Articles | Volume 17
https://doi.org/10.5194/asr-17-153-2020
https://doi.org/10.5194/asr-17-153-2020
22 Jul 2020
 | 22 Jul 2020

TITAN automatic spatial quality control of meteorological in-situ observations

Line Båserud, Cristian Lussana, Thomas N. Nipen, Ivar A. Seierstad, Louise Oram, and Trygve Aspelien

Related authors

Evaluation of forecasts by a global data-driven weather model with and without probabilistic post-processing at Norwegian stations
John Bjørnar Bremnes, Thomas N. Nipen, and Ivar A. Seierstad
Nonlin. Processes Geophys., 31, 247–257, https://doi.org/10.5194/npg-31-247-2024,https://doi.org/10.5194/npg-31-247-2024, 2024
Short summary
Exploratory analysis of citizen observations of hourly precipitation over Scandinavia
Cristian Lussana, Emma Baietti, Line Båserud, Thomas Nils Nipen, and Ivar Ambjørn Seierstad
Adv. Sci. Res., 20, 35–48, https://doi.org/10.5194/asr-20-35-2023,https://doi.org/10.5194/asr-20-35-2023, 2023
Short summary
Ensemble-based statistical interpolation with Gaussian anamorphosis for the spatial analysis of precipitation
Cristian Lussana, Thomas N. Nipen, Ivar A. Seierstad, and Christoffer A. Elo
Nonlin. Processes Geophys., 28, 61–91, https://doi.org/10.5194/npg-28-61-2021,https://doi.org/10.5194/npg-28-61-2021, 2021
Short summary
seNorge_2018, daily precipitation, and temperature datasets over Norway
Cristian Lussana, Ole Einar Tveito, Andreas Dobler, and Ketil Tunheim
Earth Syst. Sci. Data, 11, 1531–1551, https://doi.org/10.5194/essd-11-1531-2019,https://doi.org/10.5194/essd-11-1531-2019, 2019
Short summary
seNorge2 daily precipitation, an observational gridded dataset over Norway from 1957 to the present day
Cristian Lussana, Tuomo Saloranta, Thomas Skaugen, Jan Magnusson, Ole Einar Tveito, and Jess Andersen
Earth Syst. Sci. Data, 10, 235–249, https://doi.org/10.5194/essd-10-235-2018,https://doi.org/10.5194/essd-10-235-2018, 2018
Short summary

Cited articles

Anderson, A. R. S., Chapman, M., Drobot, S. D., Tadesse, A., Lambi, B., Wiener, G., and Pisano, P.: Quality of mobile air temperature and atmospheric pressure observations from the 2010 development test environment experiment, J. Appl. Meteorol. Clim., 51, 691–701, https://doi.org/10.1175/JAMC-D-11-0126.1, 2012. a
Anderson, A. R. S., Walker, C. L., Wiener, G., Iii, W. P. M., and Haupt, S. E.: Transportation Research Interdisciplinary Perspectives An adaptive big data weather system for surface transportation, Transport. Res. Interdisciplin. Perspect., 3, 100071, https://doi.org/10.1016/j.trip.2019.100071, 2019. a
Bell, S., Cornford, D., and Bastin, L.: How good are citizen weather stations? Addressing a biased opinion, Weather, 70, 75–84, https://doi.org/10.1002/wea.2316, 2015. a
Chapman, L., Bell, C., and Bell, S.: Can the crowdsourcing data paradigm take atmospheric science to a new level? A case study of the urban heat island of London quantified using Netatmo weather stations, Int. J. Climatol., 37, 3597–3605, https://doi.org/10.1002/joc.4940, 2017. a
De Vos, L., Leijnse, H., Overeem, A., and Uijlenhoet, R.: The potential of urban rainfall monitoring with crowdsourced automatic weather stations in Amsterdam, Hydrol. Earth Syst. Sci., 21, 765–777, https://doi.org/10.5194/hess-21-765-2017, 2017. a
Download
Short summary
We present the open source project Titan for automatic quality control of meteorological in-situ observations. The quality control strategy adopted is a sequence of tests, where several of them utilize the expected spatial consistency between nearby observations. Titan serves real-time operational applications that process massive amounts of observations measured by networks of automatic weather stations. Further developments include transforming Titan into a more flexible library of functions.