Weather prediction accuracy as increased over the last several decades with a few quantum leaps that resulted in much greater accuracy. Examples of the quantum leaps in weather prediction include the first use of weather satellites, the first use of radar, the first use of Doppler radar, use of computerized forecast models, the use of ensemble model predictions, use of the network of rawinsondes, increasing use of mesoscale forecast data, and wind profilers. Each of these quantum leaps is also accompanied with a more or less steady improvement of each of these. Examples of these improvements include better resolution, better coverage, integration of more weather data, better integration of physical processes, a more rapid updating of data, a more realistic use of the meteorological equations and a greater number of models to compare potential outcomes. Improvements will continue as technology improvements, data is sampled and updated more rapidly, improvements are made to models, newer state of the art satellite and radar systems are developed, coverage improves and physical processes in the computer models more accurately reflect reality. There is a limit to how much weather prediction can improve but we are at a current stage where improvements can continue to occur. Below are examples of improvements that can be expected over the next several decades if increased technology and funding for these improvements are a priority: 1. Faster lead times for tornado and severe thunderstorm warnings 2. Better accuracy with not only short term forecasts but long term forecasts also 3. A more accurate cone of influence prediction for hurricanes 4. High and low temperature predictions that are more accurate 5. Less “surprises” from severe weather for weather forecasters 6. A more detailed forecast with respect to how the temperature, cloud cover and precipitation threat changes throughout the day 7. Greater accuracy in storm system (low pressure) development, movement and intensity. A few problems that will continue to occur that will eventually need to be addressed include: 1. Relative lack of data over ocean areas 2. Relative lack of data from countries where weather data collection is not as advanced 3. Continued unrealistic expectation for much better forecasts even when forecast accuracy improves 4. Weather prediction not being a funding priority. Increasing accuracy in many ways results in a need for greater funding. 5. Mesoscale resolution |