My comments are listed below. Hope this will help to figure out solution to your problem.
To address the discrepancy between the band-pass filtered quiver plot and the original hourly resolution data plot, several factors need consideration. Let's delve into potential reasons for the observed differences and explore ways to enhance the visualization for better interpretation.
1. Band-Pass Filtering Parameters
The band-pass filter parameters (e.g., filter length, cutoff frequencies) play a crucial role in shaping the filtered output. Ensure that the parameters used in the pl33tn function align with the desired band-pass characteristics. Adjusting these parameters might help in obtaining a more representative filtered signal.
2. Data Preprocessing
Before applying the band-pass filter, preprocess the data to handle any outliers, missing values, or inconsistencies that could affect the filtering process. Ensure data integrity and quality to improve the accuracy of the filtered results.
3. Quiver Plot Scaling
The scaling of the quiver plot vectors can impact the visual representation of wind magnitude. Check the scaling factors applied to the quiver plot (U_bp27 and V_bp27) to ensure they accurately reflect the magnitude and direction of the wind after filtering. Adjusting the scaling factors might align the quiver plot with the expected wind behavior.
4. Plot Interpretation
Consider the interpretation of the quiver plot in the context of wind patterns and weather events. The band-pass filtered data may emphasize specific frequency components of the wind signal, potentially highlighting variations that are not as prominent in the original hourly data. Understanding the characteristics of the filtered signal is essential for meaningful interpretation.
5. Visualization Enhancement
To improve the visual clarity and interpretability of the quiver plot, experiment with different plotting styles, color schemes, or overlays. Adding annotations, legends, or reference lines can provide additional context to the plot, aiding in the identification of weather events or patterns.
By revisiting the band-pass filtering parameters, ensuring data quality, adjusting plot scaling, interpreting the filtered signal, and enhancing visualization techniques, you can refine the quiver plot representation of the band-pass filtered wind data. These steps can help you achieve a more informative and visually appealing visualization that facilitates the identification and analysis of wind patterns over time.
