Просмотр новости
Найдите то, что Вас интересует
Спутники
РН
SpaceX
Роскосмос
NASA
СпутникиРНSpaceXРоскосмосNASAClimate change and extreme climate events adversely impact human health globally, but there are substantial challenges for health users in accessing climate datasets and assessing their quality, as well as in obtaining data at a relevant spatio-temporal resolution for decision-making. These include a lack of training in where to access suitable sources of climate data, and how to spatially and temporally harmonise climate data with epidemiological data. Health impact studies typically require climate data at a fine spatial resolution, meaning that spatial downscaling methodologies are often required to obtain data at a relevant spatial scale due to the native large scale of the models. Furthermore, bias correction methodologies are necessary to overcome potentially large biases of climate models and forecasts, which can present a challenge for health users unfamiliar with these steps. The R package clim4health is developed as a freely-available and easy-to-use tool for researchers, data scientists and public health agencies to build analysis-ready datasets for conducting climate and health analyses (or modelling studies). clim4health contains a suite of functions allowing the user to download, load, plot and save multi-scale spatio-temporal climate datasets, as well as more advanced post-processing tools for calibration, downscaling, and quality assessment. Detailed vignettes and case study examples in climate change hotspots (sites in Colombia, the Dominican Republic, and Brazil) demonstrate and explain methods of calibration, downscaling, verification and postprocessing for different sources of climate data, including reanalyses and weather station data, and seasonal climate predictions.