Aneesh Goly

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

Understanding the influences of Atlantic multidecadal oscillation (AMO) and El Nino southern oscillation (ENSO) on regional precipitation extremes and characteristics in the state of Florida is the focus of this study. Exhaustive evaluations of individual and combined influences of these oscillations using, descriptive indices-based assessment of statistically significant changes in rainfall characteristics, identification of spatially varying influences of oscillations on dry and wet spell transition states, antecedent precipitation prior to extreme events, intraevent temporal distribution of precipitation and changes in temporal occurrences of extremes including dry/wet cycles are carried out. Rain gage and gridded precipitation data analysis using parametric hypothesis tests confirm statistically significant changes in the precipitation characteristics from one phase to another of each oscillation and also in coupled phases. Spatially nonuniform and uniform influences of AMO and ENSO, respectively, on precipitation are evident. AMO influences vary in peninsular and continental parts of Florida and the warm (cool) phase of AMO contributes to increased precipitation extremes during wet (dry) season. The influence of ENSO is confined to dry season with El Nino (La Nina) contributing to increase (decrease) in extremes and total precipitation. Wetter antecedent conditions preceding daily extremes are dominant in AMO warm phase compared to the cool and are likely to impact design floods in the region. AMO influence on dry season precipitation extremes is noted for ENSO neutral years. The two oscillations in different phases modulate each other with seasonal and spatially varying impacts and implications on flood control and water supply in the region.

Development and Evaluation of Statistical Downscaling Models for Monthly Precipitation

AbstractSeveral 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 Climate Model, ...

INFLUENCE OF TELECONNECTIONS ON SPATIAL AND TEMPORAL VARIABILITY OF EXTREME PRECIPITATION EVENTS IN FLORIDA

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.

Precipitation Extremes and Climate Change: Evaluation Using Descriptive WMO Indices

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.

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

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 WCRPs(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.

Assessment of Various Statistical Downscaling Methods for Downscaling Precipitation in Florida

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). GCMs have a good chance to capture the large-scale circulation patterns and correctly model smoothly varying fields such as surface pressure, but it is extremely unlikely that these models properly reproduce non smooth fields such as precipitation. This study compares five statistically downscaling models, viz, Multiple Linear Regression (MLR), Positive Coefficient Regression (PCR), Stepwise Regression (SWR), Support Vector Machine (SVM) and BiasCorrection Spatial Disaggregation (BCSD) for estimation of rainfall in the state of Florida, USA. The performance of the models is evaluated using various performance measures and it was found that the SVM model performed better than all the other models in reproducing most monthly rainfall statistics at 18 locations. Output from the third generation Canadian Global Climate Model (CGCM3) GCM for A1B scenario was used for future precipitation projections.

Comprehensive Framework for Assessment of Radar-Based Precipitation Data Estimates

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...

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

Statistical downscaling modeling-based results often depict biases and are incapable of reproducing the variances and approximating the distributions of the predictands. This paper proposes a multi-objective optimization framework to reduce biases resulting from the limitations of the transfer function methods adopted under statistical downscaling procedures. The proposed formulations use a combination of several error measures including Linear Error in Probability Space (LEPS) to reduce any systematic biases. Also, different optimization algorithms both linear and nonlinear are evaluated for this purpose. The optimization formulations are applied and tested at 18 different locations for Support Vector Machine (SVM) based statistical downscaling model results obtained from CGCM3 simulations for precipitation. Results indicate that the multi-objective optimization framework helps is reducing the biases of the statistically downscaled model results.

INFLUENCE OF TEMPORAL RESOLUTION OF RAIN GAGE DATA ON BIAS CORRECTION PROCEDURES FOR NEXRAD ESTIMATES

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.