Understanding Anomalies and Spaghetti Plots

Two colorful maps can answer very different questions. An anomaly asks how a forecast compares with a reference climate. A spaghetti plot asks where individual ensemble members place a selected contour.

A standardized anomaly is not ensemble spread

In the inspected SKC GEFS 500-mb height implementation, the ensemble-mean height is interpolated to the ERA5 climatology grid and compared with the climatological mean for the valid calendar day and UTC synoptic hour. The calculation is:

standardized anomaly = (forecast ensemble mean − climatological mean) ÷ climatological standard deviation

Invalid or nonpositive climatological standard deviations are masked. A value of +2 means two reference standard deviations above that climatological mean. It does not mean two degrees warmer, a 2% probability, or two standard deviations of disagreement among today’s ensemble members.

That example describes the verified 500-mb calculation. Do not assume every anomaly product uses an identical preprocessing step. Read the field, level and legend. This guide does not assign a climatological reference-year range that has not been verified from the underlying dataset.

What unusual does—and does not—tell you

A large standardized departure can flag a pattern worth examining. It is not by itself a return period, a record, or a local impact forecast. The reference distribution may not be normally distributed, and local impacts depend on other atmospheric fields. Compare the anomaly with the actual height, pressure, temperature or moisture pattern being shown.

Follow the same contour across members

SKC’s 500-mb spaghetti product draws selected height contours from individual members. Closely grouped contours indicate agreement about that contour’s position. Widely separated or differently shaped contours indicate disagreement about position or pattern. Consult the legend for the chosen contour value; the lines are not storm tracks.

Use successive forecast times to distinguish a feature moving at different speeds from a feature consistently taking different paths. A plot may also include a mean or spread layer. Those are separate summaries; do not interpret every line or shaded color as a member probability.

A useful workflow

  1. Find an unusual area on a height-anomaly map.
  2. Switch to height/spaghetti guidance at the same valid time.
  3. Check whether members agree on the large-scale feature.
  4. Use precipitation or temperature guidance to explore possible impacts.

Open GEFS. Narrow spread still allows shared model errors, and a strong anomaly does not replace an official forecast or warning.