A Methodology Using Cyclone Tracking
Vref is defined as the maximum 10-minute average of wind speed with a recurrence period of 50 years and is therefore one of the key parameters to be determined in the process of wind resource assessment for the planning of wind farms. Vref is directly related to extreme winds and its value decides the class of wind turbine that can be used on a site. But there is a big problem with the measurements of winds because there are no wind farms with a data record this long. Cyclone tracking provides a possible method of studying the winds experienced at a wind farm site, together with calculating a probability estimate for extreme winds. In this article we describe how CENER analysed the existing database of hurricanes, with data on wind speeds, for a period of more than 50 years.
By Y. loureiro and P. Benito, CENER National Renewable Energy Centre of Spain, Spain
With a combination of the maximum sustained wind speed and the minimum central pressure at landfall, it is possible to study the cyclone intensity (tropical storm or hurricane level 1, 2, 3, 4), the radius of action for cyclones of different intensities, and the future probability of hurricanes in an area of interest. It is important to know that due to the broad radial extent of cyclone winds, wind farms can experience hurricane winds from a cyclone making landfall in a neighbouring area.
Validation is possible when the wind farm registers data in the same period that a hurricane occurs.
Based on more than 100 years of data on the trajectory and impact radius of cyclones, CENER can estimate, with some precision, the probability of an extreme event in a given location (M.D. Powel, S.H. Houston and T.A. Reinhold, 1996). The implemented methodology allows the visualisation of every cyclone’s trajectory and impact radius, from tropical cyclones which occurred in an area to probability forecasts of future extreme events. By undertaking this preliminary study before the wind farm construction, wind energy developers will have an additional tool to help choose the best location and type of wind tower to use in that location. It will be useful, especially in areas at major risk of extreme weather events.
An Example Case
A real case is shown below. The location was Havana, Cuba (see Figure 1). It is possible to study the trajectories and impact radius using Google Earth.
With the previously mentioned parameters calculated (the radius of action for cyclones of different intensities and the probability of future hurricanes in an interest area), we can see that in this region a level 3, 4 or 5 hurricane was recorded in 1948 at a distance of 23.233 kilometres from Havana. Five level 1 or 2 hurricanes and 34 tropical storms were also recorded. Based on this data, the probability of the occurrence of any tropical storm is 19.92%, that for any level 1 or 2 hurricane is 3.22%, and finally the probability of the occurrence of a level 3, 4 or 5 hurricane is 0.65% (in an average year). By looking at the strongest hurricane recorded in the region, we can calculate that a wind speed of 53 m/s was experienced.
Table 1. Hurricane category
Table 2. Cyclone impact probability
Biography of the Author
Yolanda Loureiro Rodríguez has a BSc in Physics. She worked in meteorological forecast production for Spanish Television between 1999 and 2002. Since January 2003 she has been at CENER in the wind energy department working on forecasting models, especially the development of the Local Pred forecasting model (specialising in complex terrain). She has taken part in several European projects (Anemos, POW’WOW, SafeWind…) and national projects.{/access}
Vref is defined as the maximum 10-minute average of wind speed with a recurrence period of 50 years and is therefore one of the key parameters to be determined in the process of wind resource assessment for the planning of wind farms. Vref is directly related to extreme winds and its value decides the class of wind turbine that can be used on a site. But there is a big problem with the measurements of winds because there are no wind farms with a data record this long. Cyclone tracking provides a possible method of studying the winds experienced at a wind farm site, together with calculating a probability estimate for extreme winds. In this article we describe how CENER analysed the existing database of hurricanes, with data on wind speeds, for a period of more than 50 years.By Y. loureiro and P. Benito, CENER National Renewable Energy Centre of Spain, Spain
{access view=!registered}Only logged in users can view the full text of the article.{/access}{access view=registered}Using a tool developed at CENER (the National Renewable Energy Centre of Spain), that includes all hurricane databases (there are six different basins in the world in which cyclones can be formed), it is possible to ascertain the actual wind speeds experienced and the Vref for potential (and existing) wind farms.
MethodologyWith a combination of the maximum sustained wind speed and the minimum central pressure at landfall, it is possible to study the cyclone intensity (tropical storm or hurricane level 1, 2, 3, 4), the radius of action for cyclones of different intensities, and the future probability of hurricanes in an area of interest. It is important to know that due to the broad radial extent of cyclone winds, wind farms can experience hurricane winds from a cyclone making landfall in a neighbouring area.
Validation is possible when the wind farm registers data in the same period that a hurricane occurs.
Based on more than 100 years of data on the trajectory and impact radius of cyclones, CENER can estimate, with some precision, the probability of an extreme event in a given location (M.D. Powel, S.H. Houston and T.A. Reinhold, 1996). The implemented methodology allows the visualisation of every cyclone’s trajectory and impact radius, from tropical cyclones which occurred in an area to probability forecasts of future extreme events. By undertaking this preliminary study before the wind farm construction, wind energy developers will have an additional tool to help choose the best location and type of wind tower to use in that location. It will be useful, especially in areas at major risk of extreme weather events.
An Example Case
A real case is shown below. The location was Havana, Cuba (see Figure 1). It is possible to study the trajectories and impact radius using Google Earth.
With the previously mentioned parameters calculated (the radius of action for cyclones of different intensities and the probability of future hurricanes in an interest area), we can see that in this region a level 3, 4 or 5 hurricane was recorded in 1948 at a distance of 23.233 kilometres from Havana. Five level 1 or 2 hurricanes and 34 tropical storms were also recorded. Based on this data, the probability of the occurrence of any tropical storm is 19.92%, that for any level 1 or 2 hurricane is 3.22%, and finally the probability of the occurrence of a level 3, 4 or 5 hurricane is 0.65% (in an average year). By looking at the strongest hurricane recorded in the region, we can calculate that a wind speed of 53 m/s was experienced.
| Wind velocity (m/s) | Tropical Storm (Radius (km) | Hurricane level 1-2: Radius (km) | Hurricane Level 3-4-5: |
| 33.53 | 90 | 30 | 0 |
| 54.09 | 227 | 76 | 32 |
Table 1. Hurricane category
| Number of tropical storm impacts: | 34 |
| Impact probability of one tropical storm | 17.79% |
| Impact probability of any tropical storm | 19.92% |
| Number of level 1 or 2 hurricane impacts | 5 |
| Impact probability of one level 1 or 2 hurricane impact | 3.16% |
| Impact probability of any level 1 or 2 hurricane impact | 3.22% |
| Number of level 3, 4 or 5 hurricane impacts | 1 |
| Impact probability of one level 3, 4 or 5 hurricane impact | 0.65% |
| Impact probability of any level 3, 4 or 5 hurricane impact | 0.65% |
Table 2. Cyclone impact probability
Biography of the Author
Yolanda Loureiro Rodríguez has a BSc in Physics. She worked in meteorological forecast production for Spanish Television between 1999 and 2002. Since January 2003 she has been at CENER in the wind energy department working on forecasting models, especially the development of the Local Pred forecasting model (specialising in complex terrain). She has taken part in several European projects (Anemos, POW’WOW, SafeWind…) and national projects.{/access}
