Curso

TRAINING COURSE ON
CLIMATE INFORMATION, APPROACHES AND TOOLS FOR ASSESSING AND MANAGING CLIMATE RISKS

General Information and Preliminary Course Program

Place:

Headquarters of the Brazilian National Institute of Meteorology (INMET)
Brasilia, Brazil

Date:

November, 19 to 23, 2007

Language:

English (50%), Portuguese and Spanish (the possibility of simultaneous interpretation is still being analyzed)

Sponsors:

Instituto Nacional de Meteorologia(INMET)
International Research Institute for Climate and Society (IRI)
World Meteorological Organization (WMO)

Collaboration:

Centro de Previsão do Tempo e Estudos Climáticos (CPTEC) /
Instituto Nacional de Pesquisas Espaciais (INPE)

Main Instructor:

Professor Tony Barnston (International Institute for Climate and Society – IRI, USA)

Co-Instructor:

Caio Coelho, PhD (CPTEC, Brazil)
Christopher Castro, MSc (CPTEC, Brazil)
Paulo Sérgio Lucio, PhD (UFRN, Brasil)

Teaching Assistants:

Mozar Salvador, MSc(INMET, Brazil)
Danielle Ferreira, MSc (INMET, Brazil)

Invited Presentations:

Alexandre Araújo Costa, PhD (FUNCEME, Brazil)
Marcelo Schneider. MSc (INMET, Brazil)

General Coordination:

INMET: Dr. Lauro T. G. Fortes
IRI: Dr. Walter Baethgen

Target Audience:

Staff members from Meteorological Services, Universities and other institutions working in Seasonal Climate Forecasts from Brazil and other countries in LAC.

Agenda

Monday, November 19

8:30 – 09:00 Welcome, Instructions and Informs

8:30 – 12:30 (including a 20 minute coffee break)

Basic concepts on seasonal climate forecast

Why it is possible to make seasonal forecast (main concepts that support the seasonal forecast) – Barnston
Introduction to the techniques used by CPTEC and INMET in their monthly meeting to produce the seasonal climate analysis and forecast (Christopher and Mozar)
The purpose here was to prepare the participants to better follow the the Summer Seasonal Climate Outlook Meeting that took place in the afternoon.

The Wavelets Technique and its Use for Intraseasonal Forecast (Marcelo Schneider)

14:00 – 17:00

Meeting for climate analysis and the 2007 December-January-February (Summer) climate forecast, with the participation of experts from IRI, CPTEC, INMET and meteorological state centers.

Tuesday, November 20

8:30 – 12:30

Overview of techniques employed for seasonal climate forecast

Dynamic models (Christopher)

         - Global atmosphere circulation models (2 tier forecasts)
         - Ocean-atmosphere coupled models (1 tier)
         - Dynamic regional models (downscaling)

Statistical methods in climate prediction (Barnston)
The Use of Downscaling and of Regional Dynamic Models at Funceme (Alexandre Costa)

14:30 – 18:30

Forecast calibration, combination (multi-model ensemble) and verification

Basic concepts (Caio)
Brier score and Reliability Diagram (Caio)

Wednesday, November 21

8:30 – 12:30

Forecast calibration, combination (multi-model ensemble) and verification

Calibration and combination at IRI (operational Multi-model) (Barnston)
Heidke score, RMSSS, RPSS, ROC, Gerrity score (Barnston)

14:30 – 18:30

Examples of application of seasonal climate forecast in:

Agriculture
Water Management

Discussion of an Alternative Methodology to Assign Tercile Probabilities at the Final Stage of the Climate Outlook Meeting (Lauro Fortes)

Thursday, November 22

8:30 – 12:30 (including 20 min Coffee-Break)

Presentation of the Climate Predictability Tool (CPT), including examples to demonstrate the use of the software

14:30 – 16:00

CPT Presentation Continues

16:30 – 18:30

Hands-on Practice: students make use of real data do practice the use of CPT

Friday, November 23

8:30 – 10:00 - Hands-on Practice continues

10:30 - 11:30 - A Conceptual and Intuitive Overview of PCR and CCA (Paulo Lucio)

11:30 - 15:00 - Practice continues

15:00 -- 16:00 - Voluntary presentations by students of the results of their practices

16:00 – 16:30 - Certificates award and closing ceremony

16:30 -- 17:00 - Farewell Coffee

Presentations Used at the 2007 Training Course

Selected Technical References

Some Climate Forecasting and Verification References
by Anthony Barnston – July 2006

Barnston, A. G., M. Chelliah, and S. B. Goldenberg, 1997: Documentation of a highly ENSO-related SST region in the equatorial Pacific. Atmosphere-Ocean, 35, 367-383.
This paper demonstrates that the Nino3.4 region best represents the ENSO phenomenon, particularly in terms of ENSO’s effects on the global climate.
Barnston, A. G., Y. He, and D. Unger, 2000: A forecast product that maximizes utility for state-of-the-art climate prediction. Bull. Amer. Meteor. Soc., 81, 1271-1279.
This paper introduces the idea of a probability of exceedance graph as a forecast product coming from a more general probability forecast.
Barnston, A. G., S. J. Mason, L. Goddard, D. G. DeWitt, and S. E. Zebiak, 2003: Multimodel ensembling in seasonal climate forecasting at IRI. Bull. Amer. Meteor. Soc., 84, 1783-1796.
This paper shows how the IRI used a multi-model ensembling method, in 2003.
Epstein, E. S., 1969: A scoring system for probability forecasts of ranked categories. J. Appl. Meteor., 985-987.
This paper introduces the ranked probability skill score for probability forecasts. (It is also described in detail in the appendix of Goddard et al. 2003.)
Gandin, L. S., and A. H. Murphy, 1992: Equitable skill scores for categorical forecasts. Mon. Wea. Rev., 120, 361-370.
This paper discusses scoring systems that do not have features that allow forecasters to use “tricks” to help their mean score when there is no real forecast skill.
Gerrity, J. P., 1992: A note on Gandin and Murphy’s equitable skill score. Mon. Wea. Rev., 120, 2709-2712.
This paper introduces a skill score that measures the skill in discriminating among the cases within the sample being scored, rather than correctly forecasting the deviation of the mean in the sample being scored relative to the overall climatological mean.
Goddard, L., S. J. Mason, S. E. Zebiak, C. F. Ropelewski, R. Basher, and M. A. Cane, 2001: Current approaches to seasonal to interannual climate predictions. Int. J. Climatol., 21, 1111-1152.
Goddard, L., A. G. Barnston, and S. J. Mason, 2003: Evaluation of the IRI's "Net Assessment" seasonal climate forecasts: 1997-2001. Bull. Amer. Meteor. Soc., 84, 1761-1781.
This paper shows the skills of the IRI’s climate forecasts for the first four years of IRI forecasting.
Gong, X., A. G. Barnston, and M. N. Ward, 2003: The effect of spatial aggregation on the skill of seasonal precipitation forecasts. J. Climate, 16, 3059-3071.
This paper shows how the skill of precipitation forecasts increases as the region of aggregation is increased from a single gage to a large region, limited by the size of the region that has a similar climate response to an SST anomaly.
Hanssen, A. W., and W. J. A. Kuipers, 1965: On the relationship between the frequency of rain and various meteorological parameters. Koninklijk Nederlands Meteorologisch Institut, Meded. Verhand., 81, 2-15.
This paper develops the Hanssen and Kuipers skill score, one of the verification measures used in the CPT.
Mason, I., 1982: A model for assessment of weather forecasts. Aust. Meteor. Mag., 30, 291-303.
This paper is the original exposition of Relative Operating Characteristics (ROC).
Mason, S. J., and L. Goddard, 2001: Probabilistic precipitation anomalies associated with ENSO. Bull. Am. Meteor. Soc., 82, 619-638.
This paper examines empirically probabilistic composites for the precipitation effects of ENSO, and the statistical significance of the deviations from random outcomes. The examination is done for four three-month periods of the calendar.
Mason, S. J., and N. E. Graham, 2002: Areas beneath the relative operating characteristics (ROC) and levels (ROL) curves: statistical significance and interpretation. Quart. J. Roy. Meteor. Soc., 128, 2145-2166.
This paper introduces a statistical significance test for the ROC area as a skill score.
Mason, S. J., and G. M. Mimmack, 2002: Comparison of some statistical methods of probabilistic forecasting of ENSO. J. Climate, 15, 8-29.
This paper applies many statistical methods to forecast ENSO-related SST. Several of these methods are not well-known.
Murphy, A. H., 1988: Skill scores based on the mean square error and their relationships to the correlation coefficient. Mon. Wea. Rev., 116, 2417-2425.
This paper discusses, and develops mathematically, the 3 components that contribute to the mean square error, which is a very general expression of the lack of accuracy.
Potts, J. M., C.K. Folland, I. T. Joliffe and D. Sexton, 1996: Revised “LEPS” scores for assessing climate model simulations and long-range forecasts. J. Climate, 9, 34-53.
This paper develops the linear error in probability space (LEPS), a verification measure for probabilistic forecasts of either the categorical or continuous type.
Richman, M. B., 1986: Rotation of principal components. J. Climatol., 6, 293-335.
This paper illustrates many options for rotating original EOFs (or principal components), and what purpose each option is intended to serve.
Robertson, A. W., U. Lall, S. E. Zebiak, and L. Goddard, 2004: Improved combination of multiple atmospheric GCM ensembles for seasonal prediction. Mon. Wea. Rev., 132, 2732-2744.
This paper illustrates the application of a Bayesian method to form multi-model ensembles from several GCM forecasts, as used in the IRI during 2003 and 2004.
Wilks, D. S., 1995: Statistical Methods in the Atmospheric Sciences. Academic Press, 467 pp.
This is a long and detailed book about statistics and verification of weather or climate forecasts. It contains a vast multitude of material.
Woodcock, F., 1976: The evaluation of yes/no forecasts for scientific and administrative purposes. Mon. Wea. Rev., 104, 1209-1214.
This paper discusses the Hanssen and Kuipers skill score, which it calls a discriminant, and explains the advantages and the fairness of this score for yes/no (or “success/fail”) forecasts.
Barnston, A. G., 1992: Correspondence among the Correlation, RMSE, and Heidke Forecast Verification Measures; Refinement Measures. Weather and Forescating. Notes and Correspondence, Vol. 7, 699-709.
Barnston, A. G. , Mason, S. J., Goddard, L., Dewitt, D. G. , AND Zebiak, S. E., 2003: Multimodel Ensembling in Seasonal Climate Forecasting at IRI. American Meteorological Society, 1783-1796 pp.

Complementary Material

Data for CPT Exercices

Presentation of Students' Selected Works (in the order of enrollment)