The models alone don't provide the final answer. Statistical techniques also are used to convert model output into probabilistic guidance. Among these variables are clouds, cloud water, ice, humidity distributions in 3-D, wind, temperature, turbulent kinetic energy, precipitation, and pressure. Many limitations exist in some of today's equipment and technology. However, using an assimilation scheme, meteorologists often can process and convert some temporally and spatially irregularly located observation data into a decent forecast model grid.
Conditions existing in remote locations can also skew prediction accuracy.
The initial errors that cause the biggest problems are coherent errors in regions without observations, which can develop into meteorologically significant errors. Service overlap and coverage redundancies are all too obvious. Dovetailing of weather-related efforts apparently could produce improved output from the public sector's various weather organizations. The trend is decreasing for sharing information. Weather forecasting models are self-contained sets of computer programs that include some means of analyzing data and computing the evolution of the atmosphere's temperatures, winds, pressures, and moisture based on the analyses.