Forecasting the future state of the atmosphere relies on various methods and models to predict weather conditions. These techniques range from sophisticated computer simulations of atmospheric physics to simpler, statistical approaches. Forecasters must select the appropriate method based on the time horizon of the prediction, whether it is a short-term outlook or a long-range projection. The goal is always to provide the most accurate assessment of future temperature, precipitation, and wind patterns for a specific location.
Climatological Forecasting: Predicting Weather Using Averages
The method that relies solely on historical averages to predict future weather is called Climatological Forecasting. This technique assumes that the most likely weather on any given day will be the average of all past weather conditions recorded for that same day. Unlike modern Numerical Weather Prediction (NWP) models, which require real-time data about the current state of the atmosphere, this statistical approach operates without any current inputs. The climatological forecast for a location is static and unchanging; the prediction for July 15th next year is identical to the forecast for July 15th this year.
This purely statistical prediction is derived from a concept known as “climate normals.” It is one of the most basic forms of weather prediction, bypassing the complex mathematics of fluid dynamics that govern the atmosphere. Because it does not account for any current atmospheric dynamics, it offers a baseline expectation of what the weather should be. The forecast is essentially a statement of probability, suggesting that the weather will be closer to the historical average than to an extreme, sudden event.
Defining and Applying Climate Normals
The foundation of climatological forecasting is the calculation and application of climate normals. These normals are standard reference values for meteorological variables, such as average daily temperature, total monthly precipitation, and mean sea-level pressure. To maintain consistency and relevance, the World Meteorological Organization (WMO) mandates that these averages be calculated over a uniform 30-year period. In the United States, for example, the current standard uses data spanning from 1991 to 2020, with updates occurring every decade. The 30-year window is designed to be long enough to smooth out year-to-year variability while remaining short enough to reflect the current climate.
If the average high temperature recorded on October 5th over the 30-year period was 68°F, the climatological forecast for October 5th would be a high of 68°F. This allows the normals to act as a benchmark, providing context for whether a specific day is “warmer than normal” or “wetter than normal.” For monthly and annual values, the normal is a simple average of the 30-year period’s data, but daily normals often use a more sophisticated statistical technique. This process, such as a harmonic fit, dampens the effect of outliers and ensures a smooth transition between the normal values of adjacent days. These calculated normals are used by various sectors, including agriculture for planting schedules and energy companies for planning seasonal usage.
When This Method is Most and Least Useful
Climatological forecasting is most useful when predicting conditions far into the future, such as for a seasonal outlook. Over a long period, the statistical average becomes a more reliable predictor than a specific, day-by-day forecast from a dynamic model. It provides a generalized expectation that is valuable for strategic planning, such as for water resource management or setting government budgets for snow removal. In regions with highly stable weather patterns, this method can offer a reasonably accurate baseline prediction.
However, this method is nearly useless for short-term, day-to-day weather predictions. Because it makes no reference to current atmospheric conditions, it cannot predict a sudden cold front, a developing thunderstorm, or any other weather event driven by real-time physics. Relying solely on climatology during an extreme event, like a blizzard or a hurricane, would lead to a severely inaccurate forecast. It is best used as a comparison tool or a foundational estimate, not as a replacement for contemporary, dynamic forecasting models.