Improved weighting methods, deterministic and stochastic data-driven models for estimation of missing precipitation records
By Ramesh S. V. Teegavarapu, V. Chandramouli. 2005
369 citations recorded by the source corpus.
Subfields: Atmospheric Science, Environmental Engineering, Global and Planetary Change
View Paper: https://www.semanticscholar.org/paper/4950e212041ab6a16f120fef326e1874d0edbbb8
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Atmospheric Science
- Maurizio Maugeri
- Ceylan Yozgatlıgil
- C. Simolo
- Cem Iyigun
- Sipan Aslan
- Michele Brunetti
- T. Nanni
- Inci Batmaz
In Environmental Engineering
- Maurizio Maugeri
- Ceylan Yozgatlıgil
- C. Simolo
- Cem Iyigun
- Sipan Aslan
- Michele Brunetti
- T. Nanni
- Inci Batmaz
In Global and Planetary Change
- Maurizio Maugeri
- Ceylan Yozgatlıgil
- C. Simolo
- Cem Iyigun
- Sipan Aslan
- Michele Brunetti
- T. Nanni
- Inci Batmaz
Citations in the corpus
- Comparison of missing value imputation methods in time series: the case of Turkish meteorological data (2013, methodology): “Another comparative study on simple methods for the completion of precipitation series is conducted by Teegavarapu and Chandramouli (2005).”
- Improving estimation of missing values in daily precipitation series by a probability density function‐preserving approach (2010, methodology): “…methods for the interpolation of precipitation data has been investigated by a number of comparative studies, both on a monthly and daily basis (e.g. Ashraf et al., 1997; Teegavarapu and Chandramouli, 2005; Ramos et al., 2008), and generally found to be greater than that of weighting approaches.”
- Interpolation of Missing Temperature Data at Meteorological Stations Using P-BSHADE (2013, neutral): “Several variants of IDW have been developed, with principal focus on weighting schemes (Teegavarapu and Chandramouli 2005; You et al. 2008).”