Barnes-Cressman Analysis
Also known as: Barnes analysis, Cressman analysis, Objective analysis, Grid interpolation
Barnes-Cressman analysis is an objective interpolation method that creates gridded meteorological fields from irregularly spaced observations (station data, radiosonde profiles, buoys). It is widely used for synoptic analysis, quality control, and initialization of numerical weather models.
Key highlights
- Objective, reproducible method; removes subjective judgment from traditional hand-drawn analyses
- Computationally efficient and simple to implement; widely available in meteorological software
- Flexible; weight functions and analysis parameters can be tuned for different variables and domains
- Excellent for sparse data networks; performs well even with large gaps in observation coverage
Intuition
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How it works
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When to use it
Use Barnes-Cressman analysis for creating surface or upper-air analysis charts for synoptic weather interpretation, for initializing numerical models using observation-based fields, for quality control of observation networks, and for research applications requiring gridded versions of station data.
Strengths & limitations
- Objective, reproducible method; removes subjective judgment from traditional hand-drawn analyses
- Computationally efficient and simple to implement; widely available in meteorological software
- Flexible; weight functions and analysis parameters can be tuned for different variables and domains
- Excellent for sparse data networks; performs well even with large gaps in observation coverage
- Can produce spurious extrema (values above max or below min of input observations) if weight functions are not chosen carefully
- Tends to smooth and suppress fine-scale features; small mesoscale variations are lost
- Sensitive to data errors; a single bad observation can propagate through the analysis
- Assumes spatial homogeneity; analysis parameters effective in one region may not work in another
Common pitfalls
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Applications
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Frequently asked
What is the difference between Barnes and Cressman methods?
Cressman method uses a simple parabolic weight function and typically runs for a fixed number of iterations. Barnes method uses Gaussian weights and allows user control of influence radius decay between passes. Barnes method generally produces smoother analyses.
How do I choose the optimal influence radius?
The influence radius controls how far away observations can affect each grid point. Optimal radius depends on observational data density and the scale of features you want to retain. Too small and the analysis is noisy; too large and detail is lost. Cross-validation against independent data is recommended.
Can the gridded values exceed observation extrema?
Yes, if weight functions are poorly chosen or influence radius is too large. This is a common pitfall. Using Shapiro's method (non-linear filtering) or limiting output to observation range can reduce this problem.
Is Barnes-Cressman still used, or have newer methods replaced it?
Both. Modern variational methods (3D-Var, 4D-Var) and kriging are preferred for formal data assimilation in operational models. However, Barnes-Cressman remains valuable for exploratory analysis, quality control, and in regions where modern methods are computationally prohibitive.
Sources
- 1.Barnes, S. L. (1964). A Technique for Maximizing Details in Numerical Weather Map Analysis. Journal of Applied Meteorology, 3(4), 396-409.
- 2.Cressman, G. P. (1959). An Operational Objective Analysis System. Monthly Weather Review, 87(10), 367-374.
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Cite this page
ScholarGate. (2026, June 3). Barnes-Cressman Analysis. ScholarGate. https://scholargate.app/meteorology/barnes-cressman-analysis