Release of a daily benchmark dataset - version 1

Kate Willett's blog post from 6th October gives a detailed over-view of the benchmarking process that forms part of the ISTI's aims. It is hoped that in the long term these benchmarks will not only be produced at the monthly level, but also for daily data.

This post announces the release of a smaller daily benchmark dataset focusing on four regions in North America. These regions can be seen in Figure 1.

Figure 1 Station locations of the four benchmark regions. Blue stations are in all worlds. Red stations only appear in worlds 2 and 3.

These benchmarks have similar aims to the global benchmarks that are currently being produced by the ISTI working group, namely to:
 

1. Assess the performance of current homogenisation algorithms and provide feedback to allow for their improvement 
2. Assess how realistic the created benchmarks are, to allow for improvements in future iterations 
3. Quantify the uncertainty that is present in data due to inhomogeneities both before and after homogenisation algorithms have been run on them

A perfect algorithm would return the inhomogeneous data to their clean form – correctly identifying the size and location of the inhomogeneities and adjusting the series accordingly. The inhomogeneities that have been added will not be made known to the testers until the completion of the assessment cycle – mid 2015. This is to ensure that the study is as fair as possible with no testers having prior knowledge of the added inhomogeneities.

The data are formed into three worlds, each consisting of the four regions shown in Figure 1. World 1 is the smallest and contains only those stations shown in blue in Figure 1, Worlds 2 and 3 are the same size as each other and contain all the stations shown.

Homogenisers are requested to prioritise running their algorithms on a single region across worlds instead of on all regions in a single world. This will hopefully maximise the usefulness of this study in assessing the strengths and weaknesses of the process. The order of prioritisation for the regions is Wyoming, South East, North East and finally the South West.

This study will be more effective the more participants it has and if you are interested in participating please contact Rachel Warren (rw307 AT exeter.ac.uk). The results will form part of a PhD thesis and therefore it is requested that they are returned no later than Friday 12th December 2014. However, interested parties who are unable to meet this deadline are also encouraged to contact Rachel.

There will be a further smaller release in the next week that is just focussed on Wyoming and will explore climate characteristics of data instead of just focusing on inhomogeneity characteristics.

A framework for benchmarking of homogenisation algorithm performance on the global scale - Paper now published

We have just had our first benchmarking paper accepted at Geoscientific Instrumentation, Methods and Data Systems:

Willett, K., Williams, C., Jolliffe, I. T., Lund, R., Alexander, L. V., Brönnimann, S., Vincent, L. A., Easterbrook, S., Venema, V. K. C., Berry, D., Warren, R. E., Lopardo, G., Auchmann, R., Aguilar, E., Menne, M. J., Gallagher, C., Hausfather, Z., Thorarinsdottir, T., and Thorne, P. W.: A framework for benchmarking of homogenisation algorithm performance on the global scale, Geosci. Instrum. Method. Data Syst., 3, 187-200, doi:10.5194/gi-3-187-2014, 2014.

Benchmarking, in this context, is the assessment of homogenisation algorithm performance against a set of realistic synthetic worlds of station data where the locations and size/shape of inhomogeneities are known a priori. Crucially, these inhomogeneities are not known to those performing the homogenisation, only those performing the assessment. Assessment of both the ability of algorithms to find changepoints and accurately return the synthetic data to its clean form (prior to addition of inhomogeneity) has three main purposes:

      1) quantification of uncertainty remaining in the data due to inhomogeneity
      2) inter-comparison of climate data products in terms of fitness for a specified purpose
      3) providing a tool for further improvement in homogenisation algorithms

Here we describe what we believe would be a good approach to a comprehensive homogenisation algorithm benchmarking system. This includes an overarching cycle of: benchmark development; release of formal benchmarks; assessment of homogenised benchmarks and an overview of where we can improve for next time around (Figure 1).

Figure 1 Overview the ISTI comprehensive benchmarking system for assessing performance of homogenisation algorithms. (Fig. 3 of Willett et al., 2014)

There are four components to creating this benchmarking system. 

Creation of realistic clean synthetic station data

Firstly, we must be able to synthetically recreate the 30000+ ISTI stations such that they have the correct variability, auto-correlation and interstation cross-correlations as the real data but are free from systematic error. In other words, they must contain a realistic seasonal cycle and features of natural variability (e.g., ENSO, volcanic eruptions etc.). There must be a realistic persistence month-to-month in each station and geographically across nearby stations. 

Creation of realistic error models to add to the clean station data

The added inhomogeneities should cover all known types of inhomogeneity in terms of their frequency, magnitude and seasonal behaviour. For example, inhomogeneities could be any or a combination of the following:

     -  geographically or temporally clustered due to events which affect entire networks or regions (e.g. change in observation time);
     -  close to end points of time series;
     -  gradual or sudden;
     -  variance-altering;
     -  combined with the presence of a long-term background trend;
     - small or large;
     - frequent;
     - seasonally varying.

Design of an assessment system

Assessment of the homogenised benchmarks should be designed with the three purposes of benchmarking in mind. Both the ability to correctly locate changepoints and to adjust the data back to its homogeneous state are important. It can be split into four different levels:

     - Level 1: The ability of the algorithm to restore an inhomogeneous world to its clean world state in terms of climatology, variance and trends.

     - Level 2: The ability of the algorithm to accurately locate changepoints and detect their size/shape.

     - Level 3: The strengths and weaknesses of an algorithm against specific types of inhomogeneity and observing system issues.

     - Level 4: A comparison of the benchmarks with the real world in terms of detected inhomogeneity both to measure algorithm performance in the real world and to enable future improvement to the benchmarks.

The benchmark cycle

This should all take place within a well laid out framework to encourage people to take part and make the results as useful as possible. Timing is important. Too long a cycle will mean that the benchmarks become outdated. Too short a cycle will reduce the number of groups able to participate.

Producing the clean synthetic station data on the global scale is a complicated task that has now taken several years but we are close to completion of a version 1. We have collected together a list of known regionwide inhomogeneities and a comprehensive understanding of the many many different types of inhomogeneities that can affect station data. We have also considered a number of assessment options and decided to focus on levels 1 and 2 for assessment within the benchmark cycle. Our benchmarking working group is aiming for release of the first benchmarks by January 2015.