Seasonal Forecasting of Tropical Cyclones, Tropical Rainfall and the El Nino-Southern Oscillation

PRINCIPAL INVESTIGATOR
Dr. Christopher W. Landsea

OTHER PERSONNEL:
Prof. William M. Gray, Colorado State University
Prof. Paul. W. Mielke, Jr., Colorado State University
Prof. Kenneth J. Berry, Colorado State University
Dr. John A. Knaff, Colorado State University
Mr. Rick Taft, Colorado State University

To investigate statistical seasonal forecasting methodologies and to issue real-time seasonal predictions for various tropical meteorology/oceanography phenomena, including Atlantic basin hurricanes, African rainfall and the El Nino/Southern Oscillation phenomena.

The understanding of climate variations on a year to year basis has potentially large positive impacts for communities around the world. To be able to say, with even a modest degree of skill, whether - for example - the coming rainy season will be quite wet or whether a drought may be expected instead, would be quite valuable in making preparations for such events. The meteorology and oceanography in the tropical latitude belts can be seen as fortunate in that seasonal forecast skill does appear to be possible up to a year or more in the future, as opposed to the mid-latitudes where little skill is available. Thus it is logical to take advantage of such a situation and pursue avenues of predictability that can be both useful and of interest to the public community.

Research is first undertaken to reveal which physical phenomena can be utilized as skillful predictors for the seasonal parameter that one wishes to make forecasts of. This preliminary work involves analyzing composite fields of the potential predictors, running Empirical Orthogonal Functions to isolate the various modes of variability, and utilizing partial correlations to remove those predictors which are simply mirroring the associations found in other predictors. Once a pool of potential predictors have been isolated, then a combination of the Least Absolute Deviations (LAD) and the Ordinary Least Squares (OLS) regressions are run to obtain the best fit of predictors to predictands. Estimates of future skill are obtained either by running cross-validated regressions over the predictors/predictands or by using an empirical estimate of the skill shrinkage on future (independent) data. The various prediction schemes should then be run in real-time (and provided to those who are interested) as independent testing is essential to give true estimates of skill.

• Real-time seasonal forecasting of Atlantic hurricanes have been performed at Colorado State University for over a dozen years. In that time, these predictions have demonstrated real-time skill over persistence and climatology.
  

• Forecasts currently are issued in early December (of the previous year), in early April, in early June (at the start of the hurricane season), and in early August (at the beginning of the active portion of the hurricane season). Predictands include the number of named storms, hurricanes, intense (or major) hurricanes, the duration that these storms last, and the Net Tropical Cyclone activity parameter - an overall measure as a percentage of normal tropical cyclone activity. Future efforts include making probabilistic forecasts of hurricane landfall along portions of the United States coast line, Mexico and the Caribbean Islands.

• Real-time seasonal forecasts of North African rainfall have been issued at Colorado State since 1992. North African rainfall can be categorized by the West and Central Sahel and the Guinea Coast regions. The Sahel is the area along northern edge of the Inter-tropical Convergence Zone that experiences large swings of rainfall from year to year.

  Wet season (June-September) rainfall variations in the Sahel are crucial for total agricultural production. The Guinea Coast has larger absolute amounts of rainfall and subsequently less susceptibility to extreme drought periods. Forecasts for the North African rainfall are issued in late November (of the previous year) and in early June (at the start of the rainy season).

• A real-time prediction scheme for the El Nino-Southern Oscillation has been recently developed that only uses an optimal combination of CLImatology and PERsistence (the ENSO CLIPER model). This scheme is to be utilized as a baseline methodology against with other more sophisticated forecast tools can be tested. The model provides forecasts out to two years in advance of the Nino 1+2, 3, 3.4 and 4 SST regions as well as the Southern Oscillation Index at the beginning of every month.

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  KEY REFERENCES:

  http://tropical.atmos.colostate.edu/forecasts/index.html
  http://typhoon.atmos.colostate.edu/~knaff/ENSO/Cliper.html

  • Landsea, C. W., 1997: Tropical cyclones, ENSO and seasonal predictability. Accepted as a chapter for the book El Nino: Impacts of Multiscale Variability on Natural Ecosystems and Society, edited by H. F. Diaz and V. Markgraf.

  • Mielke, Jr., P.W., K.J. Berry, C.W. Landsea, and W.M. Gray, 1996: Artificial skill and validation in weather forecasting. Wea. Forecasting, 11, 153-169.

  • Landsea, C.W., W.M. Gray, P.W. Mielke, Jr., and K.J. Berry, 1994: Seasonal forecasting of Atlantic hurricane activity. Weather, 49, 273-284.

  • Gray, W.M., C.W. Landsea, P.W. Mielke, Jr., and K.J. Berry, 1994: Predicting Atlantic basin seasonal tropical cyclone activity by 1 June. Wea. Forecasting, 9, 103-115.

  • Gray, W.M., C.W. Landsea, P.W. Mielke, Jr., and K.J. Berry, 1993: Predicting Atlantic basin seasonal tropical cyclone activity by 1 August. Wea. Forecasting, 8, 73-86.

  • Landsea, C.W., W.M. Gray, P.W. Mielke, Jr., and K.J. Berry, 1993: Predictability of seasonal Sahelian rainfall by 1 December of the previous year and 1 June of the current year. Preprints of the 20th Conference on Hurricanes and Tropical Meteorology, San Antonio, TX, Amer. Meteor. Soc., 473-476.

  • Gray, W.M., C.W. Landsea, P.W. Mielke, Jr., and K.J. Berry, 1992: Predicting Atlantic seasonal hurricane activity 6-11 months in advance. Wea. Forecasting, 7, 440-455.

  • Gray, W.M., and C.W. Landsea, 1992: African rainfall as a precursor of hurricane-related destruction on the U.S. East Coast. Bull. Amer. Meteor. Soc., 73, 1352-1364.

  • Henderson-Sellers, A., H. Zhang, G. Berz, K. Emanuel, W. Gray, C. Landsea, G. Holland, J. Lighthill, S-L. Shieh, P. Webster, K. McGuffie, 1998: Tropical cyclones and global climate change: A post-IPCC assessment. Bull. Amer. Meteor. Soc.,79, 19-38.
  • Knaff, J. A., and C. W. Landsea, 1997: An El Nino-Southern Oscillation CLImatology and PERsistence (CLIPER) Forecasting Scheme. Wea. Forecasting, 12, 633-652.
  • Landsea, C.W., 1997: Comments on "Will greenhouse gas-induced warming over the next 50 years lead to higher frequency and greater intensity of hurricanes?". Tellus, 49A, 622-623.
  • Landsea, C.W., N. Nicholls, W.M. Gray, and L.A. Avila, 1997: Reply to Comments on "Downward trends in the frequency of intense Atlantic hurricanes during the past five decades". Geo. Res. Letters, 24, 2205.
  • Landsea, C. W., G. D. Bell, W. M. Gray, and S. B. Goldenberg, 1998: The extremely active 1995 Atlantic hurricane season: Environmental conditions and verification of seasonal forecasts. Mon. Wea. Rev., 126, 1174-1193.
  • Nicholls, N., C. W. Landsea, and J. Gill, 1998: Recent trends in Australian tropical cyclone activity. Met. Atmos. Phys., 65, 197-205.
  • Pielke, Jr., R. A., and Landsea, C. W., 1998: Normalized U. S. hurricane damage, 1925-1995. Wea. Forecasting, 13, 621-631.

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