In the development of the SANGOMA tools, one focus is on the diagnostic tools. By now, the tools contain functions to compute
- rank histograms
- reduced centered random variable (RCRV)
- continuous ranked probability score (CRPS)
- Brier score and entropy
- mutual information
- higher-order ensemble statistics (skewness and kurtosis)
- array modes and spectrum
The tools are described in the Deliverable D2.4 ( The diagnostics that are used in the benchmarks are explained in detail in Deliverable 4.3 ( The tools allow to assess the statistical properties and quality of the ensemble and the observations."

For further development, we would like to learn about your preferences on the following diagnostics. For compactness, we use the notation y=observation vector, H: observation operator, x^f forecast state vector, x^a analysis state vector. The following statistics can be used to access the statistical consistency of an ensemble filter. "

* 1. Please select the tools you want to see by attibuting a level of importance for you (try to use the range of choices and to not select Essential for all of them ;-))

  Essential High priority Medium priority Low priority Not interesting at all
Standard statistics (min, max, rms, mean ...) on innovation y-Hx^f:
Standard statistics (min, max, rms, mean ...) on innovation y-Hx^a:
Standard statistics (min, max, rms, mean...) on analysis increment x^a-x^f:
Test on whiteness of innovation y-Hx^f
Minimum spanning tree rank histogram; a multivariate extension for rank histograms
More consistency checkings as in Desroziers et al 2005 Q.J.R. Met. Soc

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