Climate Tools (Series Homogenization and Derived Products)

Functions for the quality control, homogenization and missing data filling of climatological series and to obtain climatological summaries and grids from the results. Also functions to display wind-roses, meteograms, Walter&Lieth diagrams, and more.

Heterogeneous Graphical Model for Non-Negative Data

Graphical model is an informative and powerful tool to explore the conditional dependence relationships among variables. The traditional Gaussian graphical model and its extensions either have a Gaussian assumption on the data distribution or assume the data are homogeneous. However, there are data with complex distributions violating these two assumptions. For example, the air pollutant concentration records are non-negative and, hence, non-Gaussian. Moreover, due to climate changes, distributions of these concentration records in different months of a year can be far different, which means it is uncertain whether datasets from different months are homogeneous. Methods with a Gaussian or homogeneous assumption may incorrectly model the conditional dependence relationships among variables. Therefore, we propose a heterogeneous graphical model for non-negative data (HGMND) to simultaneously cluster multiple datasets and estimate the conditional dependence matrix of variables from a non-Gaussian and non-negative exponential family in each cluster.

Model-Based Clustering for Functional Data with Covariates

Routines for model-based functional cluster analysis for functional data with optional covariates. The idea is to cluster functional subjects (often called functional objects) into homogenous groups by using spline smoothers (for functional data) together with scalar covariates. The spline coefficients and the covariates are modelled as a multivariate Gaussian mixture model, where the number of mixtures corresponds to the number of clusters. The parameters of the model are estimated by maximizing the observed mixture likelihood via an EM algorithm (Arnqvist and Sjöstedt de Luna, 2019) . The clustering method is used to analyze annual lake sediment from lake Kassjön (Northern Sweden) which cover more than 6400 years and can be seen as historical records of weather and climate.

Interface to Download Meteorological (and Hydrological) Datasets

Automatize downloading of meteorological and hydrological data from publicly available repositories: OGIMET (< http://ogimet.com/index.phtml.en>), University of Wyoming - atmospheric vertical profiling data (< http://weather.uwyo.edu/upperair/>), Polish Institute of Meteorology and Water Management - National Research Institute (< https://danepubliczne.imgw.pl>), and National Oceanic & Atmospheric Administration (NOAA). This package also allows for searching geographical coordinates for each observation and calculate distances to the nearest stations.

Using CF-Compliant Calendars with Climate Projection Data

Support for all calendars as specified in the Climate and Forecast (CF) Metadata Conventions for climate and forecasting data. The CF Metadata Conventions is widely used for distributing files with climate observations or projections, including the Coupled Model Intercomparison Project (CMIP) data used by climate change scientists and the Intergovernmental Panel on Climate Change (IPCC). This package specifically allows the user to work with any of the CF-compliant calendars (many of which are not compliant with POSIXt). The CF time coordinate is formally defined in the CF Metadata Conventions document available at < https://cfconventions.org/Data/cf-conventions/cf-conventions-1.12/cf-conventions.html#time-coordinate>.

Statistical Transformations for Post-Processing Climate Model Output

Empirical adjustment of the distribution of variables originating from (regional) climate model simulations using quantile mapping.

Datasets to Measure the Alignment of Corporate Loan Books with Climate Goals

These datasets support the implementation in R of the software 'PACTA' (Paris Agreement Capital Transition Assessment), which is a free tool that calculates the alignment between corporate lending portfolios and climate scenarios (< https://www.transitionmonitor.com/>). Financial institutions use 'PACTA' to study how their capital allocation decisions align with climate change mitigation goals. Because both financial institutions and market data providers keep their data private, this package provides fake, public data to enable the development and use of 'PACTA' in R.

PCIC Implementation of Climdex Routines

PCIC's implementation of Climdex routines for computation of extreme climate indices. Further details on the extreme climate indices can be found at < http://etccdi.pacificclimate.org/list_27_indices.shtml> and in the package manual.

Tools to Match Corporate Lending Portfolios with Climate Data

These tools implement in R a fundamental part of the software 'PACTA' (Paris Agreement Capital Transition Assessment), which is a free tool that calculates the alignment between financial portfolios and climate scenarios (< https://www.transitionmonitor.com/>). Financial institutions use 'PACTA' to study how their capital allocation decisions align with climate change mitigation goals. This package matches data from corporate lending portfolios to asset level data from market-intelligence databases (e.g. power plant capacities, emission factors, etc.). This is the first step to assess if a financial portfolio aligns with climate goals.

Forecast Verification Routines for Ensemble Forecasts of Weather and Climate

A collection of forecast verification routines developed for the SPECS FP7 project. The emphasis is on comparative verification of ensemble forecasts of weather and climate.