A short and probably incomplete thread of datasets made by plugging holes in HadCRUT.

1. Cowtan and Way (2014). Kriging of various flavours including one using atmospheric temperatures

https://rmets.onlinelibrary.wiley.com/doi/full/10.1002/qj.2297

2. Ilyas et al. (2017). Multi resolution lattice kriging. 10,000 member ensemble

https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2017GL074596

3. Benestad et al. (2019) reconstruction using EOFs

https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2019GL083474

4. Kadow et al. (2020). Neural network infilling proof of concept based on methods used to reconstruct damaged photographs.

https://www.nature.com/articles/s41561-020-0582-5

5. Morice et al. (2020) HadCRUT5. Kriging using full HadCRUT error model to generate an ensemble

https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2019JD032361

6. Ilyas et al. (submitted) Multi-resolution lattice kriging including uncertainty in hyperparameter estimation.

https://amt.copernicus.org/preprints/amt-2020-454/

7. Rohde and Hausfather (2020) not strictly HadCRUT, but it does fill the gaps in the sea part of HadCRUT using kriging

https://essd.copernicus.org/articles/12/3469/2020/essd-12-3469-2020.html

And… Vaccaro et al. (2021) using GraphEM which leverages Gaussian Markov random fields (aka Gaussian graphical models) to better estimate covariance relationships within a climate field

https://journals.ametsoc.org/view/journals/clim/aop/JCLI-D-19-0814.1/JCLI-D-19-0814.1.xml

Originally tweeted by John Kennedy (@micefearboggis) on January 20, 2021.

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