Continuity of daily temperature time series in the transition from conventional to automated stations for the Colombian coffee network

• Published in: Meteorological applications Vol. 29; no. 3
• Main Authors: Ramírez Carabalí, Carolina, Sarmiento Herrera, Ninibeth Gibelli, Imbachi Quinchua, Luis Carlos, García López, Juan Carlos
• Format: Journal Article
• Language: English
• Published: Chichester, UK John Wiley & Sons, Ltd 01-05-2022; John Wiley & Sons, Inc; Wiley
• Subjects: additive constant; Additives; automatic weather stations; Automation; Bias; Coffee; Continuity; conventional weather station; daily temperature; Daily temperatures; Homogeneity; Mapping; Mathematical analysis; Maximum temperatures; meteorological observation; parallel observations; quantile mapping; Statistical analysis; Statistical methods; temperature bias; Temperature differences; Temperature gradients; Temperature measurement; Temperature requirements; Weather; Weather stations
• ISSN: 1350-4827; 1469-8080
• DOI: 10.1002/met.2054

The transition from conventional weather stations (CWSs) to automated weather stations (AWSs) of the Colombian coffee network has required testing their performance and adjusting the temperature measurements to ensure the continuity of the historical CWS series. In this study, the mean (Tmean), minimum (Tmin), and maximum temperature (Tmax) measurements of CWS and AWS operating in parallel at 36 locations between 2014 and 2019 were compared, and the biases of the daily temperature differences (CWS − AWS), the agreement index (d), and the percentage of data within the allowed range (PR05) were calculated. The most consistent method for calculating Tmean and Tmax for CWS was selected for use on the AWS data. With the standard normal homogeneity test and with the metadata, we found that the series of temperature differences between CWS and AWS was not homogeneous, instrument failures and sensor changes being the main causes of the lack of homogeneity. The statistical analyses indicated that the AWS data need to be adjusted to be continuous with the CWS series. To correct the temperature bias, two approaches were applied: quantile mapping and the additive constant. The results suggest that the quantile mapping adjustments improve the average bias at all stations but do not necessarily bring the percentage to within ±0.5°C. In Tmin and Tmean, 12 AWSs can give continuity with the historical series of the CWS, and for the rest of the stations and variables, the series of the AWSs are independent of the CWSs.
The method to calculate the mean and maximum temperature in the automatic meteorological stations was selected to have a better concordance with the records of the conventional stations, and the automatic stations that give continuity to the historical series of the conventional network were specified. In the temperature variable, most of the automatic stations that operated in parallel with the conventional station have an independent series and will not give continuity to the historical series.