scholarly journals Review to the paper ESSD-2020-38: Rescue and quality control of sub-daily meteorological data collected at Montevergine Observatory (Southern Apennines), 1884-1963

2020 ◽  
Author(s):  
Alba Gilabert Gallart
Keyword(s):  
Quality Control ◽  
Meteorological Data ◽  
Southern Apennines

scholarly journals Rescue and quality control of sub-daily meteorological data collected at Montevergine Observatory (Southern Apennines), 1884–1963

2020 ◽  
Vol 12 (2) ◽  
pp. 1467-1487
Author(s):  
Vincenzo Capozzi ◽  
Yuri Cotroneo ◽  
Pasquale Castagno ◽  
Carmela De Vivo ◽  
Giorgio Budillon
Keyword(s):  
Quality Control ◽  
Atmospheric Pressure ◽  
Meteorological Data ◽  
Tolerance Test ◽  
Rate Of Change ◽  
Distribution Model ◽  
Water Vapour Pressure ◽  
Southern Apennines ◽  
Meteorological Observations ◽  
Standard Practices

Abstract. Here we present the rescue of sub-daily meteorological observations collected from 1884 to 1963 at Montevergine Observatory, located in the Southern Apennines in Italy. The recovered dataset consists of 3-daily observations of the following atmospheric variables: dry-bulb temperature, wet-bulb temperature, water vapour pressure, relative humidity, atmospheric pressure, cloud type, cloud cover, rainfall, snowfall and precipitation type. The data, originally available only as paper-based records, have been digitized following the World Meteorological Organization standard practices. After a cross-check, the digitized data went through three different automatic quality control tests: the gross error test, which verifies whether the data are within acceptable range limits; the tolerance test, which flags whether values are above or below monthly climatological limits that are defined in accordance with a probability distribution model specific to each variable; and the temporal coherency test, which checks the rate of change and flags unrealistic jumps in consecutive values. The result of this process is the publication of a new historical dataset that includes, for the first time, digitized and quality-controlled sub-daily meteorological observations collected since the late 19th century in the Mediterranean region north of the 37th parallel. These data are critical to enhancing and complementing previously rescued sub-daily historical datasets – which are currently limited to atmospheric pressure observations only – in the central and northern Mediterranean regions. Furthermore, the Montevergine Observatory (MVOBS) dataset can enrich the understanding of high-altitude weather and climate variability, and it contributes to the improvement of the accuracy of reanalysis products prior the 1950s. Data are available on the NOAA's National Centers for Environmental Information (NCEI) public repository and are associated with a DOI: https://doi.org/10.25921/cx3g-rj98 (Capozzi et al., 2019).

scholarly journals Rescue and quality control of sub-daily meteorological data collected at Montevergine Observatory (Southern Apennines), 1884–1963

2020 ◽  
Author(s):  
Vincenzo Capozzi ◽  
Yuri Cotroneo ◽  
Pasquale Castagno ◽  
Carmela De Vivo ◽  
Giorgio Budillon
Keyword(s):  
Quality Control ◽  
Atmospheric Pressure ◽  
Meteorological Data ◽  
Tolerance Test ◽  
Rate Of Change ◽  
Distribution Model ◽  
Water Vapour Pressure ◽  
Southern Apennines ◽  
Meteorological Observations ◽  
Standard Practices

Abstract. Here we present the rescue of sub-daily meteorological observations collected from 1884 to 1963 at Montevergine Observatory, located on the Italian Southern Apennines. The recovered dataset consists of three daily observations of the following atmospheric variables: dry bulb temperature, wet bulb temperature, water vapour pressure, relative humidity, atmospheric pressure, cloud type, cloud cover, rainfall, snowfall and precipitation type. The data, originally available only as paper-based records, have been digitized following the World Meteorological Organization standard practices. After a cross-check, the digitized data went through three different automatic quality control tests: the gross error test which verifies if the data are within acceptable range limits; the tolerance test that flags if values are above or below monthly climatological limits which are defined in accordance with a probability distribution model specific for each variable; and the temporal coherency test that checks the rate of change flagging unrealistic jumps in consecutive values. The result of this process is the publication of a new historical dataset that includes, for the first time, digitized and quality-controlled sub-daily meteorological observations collected since the late 19th century in the Mediterranean region north of the 37th parallel. These data are critical to enhance and complement previously rescued sub-daily historical datasets in central and northern Mediterranean regions, currently limited to the atmospheric pressure observations only. Furthermore, MVOBS dataset can enrich the understanding of high altitude weather and climate variability and contributes to improve the accuracy of reanalysis products prior the 1950s. Data are available on the NOAA’s National Centers for Environmental Information (NCEI) public repository and are associated to a DOI (https://doi.org/10.25921/cx3g-rj98) (Capozzi et al., 2019).

scholarly journals CrowdQC+—A Quality-Control for Crowdsourced Air-Temperature Observations Enabling World-Wide Urban Climate Applications

2021 ◽  
Vol 9 ◽  
Author(s):  
Daniel Fenner ◽  
Benjamin Bechtel ◽  
Matthias Demuzere ◽  
Jonas Kittner ◽  
Fred Meier
Keyword(s):  
Quality Control ◽  
Air Temperature ◽  
World Wide ◽  
Meteorological Data ◽  
Urban Climate ◽  
Specific Humidity ◽  
Data Sets ◽  
User Friendliness ◽  
Weather Stations ◽  
Further Development

In recent years, the collection and utilisation of crowdsourced data has gained attention in atmospheric sciences and citizen weather stations (CWS), i.e., privately-owned weather stations whose owners share their data publicly via the internet, have become increasingly popular. This is particularly the case for cities, where traditional measurement networks are sparse. Rigorous quality control (QC) of CWS data is essential prior to any application. In this study, we present the QC package “CrowdQC+,” which identifies and removes faulty air-temperature (ta) data from crowdsourced CWS data sets, i.e., data from several tens to thousands of CWS. The package is a further development of the existing package “CrowdQC.” While QC levels and functionalities of the predecessor are kept, CrowdQC+ extends it to increase QC performance, enhance applicability, and increase user-friendliness. Firstly, two new QC levels are introduced. The first implements a spatial QC that mainly addresses radiation errors, the second a temporal correction of the data regarding sensor-response time. Secondly, new functionalities aim at making the package more flexible to apply to data sets of different lengths and sizes, enabling also near-real time application. Thirdly, additional helper functions increase user-friendliness of the package. As its predecessor, CrowdQC+ does not require reference meteorological data. The performance of the new package is tested with two 1-year data sets of CWS data from hundreds of “Netatmo” CWS in the cities of Amsterdam, Netherlands, and Toulouse, France. Quality-controlled data are compared with data from networks of professionally-operated weather stations (PRWS). Results show that the new package effectively removes faulty data from both data sets, leading to lower deviations between CWS and PRWS compared to its predecessor. It is further shown that CrowdQC+ leads to robust results for CWS networks of different sizes/densities. Further development of the package could include testing the suitability of CrowdQC+ for other variables than ta, such as air pressure or specific humidity, testing it on data sets from other background climates such as tropical or desert cities, and to incorporate added filter functionalities for further improvement. Overall, CrowdQC+ could lead the way to utilise CWS data in world-wide urban climate applications.

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