A comparison of three stochastic multi-site precipitation occurrence generators
By Rajeshwar Mehrotra, R. Srikanthan, Ashish Sharma. 2006
124 citations recorded by the source corpus.
Subfields: Ecology, Evolution, Behavior and Systematics, Global and Planetary Change, Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/539b1427ab3ef0fbc96df09c1a6f79e0cd7cc409
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In Ecology, Evolution, Behavior and Systematics
In Global and Planetary Change
- Balaji Rajagopalan
- William Kleiber
- R. Katz
- Andrew Verdin
- Guillaume Évin
- Anne-Catherine Favre
- B. Hingray
- Vijay P. Singh
In Management Science and Operations Research
Citations in the corpus
- Coupled stochastic weather generation using spatial and generalized linear models (2015, background): “Multi-site extensions of single-site weather generators can be unwieldy with large number of parameters to capture the statistics at each site and their spatial correlation (Mehrotra et al. 2006), more so with a large number of locations.”
- Copula based multisite model for daily precipitation simulation (2009, unknown): “(Mehrotra et al., 2006) compared three multi-site stochastic weather generators, including a parametric hidden Markov model (HMM), (Srikanthan, 2005) multisite stochastic precipitation generation model proposed by (Wilks, 1998) and a non10 parametric K-nearest neighbour (KNN) model, concluding that ”
- Stochastic generation of multi-site daily precipitation focusing on extreme events (2018, methodology): “Mehrotra et al. (2006) compare three different precipitation models, the Wilks model, a HMM and a resampling approach, and they provide strong arguments in favor of the Wilks model in terms of performance, computation time, model, and level of complexity of the model structure.”