# Presentation: 097200

## ENSEMBLE PERTURBATIONS FOR SUBSEASONAL FORECASTING

- CTB PROPOSAL July 2007
- Ensemble group at EMC/NCEP. Collaborators from GSFC/NASA

## NCEP Global Ensemble Systems

- Uncoupled prediction system
- No lower boundary conditions & uncertainty
- Runs out to 16 days
- Lacks large hindcast dataset
- Adaptive bias correction scheme in place
- ET ensemble method
- State-of-the-art method
- Upgrades frequently:
- DA/model systems

- Fully coupled OLA model
- Captures variability and uncertainty of coupled system
- out to 9 months
- Large hindcast dataset
- Operational forecast system frozen to ensure high quality bias correction
- Lacks periodic upgrades
- Lagged ensemble  method
- Is not centered on the latest and best analysis. Initial variance arbitrary.

- Fully coupled OLA model
- Captures variability and uncertainty of coupled system
- out to 30 days
- Large hindcast dataset
- Real-time
- Upgrades frequently
- ET coupled
- Represents analysis uncertainty of the coupled ocean-atmosphere system

**GEFS**

**CFS**

**Proposed GEFS**

## Perturbation Schemes: Atmosphere

- 0Z

- 6Z

- 12Z

- 18Z

-  CURRENT: Ensemble Transform (ET)

-  GFS (atmosphere only) 28 Levels, T126
-  All variables are perturbed in each level
-  80 ET perturbations every 6 hrs. 60 are short-range (6 hrs)
-  20 perturbations every 6 hrs integrated forward **16 days**.

- 6 hrs

- 16 days

## ET formulation 

- The transformed perturbations   (              )  are orthogonal with respect to an inverse error variance norm.
- However, they are not centered, i.e. the sum of perturbations is not zero.

- ET transform based on analysis error variances

- From Mozheng Wei

**Notes:**
CIRA operational and research experience

- Centering perturbations

- From Mozheng Wei

- ET ensures perturbations are orthogonal and uniformly centered around the best initial conditions.

**Notes:**
CIRA operational and research experience

## Extending set of variables to include ocean

-  PROPOSED: Ensemble Transform (ET)

-  GFS **coupled** with MOM4
-  All variables are perturbed in each level
-  80 ET perturbations each 6 hrs
-  Once a day 20 perturbations will be **integrated forward for 30 days** and 60 for 16 days

- 0Z

- 6Z

- 12Z

- 18Z

- 30 days

- 16 days

- 6 hrs

## ET for the coupled system

- Goal: represent well the analysis uncertainty of the coupled system.
-  Choice of rescaling factor
-  atmosphere: KE for one layer (2D); later, TE 3D
-  ocean: heat content of top layers or depth of ocean mixed layer (2D); 3D in future
-  Ocean data assimilation every 6 hrs, thus ocean variables to rescale every 6 hrs as the atmosphere
-  Case dependent estimate of analysis uncertainty is needed
-  Research to asses whether exciting slow modes affect analysis uncertainty
-  Ocean analysis is in 24 hrs delayed mode

## Objective of CTB proposal:

- Construct and test an ensemble system for sub-seasonal (out to 30-45 days) forecasting
- Characteristics:
- Coupled OLA model (from GC&WMB/EMC)
- Latest version of DA/modeling systems ( “ )
- Real-time hindcast (piggy backing on GEFS project)
- State-of-the-art ensemble generation scheme (GEFS)
- Large portion of the project leveraged
- Next generation GEFS

## Existing approaches at NCEP: GEFS

- Uncoupled prediction system
- Runs out to 16 days
- Lower boundary conditions and its uncertainty not realistically represented
- Lacks large hindcast dataset
- No high quality-bias estimation for long leads
- Adaptive bias correction scheme in place
- State-of-the-art ensemble scheme
- Upgrades frequently to incorporate best available:
- Data assimilation system, Numerical model, Ensemble technique

## Frequent upgrades: Benefits

**1-day gain in skill in 4 yrs**

## Existing approaches: GEFS (contin.)

- Ensemble size: 20 members every 6hrs
- 80 ET members daily
- Model resolution: T126 L28
- Lower resolution than current operational GFS
- Non-negligible skill at day 16
- Evaluation scheme for deterministic and probabilistic forecasts available

## Probabilistic forecast skill

- Skill of Climatology

- GFS

- WINTER

## Existing approaches: CFS

- Fully coupled OLA model
- Captures variability and uncertainty of coupled system out to 9 months
- Large hindcast dataset
- Operational forecast system frozen to ensure high quality bias correction
- Lacks periodic upgrades of DA/modeling system
- Lagged ensemble generation scheme
- Is not centered on the latest and best analysis.
- Initial error introduced in ensemble hurts performance of sub-seasonal forecasts
- Initial variance arbitrary; does not reflect analysis uncertainty (too large)

## Example of 16-day ensemble forecast

- Initial perturbations on Feb 3rd 2007

- Lagged perturbations

- Instantaneous

- 30-member ensemble generated from previous 15 days

- Ensemble Transform perturbations

- Most recent forecast

**f00**

**f00**

- Best analysis at initial time

- Ensemble mean at initial time

- Lagged ensemble at initial time
**Not centered on best analysis**
- Increased forecast error
**Has unrealistically large spread**
- Unreliable prob. forecasts
- Decreased forecast skill

## Potential skill gain if initial errors due to lagged were removed

- RMSE Individual and lagged ensemble means

- Current vs repositioned

- Lead (in days)

- Lead (in days)

-  RMSE of CFS monthly forecasts of Nino 3.4. Annual average
-  Paremeterization of internal and model error

- No model error

- lagged

- Individual members repositioned

- Running all members simulateneously

-  Ensemble mean outperforms the individual members
-  Repositioning does not require additional computer resource or model improvement

## Existing approaches: CFS (Contin.)

- Ensemble size: Twice daily
- 60 members are formed per month for seasonal predictions
- Not optimal for sub-seasonal predictions:
- Weak predictive signal requires large ensemble
- Yet, skill is deteriorated if lagged members included
- Atmospheric model resolution: T62 L64
- Skill of CFS lower than GFS for short-range
- System not optimized for this short period

## Anomaly Correlation NH. Single Forecast

- GFS

- CFS

**GFS**
**CDAS**
**CFS**

- Difference must be larger for ensembles

## Science Readiness

- To design a system that optimally captures initial and forecast states of both fast and slow sub-components
- Follows from recommendations from a recent workshop on bridging the gap between weather and climate numerical predictions (Toth et al 2007)
- Initial ensemble perturbations for coupled systems
- Research results available with the breeding technique (Cai et al 2003, Yang et al 2006)
- Ensemble Transform method
- Tested for the atmosphere (Mozheng et al 2006)

## Computational Readiness

- Proposed ensemble system will replace current GEFS
- The Central Computing System for 2007 can accommodate the extra CPU needed if model resolution is kept
- The extra CPU needed due to:
- Increase in forecast lead	**Factor of****	****1.25**
- Ocean/Ice model integrations		**2.0 ?**
- Real-time hindcast				**1.3**
- Physics upgrade				**1.5**

**Total  ~4.8 times**

## Technical Readiness

- Initial conditions
- The project will use a truncated version of the operational GFS analysis (at T126 resolution), and the operational ocean analysis
- Models
- will use operational GFS and MOM4 with ice model and a coupler expected available soon for testing.
- Hindcast generation
- Ongoing EMC development effort to test the real-time GEFS hindcast.

## REAL-TIME GENERATION OF HIND-CAST DATASET?

- Today’s Julian Date
- TJD

- TJD + 30

- TJD - 30

- Actual ensemble **generated today**

- Hind-casts for TJD+30 **generated today**

- Hind-casts (or its statistics) for TJD+/- 30 **saved on disc**

- Time

- 2005

- 2004

- 2003

- 1968

- 1967

- 2006

## Synergies/Leverages

**GEFS developments**: Full use of ensemble generation, hind-cast generation, evaluation/verification infrastructure
**CFS**: Latest versions of ocean DA, model, new reanalysis will be operationally implemented into sub-seasonal ensemble as/when they become available
**GEOS-5**:  Latest versions of ocean DA for MOM4, NASA-funded R&D for subseasonal and seasonal forecasts with GEOS-5
**NAEFS**: Sub-seasonal forecasting benefiting from major past and ongoing developments in bias correction, downscaling, product generation.
- _Week-2_ 2m temperature (precipitation) products. CPC,  EMC, and the Canadian Meteorological Center (CMC). New system could provide weeks 3 & 4 forecasts.
**THORPEX** Pacific-Asian Regional Campaign (T-PARC) and International Polar Year (IPY): New probabilistic sea ice forecast products developed jointly with T-PARC / IPY collaborators
**River flow / drought forecasting**: Sub-seasonal ensemble forecasts will be coupled with river flow model used experimentally with GEFS system and drought monitoring tools to be applied with GEFS
**Inter-comparisons**: Various forecast products from the proposed system, including MJO predictions, will be compared with those from alternative forecast systems such as the NASA GEOS-5 coupled system and the linear inverse model from NOAA-ESRL.

## Broader impact

**Short-range prediction improvement**. Due to the use of a coupled OLA model, the sub-seasonal GEFS may improve upon the performance of current GEFS in areas where coupling plays an important role, such as _tropical cyclone prediction_, even on the shorter, 1-2 weeks time scale.
**Seamless suite of forecasts**. The proposed system offers an opportunity to produce forecasts from weather to sub-seasonal lead times with the same forecast system, a stated goal of the THORPEX and climate research communities (Toth et al 2007)
**Future designs of climate prediction systems**. Ensemble perturbation and hind-cast generation methods proposed may influence the design of future prediction systems, possibly leading toward a more unified weather and climate forecast systems.
**Multi-model approach.** The two independent prediction systems (GEOS-5 and subseasonal GEFS) assessed in this project will contribute to the national multi-model prediction approach. Multi-model forecasts have demonstrated benefit for seasonal predictions. This project will help assess whether combining the two systems improves the quality of subseasonal predictions as well.