Atmospheric Variables Plotting Page

This page plots the operational NCEP analysis. The model used to compute these values changes through time so it is not as appropriate for anomalies or examinging climate. For most purposes, using the NCEP/NCAR Reanalysis with its consistent model and longer climatology is preferred. That page can be found at http://psl.noaa.gov/data/composites/day/. Some of these plots can be found on the maproom. Note the climatology has changed to 1991-2020 as of Jan 21 2022.

This form allows you to plot the NCEP daily analysis data and OLR. To plot daily SST, please to go Daily Composites or for monthly/seasonal SST, go to Seasonal Composites. Data coverage is from 1980 to the present. You have the option of averaging the last n days. Climatology is based on a 5 day running mean of the annual cycle (based on 1991-2020). Anomalies are formed by subtracting this daily climatology. Note that no smoothing is done in the case of vector wind anomalies and climatologies.

Enter EITHER the first date and last date OR the first date and number of days OR the last date and number of days.
First date:(yyyymmdd): to Last date:
and/or enter the number of days to average:
Which variable? Analysis level?

Plot type? Mean   Anomaly   Climatology  

Map Domain: If you wish to select a specific lat/lon region, you must choose Custom from the map domain menu, and complete the lat, lon, and projection selections that immediately follow.

If CUSTOM selected, choose projection:

Lowest lat (-90 to 90) Highest lat (-90 to 90)
Western-most longitude (0 to 360) Eastern-most longitude (0 to 720)
Note: eastern longitude must be greater than western longitude
Color? Shading Type:
Override default contour interval? New interval: Range: low high
Scale Plot Size (%): (default is 100%)

Help
Background Information
Related Plot/Analysis
Feedback
  • Feedback (good, bad, suggested enhancements) is always welcome! Mail to psl at (psl.data@noaa.gov). If you find this page useful for teaching or research, I would especially like to know.