Bias Assessment and Random Forest-Based Correction of Temperature Observations from the Level-Drift Phase of Ascent-Drift-Descent Radiosonde System

Abstract. To facilitate the quantitative application of Ascent-Drift-Descent Radiosonde System (ADDRS) temperature data from the level-drift phase in numerical weather prediction (NWP) models, this study performs a comprehensive quality assessment of the temperature observations using the fifth generation European Centre for Medium-Range Weather Forecasts reanalysis (ERA5) temperature field data as a reference. The results indicate that, due to solar radiative heating, the temperature biases between the level-drift observations and the ERA5 are positive during daytime and tend to increase with the solar elevation angle (SEA). During nighttime, temperature biases are slightly negative. Statistical analysis of temperature biases reveals that the biweight mean values are 3.48 K (daytime) and −0.91 K (nighttime), with corresponding biweight standard deviations of 5.3 K and 2.3 K. Thus, the daytime biweight standard deviation meets the World Meteorological Organization (WMO) "threshold" target, while its nighttime counterpart meets the WMO breakthrough target. Furthermore, a random forest-based temperature bias correction model is developed. After correction, the biweight mean values of temperature biases decrease to 0.52 K (daytime) and −0.23 K (nighttime), and the biweight standard deviations decrease to 2.03 K (daytime) and 1.54 K (nighttime). Both evaluation metrics meets the WMO breakthrough target, with the nighttime metrics notably exceeding it. The probability distribution function of the corrected biases aligns more closely with a normal distribution, demonstrating the effectiveness of the random forest-based model in correcting the biases of level-drift temperature observations. This research establishes a critical foundation for the future application of ADDRS data assimilation in NWP models.