ABSTRACT:The monitoring of hydro-meteorological variables for operational and research purposes requires accurate measurements. The reliability of such measurements varies depending on the need to meet different requirements in several sectors, including aviation, agriculture, civil protection, entertainment and weather forecast. In the case of rain gauges, many factors and variables affect the measurement of liquid and solid precipitation in the field. Calibration is a primary tool for quality control and requires suitable infrastructure to perform the tests, and is done by using properly evaluated testing methods and procedures. The current study presents the analysis of a typical calibration system for tipping-bucket rain gauges, using the gravimetric method, in accordance with the recommendations and requirements of both meteorology and metrology. As a result, the uncertainty contribution of each component of the system and an assessment of the resulting overall uncertainty budget are obtained.
Different kinds of sensors compose a meteorological observation system that measures meteorological variables. Sensors can collect data for a long period of time in a high sampling frequency. Some meteorological parameters can be determined by making measurements that ranges from a few seconds to annual measurements which depends on the kind of equipment and application needs. In this scenario, data management is not a trivial task due to heterogeneity, large amount of data and also to the usage of proprietary software for data gathering and handling. We used a data acquisition system (datalogger) to collect and store data from a thermo-baro-hygrometer, and a pyranometer, which were calibrated previously in the laboratory. This paper aimed to analyze the open source Elasticsearch, Logstash and Kibana (ELK) stack to capture, transform, enrich, store, index, select relevant time slots and generate graphs that were integrated in a dashboard for combined visualization and analysis. Additionally, we explored its capacity to embed metadata from sensors and correct data based on a calibration certificate, also showing some relevant graphics. In this weather application, we observed that this set of computational tools are well suited to manage the daily difficulties in handling meteorological data and metadata.
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