scholarly journals Minimum data requirements for parameter estimation of stochastic weather generators

2003 ◽  
Vol 25 ◽  
pp. 109-119 ◽  
Author(s):  
A Soltani ◽  
G Hoogenboom
Keyword(s):  
Parameter Estimation ◽  
Minimum Data ◽  
Weather Generators ◽  
Stochastic Weather Generators ◽  
Data Requirements

scholarly journals Comparative Study of Different Stochastic Weather Generators for Long-Term Climate Data Simulation

Climate ◽  
2017 ◽  
Vol 5 (2) ◽  
pp. 26 ◽  
Author(s):  
Sushant Mehan ◽  
Tian Guo ◽  
Margaret Gitau ◽  
Dennis C. Flanagan
Keyword(s):  
Comparative Study ◽  
Climate Data ◽  
Data Simulation ◽  
Weather Generators ◽  
Stochastic Weather Generators

scholarly journals Comparison of LARS-WG and AAFC-WG stochastic weather generators for diverse Canadian climates

2004 ◽  
Vol 26 ◽  
pp. 175-191 ◽  
Author(s):  
B Qian ◽  
S Gameda ◽  
H Hayhoe ◽  
R De Jong ◽  
A Bootsma
Keyword(s):  
Weather Generators ◽  
Stochastic Weather Generators

scholarly journals Quantifying Uncertainty Due to Stochastic Weather Generators in Climate Change Impact Studies

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Fosco M. Vesely ◽  
Livia Paleari ◽  
Ermes Movedi ◽  
Gianni Bellocchi ◽  
Roberto Confalonieri
Keyword(s):  
Climate Change ◽  
Climate Change Impact ◽  
Impact Studies ◽  
Weather Generators ◽  
Change Impact ◽  
Stochastic Weather Generators ◽  
Quantifying Uncertainty

scholarly journals Scaling, similarity, and the fourth paradigm for hydrology

2017 ◽  
Vol 21 (7) ◽  
pp. 3701-3713 ◽  
Author(s):  
Christa D. Peters-Lidard ◽  
Martyn Clark ◽  
Luis Samaniego ◽  
Niko E. C. Verhoest ◽  
Tim van Emmerik ◽  
...  
Keyword(s):  
Boundary Condition ◽  
Parameter Estimation ◽  
Computational Simulation ◽  
Data Driven ◽  
Data Intensive ◽  
The Third ◽  
Hydrologic Data ◽  
Condition State ◽  
Information Framework ◽  
Data Requirements

Abstract. In this synthesis paper addressing hydrologic scaling and similarity, we posit that roadblocks in the search for universal laws of hydrology are hindered by our focus on computational simulation (the third paradigm) and assert that it is time for hydrology to embrace a fourth paradigm of data-intensive science. Advances in information-based hydrologic science, coupled with an explosion of hydrologic data and advances in parameter estimation and modeling, have laid the foundation for a data-driven framework for scrutinizing hydrological scaling and similarity hypotheses. We summarize important scaling and similarity concepts (hypotheses) that require testing; describe a mutual information framework for testing these hypotheses; describe boundary condition, state, flux, and parameter data requirements across scales to support testing these hypotheses; and discuss some challenges to overcome while pursuing the fourth hydrological paradigm. We call upon the hydrologic sciences community to develop a focused effort towards adopting the fourth paradigm and apply this to outstanding challenges in scaling and similarity.

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