The spatial and temporal interpolation of environmental data white paper is now available for discussion. When making posts please remember to follow the house rules. Please also take time to read the full pdf before commenting and where possible refer to one or more of section titles, pages and line numbers to make it easy to cross-reference your comment with the document.
Update 8/2: A supplement has also been published for comment / consideration. Please be sure top delineate in your comments whether you are discussing the main white paper or the supplement.
The recommendations from the main white paper are reproduced below:
• The choice of interpolation technique for a particular application should be guided by a full characterization of the input observations and the field to be analyzed. No single technique can be universally applied. It is likely that different techniques will work best for different variables, and it is likely that these techniques will differ on different time scales.
• Data transformations should be used where appropriate to enhance interpolation skill. In many cases, the simple transformation of the input data by calculating anomalies from a common base period will produce improved analyses. In many climate studies, it has been found that separate interpolations of anomaly and absolute fields (for both temperature and precipitation) work best.
• With all interpolation techniques, it is imperative to derive uncertainties in the analyzed gridded fields, and it is important to realize that these should additionally take into account components from observation errors, homogeneity adjustments, biases, and variations in spatial sampling.
• Where fields on different scales are required, interpolation techniques should incorporate a hierarchy of analysis fields, where the daily interpolated fields should average or sum to monthly interpolated fields.
• Research to develop and implement improved interpolation techniques, including full spatio-temporal treatments is required to improve analyses. Developers of interpolated datasets should collaborate with statisticians to ensure that the best methods are used.
• The methods and data used to produce interpolated fields should be fully documented and guidance on the suitability of the dataset for particular applications provided.
• Interpolated fields and their associated uncertainties should be validated.
• The development, comparison and assessment of multiple estimates of environmental fields, using different input data and construction techniques, are essential to understanding and improving analyses.