Scales of Climate

Sarma, A. A. L. N.

doi:10.1007/1-4020-3266-8_177

Cited by 3 publications

(3 citation statements)

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“…A particular example concerns daily air temperature data that are needed in hydrological models for assessing near-real-time crop and irrigation water requirements and for improving crop water productivity in agricultural areas. At local scales (Sarma 2005), sparsely and irregularly distributed data from weather stations are a challenge to be used in hydrological models at unvisited locations in irrigation networks. To address this problem, additional spatially distributed data may be included as proxies.…”

Section: Introductionmentioning

confidence: 99%

Multivariate copula quantile mapping for bias correction of reanalysis air temperature data

Alidoost

Stein

et al. 2019

Journal of Spatial Science

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Reanalysis data retrieved from the European Centre for Medium-range Weather Forecasts (ECMWF) are commonly used for hydrological studies. Their use requires bias correction, defined as the difference between reanalysis values and measurements. We propose three multivariate copula quantile mappings (MCQMs) to predict bias-corrected values at unvisited locations. We apply the methods to the Qazvin Plain, Iran, for daily air temperature retrieved from weather stations and the ECMWF archive. Results showed that MCQMs reduced bias by 46% as compared with classical quantile mapping. The study concludes that MCQMs are well able to represent the spatial and temporal variation of air temperature.

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Section: Introductionmentioning

confidence: 99%

Multivariate copula quantile mapping for bias correction of reanalysis air temperature data

Alidoost

Stein

et al. 2019

Journal of Spatial Science

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“…Hydrological studies refer to air temperature as a key variable to support water management in an irrigation network. At local scales (Sarma, 2005), sparsely and irregularly distributed data from weather stations are a challenge for hydrological studies at unvisited locations in irrigation networks. To address the problem, additional spatially distributed data may be included, e.g., gridded reanalysis weather data from the European Centre for Medium-range Weather Forecasts (ECMWF).…”

Section: Introductionmentioning

confidence: 99%

“…Drought increases crop water stress and on the other hand, intensive rainfall may cause a flood and waterlogged soils (Lobell and Gourdji 2012). The assessment of impacts is primarily based upon extremes obtained from a long time-series of data from weather stations which are, however, sparse at local scales (Sarma 2005). Global assessments of crop production easily ignore variation at local scales (Lobell and Gourdji 2012).…”

Section: Introductionmentioning

confidence: 99%

Copulas for integrating weather and land information in space and time

Alidoost

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Thank God for the wisdom bestowed upon me, the strength and the talents. I'm thankful for many gifts God has given me like the great teacher who inspires me. I do not know how to express my deepest gratitude to Prof. Alfred Stein. His support throughout my research was like a heart that pumped lots of professional guidance, encouragements, and enthusiasms. With superior insight and vast experience, he suggested solutions to handle the problems that I faced. My profound appreciation is extended to Prof. Zhongbo (Bob) Su. He gave me the insights that sparked my imagination helping me to be innovative. His vision has deeply inspired me to develop ideas. Completion of this research became possible with standing upon the shoulder of a giant: Dr. Ali Sharifi. He helped me to see further with his advice about both scientific research and life in general. Words are not enough to express my appreciation for many insightful conversations that we had. Undertaking this Ph.D. gave me the pleasure to know amazing people. I am grateful to Gabriel Parodi and Prof. John van Genderen for their valuable friendship. I want to extend my sincere thanks to Prof. Marinus G. Bos, Dr. Benedikt Gräler, Dr. Chris Mannaerts and Dr. Yijian Zeng for their suggestions and comments through developing my Ph.D. proposal and methodology. I am gratefully indebted to Tonny Boeve, Jeroen Jansen and Dr. Ad van Dommelen for their help and supports throughout my Ph.D. journey. One of the beautiful gifts that I am extremely grateful for having is my family. I extend my heartfelt gratitude to them for their love and empathy. Especially, my mother who is one of the strongest women I know and my identical twin sister who is an excellent researcher in remote sensing and photogrammetry. My success in achieving a Ph.D. is due to what they have done for me. Another valuable gift that I have received is an intimate friendship with lovely and cheerful persons in Enschede, Mississauga, Tehran, and Lincoln. All of the memorable times that we shared helped me emotionally through the rough road to finish this thesis. Thank You, my dear friends. My cordial gratitude go to my paranymphs, officemates, and colleagues for offering me a helping hand in this endeavor. My sincere thanks also go to the staff of the University of Twente and the faculty of ITC who provided me a friendly smile, a kind look, a gentle word and a hello every time we met. Many thanks to all the people who have supported me to complete the research directly or indirectly. I acknowledge the kind cooperation of the European Centre for Medium-Range Weather Forecasts, the SAJ Consulting firm in Iran for weather data, Land Processes Distributed Active Archive Center of the U.S. Geological Survey for MODIS and Landsat 8 images, the Central Bureau for Statistics, European statistics database, the Royal Netherlands Meteorological Institute for weather data, and Cressie Communication Services for improving the English wording of chapters one, three and eight. 1 multivariate copula quantile mapping 2 weather ex...

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Multiscale urban design based on the optimization of the wind and thermal environments: A case study of the core area of Suzhou Science and Technology City

Liu,

Zhou,

et al. 2024

Frontiers of Architectural Research

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Scales of Climate

Cited by 3 publications

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Multivariate copula quantile mapping for bias correction of reanalysis air temperature data

Multivariate copula quantile mapping for bias correction of reanalysis air temperature data

Copulas for integrating weather and land information in space and time

Multiscale urban design based on the optimization of the wind and thermal environments: A case study of the core area of Suzhou Science and Technology City

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