Nowcasting and Statistical Forecasting

For the next few hours, the fastest path to a good forecast is often not a physics model but the radar echo extrapolated forward and statistics that translate model output into local weather.

Definition

Nowcasting and statistical forecasting are the techniques that produce short-range and locally calibrated weather predictions by extrapolating recent observations and by applying statistical relationships to numerical model output rather than by direct physical simulation alone.

Scope

This topic covers very short-range forecasting and statistical forecast methods, including the extrapolation of radar and satellite imagery for nowcasting, model output statistics and statistical post-processing that calibrate and localize model guidance, analog and regression techniques, and emerging machine-learning approaches.

Core questions

• How are radar and satellite observations extrapolated to forecast the next few hours?
• How do statistical methods convert raw model output into local forecasts?
• How are model biases corrected and forecasts calibrated against observations?
• What roles do analog, regression, and machine-learning methods play?

Key theories

Nowcasting by extrapolation

At very short range, tracking and extrapolating observed features such as radar echoes and satellite cloud patterns often outperforms numerical models, which need time to spin up convective detail.

Statistical post-processing

Model output statistics and related methods relate numerical-model predictors to observed weather through statistical relationships, correcting systematic biases and producing calibrated, location-specific forecasts including probabilities.

Mechanisms

Nowcasting identifies coherent features in rapidly updated radar and satellite data and projects their recent motion and evolution forward over minutes to a few hours, sometimes blending in early model output as the lead time grows. Statistical forecasting instead treats numerical-model fields as predictors and uses regression, analogs, or machine learning, trained on past forecast-observation pairs, to correct biases, downscale to specific sites, and produce calibrated deterministic and probabilistic forecasts of variables the model represents only crudely.

Clinical relevance

Nowcasting provides the rapid warnings of thunderstorms, heavy rain, and flash floods on which aviation, public safety, and event management depend, while statistical post-processing turns coarse model output into the reliable, site-specific forecasts delivered to the public and used in automated decision systems.

History

Statistical forecasting grew from early regression and analog methods and was formalized as model output statistics by Glahn and Lowry in the 1970s, becoming a standard bridge between numerical models and local forecasts. Nowcasting advanced with weather radar and geostationary satellites, and both areas are now being reshaped by machine-learning techniques trained on large observational and model datasets.

Key figures

• Harry Glahn
• Daniel Wilks

Seminal works

• glahn1972
• wilks2011

Frequently asked questions

What is the difference between nowcasting and forecasting?

Nowcasting is very short-range prediction, typically out to a few hours, made mainly by extrapolating current radar and satellite observations, whereas longer-range forecasting relies primarily on numerical models of atmospheric physics.

Why are statistics applied to model output?

Numerical models have systematic biases and represent local conditions only coarsely; statistical methods trained on past forecasts and observations correct these biases and tailor the output to specific places and variables, including probabilities.