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.

Summary of Regional Inhomogeneities

A few months ago the Benchmarking group made a call to the homogenisation community to submit any times/characters of known inhomogeneities occurring in different regions.

Thanks to the many contributors we now have an overview for a number of countries:
Spain
Poland
Canada
Switzerland
Australia
Tropics
Italy
Central Europe
Russia
Slovenia
Netherlands
UK
Austria
USA

There is always room for more. If you come across any useful information or would just like to see what is there so far please go to:

https://docs.google.com/spreadsheet/ccc?key=0Al6ocsUAaINSdHpTREJzVkRZUTdfVjNPRlh0Q1V3WUE&usp=sharing#gid=0

This is an online editable document so please do add info. A static version will be scraped once a month.




Kate

ISTI/Benchmarking talk at Reading University Meteorology Department lunchtime seminar

I was invited to present the work of the ISTI at Reading University Meteorology Department.

The talk covered the aims and workings of the ISTI: to facilitate robust climate analysis of land surface temperature.

1) Provide a version controlled, comprehensive, traceable to known origin, openly shared databank of surface temperature data with digitally attached metadata.

2) Set up an international standard for benchmark testing homogenisation algorithms and other methodological choices for building climate data-products to help quantify homogenisation and methodological uncertainty and aid product selection and methodological advancement.

3) Provide a portal for all ISTI related products with user tools to help inform choice of product, visualisation and intercomparison of products.

I then focussed on the benchmarking side of things to explain why this is needed and how we are going about it.

The slides for the talk are available here: The ISTI: Dragging the land surface temperature data kicking and screaming into the 21st Century

A few of the interesting questions that I remember:

Will the ISTI databank include non-standard station data?
Yes, there are plans to incorporate data from amateur stations. This will be flagged and prioritised appropriately.

How do you deal with uncertainties between the true shaded air temperature verses the biases created by poor ventilation or insolation issues in screen temperatures?
This is a big problem as we need to quantify changes/impacts on the smaller, more local scale. By making as much data available as possible, in addition to metadata, the ISTI can help address this problem. Having a higher density of stations increases the statistical power in detection of errors. Making it easier to build surface temperature products enables more people to tackle the problem and apply a wider spread of methodological choices. This may help to constrain the uncertainty to come extent.

Can you provide a list of stations used to make up each gridbox in gridded products?
This is something that we would encourage anyone building data-products to do. Ideally they would list the stations and which stage and version they come from. The data-portal will have to be able to cope with hosting ancillary information pertaining to the data-products in an easily searchable and usable manner.

Can other people make their own error worlds/synthetic data?
Yes - all of our code will be written in R and published on the website. The idea is that others can use the code and tweak various parameters to make their own clean synthetic stations and add errors as they wish.

Benchmarking Workshop Agenda and Report

The workshop agenda and full report can be found here: https://sites.google.com/a/surfacetemperatures.org/home/benchmarking-and-assessment-working-group#Minutes

Below is the executive summary.

1st – 3rd July 2013 Benchmarking Working Group Workshop Report Executive Summary National Climatic Data Center (NCDC) of the National Oceanic and Atmospheric Administration (NOAA), Asheville, NC, USA

Attended in person:
Kate Willett (UK), Matt Menne (USA), Claude Williams (USA), Robert Lund (USA), Enric Aguilar (Spain), Colin Gallagher (USA), Zeke Hausfather (USA), Peter Thorne (USA), Jared Rennie (USA)
 
Attended by phone:
Ian Jolliffe (UK), Lisa Alexander (Australia), Stefan Brönniman (Switzerland), Lucie A. Vincent (Canada), Victor Venema (Germany), Renate Auchmann (Switzerland), Thordis Thorarinsdottir (Norway), Robert Dunn (UK), David Parker (UK)

A three day workshop was held to bring together some members of the ISTI Benchmarking working group with the aim of making significant progress towards the creation and dissemination of a homogenisation algorithm benchmark system. Specifically, we hoped to have: the method for creating the analog-clean-worlds finalised; the error-model worlds defined and a plan of how to develop these; and the concepts for assessment finalised including a decision on what data/statistics to ask users to return. This was an ambitious plan for three days with numerous issues and big decisions still to be tackled.

The complexity of much of the discussion throughout the three days really highlighted the value of this face-to-face meeting. It was important to take time to ensure that everyone understood and had come to the same conclusion. This was aided by whiteboard illustrations and software exploration, which would not have been possible over a teleconference.

In overview, we made significant progress in terms of developing and converging on concepts and important decisions. We did not complete the work of Team Creation as hoped, but necessary exploration of the existing methods was undertaken revealing significant weaknesses and ideas for new avenues to explore have been found.

The blind and open error-worlds concepts are 95% complete and progress was made on the specifics of the changepoint statistics for each world. Important decisions were also made regarding missing data, length of record and changepoint location frequency. Seasonal cycles were discussed at length and more research has been actioned. A significant first go was made at designing a build methodology for the error-models with some coding examples worked through and different probability distributions explored.

We converged on what we would like to receive from benchmark users for the assessment and worked through some examples of aggregating station results over regions. We will assess both retrieval of trends and climate characteristics in addition to ability to detect changepoints. Contingency tables of some form will also be used. We also hope to have some online or assessment software available so that users can make their own assessment of the open worlds and past versions of benchmarks. We plan to collaborate with the VALUE downscaling validation project where possible.

From an intense three days all participants and teleconference participants gained a better understanding of what we're trying to achieve and how we are going to get there. This was a highly valuable three days, not least through its effect of focussing our attention prior to the meeting and motivating further collaborative work after the meeting. Two new members have agreed to join the effort and their expertise is a fantastic contribution to the project.

Specifically, Kate and Robert are to work on their respective methods for Team Creation, utilising GCM data and the vector autoregressive method. This will result in a publication describing the methodology. We aim to finalise this work in August.

Follow on teleconferences, Team Corruption will focus on completing the distribution specifications and building the probability model to allocate station changepoints. This work is planned for completion by October 2013. Release of the benchmarks is scheduled for November 2013.

Team Validation will continue to develop the specific assessment tests and work these into a software package that can be easily implemented. This work is hoped to be completed by December 2013, but there is more time available as assessment will take place at least 1 year after benchmark release.

Call for regional inhomogeneity info

To create realistic benchmarks we would like to reproduce times and locations of known sources of inhomogeneity as best we can. Please can you help us. If you know of any regional/countrywide changes to the observing system over time please can you list them here or point us to some documentation/reference. Any information is valuable - even if its quite vague.

Ideally we'd like to know:

WHEN - specific date or month or year or even decade etc.
WHERE - a region, a country, an international GTS/WMO change etc.
WHAT - a change in shelter, thermometer type, automation, observing time/practice etc.
HOW - are there any estimates of the size/direction/nature of the effect of this change?

Please post here and encourage others to do so. We then hope to reward you with some realistic error-worlds to play with.

Kate

Mailing list on homogenisation of climate data

There is now a homogenisation mailing list, which aims to strengthen the communication and co-operation in the scientific community on homogenenisation. Suggested uses of the list are announcements on:

• Conferences, workshops, etc.
• Important papers
• Discussions
• Job opportunities
• New projects started
• Requests for data, information, and cooperation partners
• ...

For more information (on how to subscribe), please to go my page on the homogenisation mailing list.