Climate 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.
Author(s): Peter Domonkos, Róbert Tóth, László Nyitrai
Series: Developments in Weather and Climate Science, 3
Publisher: Elsevier
Year: 2022
Language: English
Pages: 303
City: Amsterdam
Front Cover
Climate Observations: Data Quality Control and Time Series Homogenization
Copyright
Contents
About the authors
Introduction
Chapter 1: Land surface observations
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
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
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
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
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
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
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
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
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
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
Back Cover
