Climate Observations
Data Quality Control and Time Series Homogenization
- 1st Edition, Volume 3 - November 15, 2022
- Imprint: Royal Meteorological Society – Elsevier
- Authors: Peter Domonkos, Róbert Tóth, László Nyitrai
- Language: English
- Paperback ISBN:9 7 8 - 0 - 3 2 3 - 9 0 4 8 7 - 2
- eBook ISBN:9 7 8 - 0 - 3 2 3 - 9 0 4 8 8 - 9
Climate Observations: Data Quality Control and Time Series Homogenization pulls together the different phases of the production of high-quality climatic datasets, allowing… Read more
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Request a sales quoteClimate Observations: Data Quality Control and Time Series Homogenization pulls together the different phases of the production of high-quality climatic datasets, allowing interested readers to obtain a coherent picture on the complexity and importance of this task. There are several new methods of time series homogenization, each very complex and fast developing. The thematic discussion of the production of high quality climatic datasets provides the opportunity to reduce errors, including the careful installation of meteorological instruments, the application of strict observing rules and inspections, and the use of sophistically developed statistical software to detect and remove errors or biases.
This book is intended for professionals working on climate data management at the national meteorological services, for the users of observed climatic data, and for students and researchers studying atmospheric and climate science.
Members of the Royal Meteorological Society are eligible for a 35% discount on all Developments in Weather and Climate Science series titles. See the RMetS member dashboard for the discount code.
- Describes the research tasks and tools for which the reliability and accuracy of climatic data is particularly important
- Includes case studies to provide real-world context to the research presented in the book
- Features benchmark datasets that have been used for testing the stable operation and efficiency of homogenization methods
- Explains the use of semiautomatic quality control software, recently developed effective homogenization methods, their testing, and related new concepts and statistical tools
Professional meteorologists and climatologists; Students and researchers in atmospheric science and climate science
- Cover image
- Title page
- Table of Contents
- Copyright
- About the authors
- Introduction
- Chapter 1: Land surface observations
- Abstract
- 1.1: Global system of weather and climate observations
- 1.2: Site selection and installation of instruments
- 1.3: Manual and automated observations
- 1.4: Temperature
- 1.5: Humidity
- 1.6: Precipitation
- 1.7: Wind direction and wind speed
- 1.8: Atmospheric pressure
- 1.9: Sunshine duration and radiation
- 1.10: Cloudiness
- 1.11: Other climate variables
- 1.12: Calibration of instruments and maintenance
- References
- Chapter 2: Upper air observation and remote sensing
- Abstract
- 2.1: Upper air observations: Climatic characteristics and tools for their observation
- 2.2: Radiosondes I. Technology and performance of observations
- 2.3: Radiosondes II. Spatial and temporal density of observations
- 2.4: Remote sensing
- 2.5: Weather radars
- 2.6: Satellites in the observation of weather and climate
- 2.7: Space-based observations
- 2.8: Other upper air observations
- 2.9: Closing notes to Chapter 1 and this chapter
- References
- Chapter 3: Data quality control and dataset development
- Abstract
- 3.1: Error sources
- 3.2: Kinds and indications of data errors
- 3.3: Phases of quality control
- 3.4: Elimination of data errors
- 3.5: Quality control of extreme values
- 3.6: Data rescue and digitation
- 3.7: Data gaps and gap filling
- 3.8: Data gridding
- 3.9: Dataset development
- References
- Chapter 4: Homogenization task and its principal approaches
- Abstract
- 4.1: Time series homogenization in the system of scientific fields
- 4.2: Basic concepts of time series homogenization
- 4.3: Kinds of inhomogeneities
- 4.4: Kinds of homogenization tasks
- 4.5: Spatial representativeness of homogenized climatic data
- 4.6: Relation with general quality control
- 4.7: Use of documented information (metadata)
- 4.8: Homogeneity test
- 4.9: Homogenization without neighbor series
- References
- Chapter 5: Relative homogenization: The basis
- Abstract
- 5.1: Concept of relative homogenization
- 5.2: Traditional approach
- 5.3: Revolution of methodology from the 1990s
- 5.4: Time series comparison
- 5.5: Detection of trend inhomogeneities
- 5.6: Detection of multiple break points
- 5.7: Correction of inhomogeneities
- References
- Chapter 6: Relative homogenization: Optional tools
- Abstract
- 6.1: Multistep procedures
- 6.2: Iteration
- 6.3: Parameterization
- 6.4: Relative time series of daily resolution
- 6.5: Ensemble homogenization
- 6.6: Transformation of probability distribution
- 6.7: Infilling data gaps within homogenization procedures
- 6.8: Pairwise detection in automatic homogenization
- 6.9: Multivariate detection
- 6.10: Combination of homogenization methods
- References
- Chapter 7: Relative homogenization: Special problems
- Abstract
- 7.1: Signal-to-noise ratio
- 7.2: Systematic bias for regional means
- 7.3: Autocorrelation
- 7.4: Cyclical components
- 7.5: Threshold distance for spatial comparisons
- 7.6: Synchronous and semi-synchronous inhomogeneities
- 7.7: Short-term inhomogeneities
- 7.8: Weather dependent inhomogeneities
- 7.9: Homogenization of probability distribution
- 7.10: Temporal resolution of homogenization results
- 7.11: Wide applicability of additive inhomogeneity model
- References
- Chapter 8: A selection of statistical homogenization methods
- Abstract
- 8.1: Methods using accumulated anomalies
- 8.2: SNHT (Standard Normal Homogeneity Test)
- 8.3: RHtests (Relative Homogenization Tests)
- 8.4: MASH (Multiple Analysis of Series for Homogenization)
- 8.5: PHA (Pairwise Homogenization Algorithm)
- 8.6: Climatol
- 8.7: PRODIGE
- 8.8: HOMER (HOMogenization softwarE in R)
- 8.9: ACMANT (Applied Caussinus-Mestre Algorithm for homogenizing Networks of climatic Time series)
- 8.10: Homogenization methods for particular climatic elements
- References
- Chapter 9: Accuracy of homogenization results
- Abstract
- 9.1: Concepts of benchmarking
- 9.2: Construction of benchmark datasets
- 9.3: Efficiency measures
- 9.4: Limitations of the reliability of test results
- 9.5: Tests for break detection methods
- 9.6: HOME benchmark experiments
- 9.7: MULTITEST benchmark experiments
- 9.8: Tests for the accuracy of homogenized daily data
- 9.9: Tests with observed data
- 9.10: Tasks for the future
- References
- Chapter 10: Use of quality controlled and homogenized data
- Abstract
- 10.1: Weather forecast and weather alarms
- 10.2: Climate modeling
- 10.3: Use of homogenized data: For which purposes is it advantageous?
- 10.4: Climate research
- 10.5: Climate services
- 10.6: Adaptation to climate change
- References
- Appendix: Basic statistical concepts
- Reference
- Further reading
- Index
- Edition: 1
- Volume: 3
- Published: November 15, 2022
- No. of pages (Paperback): 302
- No. of pages (eBook): 302
- Imprint: Royal Meteorological Society – Elsevier
- Language: English
- Paperback ISBN: 9780323904872
- eBook ISBN: 9780323904889
PD
Peter Domonkos
Peter Domonkos is a Hungarian climatologist living in Spain since 2009. He is expert on statistical climatology, analysis of extreme climatic events, data quality control and time series homogenization. He is member of the Hungarian Meteorological Society and secretary of ESPERE (Environmental Science for Everybody Round the Earth). Between 2009 – 2015 Dr. Domonkos was a researcher of the University Rovira i Virgili (Tarragona, Spain), and is a free researcher since then. He has been developed an automatic homogenization method (ACMANT), which was found to be one of the most accurate methods by various international test experiments. Between 2013 and 2015 he led 4 international trainings on time series homogenization, sponsored by the World Meteorological Organization. He has 104 printed scientific publications.
Affiliations and expertise
Climate ResearcherRT
Róbert Tóth
Róbert Tóth is an experienced meteorologist with a demonstrated history of working in the environmental services industry. Skilled in meteorological measurements, international agreements on air quality protection, sustainable development, environmental compliance and emergency management. Strong research professional with a Master's Degree focused in Public Administration from University of Economy, Budapest. Now Head of Unit for Data Quality Control at Hungarian Meteorological Service since 2020. Responsible for the conventional precipitation monitoring network. Deputy Editor in Chief of Légkör (quarterly journal of Hungarian Meteorological Service and Hungarian Meteorological Society). In 2008-2009 president of UNEP Montreal Protocol Bureau. He gave lectures on meteorological observation and instruments at Eötvös Loránd University, Budapest in the 90s.
Affiliations and expertise
Head of Unit for Data Quality Control at Hungarian Meteorological Service, Budapest, HungaryLN
László Nyitrai
László Nyitrai is a certified meteorologist, he graduated from meteorology at Eötvös Loránd University in Budapest in 1985. He has worked for Hungarian Meteorological Service in the field of meteorological measurements, data archiving and climate tables. In addition, he dealt with the aspects of ionospheric physics affecting radio wave propagation, he collected records of ionospheric conditions for shortwave radio signal transport, and corresponded internationally in this topic.
He investigated the global trends, completeness and deficiencies of meteorological tropospheric and stratospheric upper air measurements and their presumed economic background in relation to the WMO member countries. He made attempts to calculate atmospheric moisture transport from radiosonde observations, and presented his studies in international conferences
Affiliations and expertise
Certified meterologist, Hungarian Meteorological Service, HungaryRead Climate Observations on ScienceDirect