Articles | Volume 13, issue 12
Atmos. Meas. Tech., 13, 6445–6458, 2020
https://doi.org/10.5194/amt-13-6445-2020
Atmos. Meas. Tech., 13, 6445–6458, 2020
https://doi.org/10.5194/amt-13-6445-2020

Research article 01 Dec 2020

Research article | 01 Dec 2020

Interpolation uncertainty of atmospheric temperature profiles

Alessandro Fassò et al.

Related authors

Radiosounding HARMonization (RHARM): a new homogenized dataset of radiosounding temperature, humidity and wind profiles with uncertainty
Fabio Madonna, Emanuele Tramutola, Souleymane Sy, Federico Serva, Monica Proto, Marco Rosoldi, Simone Gagliardi, Francesco Amato, Fabrizio Marra, Alessandro Fassò, Tom Gardiner, and Peter William Thorne
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2020-183,https://doi.org/10.5194/essd-2020-183, 2020
Revised manuscript not accepted
Short summary
Statistical modelling of collocation uncertainty in atmospheric thermodynamic profiles
A. Fassò, R. Ignaccolo, F. Madonna, B. B. Demoz, and M. Franco-Villoria
Atmos. Meas. Tech., 7, 1803–1816, https://doi.org/10.5194/amt-7-1803-2014,https://doi.org/10.5194/amt-7-1803-2014, 2014

Related subject area

Subject: Others (Wind, Precipitation, Temperature, etc.) | Technique: In Situ Measurement | Topic: Data Processing and Information Retrieval
Sampling error in aircraft flux measurements based on a high-resolution large eddy simulation of the marine boundary layer
Grant W. Petty
Atmos. Meas. Tech., 14, 1959–1976, https://doi.org/10.5194/amt-14-1959-2021,https://doi.org/10.5194/amt-14-1959-2021, 2021
Short summary
Separation of convective and stratiform precipitation using polarimetric radar data with a support vector machine method
Yadong Wang, Lin Tang, Pao-Liang Chang, and Yu-Shuang Tang
Atmos. Meas. Tech., 14, 185–197, https://doi.org/10.5194/amt-14-185-2021,https://doi.org/10.5194/amt-14-185-2021, 2021
Short summary
An approach to minimize aircraft motion bias in multi-hole probe wind measurements made by small unmanned aerial systems
Loiy Al-Ghussain and Sean C. C. Bailey
Atmos. Meas. Tech., 14, 173–184, https://doi.org/10.5194/amt-14-173-2021,https://doi.org/10.5194/amt-14-173-2021, 2021
Short summary
Unsupervised classification of snowflake images using a generative adversarial network and K-medoids classification
Jussi Leinonen and Alexis Berne
Atmos. Meas. Tech., 13, 2949–2964, https://doi.org/10.5194/amt-13-2949-2020,https://doi.org/10.5194/amt-13-2949-2020, 2020
Short summary
An improved post-processing technique for automatic precipitation gauge time series
Amber Ross, Craig D. Smith, and Alan Barr
Atmos. Meas. Tech., 13, 2979–2994, https://doi.org/10.5194/amt-13-2979-2020,https://doi.org/10.5194/amt-13-2979-2020, 2020
Short summary

Cited articles

Alegria, A., Caro, S., Bevilacqua, M., Porcu, E., and Clarke, J.: Estimating covariance functions of multivariate skew-Gaussian random fields on the sphere, Spat. Stat.-Neth., 22, 388–402, 2017. a
Bodeker, G. E., Bojinski, S., Cimini, D., Dirksen, R. J., Haeffelin, M., Hannigan, J. W., Hurst, D. F., Leblanc, T., Madonna, F., Maturilli, M., Mikalsen, A. C., Philipona, R., Reale, T., Seidel, D. J., Tan, D. G. H., Thorne, P. W., Vömel, H., and Wang, J.: Reference Upper-Air Observations for Climate: From Concept to Reality, B. Am. Meteorol. Soc., 97, 123–135, https://doi.org/10.1175/BAMS-D-14-00072.1, 2016. a
Ceccherini, S., Carli, B., Tirelli, C., Zoppetti, N., Del Bianco, S., Cortesi, U., Kujanpää, J., and Dragani, R.: Importance of interpolation and coincidence errors in data fusion, Atmos. Meas. Tech., 11, 1009–1017, https://doi.org/10.5194/amt-11-1009-2018, 2018. a
Cressie, N. and Wikle, C.: Statistics for Spatio-Temporal Data, Wiley, New York, USA, 2011. a, b
Dirksen, R. J., Sommer, M., Immler, F. J., Hurst, D. F., Kivi, R., and Vömel, H.: Reference quality upper-air measurements: GRUAN data processing for the Vaisala RS92 radiosonde, Atmos. Meas. Tech., 7, 4463–4490, https://doi.org/10.5194/amt-7-4463-2014, 2014. a
Download
Short summary
Modern radiosonde balloons fly from ground level up to the lower stratosphere and take temperature measurements. What is the uncertainty of interpolated values in the resulting atmospheric temperature profiles? To answer this question, we introduce a general statistical–mathematical model for the computation of interpolation uncertainty. Analysing more than 51 million measurements, we provide some understanding of the consequences of filling missing data with interpolated ones.