A new technique that integrates low dimensional model (LDM) based on proper orthogonal decomposition (POD) and the flight data of a commercial aircraft is proposed to realize real-time prediction of wind and atmospheric turbulence for aviation safety and efficiency. The proposed technique sequentially assimilates flight data into LDM and predicts the wind and atmospheric turbulence at lower computational cost than the general numerical weather prediction (NWP). Actual experiments were conducted for two cases: First, weather conditions of an extratropical cyclone approaching Japan, and second, stationary front in the sea near Japan. The actual experiments consisted of two cases: under the condition of an extra-tropical cyclone approaching Japan (Case 1) and a stationary front at Pacific Ocean near Japan (Case 2). In Case 1, the proposed method was able to produce matches between the areas predicted for turbulence and the locations where turbulence was actually encountered. The proposed method is able to correct these spatiotemporal uncertainties by using the flight data. In Case 2, NWP predicted weaker wind than the flight data, and the difference between the wind rates of the NWP and the flight data was about 10 ms−1 at 55 min after the take-off, which is the time of maximum wind magnitude by the flight data. The proposed method was able to correct this difference, and predict the maximum wind magnitude accurately.