Credit: NOAA. They zip around our planet from pole to pole 14 times per day. Because they orbit while the Earth is rotating below, these satellites can see every part of Earth twice each day. By watching these global weather patterns, polar orbiting satellites can help meteorologists accurately predict long-term forecasts—up to 7 days in the future. Polar orbiting satellites get a complete view of Earth each day by orbiting from pole to pole.
Because the Earth spins, the satellite sees a different part of Earth with each orbit. It captures a picture of the entire planet as a series of wedges that then be pieced back together, as in the image above. It provides space weather alerts and forecasts while also monitoring the amounts of solar energy absorbed by Earth every day. These factors are important in making air quality forecasts. Polar orbiting satellites provide the information most useful for long-term weather forecasting.
These satellites use instruments to measure energy, called radiation, emitted by the Earth and atmosphere. This information is incorporated into weather models, which in turn leads to more accurate weather forecasts. But is this skill increase likely to continue into the future? This partly depends on what progress we can make with supercomputer technology.
Faster supercomputers mean that we can run our models at higher resolution and represent even more atmospheric processes, in theory leading to further improvement of forecast skill. However, this has been slowing down recently , so other approaches may be needed to make future progress, such as increasing the computational efficiency of our models. In short, no. The chaotic nature of weather means that as long as we have to make assumptions about processes in the atmosphere, there is always the potential for a model to develop errors.
Progress in weather modelling may improve these statistical representations and allow us to make more realistic assumptions, and faster supercomputers may allow us to to add more detail or resolution to our weather models but, at the heart of the forecast is a model that will always require some assumptions. However, as long as there is research into improving these assumptions, the future of weather forecasting looks bright.
How close we can get to the perfect forecast, however, remains to be seen. Edition: Available editions United Kingdom. Become an author Sign up as a reader Sign in. Jon Shonk , University of Reading. The whether in the weather Forecasting the weather is a huge challenge. Maybe we should stop saying we are sorry and stop saying forecasts are always wrong. We need to stop using the crutch of inevitable forecast error and start having honest conversations and focus on what we can predict and what we can control.
Yes, it really is. But let us start with what constitutes accuracy. Demand variability is an expression of how much the demand changes over time and, to some extent, the predictability of the demand. Forecast accuracy is an expression of how well one can predict the actual demand, regardless of its volatility. This is the difference between trying to precisely predict the exact point and accurately predicting a range or the expected variability. A common example of this is trying to guess the outcome of rolling two fair dice compared to accurately predicting the range of possible outcomes.
For the throw of the two dice, any exact outcome is equally probable and there is too much variability for any prediction to be useful. But the different possibilities for the total of the two dice to add up to are not equally probable because there are more ways to get some numbers than others.
We can accurate predict that While we may not know exactly what will happen, we can exactly predict the probability of it occurring.
And if you predict the outcome within the probabilities, guess what? You are correct. Accurately predicting an outcome within a range of probabilities is more valuable than trying to forecast a single number. Besides being able to more accurately predict the probabilities of outcomes and ranges, we are also providing more relevant and useful information. When you predict the variability, this not only grounds our initiatives in reality but also gives us the power to make better business decisions.
One way to counteract variability is to ask for range forecasts, or confidence intervals. Range forecasts are more useful than point predictions. With a range you are providing four pieces of valuable information: we not only know the point or mean but we also know the top, the bottom, and the magnitude of possible variability. Measuring the reduction in error rather than the increase in accuracy is more valuable to us because there is a stronger correlation between error and business impact than there is between accuracy and business effect.
Now we know how much variability we need to plan for and can better understand the upside or downside risk involved. In addition, accurately predicting uncertainty can add enormous value.
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