Quality Control Program for Real-Time Hourly Temperature Observation in Taiwan

Abstract

This paper introduces a quality control (QC) program for the real-time hourly land surface temperature observation developed by the Central Weather Bureau in Taiwan. There are three strategies involved. The first strategy is a range check scheme that inspects whether the observation falls inside the climatological limits of the station to screen out the obvious outliers. Limits are adjusted according to the station’s elevation. The second strategy is a spatial check scheme that scrutinizes whether the observation falls inside the derived confidence interval, according to the data from the reference stations and the correlations among the stations, to judge the reliability of the data. The scheme is specialized, as it employs the theorems of unbiased and minimum error estimators to determine the weights. The performance evaluation results show that the new method is in theory superior to the spatial regression test (You et al.). The third strategy is a temporal check scheme that examines whether the temperature difference of two successive observations exceeds the temperature variation threshold for judging the rationality of the data. Different thresholds are applied for the data observed in different times under different rainfall conditions. Procedurally, the observation must pass the range check first and then go through the spatial or the temporal check. The temporal check is applied only when the spatial check is unavailable. Post-examinations of the data from 2014 show that the QC program is able to filter out most of the significant errors.

Homogenization of a combined hourly air temperature dataset over Romania

“…Four automated data QC tests were performed in order to assess the quality of the hourly air temperature measurements (Estévez et al, 2011;Steinacker et al, 2011), namely: climatic limits consistency (Cheng et al, 2016), persistence (Zahumenský, 2004;Schroeder et al, 2005), step (Lott, 2004) and spatial consistency test (Shafer et al, 2000).…”

Research on quality control methods for surface temperature observations via spatial correlation analysis

“…For temporal dimension, Lanzante proposed the Biweight average method and used the dual weight standard deviation to find out the potential outliers (Lanzante, 1996). Combining spatial and temporal consistency methods, Cheng performed a hybrid quality control method to find out the potential outliers in the surface temperature observations in Taiwan (Cheng et al ., 2016). In recent years, machine learning algorithms had been employed to build the quality control model.…”

Preliminary Data Validation and Reconstruction of Temperature and Precipitation in Central Italy

“…To date, the increasing development of computer technology has generated automated systems for the analysis of meteorological and climatological data. Some investigations have considered data in real-time at hourly or semi-hourly scale (for example, [8][9][10]) in order to detect the error immediately, while other studies of automatic analyses for quality control were based on daily data [11]. There are objective difficulties in quality control through daily precipitation data because of the spatial discontinuity of the variable.…”