# Presentation: 427962

## THE NORTH AMERICAN ENSEMBLE FORECAST SYSTEM:

**AN OPERATIONAL MULTI-CENTER FORECAST SYSTEM**

*http://wwwt.emc.ncep.noaa.gov/gmb/ens/index.html*

**Zoltan Toth (NWS), Andre Methot (MSC), Michel Rosengaus (NMSM)**
- Acknowledgements: Yuejian Zhu, Bo Cui, Philippe Bougeault, David Parsons, Lawrence Wilson

## OUTLINE / SUMMARY

**PROJECT DESCRIPTION**
**Multi-center ensemble forecast system**
**BENEFITS**
**Joint development, production (cost savings)**
**Better representation of uncertainties (improvements)**
**COMPONENTS**
**20 ensemble members from Canada and USA**
**CONCEPT OF OPERATIONS**
**Exchange raw data (now), basic products (future)**
**Basic products (Bias correction, anomalies)**
**Derived products**
**PLANS**
**Consider links with future GIFS**

## PROJECT DESCRIPTION

***International project to produce operational multi-center ensemble products***
- Combines global ensemble forecasts from Canada & USA
-  40 members per cycle, 2 cycles per day from MSC & NWS
- 6-hourly output frequency
- Forecasts out to 16 days
- Generates products for
- Weather forecasters
- E.g., NCEP Service Centers (US NWS)
- Specialized users
- E.g., hydrologic applications in all three countries
- End users
- E.g., forecasts for public distribution in Canada (MSC) and Mexico (NMSM)
- Operational outlet for THORPEX research using TIGGE archive
- Prototype ensemble component of THORPEX Global Interactive Forecast System (GIFS)

## BENEFITS

**Improves probabilistic forecast performance**
- Earlier warnings for severe weather
- Lower detection threshold due to more ensemble members
- Uncertainty better captured via analysis/model/ensemble diversity (assumed)
**Provides Seamless suite of forecasts** across
- International boundaries
- Canada, Mexico, USA
- Different time ranges (1-14 days)
**Saves development costs** by
-  Sharing scientific algorithms, codes, scripts
- Accelerated implementation schedule
- Low-cost diversity via multi-center analysis/model/ensemble methods
-  Exchanging complementary application tools
- MSC focus on end users (public)
- NWS focus on intermediate user (forecaster)
**Saves production costs** by
- Leveraging computational resources
- Each center needs to run only fraction of total ensemble members
-  Providing back-up for operations in case of emergencies
- Use nearly identical operational procedures at both centers to provide basic products
- Offers as default basic products based on unaffected center’s ensemble

## PROJECT HISTORY & MILESTONES

- February 2003, Long Beach, CA
- NOAA / MSC high level agreement about joint ensemble research/development work (J. Hayes, L. Uccellini, D. Rogers, M. Beland, P. Dubreuil, J. Abraham)
- May 2003, Montreal (MSC)
- 1st NAEFS Workshop, ***planning started***
- November 2003, MSC & NWS
- 1st draft of NAEFS Research, Development & Implementation Plan complete
- May 2004, Camp Springs, MD (NCEP)
- Executive Review
- September 2004, MSC & NWS
***Initial Operational Capability*** implemented at MSC & NWS
- November 2004, Camp Springs
- Inauguration ceremony & 2nd NAEFS Workshop
- Leaders of NMS of Canada, Mexico, USA signed memorandum
- 50 scientists from 5 countries & 8 agencies
- May 2006, Montreal
- 3rd NAEFS Workshop
- May-Oct 2006, MSC & NWS
***1******st****** Operational Implementation***
- Bias correction
- Climate anomaly forecasts
- 2007-2008, MSC, NWS
- Follow-up implementations
- Improved and expanded product suite

## NAEFS ORGANIZATION

***Meteorological Service of Canada******	******National Weather Service, USA***
***	******	******MSC ******	******	******	******	******	******NWS******	***

- _Andre Methot (Implementation)_
- Peter Houtekamer (Science)

- _Zoltan Toth (Science)_
- Brent Gordon (Impl.)

***PROJECT OVERSIGHT***

***JOINT TEAM MEMBERS***

*Meteorological Research Branch MRB*
- Pierre Gauthier, Lawrence Wilson, Vincent Fortin, Guillem Candille
*Canadian Meteorological Center CMC*
- Richard Verret, Yves Pelletier, Gerard
-  Pellerin, Stephane Beauregard, Norman
-  Gagnon, Lewis Poulin,  Jacques Hodgson

*Environmental Modeling Center EMC*
- Yuejian Zhu, Bo Cui, Richard Wobus, Dingchen Hou, Malaquias Pena
*NCO *John Huddleston  *HPC  Keith Brill*
*Storm Prediction Center *David Bright
*Climate Prediction Center CPC*
- Ed O’Lenic, David Unger, Dan Collins
*NWS* Richard Grumm, Fred Branski

- Gilbert Brunet, Director, RPN
- Angele Simard, Director, AEPD

- Louis Uccellini (Director, NCEP/NWS)
- Greg Mandt (Director, OST/NWS)
- Steve Lord, EMC

***PROJECT CO-LEADERS***

***National Meteorological Service of Mexico (NMSM) - Rene Lobato***
***Fleet Numerical Meteorology & Oceanography Center (FNMOC) – Michael Sestak***
- Acknowledgements to: J. Whitaker, T. Hamill, Y. Gel, R. Krzysztofowicz

## INAUGURATIONCEREMONY

## Canadian EPS: new system (implemented July 10 2007)

- Members:
- 20+1 members:
- GEM 0.9° L28 (~100 km resolution).
- 16-day integration.
- Twice a day (00 and 12 UTC).
- Simulation of initial condition uncertainties:
- Perturbed ensemble Kalman filter data assimilation.
- Simulation of model uncertainties:
- A multi-model approach, each member having its own physics parameterization.
- Stochastic perturbations added to tendencies in the parameterized physical processes.
- Shutts (2005) energy back-scattering parameterization is used.
- Products on the web:
- _[http://](http://www.weatheroffice.gc.ca/ensemble/index_e.html)__[www.weatheroffice.gc.ca/ensemble/index_e.html](http://www.weatheroffice.gc.ca/ensemble/index_e.html)_ _[http://](http://www.meteo.gc.ca/ensemble/index_f.html)__[www.meteo.gc.ca/ensemble/index_f.html](http://www.meteo.gc.ca/ensemble/index_f.html)_

## Canadian EPS

- Data assimilation component – **NEW!**

- Ensemble of 96 analyses

- Addition of isotropic model error

- Ensemble of 96 perturbed analyses

- Ensemble of 96 trial fields at several time levels

- Weighted mean
- (Kalman Filter)

- 4-D selection of observations

- P. Houtekamer, ARMA

- Ensemble of 96 perturbed observation datasets

- Perturbations

- Observations

**Notes:**
The new set-up for the data assimilation cycle is similar to the old one. The main differences come from the fact that now the data are assimilated over a 6-h time window. In order to do so, the ensemble of 96 trial fields are generated at different time levels, namely 3, 4.5, 6, 7.5 and 9-h projection time so that interpolation can be done at the time of the observations. This approach results in a 4-D data assimilation cycle. 24 different configurations of the GEM model are used to generate the trial fields, each configuration being used 4 times. Note that in the new set-up, no random perturbations are added onto the trial fields. This is not necessary since 24 versions of the GEM model used to generate the trial fields.

## Canadian EPS

- R. Verret, CMC

**Notes:**
This diagram shows the principles of data assimilation described in the previous slide but applied to the particular problem of data assimilation in our Ensemble Prediction System. The cycle is done 96 times because we want to have 96 perturbed analyses. The advantage of having a large number of analyses is that we can calculate dynamically the trial field error covariance statistics. The variance amongst the 96 analyses is used to assess the trial field error covariance matrices. The trial field error statistics can thus vary in space and time from cycle to cycle. The variance amongst the 96 analyses of the previous cycle is used to calculate the trial error statistics for the next cycle. In such a set-up, the trial field error statistics that are used to weight the trial field against the observations in the weighted means (Kalman Filters) are not fixed, but are updated dynamically from cycle to cycle. The observations error statistics are not updated from cycle to cycle.

## Canadian EPS

**Canadian EPS**

- Initial perturbations – member 6 (GEM)
- Temperatures at 700 hPa
- 00 UTC 01-10-2007

- N. Gagnon, CMC

**Perturbation  = Analysis(Member 6) - Mean of 96 analyses**

- Example of initial perturbation on 700 hPa temperature field:

**Notes:**
Here is an example of perturbed initial conditions that are used to integrate the ensemble members. This example represents the initial perturbations on the temperature field at 700 hPa that were presented to member 6 (new system, GEM model) at 00 UTC October 1 2007. The graph in the lower left-hand side corner shows the vertical structure of the initial perturbations along a West-East line across Canada. This picture was produced by subtracting the perturbed analysis used by member 6 and the mean of the 96 perturbed analyses. 

## Canadian EPS Forecast component

| # | Deep convection | Land surface scheme | Mixing length | Vertical mixing parameter | GWD | Back-scattering | Stochastic Physics |
| --- | --- | --- | --- | --- | --- | --- | --- |
| 0 | Kain & Fritsch | ISBA  | Bougeault | 1.0 | std | No | No |
| 1
2
3
4
5 | Kain & Fritsch 
Oldkuo
Relaxed Arakawa Schubert 
Kuo Symmetric 
Oldkuo | ISBA 
ISBA 
Force-restore
Force-restore
Force-restore  | Bougeault
Blackadar 
Bougeault 
Blackadar 
Bougeault | 1.0
0.85 
0.85
1.0 
1.0 | Weak
Strong
Weak
Strong
Weak | Yes
Yes
Yes
Yes
Yes | Yes
Yes
Yes
Yes
Yes |
| 6
7
8
9
10 | Kain & Fritsch 
Kuo Symmetric
Relaxed Arakawa Schubert
Kain & Fritsch
Oldkuo | Force-restore 
ISBA
ISBA 
ISBA
ISBA  | Blackadar 
Bougeault 
Blackadar 
Blackadar 
Bougeault  | 0.85 
0.85
1.0 
0.85
1.0  | Strong
Weak
Strong
Weak
Strong | Yes
Yes
Yes
Yes
Yes | Yes
Yes
Yes
Yes
Yes |
| 11
12
13
14
15 | Relaxed Arakawa Schubert
Kuo Symmetric
Oldkuo
Kain & Fritsch
Kuo Symmetric | Force-restore
Force-restore 
Force-restore 
Force-restore 
ISBA | Blackadar 
Bougeault 
Blackadar 
Bougeault 
Blackadar | 1.0 
0.85 
0.85 
1.0 
1.0  | Weak
Strong
Weak
Strong
Weak | Yes
Yes
Yes
Yes
Yes | Yes
Yes
Yes
Yes
Yes |
| 16
17
18
19
20 | Relaxed Arakawa Schubert
Kuo Symmetric 
Kain & Fritsch
Oldkuo
Relaxed Arakawa Schubert | ISBA 
Force-restore 
ISBA
ISBA 
Force-restore | Bougeault 
Bougeault 
Blackadar 
Bougeault
Blackadar  | 0.85 
1.0 
0.85 
0.85
1.0  | Strong
Weak
Strong
Weak
Strong | Yes
Yes
Yes
Yes
Yes | Yes
Yes
Yes
Yes
Yes |

- P. Houtekamer, ARMA

**Notes:**
This table gives the configuration of the 20 GEM models used in forecast mode (members) in the new version of the Canadian Ensemble Prediction System. The members in this table match those used in the Ensemble Kalman Filter data assimilation that we have seen earlier but with additions (gravity wave drag (GWD), back scattering and stochastic Physics). Note that the control member (member 0) has the same configuration as member 1, except that it does not have the back-scattering of energy and the stochastic Physics. Click on the word “definitions” to get a definition of the columns in this table.

Note that back-scattering of energy means that the energy at the small scales (waves) that is lost by the model is randomly redistributed onto the small scales. Stochastic Physics means that the Physics tendencies are randomly perturbed before being added to the dynamics.

It is very important to realize here that the Canadian Ensemble Prediction System is a multi-model ensemble and furthermore, the new version of the EPS includes stochastic Physics. This is to simulate uncertainties related to numerical modeling of the atmosphere.

## Canadian EPS

- RMS – total energy norm – Globe
- January 2006

- RMS error (m/s)

- Projection time (hours)

- RMS error ensemble mean – New

- Spread – New

- RMS error ensemble mean – Old

- Spread – Old

**Reduced error and increased spread**

- G. Pellerin, CMC

**Notes:**
This graph compares the old and new Ensemble Prediction Systems. Again, the total energy norm is used and the domain is the whole Globe. The continuous lines represent the root mean square error of the ensemble mean. It can be seen that the ensemble mean of the new EPS (thick line) has a lower RMS error than that of the old EPS at all projection time. The two dotted lines represent the spread of the ensemble, the thick line being for the new system while the thin line is for the old system. It can be seen that after 48-72 hours projection time, the new system shows a larger ensemble spread compared to the old system. Furthermore, the old system was experiencing an abnormal behavior in the first 48-72 hours of integration. The spread was artificially boosted at initial time, but that larger initial spread was lost during the first few hours of integration. The new system shows a more normal and realistic behavior in that respect. In summary, the new Ensemble Prediction System shows reduced root mean square error for the ensemble mean and an increased ensemble spread. We have seen earlier that in a well balanced and well calibrated Ensemble Prediction System, the spread of the ensemble should be equal to the root mean square error of the ensemble mean. The new EPS shows a tremendous improvement in that aspect compared to the old system. 

## The operational implementation of the new system

- Parallel run started on April 19 2007
- Implemented on July 10 2007.
- Easier than ever with the possibility of developer to use operational software OCM.
- Pre-feed testing with our NCEP partner to ease the transition.
- Bias removal started with the parallel run.

## EPS Training Tour

- A 2-day workshop developed essentially by Richard Verret on a special assignment for a few months. A colossal work of more than 800 slides!
- Given twice in each Canadian forecast centers in the Spring of 2007 by two teams of 2 members with operational and development background.
- French and English.
- Local case studies developed by regional forecasters.
- Reach a majority of operational forecasters.
- Additional sites will be visited this fall.
- Some of the content will be included in the initial training for new forecasters.
- Available at:
- _[http://collaboration.cmc.ec.gc.ca/cmc/ensemble/Formation-Training/Lisez-moi.html](http://collaboration.cmc.ec.gc.ca/cmc/ensemble/Formation-Training/Lisez-moi.html)_
- _[http://collaboration.cmc.ec.gc.ca/cmc/ensemble/Formation-Training/Read-me.html](http://collaboration.cmc.ec.gc.ca/cmc/ensemble/Formation-Training/Read-me.html)_

## NCEP GEFS configurations

|  | OLD | NEW |
| --- | --- | --- |
| Model | GFS | GFS (current) |
| Initial uncertainty | ETR | ETR |
| Model uncertainty | None | None |
| Tropical storm | Relocation | same |
| Daily frequency | 00,06,12 and 18UTC | same |
| Hi-re control 
(GFS) | T382L64 (d0-d7.5)
T190L64 (d7.5-d16) | same |
| Low-re control (ensemble control) | T126L28 (d0-d16)
00,0612 and 18UTC  | same |
| Perturbed members | 14 for each cycle | 20 for each cycle |
| Forecast length | 16 days (384 hours) | same |
| Implementation  | May 30th 2006 | March 27th 2007 |

_**NCEP GEFS configurations**_

## 2007 Changes - Summary

- Increasing NAEFS (NCEP/GEFS) membership
- 80 perturbations in cycling (see schematic plot)
- Replaced operational 56 perturbations in cycling
- 20 perturbed long forecasts (16-d) in each cycle
- Replaced operational 14 long forecasts in each cycle
- NAEFS (NCEP/GEFS) post-product
- Post-process 20 ensemble forecasts instead of operational 14 ensemble forecasts for:
- Bias correction
- Anomaly forecast

## 6 hours breeding cycle

- T00Z
- 56m

- T06Z
- 56m

- T18Z
- 56m

- T12Z
- 56m

- 6hrs

- Next T00Z

**Re-scaling**

**Re-scaling**

**Re-scaling**

**Up to 16-d**

**Up to 16-d**

**Up to 16-d**

**Up to 16-d**

- T00Z
- 80m

- 6hrs

- T06Z
- 80m

- T12Z
- 80m

- T18Z
- 80m

**Up to 16-d**

**Up to 16-d**

**Up to 16-d**

**Up to 16-d**

**Re-scaling**

**Re-scaling**

**Re-scaling**

**Re-scaling**

-                  Production

-               Planned Change

- Next T00Z

**Re-scaling**

## Ensemble Transform Bred Vector

- P1

- N1

- P2

- N2

- P#, N# are the pairs of positive and negative
- P1 and P2 are independent vectors
- Simple scaling down (no direction change)

- _Ensemble Transform Bred Vector_
- (New)

- P1, P2, P3, P4 are orthogonal vectors
- No pairs any more
- To centralize all perturbed vectors (sum of all vectors are equal to zero)
- Scaling down by applying mask,
- The direction of vectors will be tuned by ET.

- _Rescaling_

**ANL**

**ANL**

**ANL**

- _Bred Vector_
- (Old)

_**P1 forecast**_

_**P4 forecast**_

_**P3 forecast**_

_**P2 forecast**_

- t=t1

- t=t0

- t=t0

- t=t2

- t=t2

- t=t1

- _Rescaling_

## Planned Changes – Dec 2007

- Bias corrected GFS forecast
- Use the same algorithm as ensemble bias correction
- Up to 180 hours
- Combine bias corrected GFS and ensemble forecast
- For short lead time
- GFS has a higher weights at short lead time
- NAEFS new products
- Combine NCEP/GEFS (20m) and CMC/GEFS (20m)
- Produce Ensemble mean, spread, mode, 10% 50%(median) and 90% probability forecast at 1*1 degree resolution
- Produce anomaly forecast from ensemble mean
- Statistical downscaling by using RTMA as reference
- At NDGD resolution (5km), CONUS only
- Generate mean, mode, 10%, 50%(median) and 90% probability forecasts

## CONCEPT OF NAEFS OPERATIONS

**Data exchange**
- Current status
- ~50 selected variables, GRIB1, ftp
- Subset of TIGGE variables
- Plan
- Variables added on annual basis, GRIB2,
- 	direct link
**Basic products**
- Types of products
- Bias corrected fields (35 variables)
- Reduce systematic error
- Combined ensemble (all variables)
- Based on weights (equal weights currently) or other algorithms (Bayesian)
- Anomalies (19 variables)
- Forecasts expressed as percentiles compared to climatological distribution
- Allows downscaling by adding local climatological distribution
- Generation
- Use same algorithms/codes at both MSC and NWS
- Duplicate procedures provide full backup in case of problems at either end
- If one component of ensemble missing, products based on rest of ensemble
- Distribution
- Ftp – e.g., *http://nomad5.ncep.noaa.gov/ncep_data/*

**Systematic Error**

- Before Bias Correction

- After Bias Correction

## End products
Types of products
Site specific
Ensemble-grams (MSC)
Geographically distributed
Host of probabilistic products for various regions (NCEP)
Temporal mean
Week-2 temperature
First joint end product
Generation
Based on common set of basic products

- Types of products
- Site specific
- Ensemble-grams (MSC)
- Geographically distributed
- Host of probabilistic products for various regions (NCEP)
- Temporal mean
- Week-2 temperature
- First joint end product
- Generation
- Based on common set of basic products

- Ensures consistency among end products whether generated
- Jointly or by individual centers
- Distribution
- Web, e.g., *http://meteo.ec.gc.ca/ensemble/index_naefs_e.html*; ftp
**Evaluation / Outreach goals**
- Verification using same algorithms
- Link with Decision Support Systems
- User feedback for improvements

**RPSS**

- Before Bias Correction

- After Bias Correction

**CONCEPT OF NAEFS OPERATIONS - 2**

## RAW DATA & BASIC PRODUCT AVAILABILITY

**RAW DATA & BASIC PRODUCT AVAILABILITY**

*2005, **2006**, **2007**, **2008*

## PRODUCT GENERATION

**PRODUCT GENERATION**

- Basic products
**Bias correction on model grid** against analysis
- Remove lead-time dependent behavior - Cheap
- Add more variables (especially precipitation)
- Develop / test new techniques
- Bayesian methods - Weighting
- Introduce **downscaling onto fine resolution grid**
- After bias correction – Can use more expensive methods
- Independent of forecasts– Low to fine resolution analysis
- NN, MOS, etc (including hires LAM NWP)
- End products
- New applications – Tropical cyclones, hydrology, etc
- Decision Support System collaboration

- Before
- Bias corr.

- After
- Bias corr.

- After Down-scaling

**Systematic error in 1x1 lat/lon U wind forecast**
** ****on 5x5 km grid, 24-hr lead**

*Before downscaling*

*After downscaling*

**Systematic Error**

**Effect of downscaling on**

## GFS bias correction based on an accumulated bias by using decaying average weight (0.02) which is the same as GEFS used

- The absolute errors are reduced after bias correction for 2-meter temperature
- (The stats are accumulated from 0.02 decaying average)

## NAEFS Combined Ensemble, NH

-   Before bias correction

-   After bias correction

**NAEFS Combined Ensemble, NH**

## NAEFS Combined Ensemble, SH

-   Before bias correction

-   After bias correction

**NAEFS Combined Ensemble, SH**

## Example of probabilistic forecast in terms of climatology

- ENSEMBLE 10-, 50- (MEDIAN) & 90-PERCENTILE FORECAST VALUES (BLACK CONTOURS) AND CORRESPONDING _**CLIMATE PERCENTILES (SHADES OF COLOR)**_

## NAEFS products – Forecast percentile values

**NAEFS products – Forecast percentile values**

## Combined GFS and GEFS forecasts at first 180hr

- _GFS has more skill than ensemble control for short lead time_

- Combined GFS and GEFS
- Forecast has more skill (red)
- than GEFS only (black)

## Truth = high resolution analysis
Operational North American Real-Time Mesoscale Analysis (RTMA)
5x5 km National Digital Forecast Database (NDFD) grid (e.g. G. DiMego et al.)
4 variables available: surface pressure, T2m, 10m U and V
Other data can also be used

Downscaling method: apply decaying averaging algorithm

-  Downscaling Vector**5km** = (1-*w*) * prior DV**5km** + *w* * (GDAS**5km** – RTMA**5km**)

* **GDAS**5km**: GDAS **1x1** analysis interpolated to RTMA**5km** grids by using copygb command*
* **4 cycles, individual grid point, DV**5km** = Downscaling Vector on 5km grids*
* **choose different weight: 0.5%, 1%, 2%, 5%, 10%*

-   Downscaling Process

-  Downscaled Forecast**5km** = Bias-corrected Forecast**5km** – DV**5km**

- Truth = high resolution analysis
- Operational North American Real-Time Mesoscale Analysis (RTMA)
- 5x5 km National Digital Forecast Database (NDFD) grid (e.g. G. DiMego et al.)
- 4 variables available: surface pressure, T2m, 10m U and V
- Other data can also be used
- Downscaling method: apply decaying averaging algorithm

*  **Bias-corrected Forecast**5km: **interpolated to RTMA grids by using** **copygb  *
* **subtract DV**5km** from bias-corrected forecast**5km** valid at analysis time*

## Statistical downscaling for NAEFS forecast

- True or reference
- RTMA at 5km resolution
- Variables (surface pressure, 2-m temperature, and 10-meter wind)
- Downscaling vector
- Interpolate GDAS analysis to 5km resolution
- Comparing the difference between GDAS and RTMA
- Apply decaying weight to accumulate this difference – downscaling vector
- Downscaling forecast
- Interpolate NAEFS at 1*1 degree to 5km resolution
- Add the difference to the 5km forecast
- Application
- Ensemble mean, mode, 10%, 50%(median) and 90% forecasts

## 00hr GEFS Ensemble Mean & Bias Before/After Downscaling 10%  

- _  __2m Temperature_

- _  __10m U Wind_

- Before

- After

- Before

- After

## Fcst: 24hr Ensemble Mean  & Bias Before/After Downscaling 10%

- Before

- Before

- After

## 2m Temperature: Continuous Ranked Probability Score (CRPS) 

- Average for 20070212 to 20070404

- Preliminary results:
***Major improvement in skill of fine-scale forecasts:*** Downscaled & bias-corrected ensemble forecasts have significant improvements compared with raw & calibrated forecast for all lead time (downscaled 5+day forecast as skillful as raw 6-hr forecast)
-  10% weighting is better than 2% and 5% weighting in short range. ~30% improvement with 10% weighting for d0-d4. The 2%, 5% and 10% weighting curves are close for long range. Will add more high weights for comparison.
**Limitation:  **
** ****small samples**
** ****more samples needed  **

- _Before downscaling_

- _After downscaling_

## ENSEMBLE-GRAMS

**ENSEMBLE-GRAMS**

**Total Cloud Cover**

**12-hr Accumulated Precipitation**

**2-m Temperature**

**10-m Wind Speed**

## Probability of precipitation over 10 mm at least one day 

**Probability of precipitation over 10 mm at least one day**
- 12-16 December 2006

- Any other end product can be generated based on basic products

## North American Ensemble Forecast System

- 2 m Temperature
- 8 to 14 Day Outlook
- 00z forecast [EXPERIMENTAL]
- Valid: Dec 09 - 15, 2006, Issued: Dec 01, 2006

- Probability of week-2 mean 2m temperature being in lower (shades of blue) or upper (shades of red) climate tercile

*Landshut*

## Meteorological application example
Tropical cyclone forecasting
Link with IWTC – Recent meeting in Costa Rica (Nov. 2006)
Great interest in ensemble / probabilistic forecasting

“Downstream” application example
Hydrological forecasting
Link with HEPEX – June 2007 meeting in Italy
NWS/OHD Ensemble Workshop
Great interest in ensemble forecasting

Decision Support Systems
Feed statistically corrected ensemble trajectories into decision systems
Links with SERA

Training
Strong need on all fronts
Link with WMO/CBS Expert Team on Ensemble Prediction – Meeting in February 2006 

- Tropical cyclone forecasting
- Link with IWTC – Recent meeting in Costa Rica (Nov. 2006)
- Great interest in ensemble / probabilistic forecasting
- “Downstream” application example
- Hydrological forecasting
- Link with HEPEX – June 2007 meeting in Italy
- NWS/OHD Ensemble Workshop
- Great interest in ensemble forecasting
- Decision Support Systems
- Feed statistically corrected ensemble trajectories into decision systems
- Links with SERA
- Training
- Strong need on all fronts
- Link with WMO/CBS Expert Team on Ensemble Prediction – Meeting in February 2006

**FUTURE APPLICATIONS – NAEFS – TIGGE2 - GIFS**

**THORPEX Interactive Grand**
**Global Ensemble (TIGGE)**

**NAEFS – Global Interactive Forecast System (GIFS)**

*OPERATIONS*

*RESEARCH*

*Transfers*
*New methods*

*Articulates*
*operational needs*

## HURRICANE WILMA

**HURRICANE WILMA**
*STRIKE PROBABILITY*
*Probability of storm within 65 nm vicinity of any point on map*

* **Forecast track*
* **Observed track*

*Strike probability =>*

## ENSEMBLE-BASED

**Mississippi, River**
**Vicksburg, MS**
**The Large Basin**
**May 4****th**** case**
- A major mid-range event well   predicted
- Significant spread in
- extended range
**April 1st case**
- Without  a major event,
- all simulations are similar and spread is small.
- Trend and events** **picked up.
- Short lead time dominated
-    by initial condition, showing
-    little spread.
- Spread Increases with time.

**May 4th**

**April 1st**

**----- GEFS members**
**----- GEFS ens. mean**
**----- GEFS control**
**----- GFS high resolution**
**----- NLDAS**

- 0           2              4             6             8            10           12           14           16
-                                        Lead Time  (days)

**ENSEMBLE-BASED**

**Total Cloud Cover**

## THORPEX GRAND GLOBAL ENSEMBLE (TIGGE) PLANS

**TIGGE PHASE-1**
- Existing system
- Design limitations
- Redundancy
- Huge amounts of data to/at 3 centers
- 		Moved daily (no real time access)
- 		Archived permanently
- Non-uniform user data access across centers
**TIGGE PHASE-2**
- Proposed design
- Distributed data access
- Unified user interface
**GIFS (Global Interactive Forecast System)**
- Built on Phase-2 data access & user interface
- Add derived product generation to distribution list
- Expand user interface – web displays of raw/derived data
**OPPORTUNITIES**
- Tropical & extra-tropical storm data for prototyping
- Small data volume
- Large interest in international community
- T-PARC campaign in 2008/09

## TIGGE Phase-1
Data generation distributed
~10 producing centers 
Archiving and data access centralized
3 archive and data access centers
Movement of large amounts of data to archive centers
Limits real time usage
Web portal / procedures vary by archive centers

TIGGE Phase-2
Physically distributed archiving and data access
At ~10 producing centers
Only data requested by user is shipped (directly to user)
Supports real time applications & archive requirements
Common web interface to real time and archived data
Jointly designed and developed web software for data access
Runs at each producing center?
Jointly designed common data / metadata format for exchange
Format of data in archive can be center specific

- Data generation distributed
- ~10 producing centers
- Archiving and data access centralized
- 3 archive and data access centers
- Movement of large amounts of data to archive centers
- Limits real time usage
- Web portal / procedures vary by archive centers
**TIGGE Phase-2**
- Physically distributed archiving and data access
- At ~10 producing centers
- Only data requested by user is shipped (directly to user)
- Supports real time applications & archive requirements
- Common web interface to real time and archived data
- Jointly designed and developed web software for data access
- Runs at each producing center?
- Jointly designed common data / metadata format for exchange
- Format of data in archive can be center specific

**TIGGE MULTI-CENTER ENSEMBLE DATA**

## Built on TIGGE Phase-2 data access/distribution protocol
New element - Product generation 
Transparent whether based on data from one or more centers
Product types
Web display of raw data (real time or archived)
Generation of products derived from raw data for web display or ftp download
Examples
Bias corrected forecasts
Downscaled forecasts
Any question that can be answered using database
Joint probabilities, etc 

Common toolbox
Examples
Data selection tools (NOMADS)
Different algorithms to derive probabilities based on sample from a distribution (AWIPS)
Some tools 
Shared across participating centers
Used only at one or some centers
Meta-tool info and format to facilitate algorithm etc exchange

**New element -** **Product generation**
- Transparent whether based on data from one or more centers
- Product types
- Web display of raw data (real time or archived)
- Generation of products derived from raw data for web display or ftp download
- Examples
- Bias corrected forecasts
- Downscaled forecasts
- Any question that can be answered using database
- Joint probabilities, etc
- Common toolbox
- Examples
- Data selection tools (NOMADS)
- Different algorithms to derive probabilities based on sample from a distribution (AWIPS)
- Some tools
- Shared across participating centers
- Used only at one or some centers
- Meta-tool info and format to facilitate algorithm etc exchange

**GLOBAL INTERACTIVE FORECAST SYSTEM - PLAN**

## CHALLENGES
Design distributed data processing system with components that run
On schedule at each (or some) center(s)
Bias correction of individual forecasts?
Downscaling of individual forecasts
Statistical or dynamical (LAM – where/what is added value?)
Need/role of TIGGE-LAM centers? 
On demand in response to user request at one (or several?) centers
Combination of forecasts based on pre-computed statistics
Generation of derived products
Web display 
Coordinate software development
Pieces developed individually must fit into a pre-designed system
Requests prompting coordinated action possibly at number of centers?
Screening requests due to limitations in
Bandwidth for data stream
Cpu availability
Application software readiness (ever increasing array)
User support
Helps inform / guide users in selecting from multitude of often similar but not identical products
Define meta-product info and design its format
Verification protocol and info included / referenced?
Role of / link with WIS
Links with operations at existing NWP centers
What parts of GIFS can be incorporated?
What new needs to be established at existing centers?
Is there a need for a WMO GIFS super-center (physical or virtual)?

- Design distributed data processing system with components that run
- On schedule at each (or some) center(s)
- Bias correction of individual forecasts?
- Downscaling of individual forecasts
- Statistical or dynamical (LAM – where/what is added value?)
- Need/role of TIGGE-LAM centers?
- On demand in response to user request at one (or several?) centers
- Combination of forecasts based on pre-computed statistics
- Generation of derived products
- Web display
- Coordinate software development
- Pieces developed individually must fit into a pre-designed system
- Requests prompting coordinated action possibly at number of centers?
- Screening requests due to limitations in
- Bandwidth for data stream
- Cpu availability
- Application software readiness (ever increasing array)
- User support
- Helps inform / guide users in selecting from multitude of often similar but not identical products
- Define meta-product info and design its format
- Verification protocol and info included / referenced?
- Role of / link with WIS
- Links with operations at existing NWP centers
- What parts of GIFS can be incorporated?
- What new needs to be established at existing centers?
- Is there a need for a WMO GIFS super-center (physical or virtual)?

**GLOBAL INTERACTIVE FORECAST SYSTEM (GIFS)**

## SUMMARY / EXAMPLES
Phase-1 (exists)
73 gridded variables archived at & accessible from 3 archive centers
24+ hr delay
User interface varies by archive center

Phase-2 (Proposed)
Data archived and accessible from ~10 producing centers
Real time access
Common web user interface
Stages of work
Tropical and extra-tropical storm info
Small amount of data, prototype development
Can this be advanced by T-PARC (Aug 08 – Mar 09)?
Expanded set of gridded variables (superset of Phase-1 variables)

GIFS (Proposed)
Products generated based on distributed data access
On schedule / on demand processing coordinated among centers
Tropical / extra-tropical storm related products considered for prototyping

- Phase-1 (exists)
- 73 gridded variables archived at & accessible from 3 archive centers
- 24+ hr delay
- User interface varies by archive center
- Phase-2 (Proposed)
- Data archived and accessible from ~10 producing centers
- Real time access
- Common web user interface
- Stages of work
- Tropical and extra-tropical storm info
- Small amount of data, prototype development
- Can this be advanced by T-PARC (Aug 08 – Mar 09)?
- Expanded set of gridded variables (superset of Phase-1 variables)
- GIFS (Proposed)
- Products generated based on distributed data access
- On schedule / on demand processing coordinated among centers
- Tropical / extra-tropical storm related products considered for prototyping

**TIGGE PHASE-1, PHASE-2, GIFS**

## OUTLINE / SUMMARY

**PROJECT DESCRIPTION**
**Multi-center ensemble forecast system**
**BENEFITS**
**Joint development, production (cost savings)**
**Better representation of uncertainties (improvements)**
**COMPONENTS**
**20 ensemble members from Canada and USA**
**CONCEPT OF OPERATIONS**
**Exchange raw data (now), basic products (future)**
**Basic products (Bias correction, anomalies)**
**Derived products**
**PLANS**
**Consider links with future GIFS**

## Slide 48

## BACKGROUND

## EXPANSION PLANS – LINK WITH TIGGE-2 / GIFS

**Other centers**
- FNMOC to join by 2008
- After a 1-year experimental data exchange, subject to evaluation
- UK Metoffice, KMA, CMA considers participation
- No detailed plans
- JMA, CPTEC expressed an interest
- ECMWF, NCMRWF want to be informed
**Alternate concept of operations**
- Current operational concept may not suite all parties
**Prototypes for TIGGE-2 / GIFS**
- Science / mechanics
- NAEFS
- IT Technology
- NOMADS
- Flexible access to distributed data
- Web-based product generation

## NOMADS  Design

*NOMADS  Design*

- _PROPOSED:_

- Seamless access across real-time to historical

- Want Data?

**NOAA NCDC Ensemble Archive TIGGE Goals:**
**Backup for Phase-1; Operational server for Phase-2 & GIFS**

## Screen shot of the web page containing prompts and user 

*Screen shot of the web page containing prompts and user *
*entered responses for the probability of frost at day 4 1⁄2.*

- Global Ensemble model data
- is from both the “a” and “b” files
- since June/2006.

- The purpose of this example is to show how to make queries to the server as well as show how to directly  obtain model values in an user application.  We click “yes” to show the temperature queries as they are made.

**Time for coordinated planning for TIGGE Phase-2**

## US CONTRIBUTIONS TO TIGGE - UPDATE

**PROVISION OF NCEP OPERATIONAL ENSEMBLE DATA**
- November 1 of 2006
- 41 of 71 TIGGE variables available
- Existing UCAR UNIDATA feed used
- NCAR changes “operational” to “TIGGE” header
- NCAR to transmit data with reformatted header to ECMWF
- Regular transmission is to be set up
- Reliability of transfer to be assessed
- September 2007
- 70 of 71 variables planned
- NCAR to process additional 30 operationally available variables into TIGGE-required format
- Subject to funding
- NCAR to transmit reformatted data to other archives
- September 2008
- 71 of 71 variables planned to be made available
**PROVISION OF FNMOC OPERATIONAL ENSEMBLE DATA**
- To be pursued next
**NCAR ARCHIVE CENTER**
- December 2006
- 2-3 wks on disk, rest in mass storage
- Access to archived data initially on original grid only
- NCEP, ECMWF, UK Met Office, JMA
- Future enhancements
- CPTEC data in the works, other centers to follow
- Regridding capability and other access software enhancements to follow

## PARADIGM SHIFT IN FORECASTING

- Distinguish clearly between
- Forecast process
- Single value focus OR
- Probability distribution focus AND
- Serving customers’ needs
- Many may not be ready for paradigm shift yet
- Optimal procedure for both
- Forecast process and
- Customer applications
- requires a           ***PROBABILISTIC APPROACH***
- Operational requirements / routine engrained in traditional paradigm
- Must move to new paradigm to
- Improve skill				***Ensemble mean is better than control BECAUSE***
- Potentially enlarge customer base		***Case dependent probability distribution is captured by ensemble***
- While maintaining ability to serve up traditional forecast products
- All existing and new products can be generated based on ensemble
- Numerical ensemble forecasting is favored approach
- Automated ensemble-based forecast process
- Systematic errors to be mitigated via statistical bias correction & downscaling
- Unified approach across scales / applications
- New, empowered role for forecasters
- Adjust entire ensemble (instead of single NWP forecast)
- Direct & quality control adaptive forecast process

## PROPAGATING FORECAST UNCERTAINTY

- z

*Single value*

*Distribution*

- Ensemble Forecasting:
- Central role – bringing the pieces together

**PROPAGATING FORECAST UNCERTAINTY**

## CONFIGURATION, OUTPUT CHARACTERISTICS

**CONFIGURATION, OUTPUT CHARACTERISTICS**

*2005, **2006**, **2007**, **2008*

##     THORPEX GOAL	
Accelerate improvements in skill & utility of high impact forecasts
All improvements related to advances in NWP skill
We must accelerate improvements in NWP skill
Maintain/improve application procedures

**IMPROVEMENT IN PROBABILISTIC SKILL OVER PAST 4 YEARS**

**    ****THORPEX GOAL**
- Accelerate improvements in skill & utility of high impact forecasts
- All improvements related to advances in NWP skill
- We must accelerate improvements in NWP skill
- Maintain/improve application procedures

**NORTH AMERICAN ENSEMBLE**
**FORECAST SYSTEM (NAEFS)**

- Operational multi-center ensemble system
**Significant acceleration** in skill
- Joint ensemble research
- More achieved in one implementation than in previous 4 yrs
*Implementations at participating centers have immediate impact for all participants*
- Shortcutting the typical 2-3 year development path that takes to adapt changes internally

- 1.5-day extension of skill in 4 yrs

- Close to 2-day extension of skill with first NAEFS implementation

- THORPEX – NAEFS
- TO DOUBLE RATE OF IMPROVEMENT

***1***

***2***

***3***

***4***

## CONCEPT OF OPERATIONS

*CURRENT - NAEFS*
- Concept
- Schedule driven
- Central product generation
- Data access
- Exchange all data among participating centers
- Large data transfer volume
- Basic products
- Generate all basic products by all participating centers
- Share all algorithms
- End products
- Generation based on basic products
- Suite of joint and center-specific products

## ALTERNATE CONCEPT OF OPERATIONS

*FUTURE – TIGGE-2 / GIFS*
- Concept
- User driven
- Web-based product generation
- Data access
- Grab selectively only what is needed
- Basic products
- Basic products generated by producing center only
- Hind-casts included if needed
- Share all algorithms
- End products
- Generation based on basic products
- Jointly develop and maintain product generation toolbox
- Web-based product generation

## BACKGROUND

## Ensemble Mean Forecast & Bias Before/After RTMA Downscaling  

- 2% experiments
-  Left top: operational ens. mean and its bias
-  Right top: bias corrected ens. mean and its bias
-  Left bottom: bias corrected ens. mean after downscaling and its bias left toward RTMA** **
- After Downscaling
-  More detailed forecast information
-  Bias reduced, especially high topography areas** **

- Before

- Before

- After

## Accumulated Bias Before/After RTMA Downscaling  

**Black- operational ensemble mean, 2%**
**Pink- bias corrected ens. mean after downscaling, 5%**
**Red- NAEFS bias corrected ensemble mean, 2%**
**Blue-bias corrected ens. mean after downscaling, 2%**
**Yellow-bias corrected ens. mean after downscaling, 10%**

- black

- red

- blue

## BACKGROUND

## BASIC PRODUCTS

- NAEFS basic product list
- Bias corrected members of joint MSC-NCEP ensemble
- 40 members, 35 of NAEFS variables, GRIB2
- Bias correction against each center’s own operational analysis
- Weights for each member for creating joint ensemble
- 40 members, independent of variables, GRIB2
- Weights depend on geographical location (low precision packing)
- Climate anomaly percentiles for each member
- 40 members, 19 of NAEFS variables, GRIB2
- Non-dimensional unit, allows downscaling of scalar variables to any local climatology
- Issues – Products to be added in future years
- Bias correction on precipitation & some other variables not corrected yet)
*Use CMORPH satellite-based analysis of precipitation rates*
- CPC collaborators (J. Janowiak)
- Climate anomalies for missing variables
- Need to process reanalysis data to describe climatology for missing variables

## END PRODUCTS

- End product generation
- Can be center specific
- Need to conform with procedures/requirements established at different centers
- End products generated at NCEP
- Based on prioritized list of requests from NCEP Service Centers
- Graphical products (including Caribbean, South American, and AMMA areas)
- NCEP official web site (gif – NA, Caribbean, SA, AMMA)
- NCEP Service Centers (NAWIPS metafile)
- Gridded products
- NAWIPS grids
- NCEP Service Centers (list of 661 products)
- GRIB2 format
- Products of general interest (Possible ftp distribution, no decision yet on products)
- NDGD (10-50-90 percentile forecast value + associated climate percentile)
- End products generated at MSC
- TBD
- End products generated jointly
- Experimental probabilistic Week-2 forecast
- Fully automated, based on basic products: bias corrected, weighted climate anomalies
- Can become official product once performance reaches current operational level

## ENSEMBLE PRODUCTS - FUNCTIONALITIES

|  | FUNCTIONALITY | CENTRALLY GENERATED | LOCALLY GENERATED | INTERACTIVE
ACCESS |
| --- | --- | --- | --- | --- |
| 1 | Mean of selected members  Done |  |  |  |
| 2 | Spread of selected members Done  |  |  |  |
| 3 | Median of selected values Sept. 2005 |  |  |  |
| 4 | Lowest value in selected members Sept. 2005 |  |  |  |
| 5 | Highest value in selected members Sept. 2005 |  |  |  |
| 6 | Range between lowest and highest values Sept. 2005 |  |  |  |
| 7 | Univariate exceedance probabilities for a selectable threshold value FY06? |  |  |  |
| 8 | Multivariate (up to 5) exceedance probabilities for a selectable threshold value FY06? |  |  |  |
| 9 | Forecast value associated with selected univariate percentile value Sept. 2005 - FY06? |  |  |  |
| 10 | Tracking center of maxima or minima in a gridded field (eg – low pressure centers) Sept. 2005, Data flow FY06? |  |  |  |
| 11 | Objective grouping of members FY08? |  |  |  |
| 12 | Plot Frequency / Fitted probability density function at selected location/time (lower priority) FY07? |  |  |  |
| 13 | Plot Frequency / Fitted probability density as a function of forecast lead time, at selected location (lower priority) FY07? |  |  |  |

- List of centrally/locally/interactively generated products required by NCEP Service Centers for each functionality are provided in attached tables (eg., *MSLP, Z,T,U,V,RH, etc, at 925,850,700,500, 400, 300, 250, 100, etc hPa)*

***Potentially useful functionalities that need further development:***
- - Mean/Spread/Median/Ranges for amplitude of specific features - Mean/Spread/Median/Ranges for phase of specific features

***Additional basic GUI functionalities:**** *- Ability to manually select/identify members - Ability to weight selected members *Sept. 2005*

## ENSEMBLE PRODUCT REQUEST LIST

- NCEP SERVICE CENTERS, OTHER PROJECTS

## Climate percentile (0 = 50 percentile)

## NAEFS & THORPEX

- Expands international collaboration
- Mexico joined in November 2004
- FNMOC to join in 2006
- UK Met Office may join in 2009
- Provides framework for transitioning research into operations
- Prototype for ensemble component of THORPEX legacy forecst system:   		***Global Interactive Forecast System (GIFS)***

**THORPEX Interactive Grand**
**Global Ensemble (TIGGE)**

**North American Ensemble**
**Forecast System (NAEFS)**

*OPERATIONS*

*RESEARCH*

*Articulates*
*operational needs*

*Transfers*
*New methods*

- THORPEX

- RESEARCH

- LEGACY (GIFS)

- OPERATIONAL

## Data exchange
Coordination needed with Yves Pelletier from MSC (Brent Gordon)
Switch to GRIB2 format
New file structure (files containing NAEFS variables only)
Operational transmission arrangements
NCEP pushes its data to MSC
Basic products
Bias correction (Bo Cui, Dave Unger)
First moment method works, accepted for use by both parties
Second moment correction
Moment adjustment & Bayesian Model Averaging, BMA methods to be compared
May or may not be included in 1st operational implementation
Weighting (Bo Cui, Dave Unger)
Skill, Ridging, BMA methods to be compared
Climate anomalies (Yuejian Zhu)
Detailed algorithm to be developed
End product generation
One stream to generate multiple product formats (Dave Michaud)
Start with highest priority items from prioritized list from Service Centers (Z. Toth)
Required NAWIPS tools ready by Sept & Dec 2005 (Maxine Brown)
Default graphical setup to be developed & JIF’d for web display (Maxine Brown)
NAWIPS graphical products using web default display (Dave Michaud)
NAWIPS & GRIB2 product generation as part of one product stream (Dave Michaud)

- Coordination needed with Yves Pelletier from MSC (Brent Gordon)
- Switch to GRIB2 format
- New file structure (files containing NAEFS variables only)
- Operational transmission arrangements
- NCEP pushes its data to MSC
- Basic products
- Bias correction (Bo Cui, Dave Unger)
- First moment method works, accepted for use by both parties
- Second moment correction
- Moment adjustment & Bayesian Model Averaging, BMA methods to be compared
- May or may not be included in 1st operational implementation
- Weighting (Bo Cui, Dave Unger)
- Skill, Ridging, BMA methods to be compared
- Climate anomalies (Yuejian Zhu)
- Detailed algorithm to be developed
- End product generation
- One stream to generate multiple product formats (Dave Michaud)
- Start with highest priority items from prioritized list from Service Centers (Z. Toth)
- Required NAWIPS tools ready by Sept & Dec 2005 (Maxine Brown)
- Default graphical setup to be developed & JIF’d for web display (Maxine Brown)
- NAWIPS graphical products using web default display (Dave Michaud)
- NAWIPS & GRIB2 product generation as part of one product stream (Dave Michaud)

**DETAILS**

## Product distribution
NAEFS basic products (Brent Gordon) 
3 new data sets, in addition to raw NCEP global ensemble data
Use GRIB2, low precision (for weights & climate anomalies) to control resource requirements
Must be made available via ftp for
Community use
Real time forecasts
Archive for research (THORPEX-TIGGE)
Backup in case of problem at either generating center
Resource implications
HPSS disc storage
Ftp servers
NCDC is to post & keep ensemble data?

NAEFS end products
Supercede current global ensemble products based on NCEP ensemble only
As NAESFproducts are introduced, they replace current NCEP products
NCEP official web site 
Public
NAEFS partners/users
Central & South America
Africa (AMMA)
Polar regions (IPY)

- NAEFS basic products (Brent Gordon)
- 3 new data sets, in addition to raw NCEP global ensemble data
- Use GRIB2, low precision (for weights & climate anomalies) to control resource requirements
- Must be made available via ftp for
- Community use
- Real time forecasts
- Archive for research (THORPEX-TIGGE)
- Backup in case of problem at either generating center
- Resource implications
***HPSS disc storage***
- Ftp servers
***NCDC is to post & ***_***keep***_*** ensemble data?***
- NAEFS end products
- Supercede current global ensemble products based on NCEP ensemble only
- As NAESFproducts are introduced, they replace current NCEP products
- NCEP official web site
- Public
- NAEFS partners/users
- Central & South America
- Africa (AMMA)
- Polar regions (IPY)

**DETAILS - 2**

##   Bias correction  

- First moment correction
- choose a fixed weigh factor (2 % as a default), or vary it as a function of lead time and location ( how to determine variations?)
- apply bias correction scheme
- 35 variables ( NCEP & CMC )
- on 1 x1 degree ensemble data (NCEP & CMC )
- on 00z and 12Z (NCEP & CMC, 06 &18Z for NCEP )
- Second moment correction
- may not be included in next spring operational implementation
-   Weighting
- BMA method: only tested for surface temperature
- Use frequency of “best member of ensemble” statistics

- BIAS CORRECTION & WEIGHTING

## CLIMATE ANOMALIES

*Express bias-corrected forecasts (each member) in terms of climate percentile*
- Forecasts bias corrected wrt NCEP & CMC oper. analysis
- 1.0*1.0 (lat/lon) grid
- Climate based on NCEP/NCAR reanalysis data
- 4 cycles (00UTC, 06UTC, 12UTC and 18UTC) per day
- 40 years (Jan. 1st 1959 – Dec. 31th 1998)
- 2.5*2.5 (lat/lon) grid
- Need to consider the systematic difference between reanalysis and oper. analysis (NCEP & CMC respectively)
- Variables (possible to add more)
- Height: 1000hPa, 700hPa, 500hPa, 250hPa
- Temperature: 2m, 850hPa, 500hPa, 250hPa
- Wind: 10m, 850hPa, 500hPa, 250hPa
- PRMSL, max/min temperature

## CLIMATE ANOMALIES

*PROCEDURE** *
- Determine climatological distribution for each day using reanalysis data
- Use first few harmonics to describe annual variations
- Compute all stats for 4 times per day
- Estimate climate mean (first moment)
- Estimate distribution around mean
- Archive data to be used on daily basis
- Determine systematic difference between reanalysis and operational analysis fields
- Use standard NAEFS “bias estimation” method
- Adjust bias corrected NAEFS forecasts by systematic difference between reanalysis & oper. analysis
- Compare bias corrected & adjusted NAEFS forecasts to reanalysis distribution
- Express each forecast as percentile of climate distribution