Package {CDSimX}

CDSimX: Simulating Climate Data for Research and Modelling

Description

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) doi:10.1029/WR017i001p00182, Wilks (1999) doi:10.1016/S0168-1923(99)00037-4, and Osei et al. (2026) doi:10.5334/jors.666.

Author(s)

Maintainer: Isaac Osei ikemillar65@gmail.com

Authors:

• Acheampong Baafi-Adomako acheampongbaafi1@gmail.com

• Jayanti Maji

See Also

Useful links:

• https://ikemillar.github.io/CDSimX/

• Report bugs at https://github.com/ikemillar/CDSimX/issues

Apply Physical Constraints to Climate Data

Description

Enforces physically realistic bounds and relationships across simulated climate variables.

Usage

apply_physical_constraints(climate_data, verbose = TRUE, tolerance = 0)

Arguments

Details

This function is designed as a quality-control layer for synthetic climate simulations and ensures that impossible atmospheric or hydrological states are removed.

The function automatically checks and corrects:

• Tmin <= Tmax

• Avg.Temp lies between Tmin and Tmax

• Relative humidity remains within 0–100%

• Dew point does not exceed air temperature

• Rainfall and rain days remain non-negative

• Wind speed remains non-negative

• Solar radiation remains non-negative

• Evapotranspiration remains non-negative

• Vapor pressure deficit remains non-negative

• Sunshine fraction remains within 0–1

• Cloud factor remains within 0–1

Value

A corrected climate dataframe with physically realistic values enforced.

Examples

stations <- create_stations(n = 3)
time_index <- generate_time_index(
  start_date = "2010-01-01",
  end_date = "2012-12-31"
)

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

climate <- apply_physical_constraints(climate)

Bias Correction for Simulated Climate Data

Description

Applies statistical bias correction to simulated climate variables using observed reference data.

Usage

bias_correction(
  simulated_data,
  observed_data,
  variables = c("Tmin", "Tmax", "Avg.Temp", "Rainfall", "RH", "WindSpeed",
    "Solar_Radiation", "ET0"),
  method = "mean_scaling",
  digits = 2,
  return_factors = TRUE
)

Arguments

Details

Supported correction methods include:

• Mean scaling

• Additive correction

• Multiplicative correction

Physical constraints are automatically enforced after correction to ensure climatological realism.

Value

A list containing:

corrected_data

Bias-corrected climate dataset.

correction_factors

Bias-correction factors and bias statistics applied to each variable.

Examples

stations <- create_stations(n = 10)
tindex <- generate_time_index("2020-01-01", "2020-12-31",
  frequency  = "monthly"
)
cd <- simulate_climate(stations, tindex)
obs_data <- cd
obs_data$Rainfall <- obs_data$Rainfall * 1.10
bc <- bias_correction(
  simulated_data = cd,
  observed_data = obs_data,
  variables = c("Rainfall", "Avg.Temp")
)

head(bc$corrected_data)
bc$correction_factors

Apply Copula Dependence Structure to Climate Variables

Description

Introduces multivariate dependence among simulated climate variables using Gaussian or t copulas.

Usage

copula_dependence(
  climate_data,
  variables = c("Tmin", "Tmax", "Avg.Temp", "Rainfall", "RH", "WindSpeed",
    "Solar_Radiation", "ET0"),
  copula_type = "gaussian",
  df = 4,
  seed = 123,
  digits = 2
)

Arguments

Details

This function improves realism by preserving correlations and inter-variable dependency structures commonly observed in climate systems.

Supported variables include:

• Tmin

• Tmax

• Avg.Temp

• Rainfall

• RH

• WindSpeed

• Solar_Radiation

• ET0

Value

A list containing:

adjusted_data

Climate dataset with copula-adjusted dependence structure.

correlation_matrix

Empirical correlation matrix used in copula fitting.

copula_type

Copula family used.

Examples

stations <- create_stations(n = 10)
tindex <- generate_time_index("2020-01-01", "2020-12-31",
  frequency  = "monthly"
)
cd <- simulate_climate(stations, tindex)
obs_data <- cd
obs_data$Rainfall <- obs_data$Rainfall * 1.10
bc <- bias_correction(
  simulated_data = cd,
  observed_data = obs_data,
  variables = c("Rainfall", "Avg.Temp")
)
cp <- copula_dependence(cd)
cp <- copula_dependence(bc$corrected_data)

head(cp$adjusted_data)

Create or load station metadata

Description

Create a station metadata table either by:

• loading from a CSV file,

• accepting an existing data.frame,

• or auto-generating synthetic stations within a bounding box.

Usage

create_stations(
  source = NULL,
  n = 10,
  bbox = c(-3.5, 1.5, 4.5, 11.5),
  derive_climate = TRUE,
  seed = NULL
)

Arguments

Details

The function also supports optional derivation of climate-related station attributes used by the CDSimX simulation engine.

Value

A data.frame containing:

Station

Station name

LON

Longitude

LAT

Latitude

ELEV

Synthetic elevation estimate (m)

CLIMATE_ZONE

Derived climate zone

COASTAL_INDEX

Relative coastal influence index

TEMP_BASE

Baseline temperature estimate

RAIN_REGIME

Derived rainfall regime

Examples

create_stations(n = 5, seed = 42)

create_stations(
  data.frame(
    Station = "Accra",
    LON = -0.18,
    LAT = 5.60
  )
)

Export CDSimX Data to CSV

Description

Exports any CDSimX dataframe to a CSV file.

Usage

export_csv(data, file, row_names = FALSE)

Arguments

Value

Invisibly returns the file path.

Examples

stations <- create_stations(n = 3)
tindex <- generate_time_index(
  "2025-01-01",
  "2025-12-31",
  frequency = "monthly"
)

climate_data <- simulate_climate(stations, tindex)
tmp <- tempfile(fileext = ".csv")
export_csv(climate_data, file = tmp)

Export CDSimX Data to NetCDF

Description

Exports climate simulation or forecasting data into NetCDF format.

Usage

export_netcdf(
  data,
  file,
  date_col = "DATE",
  station_col = "Station",
  lon_col = "LON",
  lat_col = "LAT",
  variables = NULL,
  fillvalue = -9999,
  overwrite = TRUE
)

Arguments

Value

Invisibly returns output filename.

Examples

stations <- create_stations(n = 3)
tindex <- generate_time_index("2025-01-01", "2025-12-31",
  frequency = "monthly"
)

climate_data <- simulate_climate(stations, tindex)
tmp <- tempfile(fileext = ".nc")
export_netcdf(climate_data, file = tmp)

Machine Learning Forecasting for Climate Variables

Description

Uses machine learning algorithms to forecast climate variables from simulated climate data.

Usage

forecasting_ml(
  climate_data,
  target = "Rainfall",
  predictors = c("Tmin", "Tmax", "Avg.Temp", "RH", "WindSpeed", "Solar_Radiation", "ET0"),
  forecast_horizon = 12,
  start_forecast = NULL,
  end_forecast = NULL,
  method = c("rf", "lm", "gbm", "arima", "xgboost", "nnet", "all"),
  frequency = c("month", "day", "year"),
  train_fraction = 0.8,
  include_lag = TRUE,
  lag_period = 1,
  ntree = 500,
  hidden_nodes = 5,
  digits = 2,
  seed = 123
)

Arguments

Details

Supported methods:

• Random Forest ("rf")

• Linear Regression ("lm")

• Gradient Boosting ("gbm")

• ARIMA Time-Series ("arima")

• Extreme Gradient Boosting ("xgboost")

• Neural Network ("nnet")

• Run All Models ("all")

Value

A list containing:

forecast_data

Forecasted future values.

model_performance

RMSE, MAE, and correlation.

importance

Variable importance table.

model

Trained ML model.

all_results

Returned only when method = "all".

Generate Climate Simulation Time Index

Description

Creates a standardized temporal framework for CDSimX simulations. Supports daily, monthly, and yearly temporal resolutions.

Usage

generate_time_index(
  start_date = "1990-01-01",
  end_date = "1999-12-31",
  frequency = "daily",
  calendar = "standard"
)

Arguments

Details

This function becomes the backbone of all climate simulations, ensuring that all variables share a synchronized temporal structure.

Value

A data.frame containing:

START_DATE

Start date

END_DATE

End date

DATE

Date index

Year

Calendar year

Month

Month number

Day

Day of month

DOY

Day of year

Week

Week number

Quarter

Quarter

Season

Climatological season

Frequency

Simulation resolution

Examples

daily_index <- generate_time_index(
  start_date = "2000-01-01",
  end_date   = "2000-12-31",
  frequency  = "daily"
)

monthly_index <- generate_time_index(
  start_date = "1990-01-01",
  end_date   = "2020-12-31",
  frequency  = "monthly"
)

Plot Station Climate Time Series

Description

Creates highly customizable climate time-series visualizations with automatic seasonal detection.

Supports:

• Custom line colors

• Seasonal coloring

• LOESS smoothing

• Trend lines

• Dark/light themes

• Flexible date handling

• Faceting

Usage

plot_station_timeseries(
  df,
  station,
  var = "Tmin",
  smooth = TRUE,
  smooth_span = 0.25,
  show_points = TRUE,
  point_size = 2,
  line_size = 1,
  line_color = NULL,
  smooth_color = NULL,
  seasonal_colors = NULL,
  use_season_colors = TRUE,
  alpha = 0.8,
  theme_style = c("minimal", "dark", "classic", "bw"),
  date_breaks = "2 years",
  date_labels = "%Y",
  facet = FALSE,
  show_trend = FALSE,
  trend_color = "black",
  title = NULL,
  subtitle = NULL
)

Arguments

Value

A ggplot object.

Examples

stations <- create_stations(n = 3)
tindex <- generate_time_index("2025-01-01", "2025-12-31",
  frequency = "monthly"
)

climate_data <- simulate_climate(stations, tindex)

plot_station_timeseries(
  climate_data,
  station = "Station_1",
  var = "Tmin"
)

Simulate Integrated Climate Dataset

Description

Generates a complete synthetic climate dataset by internally calling all climate simulation modules and merging their outputs into a single dataframe.

Usage

simulate_climate(stations, time_index, seed = NULL)

Arguments

Details

The function currently simulates:

• temperature

• rainfall

• relative humidity

• dew point

• wind speed

• wind direction

• solar radiation

• evapotranspiration

Value

A merged dataframe containing all simulated climate variables.

Examples

stations <- create_stations(
  n = 10
)

tindex <- generate_time_index(
  start_date = "2020-01-01",
  end_date   = "2020-12-31",
  frequency  = "monthly"
)

climate <- simulate_climate(
  stations,
  tindex
)

head(climate)

Simulate Dew Point Temperature

Description

Generates synthetic dew point temperature series for multiple stations using simulated air temperature and relative humidity.

Usage

simulate_dewpoint(
  temperature,
  rh,
  min_dewpoint = -5,
  max_dewpoint = 35,
  noise_sd = 0.5,
  seed = NULL
)

Arguments

Details

Dew point is physically linked to:

• air temperature,

• atmospheric moisture,

• rainfall regimes,

• coastal humidity effects,

• elevation controls.

The simulation uses a Magnus-type approximation for realistic atmospheric thermodynamics.

Value

data.frame containing:

Station

Station name

LON

Longitude

LAT

Latitude

ELEV

Elevation

DATE

Simulation timestamp

Year

Calendar year

Month

Calendar month

Season

Climatological season

Tmean

Mean air temperature (°C)

RH

Relative humidity (%)

DewPoint

Simulated dew point temperature (°C)

Dewpoint_Depression

Tmean - DewPoint

Examples

stations <- create_stations(
  n = 3,
  seed = 123
)

tindex <- generate_time_index(
  start_date = "2000-01-01",
  end_date = "2005-12-31",
  frequency = "monthly"
)

temp <- simulate_temperature(
  stations,
  tindex
)

rh <- simulate_rh(
  stations,
  tindex
)

dew <- simulate_dewpoint(
  temperature = temp,
  rh = rh
)

head(dew)

Simulate Evapotranspiration

Description

Simulates reference evapotranspiration (ET0) using temperature, relative humidity, solar radiation, and wind speed.

Usage

simulate_evapotranspiration(
  temperature,
  rh,
  solar_radiation,
  wind_speed,
  method = "FAO56",
  crop_factor = 1,
  humidity_sensitivity = 0.35,
  wind_sensitivity = 0.08,
  radiation_sensitivity = 0.12,
  noise_sd = 0.25,
  min_et = 0,
  max_et = 15,
  seed = NULL
)

Arguments

Details

The function incorporates:

• temperature-driven evaporation

• humidity suppression

• solar radiation forcing

• aerodynamic wind enhancement

• elevation-based pressure adjustment

• stochastic environmental variability

Value

A data frame containing:

Station

Station identifier

LON

Longitude

LAT

Latitude

ELEV

Elevation (m)

DATE

Date

Year

Year

Month

Month

Season

Season category

Avg.Temp

Average temperature (°C)

RH

Relative humidity (%)

Solar_Radiation

Solar radiation (MJ/m²/day)

WindSpeed

Wind speed (m/s)

Atmospheric_Pressure

Estimated atmospheric pressure (kPa)

VPD

Vapour pressure deficit (kPa)

ET0

Reference evapotranspiration (mm/day)

Dryness_Index

Normalized dryness indicator

Dryness_Class

Categorical atmospheric moisture condition derived from the dryness index.Classes include: Humid, Moderate, Dry, and Very Dry

ET_Anomaly

Evapotranspiration anomaly

Examples

stations <- create_stations(n = 3)
tindex_month <- generate_time_index("2025-01-01", "2025-12-31",
  frequency = "monthly"
)
temp <- simulate_temperature(stations, tindex_month)
rh   <- simulate_rh(stations, tindex_month)
sr   <- simulate_solar_radiation(stations, tindex_month)
ws   <- simulate_wind_speed(stations, tindex_month)

et <- simulate_evapotranspiration(
  temperature = temp,
  rh = rh,
  solar_radiation = sr,
  wind_speed = ws
)

Simulate Rainfall Time Series

Description

Generates synthetic rainfall series for multiple stations using stochastic climate dynamics and climate-zone controls.

Usage

simulate_rainfall(
  stations,
  time_index,
  wetday_prob = 0.35,
  gamma_shape = 2,
  gamma_scale = 8,
  ar_coeff = 0.4,
  seasonal_strength = 1,
  extreme_event_prob = 0.01,
  extreme_multiplier = 3,
  max_rainfall = 800,
  seed = NULL
)

Arguments

Details

The simulation incorporates:

• wet/dry occurrence processes,

• seasonal rainfall regimes,

• spatial climate variability,

• coastal moisture effects,

• elevation enhancement,

• temporal persistence,

• extreme rainfall events,

• Gamma-distributed rainfall amounts.

Supports:

• daily simulations,

• monthly simulations,

• yearly simulations.

Value

data.frame containing:

Station

Station name

LON

Longitude

LAT

Latitude

ELEV

Elevation

DATE

Simulation timestamp

Year

Calendar year

Month

Calendar month

Season

Climatological season

Rainfall

Simulated rainfall amount (mm)

Wet_Day

Wet occurrence indicator

Extreme_Event

Extreme rainfall indicator

Rain_Anomaly

Rainfall anomaly

Examples

stations <- create_stations(
  n = 5,
  seed = 123
)

time_index <- generate_time_index(
  start_date = "2000-01-01",
  end_date = "2005-12-31",
  frequency = "monthly"
)

rain <- simulate_rainfall(
  stations = stations,
  time_index = time_index,
  seed = 123
)

head(rain)

Simulate Relative Humidity Time Series

Description

Generates synthetic Relative Humidity (RH) series for multiple climate stations using stochastic hydro-climatic relationships.

Usage

simulate_rh(
  stations,
  time_index,
  rainfall = NULL,
  temperature = NULL,
  ar_coeff = 0.7,
  seasonal_strength = 8,
  rain_sensitivity = 0.04,
  temp_sensitivity = 0.6,
  coastal_moisture = 12,
  noise_sd = 3,
  min_rh = 15,
  max_rh = 100,
  seed = NULL
)

Arguments

Details

The simulation incorporates:

• seasonal humidity cycles,

• rainfall-humidity coupling,

• temperature-humidity interaction,

• coastal moisture effects,

• elevation drying effects,

• temporal persistence,

• stochastic atmospheric variability,

• physically realistic RH bounds.

Higher rainfall generally increases RH, while higher temperature lowers RH.

Value

data.frame containing:

Station

Station name

LON

Longitude

LAT

Latitude

ELEV

Elevation

DATE

Simulation timestamp

Year

Calendar year

Month

Calendar month

Season

Climatological season

RH

Relative humidity (%)

Humidity_Anomaly

Humidity anomaly

Examples

stations <- create_stations(
  n = 5,
  seed = 123
)

time_index <- generate_time_index(
  start_date = "2000-01-01",
  end_date = "2005-12-31",
  frequency = "monthly"
)

rain <- simulate_rainfall(
  stations,
  time_index
)

temp <- simulate_temperature(
  stations,
  time_index
)

rh <- simulate_rh(
  stations,
  time_index,
  rainfall = rain,
  temperature = temp
)

head(rh)

Simulate Solar Radiation Time Series

Description

Generates synthetic incoming solar radiation series for multiple stations using physically consistent astronomical and atmospheric controls.

Usage

simulate_solar_radiation(
  stations,
  time_index,
  rainfall = NULL,
  rh = NULL,
  atmospheric_transmissivity = 0.65,
  cloud_attenuation = 0.12,
  humidity_attenuation = 0.08,
  elevation_factor = 0.00012,
  seasonal_strength = 1,
  noise_sd = 1.5,
  min_radiation = 0,
  max_radiation = 35,
  seed = NULL
)

Arguments

Details

The simulation incorporates:

• annual solar cycle,

• latitude-dependent extraterrestrial radiation,

• cloud/rainfall attenuation,

• humidity effects,

• elevation enhancement,

• seasonal variability,

• stochastic atmospheric variability,

• physically bounded radiation values.

Solar radiation is simulated as:

• daily total radiation (MJ/m²/day) for daily simulations

• monthly mean radiation for monthly simulations

• yearly mean radiation for yearly simulations

Value

data.frame containing:

Station

Station name

LON

Longitude

LAT

Latitude

ELEV

Elevation

DATE

Simulation timestamp

Year

Calendar year

Month

Calendar month

Season

Climatological season

Solar_Radiation

Simulated solar radiation (MJ/m²/day)

Clear_Sky_Radiation

Potential clear-sky radiation

Cloud_Factor

Cloud attenuation factor

Sunshine_Fraction

Fraction of available sunshine reaching the surface

Radiation_Anomaly

Solar radiation anomaly

Examples

stations <- create_stations(
  n = 3,
  seed = 123
)

time_index <- generate_time_index(
  start_date = "2000-01-01",
  end_date = "2005-12-31",
  frequency = "monthly"
)

rain <- simulate_rainfall(
  stations,
  time_index
)

rh <- simulate_rh(
  stations,
  time_index
)

solar <- simulate_solar_radiation(
  stations = stations,
  time_index = time_index,
  rainfall = rain$Rainfall,
  rh = rh$RH,
  seed = 123
)

head(solar)

Simulate Temperature Time Series

Description

Generates synthetic Tmin, Tmax, and Tmean climate series for multiple stations using stochastic climate dynamics.

Usage

simulate_temperature(
  stations,
  time_index,
  ar_coeff = 0.7,
  seasonal_amplitude = 3,
  warming_trend = 0.02,
  noise_sd = 1,
  mean_dtr = 6,
  rainfall = NULL,
  cooling_factor = 0.15,
  min_tmin = 10,
  max_tmin = 35,
  min_tmax = 15,
  max_tmax = 45,
  seed = NULL
)

Arguments

Details

The simulation incorporates:

• seasonal variability,

• autoregressive temporal persistence,

• spatial station effects,

• climate-zone variability,

• coastal moderation,

• elevation lapse-rate adjustment,

• long-term warming trends,

• stochastic climate variability,

• physically consistent Tmax > Tmin relationships,

• optional rainfall-temperature coupling.

Value

data.frame containing:

Station

Station name

LON

Longitude

LAT

Latitude

ELEV

Elevation

DATE

Simulation timestamp

Year

Calendar year

Month

Calendar month

Season

Climatological season

Tmin

Simulated minimum temperature (°C)

Tmax

Simulated maximum temperature (°C)

Avg.Temp

Mean temperature (°C)

DTR

Diurnal temperature range (°C)

Examples

stations <- create_stations(
  n = 3,
  seed = 123
)

time_index <- generate_time_index(
  start_date = "2000-01-01",
  end_date = "2005-12-31",
  frequency = "monthly"
)

temp <- simulate_temperature(
  stations = stations,
  time_index = time_index,
  seed = 123
)

head(temp)

Simulate Wind Direction Time Series

Description

Generates synthetic wind direction fields for multiple stations using stochastic atmospheric circulation dynamics.

Usage

simulate_wind_direction(
  stations,
  time_index,
  wind_speed = NULL,
  base_direction = 225,
  seasonal_shift = 30,
  noise_sd = 30,
  extreme_shift_prob = 0.01,
  max_shift = 90,
  seed = NULL
)

Arguments

Details

The simulation incorporates:

• prevailing regional wind regimes,

• seasonal directional shifts,

• coastal circulation effects,

• temporal persistence,

• wind-speed-dependent directional variability,

• stochastic directional turbulence,

• extreme wind-direction shifts,

• optional wind-speed coupling,

• vector wind components (u and v).

Value

data.frame containing:

Station

Station name

LON

Longitude

LAT

Latitude

ELEV

Elevation

CLIMATE_ZONE

Climate classification

DATE

Simulation timestamp

Year

Calendar year

Month

Calendar month

Season

Climatological season

WindSpeed

Wind speed (m/s)

WindDirection

Wind direction (degrees)

WindSector

Compass sector

Prevailing_Direction

Mean prevailing direction

Direction_Variability

Directional variability

Extreme_Shift

Extreme directional event flag

Wind_u

Zonal wind component

Wind_v

Meridional wind component

Examples

stations <- create_stations(
  n = 3,
  seed = 123
)

time_index <- generate_time_index(
  start_date = "2000-01-01",
  end_date = "2005-12-31",
  frequency = "monthly"
)

ws <- simulate_wind_speed(
  stations,
  time_index
)

wd <- simulate_wind_direction(
  stations,
  time_index,
  wind_speed = ws$WindSpeed
)

head(wd)

Simulate Wind Speed Time Series

Description

Generates synthetic wind speed climate series for multiple stations using stochastic atmospheric dynamics.

Usage

simulate_wind_speed(
  stations,
  time_index,
  ar_coeff = 0.6,
  seasonal_strength = 1,
  noise_sd = 1.5,
  extreme_event_prob = 0.01,
  extreme_multiplier = 2,
  min_ws = 0,
  max_ws = 40,
  seed = NULL
)

Arguments

Details

The simulation incorporates:

• seasonal circulation variability,

• coastal enhancement,

• elevation acceleration,

• temporal persistence,

• stochastic turbulence,

• extreme wind events,

• climate-zone effects.

Wind speed is simulated in m/s.

Value

data.frame containing:

Station

Station name

LON

Longitude

LAT

Latitude

ELEV

Elevation

DATE

Simulation timestamp

Year

Calendar year

Month

Calendar month

Season

Climatological season

WindSpeed

Simulated wind speed (m/s)

Extreme_Wind

Extreme wind event indicator

Wind_Anomaly

Wind speed anomaly

Examples

stations <- create_stations(
  n = 5,
  seed = 123
)

time_index <- generate_time_index(
  start_date = "2000-01-01",
  end_date = "2005-12-31",
  frequency = "monthly"
)

wind <- simulate_wind_speed(
  stations,
  time_index
)

head(wind)

Validate Simulated Climate Dataset

Description

Performs statistical and physical validation checks on a simulated climate dataset generated using simulate_climate().

Usage

validate_climate(climate_data, digits = 2, return_data = TRUE)

Arguments

Details

The function computes:

• Descriptive statistics

• Missing value diagnostics

• Physical consistency checks

• Correlation structure

• Seasonal summaries

• Station summaries

• Extreme-event frequencies

• Validation summary metrics

Value

A list containing:

summary_statistics

Descriptive statistics for all numeric climate variables.

missing_values

Count and percentage of missing values.

physical_checks

Number of physical inconsistencies detected.

correlation_matrix

Correlation matrix among major climate variables.

seasonal_summary

Mean climate conditions by season.

station_summary

Mean climate conditions by station.

extreme_summary

Frequency of extreme rainfall and wind events.

validation_summary

Overall validation metrics for the climate dataset.

Examples

stations <- create_stations(n = 3)
tindex_month <- generate_time_index("2025-01-01", "2025-12-31",
  frequency = "monthly"
)
cd <- simulate_climate(stations, tindex_month)

vc <- validate_climate(cd)

vc$summary_statistics
vc$correlation_matrix
vc$physical_checks
vc$validation_summary

Visualization Functions for Climate Data

Description

Flexible visualization tools for CDSimX climate datasets.