Meteorological Data Manipulation

A set of functions for weather and climate data manipulation, and other helper functions, to support dynamic ecological modeling, particularly crop and crop disease modeling.

Interface to Climatic Research Unit Time-Series Version 3.21 Data

Functions for reading in and manipulating CRU TS3.21: Climatic Research Unit (CRU) Time-Series (TS) Version 3.21 data.

Monthly Climate Data for Germany, Usable for Heating and Cooling Calculations

This data package contains monthly climate data in Germany, it can be used for heating and cooling calculations (external temperature, heating / cooling days, solar radiation).

Easy Access to High-Resolution Daily Climate Data for Europe

Get high-resolution (1 km) daily, monthly and annual climate data (precipitation, and average, minimum and maximum temperatures) for points and polygons within Europe.

Weather and Climate Inputs for 'SWAT'

Provides workflows to prepare weather and climate time series from gridded and station data for 'SWAT' ('Soil and Water Assessment Tool'). Supports data extraction, aggregation, interpolation, quality control, unit conversion, and export of per-location model input files. For the underlying model, see Arnold et al. (1998) "Large Area Hydrologic Modeling and Assessment Part I: Model Development" .

Species Sensitivity Distributions

Species sensitivity distributions are cumulative probability distributions which are fitted to toxicity concentrations for different species as described by Posthuma et al.(2001) . The ssdtools package uses Maximum Likelihood to fit distributions such as the gamma, log-logistic, log-normal and log-normal log-normal mixture. Multiple distributions can be averaged using Akaike Information Criteria. Confidence intervals on hazard concentrations and proportions are produced by bootstrapping.

Detection of Structural Changes in Climate and Environment Time Series

Tools for automatic model selection and diagnostics for Climate and Environmental data. In particular the envcpt() function does automatic model selection between a variety of trend, changepoint and autocorrelation models. The envcpt() function should be your first port of call.

Compiles and Visualizes Wildfire, Climate, and Air Quality Data

Fetches data from three disparate data sources and allows user to perform analyses on them. It offers two core components: 1. A robust data retrieval and preparation infrastructure for wildfire, climate, and air quality index data and 2. A simple, informative, and interactive visualizations of the aforementioned datasets for California counties from 2011 through 2015. The sources of data are: wildfire data from Kaggle < https://www.kaggle.com/rtatman/188-million-us-wildfires>, climate data from the National Oceanic and Atmospheric Administration < https://www.ncdc.noaa.gov/cdo-web/token>, and air quality data from the Environmental Protection Agency < https://aqs.epa.gov/aqsweb/documents/data_api.html>.

Tools for Analyzing Climate Extremes

Functions for fitting GEV and POT (via point process fitting) models for extremes in climate data, providing return values, return probabilities, and return periods for stationary and nonstationary models. Also provides differences in return values and differences in log return probabilities for contrasts of covariate values. Functions for estimating risk ratios for event attribution analyses, including uncertainty. Under the hood, many of the functions use functions from 'extRemes', including for fitting the statistical models. Details are given in Paciorek, Stone, and Wehner (2018) .

Access Data from the Oregon State Prism Climate Project

Allows users to access the Oregon State Prism climate data (< https://prism.nacse.org/>). Using the web service API data can easily downloaded in bulk and loaded into R for spatial analysis. Some user friendly visualizations are also provided.