https://research-explorer.ista.ac.at 2000-01-01T00:00+00:00 1 daily https://research-explorer.ista.ac.at/record/22543 Peleg, Nadav Fatichi, Simone Paschalis, Athanasios Molnar, Peter Burlando, Paolo 2017 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. https://research-explorer.ista.ac.at/record/22543 eng American Geophysical Union info:eu-repo/semantics/altIdentifier/doi/10.1002/2016ms000854 info:eu-repo/semantics/altIdentifier/e-issn/1942-2466 https://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess Peleg N, Fatichi S, Paschalis A, Molnar P, Burlando P. An advanced stochastic weather generator for simulating 2‐D high‐resolution climate variables. <i>Journal of Advances in Modeling Earth Systems</i>. 2017;9(3):1595-1627. doi:<a href="https://doi.org/10.1002/2016ms000854">10.1002/2016ms000854</a> ddc:550 An advanced stochastic weather generator for simulating 2‐D high‐resolution climate variables info:eu-repo/semantics/article doc-type:article text http://purl.org/coar/resource_type/c_2df8fbb1 https://research-explorer.ista.ac.at/record/22551 Peleg, Nadav Molnar, Peter Burlando, Paolo Fatichi, Simone 2019 Exploring the effects of climate change on the hydrological response at the local scale requires climate data at high spatial and temporal resolutions. This is best achieved by generating downscaled ensembles of future climate variables derived from climate models. For this purpose we present a methodology to re-parameterize the AWE-GEN-2d model (Advanced WEather GENerator for a two-dimensional grid). The model simulates key meteorological variables needed by hydrological models and is particularly suitable to explore the effects of stochastic (natural) climatic uncertainty, which is fundamental for hydrological applications, especially at sub-kilometer and hourly scales. Factors of change for different climate statistics are calculated from climate model simulations of present and future climates and subsequently applied to the statistics derived from observations to re-parameterize AWE-GEN-2d. The model abilities in generating an ensemble of future climate variables for the transient period 2020–2089 is presented with examples of precipitation and near-surface air temperature fields from hourly to multi-annual scales for a small mountainous region in the Swiss Alps. The stochastic uncertainty is examined for present and future periods and for spatial scales from the RCM scale (12-km, daily) to 2-km demonstrating the potential use of AWE-GEN-2d outputs. At the RCM scale, model results yield a small increase in annual precipitation (4%) which is within the stochastic uncertainty range for present and future periods (7%). At the fine scale of 2-km, the increase in annual precipitation can exceed the stochastic uncertainty, but for less than 10% of the domain area. On the contrary, changes in annual near-surface air temperature exceed stochastic uncertainty both at the RCM and finer scales. Stochastic climate uncertainty was concluded to be very similar when comparing present and future periods and 12-km and 2-km scales. The benefits of using AWE-GEN-2d in hydrological climate change impact assessments are finally discussed. https://research-explorer.ista.ac.at/record/22551 eng Elsevier info:eu-repo/semantics/altIdentifier/doi/10.1016/j.jhydrol.2019.02.010 info:eu-repo/semantics/altIdentifier/issn/0022-1694 info:eu-repo/semantics/altIdentifier/e-issn/1879-2707 info:eu-repo/semantics/closedAccess Peleg N, Molnar P, Burlando P, Fatichi S. Exploring stochastic climate uncertainty in space and time using a gridded hourly weather generator. <i>Journal of Hydrology</i>. 2019;571:627-641. doi:<a href="https://doi.org/10.1016/j.jhydrol.2019.02.010">10.1016/j.jhydrol.2019.02.010</a> Weather generator Stochastic downscaling Climate change Internal climate variability Climate uncertainty High-resolution rainfall model Exploring stochastic climate uncertainty in space and time using a gridded hourly weather generator info:eu-repo/semantics/article doc-type:article text http://purl.org/coar/resource_type/c_2df8fbb1