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From the 1920s to today: How has numerical weather prediction changed dramatically in 60 years?
Numerical Weather Prediction (NWP) uses mathematical models to simulate the atmosphere and oceans to predict weather based on current weather conditions. The technique was first explored in the 1920s, but it was not until the advent of computer simulations in the 1950s that numerical weather forecasts began to produce reliable results. Today, countries run multiple global and regional forecast models using the latest weather observations received from weather detectors, weather satellites and other observing systems.
“Mathematical models are based on the same physical principles and are able to generate short-term or long-term climate predictions and are widely used in understanding and predicting climate change.”
As regional models advance, tropical cyclone track predictions and air quality forecasts have also improved significantly. However, climate models do not perform well when dealing with processes that occur in relatively concentrated areas, such as wildfires. The current numerical weather forecasting technology is supported by the most powerful supercomputers in the world today. Even with the increasing computing power of supercomputers, numerical forecasting models can still only accurately predict up to about six days.
"Factors that affect the accuracy of numerical predictions include the density and quality of the observational data used for forecasting, as well as the inadequacies of the numerical models themselves."
With the improvement of observation technology, the initialization process of the model becomes more and more critical. Current numerical weather forecasting not only requires inputting observational data into the model to generate initial conditions, but also requires the use of data assimilation and objective analysis methods for quality control, extracting useful values from irregular observational data as the starting point of the forecast.
The passage of time has brought about the advancement of meteorological models. From Richardson's first six-hour weather forecast using hand calculations in 1922 to ENIAC's first weather forecast based on simplified atmospheric equations generated by computers in 1950, the history of numerical weather forecasting has come a long way. With the rapid development of computing power.
"In 1956, Norman Phillips developed a mathematical model that could realistically depict the monthly and seasonal variations in the inner troposphere."
From a historical perspective, research and development between the 1950s and 1980s led to significant improvements, coupled with integrated forecasting beginning in the 1990s, in order to address uncertainties in the climate system. . We are gradually beginning to use ensemble forecasts to increase the confidence in our forecasts and make more meaningful predictions about the future.
Currently, numerical weather prediction models rely on accurate input of initial conditions and use equations of fluid dynamics and thermodynamics to predict future meteorological conditions. However, these equations are inherently chaotic, so that even small initial errors can affect the prediction results in an exponentially growing manner, which poses a challenge for long-term predictions.
"Even with specific data and a perfect model, chaotic behavior limits accurate forecasts to about 14 days."
For small-scale or overly complex meteorological processes, the parameterization process in the model plays an important role. This allows processes to be connected to the variables resolved by the model without having to specifically show their physical processes. As technology advances, the accuracy and usefulness of numerical weather forecasts have gradually increased, and this has been expanded in various climate prediction models.
In the development of forecast models, a further challenge is how to deal with the model output statistics (MOS) problem. This process emerged precisely to address the imperfections in the output of numerical weather models by combining sensor observations and climate conditions to make forecast adjustments. However, the outputs of these models may not fully capture changes in ground conditions, making statistical methods particularly important.
“Ensemble forecasting methods involve analyzing multiple forecasts using different physical parameterizations or varying initial conditions.”
Faced with a changing climate and growing challenges, data demands and technology development for numerical weather prediction continue to advance rapidly. How will future forecasts cope with more extreme weather events, ocean-atmosphere interactions, and wider ecological impacts? Let us look forward to and think about the future of predictive technology over time?
