Various players have looked at changes in trends due to loss of stations, loss of rural stations, loss of high latitude, and loss of high altitude stations. Other cuts have included brightness and GPW population or population density. Recently, Zeke added airports to the list.
Pearson’s Chi-squared test is used to test independence of variables in categories. Make no mistake, I am only playing at being a statistician in this post. I welcome comments and corrections in what follows and suggestions of more appropriate category tests.
Joseph D’Aleo made an appearance at the Heartland Institute’s recent conference. This post is my turn at the cracker barrel, a response mostly to D’Aleo’s Slide #50
If people were looking for a ‘citizen science’ project to work on, coming up with a way for the SYNOP data (available via WeatherUnderground etc.) to be made commensurate with the CLIMAT data (available via GHCN), would be a great one. There are some subtleties involved (definitions of daily and monthly means vary among providers), but that would provide an interesting back-up and comparison to the CLIMAT-derived summaries from GISTEMP, HadCRU or NCDC.
I looked briefly at the SYNOP data available at the OGIMET site. But OGIMET is not set up for bulk tranfer of data. Only later did I find the GSOD data at NCDC. And still later, before a post at Lucia’s Blackboard helped me put the two together, although I’m still not fully clear on the relationship between OGIMET synop and GSOD synop (OGIMET draws on GSOD or is a parallel collection?)
Herein lie some sample runs with the new GISTEMP code, comparing and contrasting my results with the web published, and comparing and contrasting the public GISS v2.inv file for metadata with my self-generated v2.giss.inv metadata file.
GHCNv2 was released in 1997. The climate data included a ‘station inventory’ file with several metadata fields designed to provide more additional data about the conditions surrounding the stations. Much of this metadata was extracted by manual interpretation of Operational Navigation Charts (ONC). A sample is shown below. During the intervening years, GIS tools and data sets have become readily available. These tools and datasets are used to examine alternate sources for the metadata provided in the GHCNv2 station inventory. An initial station inventory reconstruction based on these data sources is provided.
Similar to the previous post, its time to review a previous thread, GHCNv2 and GRUMP Rural and Urban Extents, in regard to the GPWv3 GRUMP data for rural/urban extants in the context of the GHCN rural/smalltown/urban (R/S/U) classification.
Population. Examining the station location on an ONC would determine whether the station was in a rural or urban area. If it was an urban area, the population of the city was determined from a variety of sources. We have three population classifications: rural, not associated with a town larger than 10 000 people; small town, located in a town with 10 000 to 50 000 inhabitants; and urban, a city of more than 50 000. In addition to this general classification, for small towns and cities, the approximate population is provided.
These population metadata represent a valuable tool for climate analysis; however, the user must bear in mind the limitations of these metadata. While we used the most recent ONC available, in some cases the charts or the information used to create the charts were compiled a decade ago or even earlier. In such cases the urban boundaries in rapidly growing areas were no longer accurate. The same is true for the urban populations. Wherever possible, we used population data from the then-current United Nations Demographic Yearbook (United Nations 1993). Unfortunately, onlycities of 100 000 or more inhabitants were listed in the yearbook. For smaller cities we used population data from several recent atlases. Again, although the atlases were recent, we do not know the date of source of the data that went into creating the atlases. Additionally, this represents only one moment in time; an urban station of today may have been on a farm 50 years ago, though it is probably valid to assume that if a station is designated rural now, it was most likely rural 50 years ago. Knowing the importance of avoiding the effect of urban warming by preferring rural stations in climate analysis, these population metadata have been used as one of the criteria in the initial selection of the Global Climate Observing System (GCOS) Surface Network (Peterson et al. 1997a).
My original post on the ‘brightness’ fields, DMSP: The Stars at Night, They are so Bright …, looked at the DMSP satellite ‘night light’ brightness data as used in GHCN and GISS. The brightness fields were not part of the original GHCN v2 metadata. GHCN adds an A/B/C indicator of brightness. GISS includes that but also adds a numerical value. The GISS value is derived from the DMSP “Radiative Calibrated”, a single data set prepared from data collected 1996-1997
Airports are getting increasing attention from those looking at surface-records as they have become an increasing fraction of the currently reporting weather/climate stations.
Airport locations. Airports are, of course, clearly marked on ONC charts. If a station is located at an airport, this information along with the distance from its associated city or small town (if present) are included as part of GHCN metadata.
Among the metadata in the GHCN station inventory file is a topographical landform classification of four types: flat (FL), hilly (HI), mountains (MV), and mountain tops (MT).
Topography. ONC make detailed orography available to pilots. We used this information to classify the topography around the station as flat, hilly, or mountainous. Additionally we differentiated between mountain valley stations and the few mountaintop stations that can provide unique insights into the climate of their regions