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David Legates
David Russell Legates is a former professor of geography at the University of Delaware. He is the former Director of the Center for Climatic Research at the same university and a former Delaware state climatologist. In September 2020, the Trump administration appointed him as deputy assistant secretary of commerce for observation and prediction at the National Oceanic and Atmospheric Administration.
Legates has spent much of his career casting doubt on the severity of climate change and the human causes of warming. He is affiliated with the Heartland Institute, a think tank that promotes climate change denial.
Legates' viewpoint, as stated in a 2015 study that he co-authored, is that the Earth will experience about 1.0 °C (1.8 °F) warming over the 2000 to 2100 period.
Legates received a bachelor's degree in 1982, a master's degree in 1985, and a Ph.D. in climatology in 1988, all from the University of Delaware.
Legates was a professor of geography at the University of Delaware. He has also taught at Louisiana State University, the University of Oklahoma, and the University of Virginia. He has been a Visiting Research Scientist at the National Climate Data Center.
Legates started his career working on precipitation probability modeling. He extended his research to the study of global precipitation and temperature measurement correlation and performed critical analyses of the quality of traditional water budgeting methods applied to recent[when?] better quality measurement data.[citation needed] He also became concerned with the study of the applicability of global circulation prognostication models at the regional and local level. Legates and his team argued for the necessity of technological progress in precipitation measurement used for validating climate change scenarios, and for validation of existing data used for that purpose. They demonstrated disagreement between satellite-based and in-situ precipitation measurements, and pointed out inconsistencies among satellite data processing algorithms.[citation needed] Legates argued for a better adequacy of observation-based climatologies compared to those compiled subjectively. His team concluded that uncorrected centered-pattern correlation statistics applied to the validation of general circulation prognostication models used to predict large-scale climate change may be inappropriate and may yield erroneous results. They proposed modified goodness of fit test methods more suitable for use in hydrologic and hydroclimate model validation.[citation needed] Legates and his coworkers became concerned with the quality of surface instrumental temperature data analysis, treatment and presentation of trends used in the communication of global warming research results.
He co-developed methods to correct biases in gauge-measured precipitation data for wind and temperature effects, with direct applicability in climate change, hydrology and environmental impact studies.[citation needed] His group observed that gauge undercatch was mostly caused by wind turbulence—especially for snow—and has a significant effect on the calculated Arctic water budget. They also studied the correlation between the observed variability in Western US snowpack accumulation and atmospheric circulation in historical measurement data and developed temperature-snowfall correlations based on first principles and observation in order to improve the global radiation balance estimation used in climate change predictions. Legates also developed a calibration method which validates NEXRAD radar precipitation data with gauge measurements to improve the accuracy of precipitation estimates.[citation needed]
Legates and his coworkers extended their research to the development of correlations between satellite crop imaging data and landscape change, crop type and its evolution, and their effects of global climate change. They have also tackled rainfed crop management, modeling and optimization. The group developed a hydrologic model based on meteorological, soil and vegetation measurement data. His groups has demonstrated poor quality of correlation between hydrological cycle data, global runoff and global warming.[citation needed]
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David Legates
David Russell Legates is a former professor of geography at the University of Delaware. He is the former Director of the Center for Climatic Research at the same university and a former Delaware state climatologist. In September 2020, the Trump administration appointed him as deputy assistant secretary of commerce for observation and prediction at the National Oceanic and Atmospheric Administration.
Legates has spent much of his career casting doubt on the severity of climate change and the human causes of warming. He is affiliated with the Heartland Institute, a think tank that promotes climate change denial.
Legates' viewpoint, as stated in a 2015 study that he co-authored, is that the Earth will experience about 1.0 °C (1.8 °F) warming over the 2000 to 2100 period.
Legates received a bachelor's degree in 1982, a master's degree in 1985, and a Ph.D. in climatology in 1988, all from the University of Delaware.
Legates was a professor of geography at the University of Delaware. He has also taught at Louisiana State University, the University of Oklahoma, and the University of Virginia. He has been a Visiting Research Scientist at the National Climate Data Center.
Legates started his career working on precipitation probability modeling. He extended his research to the study of global precipitation and temperature measurement correlation and performed critical analyses of the quality of traditional water budgeting methods applied to recent[when?] better quality measurement data.[citation needed] He also became concerned with the study of the applicability of global circulation prognostication models at the regional and local level. Legates and his team argued for the necessity of technological progress in precipitation measurement used for validating climate change scenarios, and for validation of existing data used for that purpose. They demonstrated disagreement between satellite-based and in-situ precipitation measurements, and pointed out inconsistencies among satellite data processing algorithms.[citation needed] Legates argued for a better adequacy of observation-based climatologies compared to those compiled subjectively. His team concluded that uncorrected centered-pattern correlation statistics applied to the validation of general circulation prognostication models used to predict large-scale climate change may be inappropriate and may yield erroneous results. They proposed modified goodness of fit test methods more suitable for use in hydrologic and hydroclimate model validation.[citation needed] Legates and his coworkers became concerned with the quality of surface instrumental temperature data analysis, treatment and presentation of trends used in the communication of global warming research results.
He co-developed methods to correct biases in gauge-measured precipitation data for wind and temperature effects, with direct applicability in climate change, hydrology and environmental impact studies.[citation needed] His group observed that gauge undercatch was mostly caused by wind turbulence—especially for snow—and has a significant effect on the calculated Arctic water budget. They also studied the correlation between the observed variability in Western US snowpack accumulation and atmospheric circulation in historical measurement data and developed temperature-snowfall correlations based on first principles and observation in order to improve the global radiation balance estimation used in climate change predictions. Legates also developed a calibration method which validates NEXRAD radar precipitation data with gauge measurements to improve the accuracy of precipitation estimates.[citation needed]
Legates and his coworkers extended their research to the development of correlations between satellite crop imaging data and landscape change, crop type and its evolution, and their effects of global climate change. They have also tackled rainfed crop management, modeling and optimization. The group developed a hydrologic model based on meteorological, soil and vegetation measurement data. His groups has demonstrated poor quality of correlation between hydrological cycle data, global runoff and global warming.[citation needed]