People interested in this blog are likely also interested in the upcoming
Seventh seminar for homogenization and quality control in climatological databases and COST ES-0601 “HOME” action management committee and final meeting in Budapest, Hungary on the 24 – 28 October 2011. Abstract deadline is the 16th of
September 2011. This is the main event in the homogenization community, in my opinion.
Purpose - To facilitate use of a robust, independent and global common benchmarking and assessment system for temperature data-product creation methodologies to aid product intercomparison and uncertainty quantification:
http://www.surfacetemperatures.org/benchmarking-and-assessment-working-group
Posting and comments are open for constructive advice and ideas.
Monday, 20 June 2011
Thursday, 16 June 2011
If I had but one analog I could create ...
I think I would look to create something that tested in some sense the limits of homogenization methods whilst still retaining some realism.
I would take a forced component run such as c20c and then look to add change points that in the net removed that trend. This would penalize any algorithm that tended to introduce adjustments with a preferential zero bias.
The breaks I would add would be a mix of step like and slope like and a large number would have changes in seasonality and timeseries variance associated.
I'd have limited metadata and what metadata there was would be poor quality.
I would assume that most breaks were small (sigma <1K, in some cases perhaps <<1K) and that they happened fairly frequently (once every 5 to ten years say on average).
A number of breaks would be quasi-contemperaneous over countries and these would have very similar characteristics to each other.
There is documented evidence that these issues all to some extent pervade the network (e.g. US network move from stevenson screen to automated sensors happened largely within 5 years over 70% of the network).
So, whilst at the outer bounds of plausibility it would not be an entirely implausible error structure.
I would take a forced component run such as c20c and then look to add change points that in the net removed that trend. This would penalize any algorithm that tended to introduce adjustments with a preferential zero bias.
The breaks I would add would be a mix of step like and slope like and a large number would have changes in seasonality and timeseries variance associated.
I'd have limited metadata and what metadata there was would be poor quality.
I would assume that most breaks were small (sigma <1K, in some cases perhaps <<1K) and that they happened fairly frequently (once every 5 to ten years say on average).
A number of breaks would be quasi-contemperaneous over countries and these would have very similar characteristics to each other.
There is documented evidence that these issues all to some extent pervade the network (e.g. US network move from stevenson screen to automated sensors happened largely within 5 years over 70% of the network).
So, whilst at the outer bounds of plausibility it would not be an entirely implausible error structure.
Monday, 13 June 2011
Big questions with which to test homogenisation algorithms
Hi, I'm hoping to get time in the call to touch on this a little. Similar to previous posts about worse nightmares I would like us to think about questions that we want to answer with the analog-error-models. The idea would be to pick 8 of these to run with for each 3 year benchmarking cycle - one for each world. If these start from something simple to something horrible this will give us a chance to see where algorithms begin to struggle. Lets focus here on monthly means to make things a little simpler for the time being - please comment with your ideas. For example:
1) Do homogenisation algorithms detect discontinuities when none are present?
Analog-error-world 1 = A historical forcing model analog-known-world with no-errors added
2) Do homogenisation algorithms cope with discontinuities that affect the variance?
Analog-error-world 2 = A historical forcing model analog-known-world with seasonally constant changes applied
Analog-error-world 3 = A historical forcing model analog-known-world with seasonally varying changes applied at the same location and approximate magnitude as analog-error-world 2
2) Can homogenisation algorithms cope with non-stationary worlds/ where there is a background trend?
Analog-error-world 4 = A control forcing (constant pre-industrial emissions) model analog-known-world with mixed error structure applied
Analog-error-world 5 = An A1B (high emissions) forcing model analog-known-world basis with identical error structure to World 4
3) Can homogenisation algorithms cope when discontinuities are small and frequent?
Analog-error-world 6 = A historical forcing model analog-known-world with many small discontinuities added of various sign biases - (seasonally varying to be realistic?).
4) Can homogenisation algorithms cope with layered gradual and abrupt discontinuities (i.e., urban warming + instrument shelter change)
Analog-error-world 7 = A historical forcing model analog-known-world with either gradual or abrupt discontinuities applied to a station (seasonally varying to be realistic?)
Analog-error-world 8 = A historical forcing model analog-known-world with both gradual and abrupt discontinuities applied to a station (seasonally varying to be realistic?) using the initial error structure from analog-error-world 7 with other errors added.
At present its probably useful just to come up with as many plausible questions as possible and examples of error world structures to explore these.
There is an argument for including a really nasty one that is perhaps unplausible - so feel free to be creative.
1) Do homogenisation algorithms detect discontinuities when none are present?
Analog-error-world 1 = A historical forcing model analog-known-world with no-errors added
2) Do homogenisation algorithms cope with discontinuities that affect the variance?
Analog-error-world 2 = A historical forcing model analog-known-world with seasonally constant changes applied
Analog-error-world 3 = A historical forcing model analog-known-world with seasonally varying changes applied at the same location and approximate magnitude as analog-error-world 2
2) Can homogenisation algorithms cope with non-stationary worlds/ where there is a background trend?
Analog-error-world 4 = A control forcing (constant pre-industrial emissions) model analog-known-world with mixed error structure applied
Analog-error-world 5 = An A1B (high emissions) forcing model analog-known-world basis with identical error structure to World 4
3) Can homogenisation algorithms cope when discontinuities are small and frequent?
Analog-error-world 6 = A historical forcing model analog-known-world with many small discontinuities added of various sign biases - (seasonally varying to be realistic?).
4) Can homogenisation algorithms cope with layered gradual and abrupt discontinuities (i.e., urban warming + instrument shelter change)
Analog-error-world 7 = A historical forcing model analog-known-world with either gradual or abrupt discontinuities applied to a station (seasonally varying to be realistic?)
Analog-error-world 8 = A historical forcing model analog-known-world with both gradual and abrupt discontinuities applied to a station (seasonally varying to be realistic?) using the initial error structure from analog-error-world 7 with other errors added.
At present its probably useful just to come up with as many plausible questions as possible and examples of error world structures to explore these.
There is an argument for including a really nasty one that is perhaps unplausible - so feel free to be creative.