nderstanding Wind Rose Plots: A Comprehensive Guide

A wind rose plot is an essential tool used to visualize the distribution of wind direction and speed at a specific location over a set period. This type of plot is commonly employed in meteorology, climatology, and various environmental and industrial applications where wind analysis is crucial.

Components of a Wind Rose Plot

Wind Direction (Radial Axis):In a wind rose plot, wind direction is displayed around a circle, typically with cardinal points (North, South, East, West) marked. Each segment of the circle corresponds to a specific range of wind directions, such as 0° to 30° for the North sector, 30° to 60° for the Northeast sector, and so forth.

Wind Speed (Spokes):The spokes radiating from the center of the circle indicate the intensity or frequency of the wind for each direction. Wind speed is often represented by the length, area, or color of the spokes, with larger or darker spokes indicating stronger or more frequent winds from that direction.

Legend:A legend is typically included to explain the wind speeds represented by the spokes. Located around or below the circle, the legend provides information on units of measurement (such as meters per second), the scale of the legend, and the colors corresponding to different wind speed intervals.

Interpreting a Wind Rose Plot

Directional Distribution:
The wind rose plot allows easy visualization of the directional distribution of winds. This is useful for identifying seasonal or diurnal patterns in wind direction.

Wind Intensity:
By examining the spokes, one can determine which directions have the strongest or most frequent winds. This information is vital for applications like wind energy, maritime navigation, and urban planning.

Local Patterns:
Depending on the geographical location, wind rose plots may reveal unique wind patterns influenced by local topography, such as mountains or valleys, or by regional climatic characteristics.

Practical Applications

Urban Planning:
Wind rose plots help in designing building orientations and park layouts to optimize natural ventilation and reduce heat island effects.

Wind Energy:
Wind rose plots are crucial in assessing the viability of wind turbine installations, considering both wind direction and intensity.

Maritime Navigation:
Mariners use wind rose plots to navigate safely by understanding the wind conditions along shipping routes.

Data Visualization: Creating a Wind Rose Plot Using ggplot2 in R

The following R code demonstrates how to create a wind rose plot using real meteorological data:

# Installing required packages if not already installed


install.packages("RNCEP")
install.packages("ggplot2")
install.packages("openair")
  

‍

# Loading necessary libraries


library(RNCEP)
library(ggplot2)
  



# Collecting U and V wind component data
‍

dados_uwnd <- NCEP.gather(variable = "uwnd", level = 1000,
                          months.minmax = c(5, 5), years.minmax = c(2024, 2024),
                          lat.southnorth = c(35, 45), lon.westeast = c(-10, 5),
                          reanalysis2 = FALSE, return.units = TRUE)

dados_vwnd <- NCEP.gather(variable = "vwnd", level = 1000,
                          months.minmax = c(5, 5), years.minmax = c(2024, 2024),
                          lat.southnorth = c(35, 45), lon.westeast = c(-10, 5),
                          reanalysis2 = FALSE, return.units = TRUE)
  

‍

# Converting data into data frames
‍

dados_uwnd_df <- as.data.frame(as.table(dados_uwnd))
colnames(dados_uwnd_df) <- c("lat", "lon", "time", "uwnd")

dados_vwnd_df <- as.data.frame(as.table(dados_vwnd))
colnames(dados_vwnd_df) <- c("lat", "lon", "time", "vwnd")
  

‍

# Combining U and V wind component data
‍

dados_combined <- merge(dados_uwnd_df, dados_vwnd_df, by = c("lon", "lat", "time"))
  

‍
# Calculating wind speed and direction
‍

dados_combined$velocidade <- sqrt(dados_combined$uwnd^2 + dados_combined$vwnd^2)
dados_combined$direcao <- atan2(dados_combined$vwnd, dados_combined$uwnd) * (180 / pi)
  

‍
# Categorizing wind direction into sectors (N, NE, E, SE, S, SW, W, NW)
‍

‍

dados_combined$direcao_cat <- cut(dados_combined$direcao, breaks = seq(-180, 180, by = 45),
                                  labels = c("N", "NE", "E", "SE", "S", "SW", "W", "NW"),
                                  include.lowest = TRUE)
  

‍

# Plotting the wind rose plot using ggplot2
‍

windrose_plot <- ggplot(dados_combined, aes(x = direcao_cat, fill = cut(velocidade, breaks = seq(0, max(velocidade), by = 2)))) +
  geom_bar(width = 1, color = "black") +
  scale_fill_viridis_d(name = "Velocidade do Vento (m/s)", guide = "legend", na.value = "gray") +
  coord_polar(start = -((90 - 45/2) / 180) * pi) +
  theme_minimal() +
  labs(title = "Wind Rose",
       x = "Direção do Vento",
       y = "Frequência")
  

‍

‍
# Displaying the plot
‍

‍

print(windrose_plot)
  

‍


The RNCEP Package: Accessing Meteorological Reanalysis Data

The RNCEP package in R is a powerful tool for accessing and working with meteorological reanalysis data from the NCEP/NCAR database. This data is crucial for climate studies, atmospheric modeling, and various scientific applications requiring historical meteorological information on a global scale.

Key Features of the RNCEP Package:

Access to Reanalysis Data:
RNCEP enables access to data from the NCEP/NCAR reanalysis dataset, which is generated by numerical models that integrate observations with simulations of the atmospheric state over several decades.

Available Variables:
The package facilitates the extraction of various meteorological variables, such as temperature, humidity, wind components (U and V), atmospheric pressure, and more, across different vertical levels and times.

Flexible Query Configuration:
Users can configure queries for specific data, including time periods (years and months), geographical locations (latitude and longitude), vertical levels in the atmosphere, and other parameters.

Data Manipulation and Processing:
RNCEP simplifies the transformation and preparation of collected data into suitable data structures, like data frames in R, for further analysis.

Supporting Scientific Studies and Modeling:
The package is widely used by scientists, climatologists, and researchers for climate analysis, trend studies, atmospheric modeling, and environmental impact assessments.

Example Workflow:

• Installation and Loading of Packages:
  • install.packages("RNCEP")
  • install.packages("ggplot2")
  • library(RNCEP)
  • library(ggplot2)
  • These commands load the RNCEP and ggplot2 packages for use in analysis and visualization.
• Collecting U and V Wind Component Data:
  • NCEP.gather(): A function in the RNCEP package to collect meteorological data from the NCEP/NCAR reanalysis dataset.
• Data Transformation and Preparation:
  • Convert the collected data into data frames.
  • Combine the data frames dados_uwnd_df and dados_vwnd_df to form dados_combined, containing information on latitude, longitude, time, U and V wind components, wind speed, and direction.
• Calculating Wind Speed and Direction:
  • Use trigonometric formulas to calculate wind speed (velocidade) and direction (direcao), converting from radians to degrees.
• Categorizing Wind Direction into Sectors:
  • cut(): A function to categorize wind direction into eight main sectors (N, NE, E, SE, S, SW, W, NW) based on calculated angles.
• Plotting the Wind Rose Plot with ggplot2:
  • ggplot(): Initializes a plot object using dados_combined.
  • aes(): Defines aesthetic mappings for the plot, such as wind direction (direcao_cat) on the x-axis and wind speed (velocidade) as the fill variable (fill).
  • geom_bar(): Adds bars to the plot, representing the frequency of wind speeds in each direction.
  • scale_fill_viridis_d(): Sets the color scale to represent wind speed.
  • coord_polar(): Configures the polar coordinate system to transform the plot into a wind rose plot.
  • theme_minimal(): Applies a minimalist theme to the plot.
  • labs(): Adds titles to the x and y axes, as well as a general title to the plot.
• Displaying the Plot:
  • print(windrose_plot): Displays the wind rose plot on the screen.

This R code provides a practical example of creating a wind rose plot using real meteorological data, visualizing wind direction and speed distribution in a specific region with the RNCEP and ggplot2 packages.

Result:

‍

‍