An advanced stochastic weather generator for simulating 2‐D high‐resolution climate variables. (10th July 2017)

• Record Type: Journal Article
• Authors: Peleg, Nadav; Fatichi, Simone; Paschalis, Athanasios; Molnar, Peter; Burlando, Paolo
• Is Part Of: Journal of advances in modeling earth systems. Volume 9:Number 3(2017)
• Page Start: 1595
• Page End: 1627
• Publication Date: 2017-07-10
• Subjects: high‐resolution climate simulations -- rainfall generator -- stochastic modeling -- spatially distributed climate variables -- subdaily resolution -- weather generator; Geological modeling -- Periodicals; Climatology -- Periodicals; Geochemical modeling -- Periodicals; 551.5011
• DOI: 10.1002/2016MS000854
• Languages: English
• ISSNs: 1942-2466

Abstract

Abstract: A new stochastic weather generator, A dvancedWE atherGEN erator 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. Key Points: A new advanced subdaily stochastic weather generator (AWE‐GEN‐2d) is presented The model combines physical and stochastic approaches to simulate key climate variables at high spatial and temporal resolution It is relatively computationally efficient and allows the generation of multiple realizations accounting for natural climate variability