Saturday, 5 October 2013

Five statistically interesting problems in homogenization

For many the term homogenization is associated with dusty archives. Surely good metadata on station histories is important for achieving best results, but homogenization is much more, it is especially a very exiting statistical problem. It provides a number of problem that are of fundamental statistical interest.

Most of the work in homogenization has been focussed on improving the monthly and annual means, for example to allow for accurate computations of changes in global mean temperature. The recent research focus on extreme and server weather and on weather variability, has made the homogenization of daily data and its probability distribution necessary. Much recent work goes in this direction.

As I see it, there are five problems for statisticians to work on. The first problems are of general climatological interest and thus also for the study of weather variability. The latter ones are more and more important for the study of weather variability.

Problem 1. The inhomogeneous reference problem
Neighboring stations are typically used as reference. Homogenization methods should take into account that this reference is also inhomogeneous
Problem 2. The multiple breakpoint problem
A longer climate series will typically contain more than one break. Methods designed to take this into account are more accurate as ad-hoc solutions based single breakpoint methods
Problem 3. Computing uncertainties
We do know about the remaining uncertainties of homogenized data in general, but need methods to estimate the uncertainties for a specific dataset or station
Problem 4. Correction as model selection problem
We need objective selection methods for the best correction model to be used
Problem 5. Deterministic or stochastic corrections?
Current correction methods are deterministic. A stochastic approach would be more elegant

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