Quality Control for Second-Level Radiosonde Data Based on Bezier Curve Fitting

Abstract. The balloon-borne radiosonde observations provide high-resolution profile observations of pressure, temperature, relative humidity, and winds from the surface to the middle stratosphere. These observations help validate space-based data and are used in climate research, weather forecasting. For the large amount of second-level radiosonde data, it is tedious and time-consuming for the manual quality control (QC). Furthermore, varying experiences and different judgment standards may lead to inconsistent judgments for abnormal data. To address these issues, we propose a two-stage QC method for second-level radiosonde data based on the Bezier curve fitting. In the stage QC1, the gross errors are filtered out according to the measurement range of the sensors, the change rates and extreme temperature values based on pressure segmentations. Also, the algorithm of the longest descending sequence(LDS) is used to identify the moment of sounding termination and eliminate items after that moment. In the stage QC2, we score each item with deviations calculated using Bezier curve fitting, and then use a decision tree model ,CART, to identify anomalies in second-level radiosonde data. The experiment results first demonstrate the efficacy of QC at each step, and finally validate the rationality of our method by comparing the statistical characteristics before and after QC. After QC, the error items are greatly reduced, and the percentile profile distribution of temperature, pressure and relative humidity becomes more reasonable. The overlap of items identified by manual QC and automatic QC reaches 86 %, verifying the effectiveness of our method. This research significantly boosts QC efficiency and unifies the QC standards, providing quality assurance for various applications.