Weather forecasting is the classic inexact science, relying on the complex mutual interactions of wind, currents, precipitation, tides, humidity and temperature variations, and a million other variables across a planet that’s rotating on its axis, revolving around its heat source, and tilted about its plane of revolution.
The work of the public weather service covers a broad spectrum, from the issuing of weather forecasts and warnings for the protection of the general public (including health-related hazards such as heat waves or cold spells), to the provision of timely support to weather-sensitive economic sectors.
The emergence of new, innovative and technologically advanced forecast and communication systems improves public weather services and effectively integrates the dissemination of information and service delivery. More specifically, digital forecasting integrates transparent weather forecast dissemination and service delivery to its users.
The advanced numerical weather prediction methods of assimilation, visualisation and information processing techniques help to generate forecast products rapidly and disseminate them in a timely matter. From the ‘60s through today, the increasing power and ubiquity of high-end computer system has allowed a quickly improving ability to process these large data sets.
Recent developments in information technology such as XML (Extensible Markup Language), CAP (Common Alerting Protocol) and RSS (Real Simple Syndication) allow the use of fast telecommunication networks including broadband, wireless and mobile systems to improve public weather services (Wankhede et al., 2014).
Together these technologies create a holistic forecast delivery and warning dissemination service, and an all-hazard decision support process that best serves the users.
Location-specific weather forecasts are heterogeneous due to the extreme complexity of microscale interactions of atmospheric phenomena that are spread over macroscale distances. Moreover, the topography, hydrology and structural features of a particular location are too specific to be included in regional models, yet these are significant variables in micro-weather events.
But weather forecasting is too imperative to be left to chance, and humans have been doing our best to predict the weather throughout history. How much closer have our sophisticated technology and global communication advances brought us to useful predictability?
Numerical weather prediction models provide quantitative values of the weather parameters at specific locations and times, depending on the model domain, resolution and the interval of model output. Numerical weather prediction (NWP) involves constructing a three-dimensional grid of the atmosphere using data on the atmosphere’s current state, and using a mathematical model to predict its evolution into future states.
Taking readings on the temperature, humidity, barometric pressure, wind speed and direction, and precipitation for each point on that grid, and repeating that process every 10 minutes or so, provides a stream of data that feeds into mathematical algorithms that seek to predict future states. These algorithms’ success is later analyzed and revised to improve predictive quality over time.
Those methods have become reasonably reliable in short-term forecasting of one to three days: If the weatherman calls on Thursday for weekend rain, cancel that picnic. But for longer-range prediction, chaos theory suggests that this incredibly complex system, so dependent on initial conditions, starts to diverge quickly, and the fifth or sixth day of that ‘Seven-day forecast’ is significantly less reliable than the third and fourth.
The chaotic nature of the atmosphere, the errors involved in measuring the initial conditions, and a limited understanding of atmospheric processes result in less accurate forecasts, which worsen as the forecast period increases. In the late 1960s and early 1970s, the US National Weather Service (NWS) began implementing guidance products that objectively interpreted the output of numerical weather prediction (NWP) models (Carter & Polger, 1986).
Thornes (1996) reported the accuracy of public forecasting in the United Kingdom, but it was limited to one verification parameter, i.e. per cent correct. Bickel & Kim (2008) examined the reliability of the probability of precipitation (PoP) forecasts produced by The Weather Channel (TWC) over 14 months for 42 locations in the USA. They observed decreasing forecast performance with lead time and a worsening during the warm season (April– September). Brooks et al. (1997) verified approximately 1 year’s forecasts from the NWS, along with those from major media outlets, for Oklahoma City.
According to National Oceanic and Atmospheric Administration U.S. Department of Commerce that ‘seven-day forecast can accurately predict the weather about 80 per cent of the time and a five-day forecast can accurately predict the weather approximately 90 per cent of the time. However, a 10-days or more extended forecast is only right about half the time.
A weather forecast can give you an excellent idea of what to expect. A seven-day forecast can accurately predict the weather about 80 per cent of the time, and a five-day forecast can accurately predict the weather approximately 90 per cent of the time.
However, a 10-day—or longer—the forecast is only right about half the time. Meteorologists use computer programs called weather models to make forecasts. Since we can’t collect data from the future, models have to use estimates and assumptions to predict future weather. The atmosphere is changing all the time, so those estimates are less reliable the further you get into the future.
On the other hand, Ignitia is the world’s first and most accurate tropical weather forecasting company. With over 84% reliability, Ignitia’s proprietary forecasting model predicts tropical weather patterns down to a 3 km square range. The forecasts are delivered to West African farmers via SMS in partnership with mobile network operators. We provide the most accurate, location-specific weather forecast for the tropics.
Brooks HE, Witt A, Eilts MD. 1997. Verification of public weather forecasts available via the media. Bull. Am. Meteorol. Soc. 78: 2167–2177.
Thornes JE. 1996. The quality and accuracy of a sample of public and commercial weather forecasts in the U.K. Meteorol. Appl. 3: 63–74.
Bickel JE, Kim SD. 2008. Verification of the weather channel probability of precipitation forecasts. Mon. Wea. Rev. 136: 4867–4881.
Carter GM, Polger PD. 1986. A 20-year summary of National Weather Service verification results for temperature and precipitation. NOAA Technical Memorandum. NWS FCST-31, 50 pp.
Wankhede P, Sharma R, Pote C. 2014. A review on weather forecasting systems using different techniques and web alerts. Int. J. Adv. Res. Comput. Sci. Software Eng. 4(2): 357.
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