How to Plot Atmospheric Data
Create publication-ready meteorological visualizations with AI. From pressure anomaly charts to barometric tendency plots - describe what you need, upload your data, and let Plotivy generate the Python code.
# NCEP Pressure Forecast
Direct Integration with Atmospheric Databases
Skip the manual download process. Plotivy connects directly to verified meteorological data sources.
NOAA Climate Data
Access historical weather and climate data directly from the National Oceanic and Atmospheric Administration.
University of Wyoming Upper Air
The gold standard for upper-air sounding data. Fetch radiosonde data for any station globally.
Expert-Curated Atmospheric Plots
Each example uses real NCEP pressure forecast data. Click "Generate This" to auto-load the data.
Pressure Anomaly (Fill Between)
Visualize high and low pressure systems by filling the area between the pressure curve and the 1013.25 hPa standard baseline. Red indicates high pressure (anticyclone), blue indicates low pressure (cyclone).
"Using the uploaded dataframe df (columns: lat, lon, time, prmslmsl), create a time series of mean pressure over time. Fill the area between the pressure curve and 1013.25 hPa baseline with red when above (high pressure) and blue when below (low pressure). Add a dashed horizontal reference line at 1013.25 hPa."
Daily Pressure Range (Candlestick)
Summarize daily pressure fluctuations with vertical bars extending from minimum to maximum pressure, similar to financial candlestick charts. Perfect for identifying daily variability patterns.
"Using the uploaded dataframe df (columns: lat, lon, time, prmslmsl), compute min and max prmslmsl for each unique time value. Create a candlestick-style chart showing vertical bars from min to max pressure. Color bars based on whether mean pressure increased or decreased."
Barometric Tendency (Dual Axis)
Analyze both the pressure value and its rate of change on a dual-axis chart. The line shows pressure, while bars on the secondary axis show the 'barometric tendency' (derivative) - how much pressure has risen or fallen.
"Using the uploaded dataframe df (columns: lat, lon, time, prmslmsl), compute mean pressure per time step. Plot as a line. On a secondary Y-axis, add bars showing the pressure change (derivative). Color bars green for rising, red for falling pressure."
Local Minima/Maxima Annotation
Automatically detect and annotate the peaks (maxima) and valleys (minima) on a pressure curve. Display the exact value only at these critical points to keep the plot clean and informative.
"Using the uploaded dataframe df (columns: lat, lon, time, prmslmsl), compute mean pressure over time. Plot as a smooth curve. Use scipy.signal.find_peaks to detect local minima and maxima. Annotate only these peak/valley points with their pressure values."
Multi-Location Forecast Comparison
Compare pressure forecasts for multiple grid points or locations on a single 'spaghetti plot'. Each line represents a different location, allowing quick comparison of regional pressure patterns.
"Using the uploaded dataframe df (columns: lat, lon, time, prmslmsl), select 5 unique lat/lon combinations. For each location, plot prmslmsl vs time as a separate colored line. Add a legend showing coordinates."
Extended Forecast with Thresholds
Visualize a 10-day pressure forecast with horizontal threshold lines marking 'high' (1020 hPa), 'normal' (1013 hPa), and 'low' (1005 hPa) pressure zones. Shade regions to indicate weather patterns.
"Using the uploaded dataframe df (columns: lat, lon, time, prmslmsl), compute mean pressure per time step. Plot the time series. Add horizontal dashed lines at 1020, 1013, and 1005 hPa. Optionally shade regions between thresholds."
How It Works
1. Load Data
Upload your CSV, fetch from NOAA/Wyoming, or use our example NCEP data
2. Describe Your Plot
Tell Plotivy what you want: "pressure anomaly with red/blue fill"
3. Get Python Code
AI generates publication-ready matplotlib/plotly code
Frequently Asked Questions
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