Sentences with phrase «rural station data»

comparing the 42 ‐ rural station data used in the 1990 GRL and Nature papers with those adjusted for homogeneity of a 728 ‐ station network yield very much the same results, implying that the station moves, if any, really did not matter when a representative set of stations (here 42 ‐ stations) was used.

For another discussion (where to find the effect of aerosols) I was looking for more or less reliable rural station data for North - West Russia for the period 1945 - 2004.

Not exact matches

These can arise from many technical issues, including data selection, substandard temperature station quality, urban vs rural effects, station moves, and changes in the methods and times of measurement

First they used the weather station data to determine how temperatures in Philadelphia's urban and surrounding rural areas had changed over time.

We carefully studied issues raised by skeptics: biases from urban heating (we duplicated our results using rural data alone), from data selection (prior groups selected fewer than 20 percent of the available temperature stations; we used virtually 100 percent), from poor station quality (we separately analyzed good stations and poor ones) and from human intervention and data adjustment (our work is completely automated and hands - off).

Quixeramobin is the nearest rural station with more or less reliable data over a longer time span, and shows very different trends than Salvador.

For instance, GISTEMP uses satellite - derived night light observations to classify stations as rural and urban and corrects the urban stations so that they match the trends from the rural stations before gridding the data.

To provide a baseline for projecting temperature to the projected maximum of solar cycle 25, data from five rural, continental US stations with data from 1905 to 2003 was averaged and smoothed.

Quixeramobin is the nearest rural station with more or less reliable data over a longer time span, and shows very different trends than Salvador.

with — it shows what happens when you average raw («unskewed») and homogenized («skewed») data from 1 to 40 random rural stations: http://tinyurl.com/ghcn-animation

This takes public domain data provided by the Met Services, homogenises it and makes a correction for urban warming based on nearby rural stations.

In my experimentation with the data, I found that it was virtually impossible to get results inconsistent with the NASA results — rural stations, urban stations, raw data, adjusted data — once you average data from a few dozen stations scattered around the world, everything settles right down to something that looks very much like the NASA land - temp.

UHI effects will generally lead to long term trends in an affected station (relative to a rural counterpart), whereas micro-site changes could lead to jumps in the record (of any sign)-- some of which can be very difficult to detect in the data after the fact.

For instance, GISTEMP uses satellite - derived night light observations to classify stations as rural and urban and corrects the urban stations so that they match the trends from the rural stations before gridding the data.

The «50 random rural stations» adjusted data results are plotted in green.

The «50 random rural stations» raw data results are plotted in red.

As Hansen indeed only used rural stations for his global temperature trend outside the USA, I need to change the challenge: find out the station density of rural stations in the GISS database for the tropics (20N - 20S or 30N - 30S) where in the 1979 - 2005 period the data show some reliability... Good luck with that!

To provide a baseline for projecting temperature to the projected maximum of solar cycle 25, data from five rural, continental US stations with data from 1905 to 2003 was averaged and smoothed.

There is good evidence that the answer to both these question is no: (The insensitivy of the results to methodology of selecting rural stations, the Parker et al windy days study, and the fact that data from satellite skin surface measurements, from sea surface temperatures, deep ocean temps as we as tropospheric temps are all in good agreement).

Is it the process of taking raw temp data from a rural site and mathematically combining it with data from a nearby station with warming to make the rural data have a warming trend?

However, they could evaluate urban bias and found that once the data were fully adjusted the 30 % most urban stations had about the same trend as the remaining more rural stations.

Since about 20 years, GISS and NOAA adapt UHI station output by homogenizing the data with that of their rural neighbours.

Only eight of the rural stations have data for at least 95 of the last 100 years!

Only EIGHT of the rural stations had data for at least 95 of the last 100 years!

And with all the one way data manipulations, the urban heat island effect, and the vanishing rural stations, one could infer that worldwide the 1930s were likely hotter than today!

------------------------------------ And here's what the proxies vs. the highly adjusted instrumental data that have been hopelessly corrupted by removing thousands of rural stations and keeping urban stations, moving rural sites to airports, «mostly made up» SH sea surface temperatures, cooling down the 1930s and 1940s artificially to remove 0.5 C from the early 20th century warming... look like.

Here is a chart comparing, for the CONUS from 1880 till today, very rural places with the rest: The data was obtained by selecting, out of all US GHCN stations, those showing in their metadata both rural mode and least nightlight.

What do warmers such as Hanson / Giss do, well, they compare data from a highly UHI contaminated urban city weather station with CLEAN data from a neighbouring RURAL station.

There is accurate CO2 data but only since 1958 at Mauna Loa, as for temperature, well there are thousands of high quality rural weather stations throughout the world and especially in the US and the northern hemisphere that have long histories and NO UHI bias.

What's more, NASA GISS takes explicit steps in their analysis to remove any such spurious signal by normalizing urban station data trends to the surrounding rural stations.

Would it not have been more logical and possibly more honest to have discarded the contaminated data from the Urban stations and used only the clean data from the Rural sites.

This causes a cooling bias in the data of the rural stations.

Since urban stations show a higher temperature (on average) compared to rural stations (on average), having more urban stations in the mix will increase the overall average temperature of even an unadjusted data set.

And the OAA data — the ocean air affected stations far from the coast — are often hill or mountain stations, quite rural.

Secondly, and this is the more important issue related to the different station data for different years — is what is the mix of rural / urban stations?

Just to be absolutely sure, we can do a further calculation using just rural unadjusted data (using the GHCN station classifications)- the blue line.

We carefully studied issues raised by skeptics: biases from urban heating (we duplicated our results using rural data alone), from data selection (prior groups selected fewer than 20 percent of the available temperature stations; we used virtually 100 percent), from poor station quality (we separately analyzed good stations and poor ones) and from human intervention and data adjustment (our work is completely automated and hands - off).

Although the global network nominally contains temperature records for a large number of rural stations, most of these records are quite short, or are missing large periods of data.

Why would a rural station that has never changed location or instrumentation have its data adjusted?

If temperatures rose because previously rural weather stations were swallowed up by expanding megalopolises then some doubt is cast on whether that data supports theories of human - caused global warming.

It concluded: Using satellite night - lights — derived urban / rural metadata, urban and rural temperatures from 289 stations in 40 clusters were compared using data from 1989 to 1991.

Using groups for urban / suburban / rural, altitude band, latitude band, station id, and year in the R Project lmer function applied to the giss.dset0 annual temperature data.

This entire debate about TOBS is a red herring, and here is why: if the difference between the raw data and the final product is mainly due to the TOBS adjustments then you would expect to see the similar raw trends for rural and urban stations, and for «compliant» and «non compliant» stations, don't you?

In the same time the raw data for the non-compliant 3,4,5 class stations show 0.212 C and for all stations, urban and rural 1,2 — 0,155, for the class 3,4,5 stations raw 0.246 and for all stations adjusted 0.3.

It is one thing to say that the rural stations may not be as reliable as some people think, because of the general problems with personal you note, but quite another to claim that the data must of necessity exhibit a strong cooling bias!

As the trend in the US rural stations, which at least until very recently employed these min / max stations, has been from early evening observation (5 pm or 7 pm in most of the sources I've found) to early morning observation (usually 7 am), this has been presumed to put an artificial cooling bias into the temperature record, so a net positive, and increasing as more stations have been converted, correction has been added to the raw data.

# 167 / In all of the above posts there is no mention of the urban heat island effect, nor of the effect of rural station drop out nor of the effect the GISS data manipulation has on surface temperature.

(Note that there is no data for the most recent years with some of the rural stations.)

At a minimum it would necessarily include the miserable state the 4 global data sets are in, the various methods and means of which this record consists, loss of ~ 70 % of reporting stations (predominately high latitude, high altitude and rural) post 1990, and what resides in my mind at least as pure horror at the state of the surface stations Anthony et al have and are documenting.

When looking at the Tmin USHCNv2 adjusted data for rural stations, we observe that it is adjusted higher in value, from 0.127 °C / decade to 0.249 °C / decade, effectively doubling the trend,