Found 316 packages in 0.06 seconds
Climate Classification According to Several Indices
Classification of climate according to Koeppen - Geiger, of aridity indices, of continentality indices, of water balance after Thornthwaite, of viticultural bioclimatic indices. Drawing climographs: Thornthwaite, Peguy, Bagnouls-Gaussen.
Bayesian Emulation of Computer Programs
Allows one to estimate the output of a computer program, as a function of the input parameters, without actually running it. The computer program is assumed to be a Gaussian process, whose parameters are estimated using Bayesian techniques that give a PDF of expected program output. This PDF is conditional on a training set of runs, each consisting of a point in parameter space and the model output at that point. The emphasis is on complex codes that take weeks or months to run, and that have a large number of undetermined input parameters; many climate prediction models fall into this class. The emulator essentially determines Bayesian posterior estimates of the PDF of the output of a model, conditioned on results from previous runs and a user-specified prior linear model. The package includes functionality to evaluate quadratic forms efficiently.
Zhang + Yue-Pilon Trends Package
An efficient implementation of the slope method described by Sen (1968)
Dataset for Climate Analysis with Data from the Nordic Region
The Nordklim dataset 1.0 is a unique and useful achievement for climate analysis. It includes observations of twelve different climate elements from more than 100 stations in the Nordic region, in time span over 100 years. The project contractors were NORDKLIM/NORDMET on behalf of the National meteorological services in Denmark (DMI), Finland (FMI), Iceland (VI), Norway (DNMI) and Sweden (SMHI).
Multivariate Bias Correction of Climate Model Outputs
Calibrate and apply multivariate bias correction algorithms
for climate model simulations of multiple climate variables. Three methods
described by Cannon (2016)
Agro-Climatic Data by County
The functions are designed to calculate the most widely-used county-level variables in agricultural production or agricultural-climatic and weather analyses. To operate some functions in this package needs download of the bulk PRISM raster. See the examples, testing versions and more details from: < https://github.com/ysd2004/acdcR>.
Uncertainties of Climate Projections using Smoothing Splines
These functions use smoothing-splines for the assessment of single-member ensembles of climate projections.
- Cheng, C.-I. and P. L. Speckman (2012)
Evaluation Tools for Assessing Climate Adaptation of Fruit Tree Species
Climate is a critical component limiting growing range of plant species, which
also determines cultivar adaptation to a region. The evaluation of climate influence on
fruit production is critical for decision-making in the design stage of orchards and
vineyards and in the evaluation of the potential consequences of future climate. Bio-
climatic indices and plant phenology are commonly used to describe the suitability of
climate for growing quality fruit and to provide temporal and spatial information about
regarding ongoing and future changes. 'fruclimadapt' streamlines the assessment of
climate adaptation and the identification of potential risks for grapevines and fruit
trees. Procedures in the package allow to i) downscale daily meteorological variables
to hourly values (Forster et al (2016)
Dengue Cases and Climate Variables in Colombo Sri Lanka
Weekly notified dengue cases and climate variables in Colombo district Sri Lanka from 2008/ week-52 to 2014/ week-21.
Partitioning Uncertainty Components of an Incomplete Ensemble of Climate Projections
These functions use data augmentation and Bayesian techniques for the assessment of single-member and incomplete ensembles of climate projections. It provides unbiased estimates of climate change responses of all simulation chains and of all uncertainty variables. It additionally propagates uncertainty due to missing information in the estimates.
- Evin, G., B. Hingray, J. Blanchet, N. Eckert, S. Morin, and D. Verfaillie. (2019)