Abstract Accurately understanding the evolution and development of cloud physical properties (CPP) in advance is crucial for extreme weather forecasting and early warning. This study utilized the Fourier neural operator (FNO) method to develop a short‐term forecasting model of Cloud (Cloud‐FNO). Using the multi‐task learning framework and autoregression strategy, the model achieves accurate 6‐hr forecasting of cloud phase (CLP), cloud top height (CTH), cloud effective radius (CER), and cloud optical thickness (COT). Evaluation results on the independent testing data set show that the Cloud‐FNO model achieves an average CLP identification accuracy exceeding 74%, and the average root mean square errors for CTH, CER, and COT forecasts are 2.28 km, 6.52 μm, and 9.01, respectively. Importantly, the Cloud‐FNO model demonstrates strong forecasting capability and promising application potential for the CPP evolution under severe weather.
Last updated: April 16, 2026 01:25 AM
CO₂
431.9ppm
+1.9
/ year
CH₄
1945.85ppb
+8.44
/ year
Temp
+1.48°C
Peak +1.73°C
Population
8.1B
+67M
/ year