Monday, January 28, 2013
More on efforts at data rescue and digitization - reposted press release
Friday, January 25, 2013
There be gremlins in the data decks constituting some of the input data to the databank algorithm - both dubious data and geolocation metadata. We knew this from the start but have stayed blacklisting until we got the algorithm doing sort of what we thought it should and everyone was happy with it. Now we have attacked the problem for several weeks. Here are the four strands of attack:
1. Placing a running F-test through the merged series to find jumps in variance. This found a handful of intra-source cases of craziness. We will delete these stations through blacklisting.
2. Running through NCDC's pairwise homogenization algorithm to see whether any really gigantic breaks in teh series are apparent. This found no such breaks (but rest assured there are breaks and the databank is a raw data holding and not a data product per se).
3. First difference series correlations with proximal neighbors. We looked for cases where correlation was high and distance was high, correlation was low and distance was low and correlation was perfect and distance low. These were then looked at manually. Many are longitude / latitude assignation errors. For example we know Dunedin on the South Island of New Zealand is in the Eastern Hemisphere:
|This is Dunedin. Beautiful place ...|
And not the Western Hemisphere:
|This is not the Dunedin you were looking for ... Dunedin is not the new Atlantis|
But sadly two sources have the sign switched. The algorithm does not know where Dunedin is so is doing what it is supposed to. So, we need to tell it to ignore / correct the metadata for these sources so we don't end up with a phantom station.
There are other issues than simple sign errors in lat / lon that these picked up. One of the data decks has many of its French stations longitudes inflated by a factor of 10, so a station at 1.45 degrees East is wrongly placed at 14.5 degrees East. Pacific island stations appear to have recorded under multiple names and ids which confounds the merging in many cases.
4. As should be obvious from the above we also needed to look at stations proverbially 'in the drink', so we have pulled a high resolution land-sea mask and run through all stations against that. All cases demonstrably wet (greater than 10Km = .1 degree resolution at equator and many sources are only to 0.1 degree accuracy) are getting investigated.
Investigations have used the trusty googlemaps and wikipedia route in general with other approaches where helpful. Its time consuming and thankless. The good news is 'we' (Jared) are (is) nearly there.
The whole blacklist file will be one small text file the algorithm reads and one very large pdf that justifies each line in that text file. As people find other issues (and there undoubtedly will be - we will only catch worst / most obvious offenders even after several weeks on this) we can update and rerun.
Tuesday, January 15, 2013
First public talk on Databank Merge Results: AMS Annual Meeting
In order to continue our aims to be open and transparent, the abstract from the conference can be found here, and the presentation used can be located here. The presentation was also recorded, and once AMS puts the audio online, we will also try and link to it.
Wednesday, January 9, 2013
How should one update global and regional estimates and maintain long-term homogeneity?
The fundamental issue of how to curate and update a global, regional or national product whilst maintaining homogeneity is a vexed one. Non-climatic artifacts are not the sole preserve of the historical portion of the station records. Still today stations move, instruments change, times of observation change etc. etc. often for very good and understandable reason (and often not ...). There is no obvious best way to deal with this issue. If ignored for long enough station, local and even regional series can become highly unrealistic if large very recent biases are not dealt with.
The problem is also intrinsically inter-linked with the question as to which period of the record we should adjust for non-climatic effects. Here, at least there is general agreement that adjustment should be made to match the most recent apparently homogeneous segment so that today's readings can be easily and readily compared to our estimates of past variability and change without performing mental gymnastics.
At one extreme of the set of approaches is the CRUTEM method. Here, real-time data updates are only made to a recent period (I think still just post-2000) and no explicit assessment of homogeneity is made at the monthly update granuality (there is QC applied). Rather adjustments and new pre-2000 data effectively are caught up with major releases or network updates (e.g. with entirely new station record additions / replacements / assessments normally associated with a version increment and manuscript). This ensures values prior to a recent decade or so remain static for most month to month updates but at a potential cost if a station inhomogeneity occurs in the recent past which is de facto unaccounted for. This can only then be caught up with through a substantive update.
At the other extreme is the approach undertaken in GHCN / USHCN. Here the entire network is reassessed based upon new data receipts every night using the automated homogenization algorithm. New modern periods of records can change the identification of recent breaks in stations that contribute to the network. Because the adjustments are time-invariant deltas applied to all points prior to an identified break the impact is to change values in the deep past to better match modern data. So, the addition of station data for Jan 2013 may change values estimated for Jan 1913 (or July 1913) because the algorithm now has enough data to find a break that occurred in 2009. This then may affect the nth significant figure of the national / global calculation in 1913 on a day to day basis. This is why with GHCNv3 a system of version control of v3.x.y.z.ddmmyyyy was introduced and each version archived. If you want bit replication to be possible of your analysis then explicitly reference the version you used.
What is the optimal solution? Perhaps this is a 'How long is a piece of string?' class of question. There are very obvious benefits to either approach or any number of others. In part it depends upon the intended uses of the product. If interested in serving homogeneous station series as well as aggregated area averaged series using your best knowledge as of today perhaps something closer to NCDC's approach. If interested mainly in large scale average determination and under a reasonable null that at least on a few years timescale the inevitable new systematic artifacts average out as gaussian over broad enough space scales the CRUTEM approach makes more sense. And that, perhaps, is fundamentally why they chose these different routes ...
Saturday, January 5, 2013
High School Students Engage in Climate Research
Wednesday, January 2, 2013
Databank update - nearby 'duplicates' issue raised by Nick Stokes
One of the issues arising from the historically fragmented way data has been held and managed is that many versions of the same station may exist across multiple holdings. Often the holding will itself be a consolidation of multiple other sources and, like a Russian doll - well, you get the picture - its a mess. So, in many cases we have little idea what has been done to the data between the original measurement and our receipt. These decks are given low priority in the merge process but ignoring them entirely would be akin to cutting one's nose off to spite one's face - they may well contain unique information.
To investigate this further and more globally we ran a variant of the code with only one line change (Jared will attest that my estimate of line changes are always an underestimate but in this case it really was one line). If the metadata and data disagreed strongly then we withheld the station. We then ran a diff on the output with and without. The check found solely stations that were likely bona fide duplicates (641 in all). This additional check will be enacted in the version 1 release (and hence there will be 641 fewer stations).
Are we there now? Not quite. We have still to do the blacklisting. This is labor-intensive stuff. We will have a post on this - what we are doing and how we are planning to document the decisions in a transparent manner - early next week time permitting.
We currently expect to release version 1 no sooner than February. But it will be better for the feedback we have received and the extra effort is worth it for a more robust set of holdings.