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Select and Download Climate Data from 'DWD' (German Weather Service)
Handle climate data from the 'DWD' ('Deutscher Wetterdienst', see < https://www.dwd.de/EN/climate_environment/cdc/cdc_node_en.html> for more information). Choose observational time series from meteorological stations with 'selectDWD()'. Find raster data from radar and interpolation according to < https://brry.github.io/rdwd/raster-data.html>. Download (multiple) data sets with progress bars and no re-downloads through 'dataDWD()'. Read both tabular observational data and binary gridded datasets with 'readDWD()'.
Climate Indices
Computes 138 standard climate indices at monthly, seasonal and annual resolution. These indices were selected, based on their direct and significant impacts on target sectors, after a thorough review of the literature in the field of extreme weather events and natural hazards. Overall, the selected indices characterize different aspects of the frequency, intensity and duration of extreme events, and are derived from a broad set of climatic variables, including surface air temperature, precipitation, relative humidity, wind speed, cloudiness, solar radiation, and snow cover. The 138 indices have been classified as follow: Temperature based indices (42), Precipitation based indices (22), Bioclimatic indices (21), Wind-based indices (5), Aridity/ continentality indices (10), Snow-based indices (13), Cloud/radiation based indices (6), Drought indices (8), Fire indices (5), Tourism indices (5).
Simulating Climate Data for Research and Modelling
Generate synthetic station-based monthly climate time-series including
temperature and rainfall, export to Network Common Data Form (NetCDF),
and provide visualization helpers for climate workflows. The approach is
inspired by statistical weather generator concepts described in Wilks (1992)
PaleoPhyloGeographic Modeling of Climate Niches and Species Distributions
Reconstruction of paleoclimate niches using phylogenetic comparative
methods and projection reconstructed niches onto paleoclimate maps.
The user can specify various models of trait evolution or estimate the best fit
model, include fossils, use one or multiple phylogenies for inference, and make
animations of shifting suitable habitat through time. This model was first used
in Lawing and Polly (2011), and further implemented in Lawing et al (2016) and
Rivera et al (2020).
Lawing and Polly (2011)
Statistical Downscaling of Climate Predictions
Statistical downscaling and bias correction of climate predictions.
It includes implementations of commonly used methods such as Analogs,
Linear Regression, Logistic Regression, and Bias Correction techniques,
as well as interpolation functions for regridding and point-based applications.
It facilitates the production of high-resolution and local-scale climate
information from coarse-scale predictions, which is essential for impact analyses.
The package can be applied in a wide range of sectors and studies,
including agriculture, water management, energy, heatwaves, and other
climate-sensitive applications. The package was developed within the framework of
the European Union Horizon Europe projects Impetus4Change (101081555) and ASPECT (101081460),
the Wellcome Trust supported HARMONIZE project (224694/Z/21/Z), and the Spanish national project
BOREAS (PID2022-140673OA-I00). Implements the methods described in
Duzenli et al. (2024)
Non-Homogeneous Markov Switching Autoregressive Models
Calibration, simulation, validation of (non-)homogeneous Markov switching autoregressive models with Gaussian or von Mises innovations. Penalization methods are implemented for Markov Switching Vector Autoregressive Models of order 1 only. Most functions of the package handle missing values.
Plotting Functions for Climate Science and Services
A plotting package for climate science and services. Provides a set
of functions for visualizing climate data, including maps, time series,
scorecards and other diagnostics. Some functions are adapted and extended
from the 's2dv' and 'CSTools' packages (Manubens et al. (2018)
Integrating Phylogenetics and Climatic Niche Modeling
Implements some methods in phyloclimatic modeling: estimation of ancestral climatic niches, age-range-correlation, niche equivalency test and background-similarity test.
Climate Crop Zoning Based in Air Temperature for Brazil
Climate crop zoning based in minimum and maximum air temperature. The data used in the package are from 'TerraClimate' dataset (< https://www.climatologylab.org/terraclimate.html>), but, it have been calibrated with automatic weather stations of National Meteorological Institute of Brazil. The climate crop zoning of this package can be run for all the Brazilian territory.
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.