Homogenizing GPS Integrated Water Vapor Time Series: Benchmarking Break Detection Methods on Synthetic Data Sets

Autor: Janusz Bogusz, E. Pottiaux, José Antonio Guijarro, A. Quarello, M. Elias, B. Chimani, Olivier Bock, Tong Ning, Fadwa Alshawaf, R. Van Malderen, S. Zengin Kazancı, Peter Domonkos, Emilie Lebarbier, Mostafa Hoseini, V. Tornatore, Anna Klos
Přispěvatelé: Royal Meteorological Institute of Belgium, Royal Observatory of Belgium [Brussels] (ROB), Military University of Technology, Unaffiliated Researcher, Research Institute of Geodesy, Cartography and Topography, Lantmäteriet (agence gouvernementale suédoise), Institut de Physique du Globe de Paris (IPGP), Institut national des sciences de l'Univers (INSU - CNRS)-IPG PARIS-Université de La Réunion (UR)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP), École nationale des sciences géographiques (ENSG), Institut National de l'Information Géographique et Forestière [IGN] (IGN)-Université Gustave Eiffel, Agencia Estatal de Meteorología (AEMet), German Research Centre for Geosciences - Helmholtz-Centre Potsdam (GFZ), Norwegian University of Science and Technology [Trondheim] (NTNU), Norwegian University of Science and Technology (NTNU), Mathématiques et Informatique Appliquées (MIA-Paris), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-AgroParisTech-Université Paris-Saclay, Zentralanstalt für Meteorologie und Geodynamik [Vienna] (ZAMG), Politecnico di Milano [Milan] (POLIMI), Karadeniz Technical University (KTU), European COST Action ES1206 GNSS4SWEC, Polish National Science Centre UMO-2016/21/B/ST10/02353, Belgian Federal Science Policy OfficeEuropean Commission, Faculty of Civil Engineering, Architecture and Geodesy, Split University, HELMHOLTZ CENTRE POTSDAM GFZ GERMAN RESEARCH CENTRE FOR GEOSCIENCES POTSDAM DEU, Partenaires IRSTEA, Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), Department of Civil and Environmental Engineering [Singapore], National University of Singapore (NUS), Université Paris Nanterre (UPN), Department of Geomatics Engineering
Jazyk: angličtina
Rok vydání: 2020
Předmět:
010504 meteorology & atmospheric sciences
lcsh:Astronomy
Computer science
GPS
ERA‐Interim
homogenization
[SDU.STU]Sciences of the Universe [physics]/Earth Sciences
[SDU.STU.ME]Sciences of the Universe [physics]/Earth Sciences/Meteorology
Environmental Science (miscellaneous)
010502 geochemistry & geophysics
01 natural sciences
Homogenization (chemistry)
Teknologi: 500 [VDP]
lcsh:QB1-991
Break detection methods
Global Positioning System
integrated water vapour
Climate change
Atmosphere and climate
ERA-Interim
break detection
Process engineering
0105 earth and related environmental sciences
Water vapor
GNSS
business.industry
Technology: 500 [VDP]
lcsh:QE1-996.5
Benchmarking
Atmosfære og klima
Synthetic data sets
lcsh:Geology
Klimaendringer
Integrated water vapour
[SDU]Sciences of the Universe [physics]
13. Climate action
General Earth and Planetary Sciences
business
Zdroj: Earth and Space Science
Earth and Space Science, American Geophysical Union/Wiley, 2020, 7 (5), pp.e2020EA001121. ⟨10.1029/2020EA001121⟩
Earth and Space Science, Vol 7, Iss 5, Pp n/a-n/a (2020)
Earth and Space Science, American Geophysical Union/Wiley, 2020, 7 (5), ⟨10.1029/2020EA001121⟩
ARCIMIS. Archivo Climatológico y Meteorológico Institucional (AEMET)
Agencia Estatal de Meteorología (AEMET)
ISSN: 2333-5084
Popis: We assess the performance of different break detection methods on three sets of benchmark data sets, each consisting of 120 daily time series of integrated water vapor differences. These differences are generated from the Global Positioning System (GPS) measurements at 120 sites worldwide, and the numerical weather prediction reanalysis (ERA‐Interim) integrated wáter vapor output, which serves as the reference series here. The benchmark includes homogeneous and inhomogeneous sections with added nonclimatic shifts (breaks) in the latter. Three different variants of the benchmark time series are produced, with increasing complexity, by adding autoregressive noise of the first order to the white noise model and the periodic behavior and consecutively by adding gaps and allowing nonclimatic trends. The purpose of this “complex experiment” is to examine the performance of break detection methods in a more realistic case when the reference series are not homogeneous. We evaluate the performance of break detection methods with skill scores, centered root mean square errors (CRMSE), and trend differences relative to the trends of the homogeneous series. We found that most methods underestimate the number of breaks and have a significant number of false detections. Despite this, the degree of CRMSE reduction is significant (roughly between 40% and 80%) in the easy to moderate experiments, with the ratio of trend bias reduction is even exceeding the 90% of the raw data error. For the complex experiment, the improvement ranges between 15% and 35% with respect to the raw data, both in terms of RMSE and trend estimations. The research has been undertaken in the framework of the European COST Action ES1206 GNSS4SWEC (GNSS for Severe Weather and Climate monitoring; http://www.cost.eu/COST_Actions/essem/ES1206), which also funded two dedicated workshops on this activity.
Databáze: OpenAIRE
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