Aneesh Goly | Florida Atlantic University (original) (raw)

Papers by Aneesh Goly

Research paper thumbnail of The Use of Embedded Sensors for Long Term Monitoring of Stress, Temperature, Corrosion Potential, and Other Variables in Precast Concrete Elements

Research paper thumbnail of Hydrodynamic Analysis of Ocean Current Turbines Using Vortex Lattice Method

iii ACKNOWLEDGEMENTS I would like to express my gratitude to my advisor, Palaniswamy Ananthakrish... more iii ACKNOWLEDGEMENTS I would like to express my gratitude to my advisor, Palaniswamy Ananthakrishnan, for his support, patience, and encouragement throughout my graduate studies. It is not often that one finds an advisor who always finds the time for listening to the little problems that unavoidably crop up in the course of performing research. His technical and editorial advice was essential to the completion of this master's thesis and has taught me innumerable lessons and insights. I would like to sincerely thank my committee members Dr. Dhanak, Dr. Hanson and Dr. Xiros for their valuable comments, suggestions and input to the thesis. My special thanks go to Ranjith for his encouragement and numerous fruitful discussions Lakitosh and Baishali who were encouraging me all through my thesis. My deepest gratitude goes to my family for their unflagging love and support throughout my life. In the first place to my parents, Srinivas and Rajani for giving me everything in my life. Se...

Research paper thumbnail of Spatial and Temporal Variation of Precipitation Extremes in Florida: Influence of Teleconnections

Research paper thumbnail of Optimization and Variants of Quantile-Based Methods for Bias Corrections of Statistically Downscaled Precipitation Data

Journal of Hydrologic Engineering, 2020

AbstractNew optimization and variants of quantile-based methods are developed for bias correction... more AbstractNew optimization and variants of quantile-based methods are developed for bias corrections of monthly and daily general circulation model (GCM)-based statistically downscaled precipitation ...

Research paper thumbnail of Optimal Selection of Predictor Variables in Statistical Downscaling Models of Precipitation

Water Resources Management, 2018

Two screening methods aimed at selection of predictor variables for use in a statistical downscal... more Two screening methods aimed at selection of predictor variables for use in a statistical downscaling (SD) model developed for precipitation are proposed and evaluated in this study. The SD model developed in this study relies heavily on appropriate predictors chosen and accurate relationships between site-specific predictand (i.e. precipitation) and general circulation model (GCM)-scale predictors for providing future projections at different spatial and temporal scales. Methods to characterize these relationships via rigid and flexible functional forms of relationships using mixed integer nonlinear programming (MINLP) formulation with binary variables, and artificial neural network (ANN) methods respectively are developed and evaluated in this study. The proposed methods and three additional methods based on the correlations between predictors and predictand, stepwise regression (SWR) and principal component analysis (PCA) are evaluated in this study. The screening methods are evaluated by employing them in conjunction with an SD model at 22 rain gauge locations in south Florida, USA. The predictor variables that are selected by different predictor selection methods are used in a statistical downscaling model developed in this study to downscale precipitation at a monthly temporal scale. Results suggest that optimal selection of variables using MINLP and ANN provided improved performance and error measures compared to two other models that did not use these methods for screening the variables. Results from application and evaluations of screening methods indicate improved downscaling of precipitation is possible by SD models when an optimal set of predictors are used and the selection of the predictors is site-specific.

Research paper thumbnail of Influences of Climate Variability and Change on Precipitation Characteristics and Extremes

Research paper thumbnail of Comprehensive Framework for Assessment of Radar-Based Precipitation Data Estimates

Journal of Hydrologic Engineering, 2017

AbstractAssessment of radar-based precipitation estimates using rain gauge observations is a crit... more AbstractAssessment of radar-based precipitation estimates using rain gauge observations is a critical exercise in evaluating pre-and postcorrected (gauge-adjusted) radar-based precipitation data. A comprehensive assessment framework combining several visual, quantitative, and statistical measures, indexes, and skill scores is proposed and developed for evaluation of radar-based precipitation estimates in space and time. Contingency measures, skill scores, and a few new metrics are proposed and are evaluated along with several indexes. Visual measures provide a quick check of agreement between radar and rain gauge data sets. Quantitative measures provide information about errors, and skill scores assess the quality of radar data for dichotomous (rain and no-rain) events. Summary statistics and hypothesis tests in statistical categories provide insights into distributional aspects of the rain gauge and radar data sets. The framework is used for evaluation of 15-min radar-based precipitation data obtained fr...

Research paper thumbnail of Assessment of Influences of Climate Variability on Storm Event Characteristics

World Environmental and Water Resources Congress 2013, 2013

ABSTRACT

Research paper thumbnail of Assessment of Various Statistical Downscaling Methods for Downscaling Precipitation in Florida

World Environmental and Water Resources Congress 2013, 2013

ABSTRACT

Research paper thumbnail of Multi-Objective Optimization Methods for Bias Correction of Statistically Downscaled Precipitation

World Environmental and Water Resources Congress 2013, 2013

ABSTRACT

Research paper thumbnail of Development and Evaluation of Statistical Downscaling Models for Monthly Precipitation

Earth Interactions, 2014

Several statistical downscaling models have been developed in the past couple of decades to asses... more Several statistical downscaling models have been developed in the past couple of decades to assess the hydrologic impacts of climate change by projecting the station-scale hydrological variables from large-scale atmospheric variables simulated by general circulation models (GCMs). This paper presents and compares different statistical downscaling models that use multiple linear regression (MLR), positive coefficient regression (PCR), stepwise regression (SR), and support vector machine (SVM) techniques for estimating monthly rainfall amounts in the state of Florida. Mean sea level pressure, air temperature, geopotential height, specific humidity, U wind, and V wind are used as the explanatory variables/predictors in the downscaling models. Data for these variables are obtained from the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis dataset and the Canadian Centre for Climate Modelling and Analysis (CCCma) Coupled Global ...

Research paper thumbnail of Influence of Temporal Resolution of Rain Gage Data on Bias Correction Procedures for NEXRAD Estimates

World Environmental and Water Resources Congress 2012, 2012

Bias corrections of radar data using ground truth is essential and critical step in generation of... more Bias corrections of radar data using ground truth is essential and critical step in generation of viable precipitation data sets. The improvement in the radar data achieved through the correction procedures depend on several factors including available rain gage data, gage density and reliability of ground truth. Availability of rain gage data at the same temporal resolution as that of radar data is essential and may not be possible in many instances. In those situations, correction procedures adopted for up-scaling or down-scaling the bias-correction factors need to be evaluated thoroughly. In the current study, bias correction procedures using spatial interpolation and optimal weighting methods used for adjustment of NEXRAD based rainfall estimates are assessed. Fifteen minute NEXRAD-based precipitation data available from South West Florida Water Management District (SWFWMD) provided by OneRain Inc. are improved using NOAA and SWFWMD rain gage data available at temporal resolutions of 15 minutes, one hour and a day. All the bias correction methods are evaluated using several performance measures. Data from a minimum of forty three and a maximum of 182 rain gages are used for improvement of NEXRAD data from years 1994-2007. Results from this study highlight the difficulties in applying bias corrections procedures with data sets of different temporal resolutions and performances of different spatial interpolation methods.

Research paper thumbnail of Evaluation of Different Climate Change Models for Future Precipitation Projections in Florida

World Environmental and Water Resources Congress 2012, 2012

Precipitation being a vital input for many hydrological modeling studies has a direct bearing on ... more Precipitation being a vital input for many hydrological modeling studies has a direct bearing on the water resources modeling and management at different spatial and temporal scales. According to Intergovernmental Panel on Climate Change (IPCC), frequency of extreme precipitation events is expected to increase in future with no consistent trend in mean precipitation across the globe. To evaluate trends in precipitation, Global Circulation Models (GCMs) combined with statistical or dynamic downscaling techniques are generally used. However, it is agreed that skill of any climate change model is lower for precipitation compared to that for temperature. The model performance also depends on spatial and temporal resolution of the simulations. In the current study, precipitation projections based on fifteen GCMs from WCRP's(World Climate Research Program) Coupled Model Inter-comparison Project, phase -3 (CMIP3) project with different SRES (Special Report on Emission Scenarios) runs are analyzed for the state of Florida. Historical precipitation data is used for evaluation of the models via several performance measures and for selection of the best model. Long term historical precipitation data from United States Historical Climatology Network (USHCN) and GCM simulations from 20th and 21st century are used in this study. Efficacy and utility of Bias-Corrected Spatial Disaggregation (BCSD) procedure used in CMIP3 project for downscaling precipitation data for the state of Florida is assessed. Performances of models in two distinct seasons (wet and dry) that dominate tropical climate of Florida are also evaluated.

Research paper thumbnail of Influence of Teleconnections on Spatial and Temporal Variability of Extreme Precipitation Events in Florida

World Environmental and Water Resources Congress 2012, 2012

Two major teleconnections, AMO (Atlantic multi-decadal oscillation) and ENSO (El Nino southern os... more Two major teleconnections, AMO (Atlantic multi-decadal oscillation) and ENSO (El Nino southern oscillation) under cool and warm cycles (phases) influencing precipitation patterns in Florida are assessed in this study. Temporal shift in the occurrences of precipitation extremes and changes in the magnitudes of these extremes are evaluated in different phases. Extreme precipitation events for nine different durations are also evaluated. Assessment of spatial variability of extreme precipitation in different rain areas (meteorologically homogeneous areas), and AMO and ENSO combined influences on precipitation is also carried out. Long-term historical precipitation data from National Climatic Data Center (NCDC) are used for the statistical analyses using parametric and non-parametric methods.

Research paper thumbnail of Precipitation Extremes and Climate Change: Evaluation Using Descriptive WMO Indices

World Environmental and Water Resources Congress 2012, 2012

Assessment of spatial and temporal extreme precipitation events due to climate variability and ch... more Assessment of spatial and temporal extreme precipitation events due to climate variability and change is critical for future hydrologic design. Evaluation of these extremes in the past has been limited to evaluation of annual and partial duration series. However, climate-change sensitive hydrologic design requires evaluation of precipitation extremes at different temporal levels using a variety of indices. This study evaluates the variability of precipitation extremes in two climatic regions in the U. S. using WMO (World Meteorological Organization) proposed and adopted eleven indices. These indices relate to precipitation extremes at a daily temporal scale and encompass a variety of conditions including user-defined precipitation thresholds. Quantitative evaluation, statistical analyses and spatial variability of indices in a region as well across different climate zones indicate that infilling of precipitation data and existence of in homogeneities influences the assessment of trends in extreme events using indices. This paper presents preliminary results of an ongoing study.

Research paper thumbnail of Influences of Atlantic multidecadal oscillation phases on spatial and temporal variability of regional precipitation extremes

Journal of Hydrology, 2013

A major teleconnection, Atlantic multidecadal oscillation (AMO) under two phases (cool and warm) ... more A major teleconnection, Atlantic multidecadal oscillation (AMO) under two phases (cool and warm) influencing precipitation extremes in Florida, USA, is the main focus of this study. Long-term extreme precipitation data from several rain gages from temporal windows that coincide with the AMO phases are evaluated for changes in spatial and temporal variability across the region. Assessments of precipitation extremes for nine durations in different meteorologically homogenous rainfall areas as well as in the entire region are carried out. Methods of assessment included parametric unpaired t-tests and nonparametric Mann-Whitney U tests, kernel density estimates using Gaussian kernel for distribution-free comparative analysis and bootstrap sampling-based confidence intervals. Depth-duration-frequency (DDF) curves are also developed using generalized extreme value (GEV) distributions characterizing the extremes. Analysis of data indicated increase in precipitation extremes in warm phases of AMO for durations greater than 24 h. The influence of warm or cool phases of AMO on precipitation extremes is not spatially uniform in the region. Temporal shifts in occurrences of extremes from the later part of the year in warm phase to earlier in the year for the cool phase are evident. These shifts will have implications on flooding events in different regions of Florida. Magnitudes of extremes for a 25 year return period based on DDF curves were higher for all nine durations when data from cool or warm phase alone were compared to those obtained from data from two phases. Precipitation extremes for durations longer than a day are associated with increased landfalls of hurricanes occurring in the region in the AMO warm phases.

Research paper thumbnail of Individual and coupled influences of AMO and ENSO on regional precipitation characteristics and extremes

Water Resources Research, 2014

ABSTRACT

Research paper thumbnail of The Use of Embedded Sensors for Long Term Monitoring of Stress, Temperature, Corrosion Potential, and Other Variables in Precast Concrete Elements

Research paper thumbnail of Hydrodynamic Analysis of Ocean Current Turbines Using Vortex Lattice Method

iii ACKNOWLEDGEMENTS I would like to express my gratitude to my advisor, Palaniswamy Ananthakrish... more iii ACKNOWLEDGEMENTS I would like to express my gratitude to my advisor, Palaniswamy Ananthakrishnan, for his support, patience, and encouragement throughout my graduate studies. It is not often that one finds an advisor who always finds the time for listening to the little problems that unavoidably crop up in the course of performing research. His technical and editorial advice was essential to the completion of this master's thesis and has taught me innumerable lessons and insights. I would like to sincerely thank my committee members Dr. Dhanak, Dr. Hanson and Dr. Xiros for their valuable comments, suggestions and input to the thesis. My special thanks go to Ranjith for his encouragement and numerous fruitful discussions Lakitosh and Baishali who were encouraging me all through my thesis. My deepest gratitude goes to my family for their unflagging love and support throughout my life. In the first place to my parents, Srinivas and Rajani for giving me everything in my life. Se...

Research paper thumbnail of Spatial and Temporal Variation of Precipitation Extremes in Florida: Influence of Teleconnections

Research paper thumbnail of Optimization and Variants of Quantile-Based Methods for Bias Corrections of Statistically Downscaled Precipitation Data

Journal of Hydrologic Engineering, 2020

AbstractNew optimization and variants of quantile-based methods are developed for bias correction... more AbstractNew optimization and variants of quantile-based methods are developed for bias corrections of monthly and daily general circulation model (GCM)-based statistically downscaled precipitation ...

Research paper thumbnail of Optimal Selection of Predictor Variables in Statistical Downscaling Models of Precipitation

Water Resources Management, 2018

Two screening methods aimed at selection of predictor variables for use in a statistical downscal... more Two screening methods aimed at selection of predictor variables for use in a statistical downscaling (SD) model developed for precipitation are proposed and evaluated in this study. The SD model developed in this study relies heavily on appropriate predictors chosen and accurate relationships between site-specific predictand (i.e. precipitation) and general circulation model (GCM)-scale predictors for providing future projections at different spatial and temporal scales. Methods to characterize these relationships via rigid and flexible functional forms of relationships using mixed integer nonlinear programming (MINLP) formulation with binary variables, and artificial neural network (ANN) methods respectively are developed and evaluated in this study. The proposed methods and three additional methods based on the correlations between predictors and predictand, stepwise regression (SWR) and principal component analysis (PCA) are evaluated in this study. The screening methods are evaluated by employing them in conjunction with an SD model at 22 rain gauge locations in south Florida, USA. The predictor variables that are selected by different predictor selection methods are used in a statistical downscaling model developed in this study to downscale precipitation at a monthly temporal scale. Results suggest that optimal selection of variables using MINLP and ANN provided improved performance and error measures compared to two other models that did not use these methods for screening the variables. Results from application and evaluations of screening methods indicate improved downscaling of precipitation is possible by SD models when an optimal set of predictors are used and the selection of the predictors is site-specific.

Research paper thumbnail of Influences of Climate Variability and Change on Precipitation Characteristics and Extremes

Research paper thumbnail of Comprehensive Framework for Assessment of Radar-Based Precipitation Data Estimates

Journal of Hydrologic Engineering, 2017

AbstractAssessment of radar-based precipitation estimates using rain gauge observations is a crit... more AbstractAssessment of radar-based precipitation estimates using rain gauge observations is a critical exercise in evaluating pre-and postcorrected (gauge-adjusted) radar-based precipitation data. A comprehensive assessment framework combining several visual, quantitative, and statistical measures, indexes, and skill scores is proposed and developed for evaluation of radar-based precipitation estimates in space and time. Contingency measures, skill scores, and a few new metrics are proposed and are evaluated along with several indexes. Visual measures provide a quick check of agreement between radar and rain gauge data sets. Quantitative measures provide information about errors, and skill scores assess the quality of radar data for dichotomous (rain and no-rain) events. Summary statistics and hypothesis tests in statistical categories provide insights into distributional aspects of the rain gauge and radar data sets. The framework is used for evaluation of 15-min radar-based precipitation data obtained fr...

Research paper thumbnail of Assessment of Influences of Climate Variability on Storm Event Characteristics

World Environmental and Water Resources Congress 2013, 2013

ABSTRACT

Research paper thumbnail of Assessment of Various Statistical Downscaling Methods for Downscaling Precipitation in Florida

World Environmental and Water Resources Congress 2013, 2013

ABSTRACT

Research paper thumbnail of Multi-Objective Optimization Methods for Bias Correction of Statistically Downscaled Precipitation

World Environmental and Water Resources Congress 2013, 2013

ABSTRACT

Research paper thumbnail of Development and Evaluation of Statistical Downscaling Models for Monthly Precipitation

Earth Interactions, 2014

Several statistical downscaling models have been developed in the past couple of decades to asses... more Several statistical downscaling models have been developed in the past couple of decades to assess the hydrologic impacts of climate change by projecting the station-scale hydrological variables from large-scale atmospheric variables simulated by general circulation models (GCMs). This paper presents and compares different statistical downscaling models that use multiple linear regression (MLR), positive coefficient regression (PCR), stepwise regression (SR), and support vector machine (SVM) techniques for estimating monthly rainfall amounts in the state of Florida. Mean sea level pressure, air temperature, geopotential height, specific humidity, U wind, and V wind are used as the explanatory variables/predictors in the downscaling models. Data for these variables are obtained from the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis dataset and the Canadian Centre for Climate Modelling and Analysis (CCCma) Coupled Global ...

Research paper thumbnail of Influence of Temporal Resolution of Rain Gage Data on Bias Correction Procedures for NEXRAD Estimates

World Environmental and Water Resources Congress 2012, 2012

Bias corrections of radar data using ground truth is essential and critical step in generation of... more Bias corrections of radar data using ground truth is essential and critical step in generation of viable precipitation data sets. The improvement in the radar data achieved through the correction procedures depend on several factors including available rain gage data, gage density and reliability of ground truth. Availability of rain gage data at the same temporal resolution as that of radar data is essential and may not be possible in many instances. In those situations, correction procedures adopted for up-scaling or down-scaling the bias-correction factors need to be evaluated thoroughly. In the current study, bias correction procedures using spatial interpolation and optimal weighting methods used for adjustment of NEXRAD based rainfall estimates are assessed. Fifteen minute NEXRAD-based precipitation data available from South West Florida Water Management District (SWFWMD) provided by OneRain Inc. are improved using NOAA and SWFWMD rain gage data available at temporal resolutions of 15 minutes, one hour and a day. All the bias correction methods are evaluated using several performance measures. Data from a minimum of forty three and a maximum of 182 rain gages are used for improvement of NEXRAD data from years 1994-2007. Results from this study highlight the difficulties in applying bias corrections procedures with data sets of different temporal resolutions and performances of different spatial interpolation methods.

Research paper thumbnail of Evaluation of Different Climate Change Models for Future Precipitation Projections in Florida

World Environmental and Water Resources Congress 2012, 2012

Precipitation being a vital input for many hydrological modeling studies has a direct bearing on ... more Precipitation being a vital input for many hydrological modeling studies has a direct bearing on the water resources modeling and management at different spatial and temporal scales. According to Intergovernmental Panel on Climate Change (IPCC), frequency of extreme precipitation events is expected to increase in future with no consistent trend in mean precipitation across the globe. To evaluate trends in precipitation, Global Circulation Models (GCMs) combined with statistical or dynamic downscaling techniques are generally used. However, it is agreed that skill of any climate change model is lower for precipitation compared to that for temperature. The model performance also depends on spatial and temporal resolution of the simulations. In the current study, precipitation projections based on fifteen GCMs from WCRP's(World Climate Research Program) Coupled Model Inter-comparison Project, phase -3 (CMIP3) project with different SRES (Special Report on Emission Scenarios) runs are analyzed for the state of Florida. Historical precipitation data is used for evaluation of the models via several performance measures and for selection of the best model. Long term historical precipitation data from United States Historical Climatology Network (USHCN) and GCM simulations from 20th and 21st century are used in this study. Efficacy and utility of Bias-Corrected Spatial Disaggregation (BCSD) procedure used in CMIP3 project for downscaling precipitation data for the state of Florida is assessed. Performances of models in two distinct seasons (wet and dry) that dominate tropical climate of Florida are also evaluated.

Research paper thumbnail of Influence of Teleconnections on Spatial and Temporal Variability of Extreme Precipitation Events in Florida

World Environmental and Water Resources Congress 2012, 2012

Two major teleconnections, AMO (Atlantic multi-decadal oscillation) and ENSO (El Nino southern os... more Two major teleconnections, AMO (Atlantic multi-decadal oscillation) and ENSO (El Nino southern oscillation) under cool and warm cycles (phases) influencing precipitation patterns in Florida are assessed in this study. Temporal shift in the occurrences of precipitation extremes and changes in the magnitudes of these extremes are evaluated in different phases. Extreme precipitation events for nine different durations are also evaluated. Assessment of spatial variability of extreme precipitation in different rain areas (meteorologically homogeneous areas), and AMO and ENSO combined influences on precipitation is also carried out. Long-term historical precipitation data from National Climatic Data Center (NCDC) are used for the statistical analyses using parametric and non-parametric methods.

Research paper thumbnail of Precipitation Extremes and Climate Change: Evaluation Using Descriptive WMO Indices

World Environmental and Water Resources Congress 2012, 2012

Assessment of spatial and temporal extreme precipitation events due to climate variability and ch... more Assessment of spatial and temporal extreme precipitation events due to climate variability and change is critical for future hydrologic design. Evaluation of these extremes in the past has been limited to evaluation of annual and partial duration series. However, climate-change sensitive hydrologic design requires evaluation of precipitation extremes at different temporal levels using a variety of indices. This study evaluates the variability of precipitation extremes in two climatic regions in the U. S. using WMO (World Meteorological Organization) proposed and adopted eleven indices. These indices relate to precipitation extremes at a daily temporal scale and encompass a variety of conditions including user-defined precipitation thresholds. Quantitative evaluation, statistical analyses and spatial variability of indices in a region as well across different climate zones indicate that infilling of precipitation data and existence of in homogeneities influences the assessment of trends in extreme events using indices. This paper presents preliminary results of an ongoing study.

Research paper thumbnail of Influences of Atlantic multidecadal oscillation phases on spatial and temporal variability of regional precipitation extremes

Journal of Hydrology, 2013

A major teleconnection, Atlantic multidecadal oscillation (AMO) under two phases (cool and warm) ... more A major teleconnection, Atlantic multidecadal oscillation (AMO) under two phases (cool and warm) influencing precipitation extremes in Florida, USA, is the main focus of this study. Long-term extreme precipitation data from several rain gages from temporal windows that coincide with the AMO phases are evaluated for changes in spatial and temporal variability across the region. Assessments of precipitation extremes for nine durations in different meteorologically homogenous rainfall areas as well as in the entire region are carried out. Methods of assessment included parametric unpaired t-tests and nonparametric Mann-Whitney U tests, kernel density estimates using Gaussian kernel for distribution-free comparative analysis and bootstrap sampling-based confidence intervals. Depth-duration-frequency (DDF) curves are also developed using generalized extreme value (GEV) distributions characterizing the extremes. Analysis of data indicated increase in precipitation extremes in warm phases of AMO for durations greater than 24 h. The influence of warm or cool phases of AMO on precipitation extremes is not spatially uniform in the region. Temporal shifts in occurrences of extremes from the later part of the year in warm phase to earlier in the year for the cool phase are evident. These shifts will have implications on flooding events in different regions of Florida. Magnitudes of extremes for a 25 year return period based on DDF curves were higher for all nine durations when data from cool or warm phase alone were compared to those obtained from data from two phases. Precipitation extremes for durations longer than a day are associated with increased landfalls of hurricanes occurring in the region in the AMO warm phases.

Research paper thumbnail of Individual and coupled influences of AMO and ENSO on regional precipitation characteristics and extremes

Water Resources Research, 2014

ABSTRACT