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Middle-Range Ensemble Forecast at CPTEC/INPE - Current Activities

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Presentation on theme: "Middle-Range Ensemble Forecast at CPTEC/INPE - Current Activities"— Presentation transcript:

1 Middle-Range Ensemble Forecast at CPTEC/INPE - Current Activities
Morning, my name is Christopher and I am the team leader of the Global Ensemble Prediction Group. My intention with this presentation is to show you a summary of our main activities and results. The objective of this presentation is to bring the audience closer to the CPTEC activities. Christopher Cunningham Ensemble Prediction Group Modeling and Development Division CPTEC/INPE

2 OUTLINE 1. TIGGE 2. Local Ensemble Transformed Kalman Filter
3. New method to obtain perturbed initial conditions 4. Combination of diferent parametrizations First I will present the status of our activities regarding TIGGE. Then I will update you about the schedule regarding the Local Ensemble Transformed Kalman Filter for generation of the analysis. The third topic intends to inform you about our next version of the current CPTEC’s Ensemble Prediction System. The last three topics are matter of research. We are investigating the potential of combine different parameterizations of the same model to produce an ensemble forecast. The EFI has been investigated as a manner to anticipate probabilities of extreme events. Finally I will show some results related to a simple method of producing ensemble from our daily runs 5. Extreme Forecast Index ( EFI ) 6. Coupled GCM Ensemble

3 We are retrieving KWBC and ECMWF
TIGGE We are retrieving KWBC and ECMWF The download rate is 2G/hour Currently we are capable of keep ½ TB which corresponds approximately to 7 days of data Currently, besides collaborate sending the forecast outputs we are also retrieveing Afterwards, tape backup Oldest backup is from December 2010

4 Local Ensemble Transformed Kalman-Filter ( LETKF )
Currently CPTEC is running a stable data assimilation cycle with an horizontal resolution T062 and 28 levels. At this point this is a test cycle, assimilating only conventional data. Up to the end of the year the Data Assimilation Group will begin tests including satellite retrievals (mostly radiances). Afterwards the group will proceed the tests increasing horizontal and vertical resolutions. CPTEC is currently migrating to a new method to produce analysis: the Local Ensemble Transformed Kalman Filter. ULTIMATE GOAL: to have an operational analysis until middle This analysis will have an horizontal resolution of 299 waves and 64 levels.

5 Toward an improved method of perturbation of the initial conditions
The main changes are: Perturb midlatitudes (20N-90N + 20S- 90S) in addition to perturbation in the tropics Perturb surface pressure Perturb specific humidity Based on Mendonca and Bonatti (2009)

6 Combination of different parameterizations
We have integrated 126 ensembles, 15 members each to investigate the potential of an ensemble of parametrizations

7 Anom. Correlation – T850 – Tropics
Combination of different parameterizations Anom. Correlation – T850 – Tropics 10-30/Nov/2008 RMSE – T850 – Tropics 10-30/Nov/2008

8 Anom. Correlation – U850 – Tropics
Combination of different parameterizations Anom. Correlation – U850 – Tropics 10-30/Nov/2008 RMSE – U850 – Tropics 10-30/Nov/2008

9 Extreme Forecast Index ( EFI )
Based on the ideia proposed by Lalaurette (2003) Measures the difference between the forecast ed probabilistic distribution and the climatological (model) distribution We developed such index for surface wind and precipitation Currently we are evaluating the results

10 Extreme Forecast Index ( EFI )

11 Extreme Forecast Index ( EFI )

12 Ensemble prediction with CGCM
CGCM – extended weather 30 days forecast 2 members per day (00 and 12 UTC) Resolution Atmos: T126L28 Ocean: ¼ x ¼ lat-lon, 10S-10N, Atlantic sector, 2 deg. extratropics O-A Coupling latitute belt: 65S – 65N Prognostic fields: SLP, Geopot. Height, Temperature, Precip., SST

13 Ensemble prediction with CGCM
There is some potential to increasing predictability regarding certain variables and regions Forecasts after fifth day shows that anomaly correlation for the ensemble mean is substantially higher that the deterministic forecast This is not true for every variable and region of the planet. There are some promising results, other not that promising…


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