There are also large year-to-year variations in the mean forecast error, depending upon the average initial latitude of storms. Errors in the initial position and motion of the tropical cyclone can have an impact on the accuracy of the forecast. Errors in forecast, although showing a slow and steady decrease, are still substantially large.
The National Hurricane Center's official track average forecast errors during the year period to ranged from approximately 91 km for the hour forecasts to km for hour forecasts.
The official intensity errors during the 6-year period to for similar forecast hours range from approximately 3 kt to 19 kt as shown in Table 2.
Forecast Interval hr 0 12 24 36 48 72 Track in km 26 91 Intensity in mph 3 7 10 13 16 19 Fixing the current cyclone position and intensity is the first step in a making a track and intensity forecast. Since the forecast quality is dependent on the accuracy of this data, considerable care is needed in the analysis stage. Highly accurate positioning is especially important for short-range forecasts in critical situations, such as near landfall, but large position errors have resulted in major forecast failures at all times.
The centre or location of a tropical cyclone is a function of both how we choose to define it and the type of observations used.
Surface pressure, wind circulation centre, and the cloud system centre are parameters used to position the cyclone centre. In many cases, these positions are not coincident. Surface observations, satellites, and land-based radar are the most common method used to locate the centre and determine the intensity. Occasionally, reconnaissance aircraft are used to supplement these data. Weak and developing systems are a particular analysis problem as they may be sheared or contain multiple centres.
During this period, one centre may tend to dominate for a period, but then be displaced by a separate centre. Major forecast errors can be made by analysts following an incorrect feature or local circulation centre during satellite analysis. The average official forecast error for the initial position is 26 km, while the initial intensity error is about 3 kt.
However, errors of the initial position for individual systems may range from 10 km in the case of a radar fix of a good eye to more than km in the case of a satellite fix of a poorly defined centre.
The initial intensity estimates may be in error by as much as 30 knots, particularly when using satellite imagery. One method of objectively defining the uncertainty inherent in cyclone forecasting is to utilise strike probabilities, derived from knowledge of past cyclones and forecast errors in the region of interest.
In the USA, this can involve the mass evacuation of a million or more people. Reinforced aircraft, fitted with various scientific instruments, fly through and over tropical storms to collect data. In the USA, this can involve the mass evacuation of a million or more people.
Reinforced aircraft, fitted with various scientific instruments, fly through and over tropical cyclones to collect data. This can be used to help track and predict the path of a tropical cyclone. Changes in the certainty of weather forecasts will have significant implications for future emergency response planners. To maintain confidence in forecasts, we can continue to develop more accurate numerical weather prediction models; this means improving understanding of atmospheric physics, together with increasing computing capability.
Another critical area for development is observations such as satellite remote sensing, as we require an accurate picture of the current state of the weather to predict it several days into the future. However, observational data will always have limitations.
Still, cyclone prediction relies on this data as storm intensity and sea temperature are linked. Uncertain initial conditions create forecast uncertainties that grow as the forecast runs. The technique of data assimilation, where models are run over time and continuously adjusted with new observations, goes a long way to reduce these uncertainties and has been a recent revolution in forecasting.
Assimilating newly available ocean data during the forecasts has a considerable potential to improve the prediction of tropical cyclones. Given the challenges of improving weather forecasts in an uncertain future, understanding and predicting extreme weather is a major area of current research.
The World Climate Research Program selected the topic as one of its Grand Challenges , allowing meteorologists and climate scientists worldwide to connect and tackle the challenge of better documenting, understanding, and simulating extreme weather events. For tropical cyclones, the focus is on improving storm intensity predictions; a particular priority is to increase forecasting capability in developing countries, which are hit hardest in terms of risk.
By improving our understanding of the complex systems driving storm intensification, we can better forecast hazards such as high winds, rainfall, and storm surges, and increase our capacity to cope with a changing climate.
Further reading and resources. Global trends in tropical cyclone risk scientific paper, Nature Climate Change. Climate change indicators: tropical cyclone activity website, United States Environmental Protection Agency. Advances in weather prediction article, Science.
Map of extreme weather events attributed to climate change interactive map, Carbon Brief. Previous blogs.
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