SPATIAL CONSISTENCY QUALITY CONTROL OF DAILY SURFACE AIR TEMPERATURE OBSERVATIONS IN VIETNAM USING IDW AND RANDOM FOREST

Abstract

This study developed and evaluated a spatial consistency quality-control method for daily surface air temperature observations from 186 SYNOP stations across Vietnam during 1961–2023. The variables examined included daily mean (Tavg), minimum (Tmin), and maximum (Tmax) temperatures. The proposed framework combines inverse distance weighting (IDW) with a machine-learning (ML) model. IDW serves as the baseline reference, whereas ML is employed to better capture nonlinear relationships associated with spatial location, elevation, seasonality, and climatic region. Final quality-control (QC) flags were derived from the complete outputs of both models, allowing alerts to be stratified into method-specific detections, concurrent detections by IDW and ML, and high-suspicion cases. The ML model consistently outperformed IDW for all three variables. Its mean absolute error was 0.47°C for Tavg, 0.59°C for Tmin, and 0.73°C for Tmax, compared with 0.58°C, 0.74°C, and 1.00°C, respectively, for IDW. Under the combined IDW–ML framework, 98.87–99.10% of the evaluated observations were classified as good quality, while alert rates ranged from 0.90% to 1.13% and high-suspicion cases accounted for only 0.05–0.13%. Approximately one-third of all alerts were detected concurrently by both methods, thereby narrowing the subset of stations requiring priority manual review. These findings demonstrate that the combined IDW–ML approach provides an effective framework for spatial quality control of daily temperature observations, particularly in networks characterized by complex terrain, uneven station distribution, and island stations, as in Vietnam.