nowcasting

Nowcasting is a short-term weather forecasting approach that predicts conditions from the present up to several hours ahead, based primarily on current observations and rapid-update data rather than longer-range model outputs. It concentrates on rapidly evolving phenomena such as convective storms, heavy rainfall, and fog, where traditional forecasts lose skill quickly.

Origins and development: nowcasting emerged with the expansion of weather radar networks in the late 20th century, which enabled extrapolation of observed precipitation echoes. Over time, techniques evolved to include storm tracking, advection estimates, data assimilation, and the integration of rapid-refresh numerical models to provide more physically consistent guidance.

Methods: radar-based extrapolation and object-tracking monitor the movement and growth of precipitation echoes. Optical flow and velocity-field methods estimate motion and development to extend forecasts a few hours ahead. Satellite infrared imagery can supplement radar data in data-sparse regions. Lightning networks help gauge storm intensity. Modern nowcasting blends physics-based rapid-refresh models with statistical or machine learning approaches, and often uses ensemble methods to express forecast uncertainty.

Applications: nowcasting supports aviation, urban weather services, and emergency management by providing timely warnings for flash floods, severe storms, and precipitation-induced hazards. It informs decision-making for outdoor events, transportation, and solar-energy operations, where near-term conditions are critical.

Limitations: convective systems are highly variable in space and time, and small-scale features can change rapidly, reducing accuracy beyond a few hours. Data gaps, radar clutter, and uncertainties in motion estimation can lead to false alarms or missed events.