2007)**
****** ****120 day mean is based on observations (the last 120 days from the reanalysis)**

## Realtime MJO Monitoring and Forecast

**After Bias correction**** **

**Before Bias correction**** **

**Big jump related to model bias**

**Notes:**
Fig. 4: Realtime MJO index forecast examples (a) after applying some of the adjustments listed above (Global Ensemble Forecast System (GEFS) and bias correction) and (b) prior to applying these adjustments (control run GFS and no bias correction).  Also shown are two statistical forecast models (autoregressive model and lagged regression technique). 

## 4. Example Products

**OLR**

**U850**

** ****forecast 15 days**

_**4. Example Products**_

**Notes:**
Fig. 6:  Time-longitude plots of MJO associated OLR, U850, and U200 anomalies from observations (shaded) and extended to include MJO associated anomalies from the GEFS (contours).

## Realtime MJO Monitoring and Forecast

**OLR**

**850hPa wind and Chi**

**Realtime MJO Monitoring and Forecast**

**Notes:**
Fig. 5:  Prediction of MJO-related OLR, velocity potential, and U850 / U200 wind anomalies from the GEFS. 

## 5. Verification

**GEFS**

**An Example of GEFS Forecast Verification**

**CFS**

- 11 days

- 8 days

- 12 days

- 11 days

**Notes:**
Fig. 8: Correlation skill of PC1 and PC2 as a function of forecast day for original and bias corrected GEFS data during the January to September 2007 time period. An example verification phase diagram from a forecast made on June 14, 2007 is shown on the right. 

## 6. Summary and Future Work

****** ****Several experimental MJO tools are available using GFS and CFS operational data and the GEFS indicates greater skill than the CFS over the limited validation periods**
****** ****These tools will be used as input to the weekly MJO update and Experimental Global Tropics Benefits/Hazards Assessment routinely produced at CPC**
****** ****These tools will be incorporated as part of CPCs MJO overall tool consolidation effort and collaboration with other operational centers as part of the US CLIVAR MJO working group**
****** ****For future work:  OLR bias correction, integration longer than 15 days, add uncertainty information...**

## 7. Monsoon Indices Forecast (CPC’s global monsoon group)

**Webster-Yang Monsoon Index**
**U850-U200 (40-110****o****E, EQ-20****o****N)**

**Australian Monsoon Index**
**U850 (110-150****o****E, 2.5-20****o****S)**

## 7. Monsoon Indices Forecast (CPC’s global monsoon group)

## 7. Monsoon Indices Forecast (CPC’s global monsoon group)

**Dynamic Indian Monsoon Index**
**U850 (40-80****o****E, 5-15oN) - U850 (70-90oE, 20-30oN)**

**Western North Pacific Monsoon Index**
**U850(100-130oE, 5-15oN)-U850(110-140oE, 20-30oN)**** **