WebMACA is a statistical downscaling method that utilizes a training dataset (i.e., meteorological observations) to remove historical biases and match spatial patterns in climate model output. The MACA dataset offers data for the following variables: tasmax—Maximum daily temperature near surface. tasmin—Minimum daily temperature … Web28 Jul 2014 · Downscaled CMIP3 and CMIP5 Climate and Hydrology Projections : “Downscaled” (finer-resolution) versions of monthly and daily temperature and precipitation from most of the GCM simulations in the PCMDI CMIP3 and CMIP5 archives, for CONUS, using two downscaling methods. In addition, derived simulations of surface hydrology are …
Climate model downscaling: explainer - Climate Futures
A good knowledge of future coastal wind and wave resources in the context of climate change is crucial for the construction of offshore wind farms. In this study, the dataset of the coupled model intercomparison project phase 6 (CMIP6) was used to evaluate the future wind resources and wave conditions in the nearshore area of … Web12 Apr 2024 · Also, irrespective of the climate regime and the machine learning technique, at the majority of stations downscaling models showed an over-estimating trend of low to mid percentiles (i.e. below ... gembloux weather
Deep Learning for Daily Precipitation and Temperature …
WebStatistical downscaling is a process used to transform large-scale climate model outputs into meaningful information that can be used to assess climate change impacts and adaptation options. It can be used to project future changes in climate variables such as temperature, precipitation, wind velocity, and other aspects of the climate. Web‘Downscaling’ is the process by which coarse-resolution GCM outputs are translated into finer resolution climate information, so that they better account for regional climatic influences, such as local topography. There are many different ways in which GCM outputs can be translated to finer resolutions or even point locations. Web26 Apr 2024 · One method, commonly referred to as “statistical downscaling”, uses the empirical-statistical relationships between large-scale weather phenomena and historical local weather data. In this method, these statistical relationships are applied to output generated by global climate models. gembok american secure