Simulating Climate Data for Research and Modelling

Advanced climate simulation, forecasting, visualization, export, and machine learning tools. Generates synthetic climate datasets for single or multiple weather stations using stochastic weather generation techniques. 'CDSimX' simulates daily climate variables including minimum and maximum temperature, rainfall, relative humidity, solar radiation, wind speed, wind direction, dew point temperature, and potential evapotranspiration. The package incorporates seasonal harmonic models, Markov chain rainfall occurrence processes, Gamma-distributed rainfall amounts, copula-based dependence structures, bias-correction procedures, and physical consistency constraints. 'CDSimX' supports climate data generation, environmental modeling, machine learning benchmarking, sensitivity analysis, and educational applications. Methods are based on established stochastic weather generation approaches described in Richardson (1981) , Wilks (1999) , and Osei et al. (2026) .

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CDSimX

Climate Data Simulation and Forecasting Toolkit for R

CDSimX is an advanced R package for:

• Climate data simulation
• Weather variable generation
• Machine learning forecasting
• NetCDF export
• Climate visualization
• Seasonal analysis
• Copula-based dependence modeling

Installation

# install.packages("remotes")
remotes::install_github("ikemillar/CDSimX")

Example

library(CDSimX)

stations <- create_stations(n = 3)

time_index <- generate_time_index(
  start_date = "2000-01-01",
  end_date = "2020-12-31"
)

climate <- simulate_climate(
  stations = stations,
  time_index = time_index
)

head(climate)

Features

• Temperature simulation
• Rainfall simulation
• Relative humidity simulation
• Wind speed simulation
• Solar radiation simulation
• Machine learning forecasting
• NetCDF export support
• Advanced climate visualization

Author

Isaac Osei