Tuesday, July 27, 2010

GHCN KML visualisation by years

This post complements Ron Broberg's nifty movie of the progress of GHCN stations over the years, and in particular, his decadal stills. I've made KML files for the decade years (1880, 1890,...) of all stations that have records (at least one) in those years, for visualisation in Google Earth. You can read them into GE and see in detail how the network of stations varied over time. These files also have the description balloons etc.

They are in a zip file called KMLGHCNyears.zip on the doc repository. Each is a kmz file. Just click on the file name in  a file browser or use "open" in GE.

If it's hot in Washington, how about Montreal?

In one of Steve Goddard's posts at WUWT, there was some mocking of interpolation in GISS. "Is the temperature data in Montreal valid for applying to Washington DC.? " was asked.

Well, it turns out, yes it is, using anomalies. I looked in the raw GHCN data at McGill Montreal (71627/003), which has the only long GHCN record there, vs Washington NA (WMO 72405/000),  which also has a very long record. I used a 4-year tapered smoothing filter (triangle) on the monthly data. Here's how it turned out:

Monday, July 26, 2010

Still more KML and Google Earth

Some more updates and capabilities. I've added descriptions to the markers. If you click on any station marker in GE, a balloon pops up with:
  • Name
  • Country
  • WMO number
  • Rural/Town/Urban (also indicated by marker color)
  • Airport (if true), Population, in 1000's (if given)
  • Years over which data exists (may have gaps)
  • Total years of data (excluding gaps)
You can also see this data by drilling down on a LH panel (long list).

I've also moved to using KMZ files, as is recommended. These are just zip files, each containing the appropriate KML file. GE will read them directly.

The docs repository file is still called KMLfiles.zip, and now includes the R code I used and a Readme.txt file.


Sunday, July 25, 2010

More Google Earth station data

I've learnt more about KML since the previous post, so I've thought of more it could do. I've amended the files in the KMLfiles zipfile on the docs repository so that the pushpins (and names) are colored according to the "Urban" criterion in the inventory file. The colors are:
  yellow for urban (C)
  green for rural (A)
  reddish orange for small town (B)
[Update - I've also varied the pushpin size according to total numbers of years in station record. Scale 1 if >50 yrs, 0.7 for 20-50 and 0.4 if <20 yrs.]

So you can see how well the rurals are spread, and, if you like, zoom in to see whether you think the classification is right.

I've also added another file, GHCN2009.kml, which has only GHCN stations that have reported since end 2008 (same pushpin colors). One GE catch in reading multiple files - the pushpins aren't cleared when you read in a new file. Below the jump -  what a local view (GSOD) looks like:

Saturday, July 24, 2010

Fun with Google Earth, GSOD and KML

Steven Mosher has been a big advocate of using KML files to pass location data for stations into Google Earth. I'm planning to add KML file output to TempLS. But in the meantime, as a follow-up to the previous post, I've made KML files for the GHCN and GSOD databases, if you want to look more closely at them in Google Earth.

The files are on the docs repository. They are in a zipfile called KMLfiles.zip, and are called GHCN.kml and GSOD.kml. You can read them into Google Earth either by just clicking on the filename in a file browser (GE pops up), or by using Open or Ctrl-O in GE. These files just read in the stations as a whole lot of placemarks and names- there's no tour provided.

Wednesday, July 21, 2010

Spatial coverage of the GHCN and GSOD station sets

The Greenland study with the NOAA GSOD surface temperature dataset looked very promising. There are many more GSOD stations than GHCN, especially in modern times. But it turned out that they weren't very well distributed, being mainly along the west coast. In that way GHCN did better, and its fewer stations may have performed at least as well as GSOD.

So I tried to compare more closely for other parts of the world. It's a big and complex picture, but this may be a start.

Tuesday, July 20, 2010

Monitoring global temperatures daily.

I've been running a macro each day to monitor JAXA sea ice. I've added to it facilities for updating UAH satellite temperatures. Now I've added an automatically updated plot of the 5 main global temperature indices, HADCrut3, Gistemp, NOAA-NCDC, UAH and MSU-RSS. Of course, these are each only updated monthly, but the macro checks each day for new info, so the plots should remain current. Below the jump:

Global Land/Ocean - GSOD and GHCN data compared




This continues the series comparing the GHCN station temperature data used by most major indices and blog reconstructions with the larger, different SYNOP-based NCDC GSOD dataset which Ron Broberg has made available. Since GSOD is patchy pre 1973, that is the start year for this check.


It turns out (below) that for Global Land/Ocean, both datasets give very similar results. In a way, this is not surprising, because both use the same ocean dataset HADSST2, which covers most of the globe. But it does suggest that any lack of coverage by GHCN is not a big source of error.

Friday, July 16, 2010

Greenland and GSOD


Greenland's temperature history has been discussed recently at WUWT. It is another region which is sparsely covered by measurements in GHCN, but has better numbers in the recent NCDC GSOD data which Ron Broberg has made available.


At WUWT Steve Goddard was criticising NASA for observing that temperatures had risen rapidly in the last 30 years. He didn't actually say whether this was right or wrong - he just said that nett movement since 1920 had been negative. So with GSOD data for the more recent coverage, let's see.


Thursday, July 15, 2010

Revisiting Bolivia

Back in April I looked at the "Bolivia effect". This was attributed to the absence of GHCN data from Bolivia since 1990. This does create a gap in the temperature data; there are stations just over the border.

The conclusion was that there was no real evidence of a major problem, but perhaps some uncertainty remained. Now with GSOD data, which is available for Bolivia through to present, we can look again.

Tuesday, July 13, 2010

Arctic Trends using GSOD Temperature data with TempLS

The previous post described the GSOD land temperature data from NCDC. This is a large set based on SYNOP data, which is plentiful from about 1973 onwards. Ron Broberg has processed it and put it into a useful format.

In particular it has a lot more Arctic stations than does the GHCN set. So my first regional application with TempLS will be to Arctic trends.

Monday, July 12, 2010

Using TempLS on an alternative land temperature data set - GSOD.

A few years ago there was a fuss about the unavailability of the main codes for preparing temperature indices. They were said to perform illegitimate data manipulations to exaggerate temperature trends. Then GISS released the code and data for Gistemp. There were various nit-pickings, but the folks demanding code seemed at that stage to not really get it working. Suspicions remained, and started to focus on the GHCN dataset which was the major resource for all the indices. Thermometers were being eaten, a southward march etc.

Recent independent codes, including TempLS, and notably Muir Russell, have verified that not only are the indices calculated properly, but the various red herrings about adjustments, airports etc do not create a noticeable bias. However, they are all based on GHCN and a few ocean data sets (mainly HADSST2) so an alternative dataset would be welcome.

Ron Broberg went in search of a set based on SYNOP reports, which include a lot more stations (as recommended by Gavin). He found the GSOD data at NCDC, and made the huge efforts needed to get the data scattered over thousands of files into a GHCN format. I downloaded his files from here.

This format is ideal for TempLS, and the expanded number of land stations is useful. So I looked to see how well the new dataset matches GHCN-based results. In future posts I'll look at regional results. There are, for example, many more Arctic stations than in GHCN. And there has been no great reduction in recent years. On the other hand, there's very little data pre-1940, and it's fairly gappy up to the mid 70's.

Tuesday, July 6, 2010

Jaxa Arctic Ice data and UAH AMSU temperatures

I'll keep posting JAXA numbers occasionally until end September, but the minimum of 4813594 km^2 on 18 Sept seems likely to stand. I'll keep updating the temperature plots.

Daily melt: Change in Jaxa extent

MoDay 2005 2006 2007 2008 2009 2010
9 20 -23282 -45625 14063 7500 25000 36718
9 21 -29531 781 -25782 21250 18281 37032
9 22 -469 3750 -7812 35469 30938 68281
9 23 20000 31875 -9375 63906 -15782 58750
9 24 42813 88907 -12813 5625 -8750 58594
9 25 29687 64531 10469 -5000 31407 65937

Jaxa Extent

MoDay 2005 2006 2007 2008 2009 2010
9 25 5407656 6036719 4265000 4873750 5439688 5166875