An advanced stochastic weather generator for simulating 2‐D high‐resolution climate variables
By N. Peleg, S. Fatichi, A. Paschalis, P. Molnar, Paolo Burlando. 2017
158 citations recorded by the source corpus.
A new stochastic weather generator, Advanced WEather GENerator for a two‐dimensional grid (AWE‐GEN‐2d) is presented. The model combines physical and stochastic approaches to simulate key meteorological variables at high spatial and temporal resolution: 2 km × 2 km and 5 min for precipitation and cloud cover and 100 m × 100 m and 1 h for near‐surface air temperature, solar radiation, vapor pressure, atmospheric pressure, and near‐surface wind. The model requires spatially distributed data for the calibration process, which can nowadays be obtained by remote sensing devices (weather radar and satellites), reanalysis data sets and ground stations. AWE‐GEN‐2d is parsimonious in terms of computational demand and therefore is particularly suitable for studies where exploring internal climatic variability at multiple spatial and temporal scales is fundamental. Applications of the model include models of environmental systems, such as hydrological and geomorphological models, where high‐resolution spatial and temporal meteorological forcing is crucial. The weather generator was calibrated and validated for the Engelberg region, an area with complex topography in the Swiss Alps. Model test shows that the climate variables are generated by AWE‐GEN‐2d with a level of accuracy that is sufficient for many practical applications.
Subfields: Atmospheric Science, Global and Planetary Change
View Paper: https://www.semanticscholar.org/paper/8d41085e226b861db1ea46fb467b4514e826a449
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
In Global and Planetary Change
- F. Serinaldi
- Simon Michael Papalexiou
- Vijay P. Singh
- Chris G. Kilsby
- Shenglian Guo
- Demetris Koutsoyiannis
- M. Janga Reddy
- Poulomi Ganguli
Citations in the corpus
- Random Fields Simplified: Preserving Marginal Distributions, Correlations, and Intermittency, With Applications From Rainfall to Humidity (2020, methodology): “…of Beads” method, and Mantoglou and Wilson (1982) investigated the “Turning bands method” (later modified by Leblois & Creutin, 2013); many more simulators can also be found in the literature (see, e.g., Baxevani & Lennartsson, 2015; Benoit et al., 2018; Paschalis et al., 2013; Peleg et al., 2017).”