New Methods for Estimation of Missing Precipitation Data at a Site and other works
This page provides links to different publications of Dr. Ramesh Teegavarapu and co-authors for estimation of missing precipitation data at a site using different new and innovative spatial interpolation, optimization and data mining methods.
Ramesh S. V. Teegavarapu and V. Chandramouli, Improved Weighting Methods, Deterministic and Stochastic Data-driven Models for Estimation of Missing Precipitation Records, Journal of Hydrology, 191-206, 312, 2005 Link to Publication
Ramesh S. V. Teegavarapu, Spatial Interpolation using Non-linear Mathematical Programming Models for Estimation of Missing Precipitation Records, Hydrological Sciences Journal, 57(3), 383-406, 2012. Link to Publication
Ramesh S. V. Teegavarapu, Statistical Corrections of Spatially Interpolated Precipitation Estimates, Hydrological processes, 28(11), 3789–3808, 2014. DOI: 10.1002/hyp.9906. Link to Publication
Ramesh S. V. Teegavarapu, Missing Precipitation Data Estimation using Optimal Proximity Metric-based Imputation, Nearest Neighbor Classification and Cluster-based Interpolation Methods, Hydrological Sciences Journal, 2013. DOI:10.1080/02626667.2013.862334. Link to Publication
Ramesh S. V. Teegavarapu, Estimation of Missing Precipitation Records Integrating Surface Interpolation Techniques and Spatio-Temporal Association Rules , Journal of HydroInformatics Vol, 11 No 2 pp 133–146, 2009. Link to Publication
Ramesh S. V. Teegavarapu, Tadesse Meskele and Chandra Pathak, Geo-Spatial Grid-based Transformation of Multi-Sensor Precipitation using Spatial Interpolation Methods, Computers and Geosciences, 40, 28-39, 2012. Doi:10.1016./j.cageo2011.07.004. Link to Publication
Ramesh S. V. Teegavarapu, Use of Universal Function Approximation in Variance-dependent Interpolation Technique: An Application in Hydrology, 332, 16-29, 2007. Link to Publication
Ramesh S. V. Teegavarapu, Mohammad Tufail and Lindell Ormsbee, Optimal Functional Forms for Estimation of Missing Precipitation Records, Journal of Hydrology, 2009, 374 (2009) 106–115. Link to Publication
Ramesh S. V. Teegavarapu, Ala Aly, Chandra S. Pathak, Jon Ahlquist, Henry Fuelberg, Jill Hood, Infilling Missing Precipitation Records using Variants of Spatial Interpolation and Data-Driven Methods: Use of Optimal Weighting Parameters and Nearest Neighbor-based Corrections, International Journal of Climatology, 2017.
Tigstu, Dullo, A. Kalyanapu and Ramesh S. V. Teegavarapu, Evaluation of Changing Characteristics of Temporal Rainfall Distribution within 24-hour Duration Storms and their Influences on Peak Discharges: A Case Study of Asheville, North Carolina, ASCE Journal of Hydrologic Engineering, 2017, Published.
Radar-based Precipitation Estimation
Ramesh S. V. Teegavarapu, Aneesh Goly, Qinglong Wu, A Comprehensive Framework for Assessment of Radar-based Precipitation Data Estimates, Journal of Hydrologic Engineering, ASCE, 2015. Link to Publication
Trends in Extreme Precipitation
Ramesh S. V. Teegavarapu and Anurag Nayak. Evaluation of Long-term Trends in Extreme Precipitation: Implications of Infilled Historical Data and Temporal-Window based Analysis,2017. Link to Publication
- Rainfall Analysis and INterpolation (RAIN) – Designed and Developed by Dr. Ramesh Teegavarapu (2012). Available in Public Domain.
RAIN provides several spatial interpolation methods for estimation of missing precipitation data at a site. Analysis of rainfall in space and time is also possible using RAIN. This software can also be used for interpolation of hydrometeorological variables. RAIN software provides over 20 deterministic and stochastic interpolation methods.