chubbs

UAH warmed in August. Expect similar jumps in other August metrics.  

9 hours ago, Chinook said:

I have never seen this type of real-time analysis map be +1.0 degrees C

I've been tracking since 2023. Over 1C daily is very warm but it has happened before mainly in the 23/24 nino. 1.04C in Nov 2023 is the highest monthly value. August was 0.942 the 5'th highest monthly value , the higher values were the last 4 months of 2023. Note that changes to the gfs model may change the metric.  

Storymap on the Nepal floods by the Center for Land for Land Surface Hazards with detailed information from flood gauges, seismic, paleo, etc. The story book is updated as new information becomes available. A couple of quotes from the storymap below. The first quote indicates the flood water came mainly from the slide materials i.e, this material was warm/partially melted. The second quote shows how unusual this event was in area impacted.

"However, more recent seismic analysis of the event as well as detailed reconstruction of flood downstream arrival timing (see above) suggests that damming and release of lake water did not likely contribute significantly to this event because the timing of the initial failure (i.e. the ice-rock avalanche) from seismic records is only seven minutes before the arrival of the flood waters in the nearby community of Gyirong, which is not enough time to have constructed a significant lake in the highland region. Instead, melting of ice and frozen sediment/rock in the initial debris flow and avalanche runout has been attributed to the initiation of the flood."

 In a 2020 study , Huber and others determined boulder-emplacement timing and estimated paleo-flood magnitudes based on boulder size. They show that most boulders in the Trishuli and Sunkoshi were last mobilized and came to rest about 5000 years ago in flood events that far exceed typical monsoon discharges by one to two orders of magnitude.  Images  and  videos from this Trishuli flood  suggest this was a larger event, moving larger boulders than the flood event that impacted the valley ~5000 years ago

https://storymaps.arcgis.com/stories/f2b2425eac544929a7d18f4c90b41d66

5 hours ago, GaWx said:

 I just watched Joe Bastardi’s free to the public Saturday Summary. He says the Nepal disaster has nothing to do with CC. Why?

-He says that there has been no net change in temperature in Nepal in 400 years!

-He shows a summer temp. anomaly map that has blue colors all over Tibet  including the Tibet-Nepal border.

 So, I’d like to know whether or not these two things are true. Anyone know?

@donsutherland1

BS, its well known that glaciers around the world are shrinking rapidly due to climate change. The Himalayas are no exception.  The increasing local risk from glacier melting was described as a "crisis" earlier this year:

https://www.icimod.org/press-releases/hindu-kush-himalaya-glaciers-losing-ice-at-double-the-rate-since-2000-new-icimod-report-confirm/

Kathmandu, 18 March 2026 – Glaciers across the Hindu Kush Himalaya (HKH) are melting at an accelerating rate, with ice loss rates doubling since the year 2000, according to two new landmark reports that will be released on 21 March 2026 by the International Centre for Integrated Mountain Development (ICIMOD) to mark World Day for Glaciers.

The reports, Changing Dynamics of Glaciers in the Hindu Kush Himalaya Region from 1990 to 2020 and HKH Glacier Outlook 2026: Insights from 50 Years of Himalayan Glacier Monitoring, provide the most comprehensive evidence yet of glacier change in the region. They reveal a total loss of up to 27 metres of ice thickness since 1975, sounding an alarm for the nearly two billion people downstream who depend on meltwater from the ‘Water Towers of Asia’.

“This isn’t a distant problem; it’s a crisis unfolding in real-time, with new disasters every summer and monsoon. The fact that ice loss rates have doubled this century should shock us all into action,” said Pema Gyamtsho, Director General of ICIMOD. “The Hindu Kush Himalaya is at a crossroads. The rapidly escalating impacts we’re seeing from water uncertainty to catastrophic floods underscore that we are in a critical decade for the cryosphere. We must scale up monitoring and invest in adaptation now. These aren’t blind spots becoming surprises anymore; they are our new reality.”

On 8/29/2026 at 6:24 AM, bluewave said:

The current record breaking +1.81° is well ahead of what any of the models were forecasting for this month.

 

 

I estimate 1.77C is needed to break a record this year vs Jacobson's 1.84. In any case August's warmth has made a record likely this year and put 2023's gobsmacking September of 1.74C in play. Sept 2023 is still the highest ERA monthly anomaly.

“We don’t see a time in the past where El Niños have been as strong as today, and we show that the strength of El Niño changes in parallel with the warming of global temperature,” said lead author Julia Cole, professor and chair of the U-M Department of Earth and Environmental Sciences. “Our findings tell us that the big El Niño events of the last 40 years are not normal in the context of the last thousand years.”

https://news.umich.edu/el-ninos-more-intense-over-last-40-years-than-previous-1000-according-to-u-m-study/

Blog article on the Nepal floods by a glaciologist providing background and recent history of the area impacted.

https://www.antarcticglaciers.org/2026/08/august-2026-nepal-tibet-floods/

Satellite OISST (through August) has risen to a new peak, 0.08C warmer than the previous peak in September 2023. So far, the nino spike this year is very similar in magnitude to the 2023 spike. Both larger and faster than previous El Nino.

7 hours ago, Typhoon Tip said:

Climate Reanalyzer has us currently warmer than any other august, ever ...

Not sure what-how to reconcile these two products - I'll leave that y'all... but it looks like it confirms that idea about Aug.  Not sure about 'all months' tho - this below looks like the current month is running along 2nd in that scope. As an aside, also defiant of the fact that all years are typically descending by now. zoink

Fwiw, this product includes 1940 - 2026/present

The chart I posted shows temperature anomaly, while your chart shows temperature. August will rank a little higher on a temperature basis than it will on an anomaly basis because August is a relatively warm month.

The rise in global temperature anomalies that began in June continues through the end of August in this gfs-based forecast.  August will easily break the previous August record and is tracking as the fourth highest monthly anomaly in any month on this metric, fifth highest on ERA. End of the month readings are near the peak monthly value for this metric, 1.04 in November 2023. Note that anomalies are usually highest in the colder months. We'll see what September brings.

http://www.karstenhaustein.com/climate.php

Here's Zeke H latest forecasts (updated daily). An August record is almost guaranteed, while a record for 2026 is currently a coin flip. Monthly and yearly record odds have both been increasing as the month continues to run warm. 

https://dashboard.theclimatebrink.com/#global

17 hours ago, GaWx said:

 Joe Bastardi brings something up that I think merits further study. The contrast in temps between the tropics and Arctic has dropped significantly since 1999 because the Arctic has warmed ~2C while the tropics have warmed significantly less. GW has been most concentrated in the Arctic.

“A reduced equator-to-pole temperature gradient lowers zonal available potential energy (ZAPE) and surface baroclinicity. Baroclinic instability is the primary energy source for extratropical cyclones (the large-scale mid-latitude storms that form along the jet stream and frontal zones). With less available energy to draw from:

• Models (including coordinated experiments focused on Arctic sea-ice loss and amplification) generally show fewer individual cyclones each season across the northern mid-latitudes and Arctic.
• The remaining storms tend to be, on average, somewhat weaker, slower-moving, and longer-lived.”

Individual events can still be intense or produce more extreme rainfall because of thermodynamic changes.

Because of the distortion of warming, it leads to some things opposite of what is portrayed in the general sense.

Your point is that this differential warming can produce effects that run counter to the simple ‘warmer world = more of everything extreme’ narrative, especially for baroclinic storm frequency and intensity. The thought experiment cleanly highlights how the pattern of warming, not just global-mean temperature, controls the dynamical response.”

 

This isn't a new thought, scientists have been looking for weather impacts from accelerated warming in the arctic for a while. One hypothesis is a weaker, wavier jetstream, amplifying and slowing the progression of weather patterns. As far as I am aware this hasn't been proven with studies on both sides. Not aware of any findings on baroclinic storms either. Note that increased moisture compensates for weaker temperature gradients in baroclinic storms through increased heat release from condensation in clouds. One weather effect that has been found is a northward shift in the jet stream and storm track.

On 8/17/2026 at 11:11 AM, bluewave said:

 

 

Climatologist49

 

@climatologist49.bsky.social

 

Follow

The first 14 days of August have blasted through the previous record for the Aug 1-14 period globally. 

 

 

 

In El Nino mode.  2026 started #5 in GISS in Jan and Feb, but reached #1 in July. August on track to easily top 2024. Can expect monthly records well into until 2027. The one exception is Sept, the "gobsmacking" warmth of 2023 will be tough to beat. 

This study provides an estimate of transient climate response (TCR) using CERES net radiation estimates and updates temperature-based estimates through 2025. The new TCR estimates for temperature are a little higher than previous studies (orange bars) and the CERES TCR estimates (red bars) are higher still, inline with TCR estimated with the warmer CMIP6 models. 

https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2026EF008356

21 hours ago, GaWx said:

 Per the following reliable and objective source, year 2026 as of week 32 has had the lowest area burnt in North America since 2012 by a good margin vs other years as of the same week (by a third). Also, globally it has been by a large margin the lowest for this point in the year since 2012 as I’ve been posting despite being near the warmest globally along with 2025 being 2nd lowest. My point in posting about 2026 for N.A. and 2026/5 globally is not to say CC is causing a decrease in wildfire activity because I’m not saying that. Instead, my point in posting this is to show that that there isn’t a proven simple relationship between CC and wildfire related activity and that different kinds of wildfire related data from various sources from various periods can be presented in various ways:
 

I don't think area burned is telling you much about CC; because, other human factors are driving the long-term trends. Per Our World in Data, the vast majority of wildfires occur in grassland and savanna in the sub-tropics and tropics, roughly 50% in Africa. Forest burning is a small portion of area burned in all regions. Our World in Data cites  a Science paper that attributes the long-term decrease in area burned to the spread and intensification of agriculture. In Europe and North America, reduction in cropland burned is another important long-term trend.

Have to look at other stats to see the effect of climate change. The blog I linked makes the point that the intensity of the largest fires is increasing due to climate change. Big, intense fires are the most problematic but aren't well captured by the area burned stats. Here's a recent paper that finds that a small number of extreme fires are having an out-sized impact on the forest ecosystem in western and boreal North America.

https://www.science.org/doi/full/10.1126/sciadv.aeg5802

Blog on wildfire smoke in the US. There's been an increase in smoky years since the late 2010s. 

https://www.theclimatebrink.com/p/smoke-gets-in-your-eyes

49 minutes ago, Mike Cycle said:

AI can do that easily.  I will put it to work.  Should we look at daily, monthly, or annual?  Tavg, or Tmin and Tmax?

I would look at Tavg, annual and monthly, to start. 

17 hours ago, Mike Cycle said:

Chubbs, I did try to expand your manual breakpoint detection process by asking AI to look at surrounding stations and identify "islands" where multiple stations are moving together.  I explored various settings, such as the minimum amount of synchronized temperatures before declaring an island, minimum stations to make an island, rules for stations leaving an island and new stations joining, and new islands forming.  The geographical area grew too large, pulling in Allentown etc. It will work, but its not a clear demonstration of using semi-local stations to identify breaks.  

However, Reading and West Chester do synchronize from 1929 to 1960 and make what looks like a solid reference against which breaks can be detected.  I have not looked at the individual breaks, but these breaks should be those that would be found by your approach.

Edit:  this does not rule out the case where Reading and West Chester have overlapping breaks.  

 

Yes, Reading and West Chester are stable in the 1929 to 1960 period and I am going to add Reading to my analyses. A couple of comments. As I said above looking at shorter time periods significantly expands the list of reference stations for individual station breaks as all stations are stable between the station breaks. Second, there is no need to restrict to short distances from Chester County. Weather station data is well correlated over hundreds of miles. An interesting first step would be to check how far good correlation with Chester County extends.

9 hours ago, ChescoWx said:

So let's address Charlie @chubbs post above 

Charlie, of course no dispute that Phoenixville stepped down at the 1948 move. But you've written the very objection into your own post: "Not surprising because the bias adjustments are calculated from the raw data." Charlie, your test is pairwise comparison of a target against raw neighbors across a break date. But so is the PHA!  You've actually hand-implemented the method NCEI automates. Agreement demonstrates reproducibility, but of clearly not correctness as you are simpling showing us the same method, same data. It's the same issue as Berkeley and NCEI agreeing being offered as independent confirmation - they are of course not!

On your "bullet proof" claim - the four available estimates of this one documented move:

  Berkeley -1.23 F / yours vs Coatesville  -1.49 F
  NOAA  -1.88 F / yours vs West Chester   -2.03 F

Four pairwise estimates of the same event spanning 0.80 F; the largest is 65% bigger than the smallest. Same sign and rough scale but the real question here as always has been the magnitude is what's in dispute here, and that spread isn't what bullet proof looks like!!. Also, your 2 references disagree by 0.54 F. If both were stable through 1947-50 they should agree on that Phoenixville's step. They don't, so they moved relative to each other in your window - and you date West Chester to 1970 and Coatesville to 1946, before your "before" period even opens.

So you also say "all of the major county COOP stations moved in the same direction and roughly the same magnitude after the war". If so, doesn't that actually represent a real problem for the method, not support for it??. Pairwise comparison sees only RELATIVE change - a shift common to the whole network differences out and is invisible by construction. The more synchronised those moves were, the less your test and the PHA can detect, in exactly the era carrying the county's largest adjustments. So Charlie while none of this says your step estimate wrong. It means the method you are using is simply being validated against itself, the spread across implementations exceeds the confidence claimed, and the scenario you offer as clinching is the one the method handles worst.!!

Paul

Lets look at the big picture. All four estimates you've listed agree that Phoenixville experienced spurious cooling in 1948. Better to recognize that Phoenixville had a cooling move vs ignoring the move and using the raw data.

However things aren't as uncertain as the four numbers make it seem. First the BEST estimate is 1.58F on Mike's chart not 1.88F, closer to NCEI. Second  If you look at Mike's charts there are many small differences between NCEI and Berkeley that cancel out over a longer time period. I don't see a big difference in the NCEI and Berkeley local climate history. Third, I wouldn't expect my estimates to be that accurate. I haven't taken the time to optimize my approach and more importantly there are many other stations besides West Chester and Coatesville that can be used to estimate the impact of Phoenixville's move.  

I say the NCEI method is bullet-proof because it has a strong scientific foundation.  Science says that there is a strong correlation in year-to-year temperature changes over a wide area and that's exactly what I have found. This chart is an example. These stations are well correlated at the time of the Phoenixville move indicating that they all can be used to estimate the timing and impact of the Phoenixville move.  Moreover, I have only scratched the surface, there are just the ones that had the time to check. 

The only way to estimate the magnitude of station breaks is from raw temperature data, by comparing stable and changed stations.  As documented in this thread, my tests using raw temperature data in Chester County agree very well with the NCEI bias adjustments. Not surprising because the bias adjustments are calculated from the raw data. My experience in Chester County has convinced me that the NCEI method is bullet proof. 

Below is raw temperature info on the Phoenixville 1948 move. NCDC has monthly means for Phoenixville for Janaury through July 1948, partial data from August through November but no monthly mean until December 1948. Phoenixville cooled relative to Coatesville and West Chester when the monthly mean started again in December 1948.  Phoenixville cooled 1.6F relative to Coatesville, and 2F relative to West Chester, due to the move. The stats using the monthly data are highly significant for both Coatesville and West Chester. 

The post-war moves at Coatesville (1946), West Chester (1970) and Phoenixville (1948) are all highly significant statistically. A coincidence that all of the major county COOP stations moved in the same direction and roughly the same magnitude after the war. Clearly not weather because the cooling occurred at different times at each station.

7 hours ago, TheClimateChanger said:

I wanted to take a closer look at the claim that NOAA/NCEI’s July 2026 temperature ranking is largely the product of “tampering” or adjustments to the historical temperature record. Tony Heller has advanced this claim on X/Twitter, and we've seen similar claims made by members here (cough, cough, ChescoWx).

So I downloaded the raw USHCN Tmax and Tmin monthly data directly from NOAA, without applying NOAA’s homogenization adjustments, and tried to see how far one can get using the raw observations alone. The results were pretty illuminating.

The first issue is that simply averaging the absolute temperatures of all available USHCN stations each year is not a very good way to construct a national temperature series.

Although USHCN contains 1,218 designated stations, nowhere near 1,218 stations report in every July. The number of stations with both Tmax and Tmin data increased from only about 528 in 1895 to roughly 1,200 by the 1930s–1960s, then began declining sharply in recent decades:

• 1990: 1,180
• 2000: 1,115
• 2010: 980
• 2020: 779
• 2025: 730
• 2026: 458 so far in the August 12 archive

That creates a compositional problem. If the stations disappearing from the network are climatologically warmer or cooler than those remaining, the simple national average can change even if temperatures at every individual station do not. Note that 2026 data is provisional, as a lot of stations are not yet reported in the dataset file.

Step 1: Calculate station anomalies

To deal with this, I calculated a 1951–1980 July climatology for each individual USHCN station. This was a particularly useful reference period because station coverage was near its peak; 1,217 of the 1,218 stations had sufficient data to calculate a usable climatology.

For every station and every July, I then calculated:

July anomaly = observed July temperature − that station’s own 1951–1980 July mean

This removes most of the changing-station-composition problem, because a hot station disappearing from the network no longer mechanically lowers the national average merely because its absolute climatological temperature was higher.

That made a surprisingly large difference in recent decades.

For July Tmax from 1990–2025:

• simple raw station-average trend: +0.35°F/decade
• station-anomaly trend: +0.55°F/decade
• composition effect: about −0.20°F/decade

From 2000–2025:

• simple raw Tmax trend: only +0.09°F/decade
• station-anomaly trend: +0.34°F/decade
• composition effect: about −0.25°F/decade

Why? Because the climatological July Tmax of the stations actually reporting has declined substantially:

Year	Mean July climatology of reporting Tmax stations
1990	87.39°F
2000	87.43°F
2010	87.31°F
2020	86.96°F
2025	86.88°F

In other words, the modern USHCN reporting network has increasingly become composed of cooler stations. Simply averaging their absolute temperatures therefore introduces an artificial cooling tendency.

The same phenomenon appears in Tmin.

Using an equal-station anomaly calculation, July 2026 came out approximately:

• Tmax: +1.66°F
• Tmin: +3.71°F
• Tavg: +2.68°F

On that basis, 2026 ranked roughly:

• 14th warmest Tmax
• 1st warmest Tmin
• 5th warmest Tavg

But there was still another problem.

Step 2: Geographic weighting

A station in a densely sampled part of Ohio should not carry the same national weight as a station representing a huge area of Nevada, Montana, or Wyoming.

So I took the raw USHCN station anomalies and placed them on a fixed 0.25° CONUS land grid. Each grid cell was assigned the anomaly of its nearest reporting USHCN station, and the resulting cells were area-weighted using the cosine of latitude.

This is essentially a simple Voronoi-style geographic weighting.

Importantly, it does not use NOAA homogenization. It does not alter the raw station observations. It merely prevents regions containing many stations from receiving disproportionately large weight in the national average.

The effect was substantial.

After rebasing our resulting national series to the same 1901–2000 reference used by NCEI, several important Julys looked like this:

Year	Equal-weight raw USHCN	Area-weighted raw USHCN	NCEI
1901	+3.25°F	+2.55°F	+2.60°F
1934	+2.99°F	+2.87°F	+2.72°F
1936	+3.32°F	+3.29°F	+3.14°F
1980	+2.02°F	+2.12°F	+2.09°F
2000	−0.59°F	+0.02°F	+0.22°F
2006	+2.32°F	+2.59°F	+2.76°F
2012	+3.15°F	+2.95°F	+3.10°F
2022	+2.02°F	+2.56°F	+2.76°F
2023	+1.25°F	+1.76°F	+2.02°F
2024	+1.47°F	+1.77°F	+2.07°F
2026	+2.67°F	+3.15°F	+3.26°F

The 1901 result is especially revealing.

Under a naïve equal-station average, 1901 appeared about 0.65°F warmer relative to NCEI. Once the raw observations were geographically weighted, the discrepancy collapsed to about 0.05°F.

The same thing happened at the other end of the record.

Our equal-station calculation put 2026 at only about +2.67°F, compared with NCEI’s +3.26°F. Geographic weighting alone moved the raw-USHCN result to +3.15°F.

No homogenization adjustment was necessary to explain most of that gap.

Final raw-USHCN ranking

Using the geographically weighted raw station anomalies, the leading Julys were:

1. 1936: +3.29°F
2. 2026: +3.15°F
3. 2012: +2.95°F
4. 1934: +2.87°F
5. 2006: +2.59°F
6. 2022: +2.56°F
7. 1901: +2.55°F

NCEI places 2026 slightly ahead of 1936 instead:

• 2026: +3.26°F
• 1936: +3.14°F

So after controlling for station composition and geographic weighting, the difference between our deliberately simple raw-data-only method and NCEI is not some enormous discrepancy.

Our raw calculation has 1936 about 0.15°F warmer than 2026.

NCEI has 2026 about 0.12°F warmer than 1936.

That leaves only about a 0.27°F swing in their relative difference between our raw-data method and the full NCEI analysis.

Now compare the entire records

This was perhaps the most surprising result.

Once the raw USHCN observations are converted to station anomalies and geographically weighted, the resulting curve lies almost directly on top of the official NCEI series.

Across 1895–2026, the mean absolute difference between the two annual July anomaly series is only about 0.11°F.

And remember what went into our series:

• raw USHCN observations
• individual station climatologies
• geographic weighting

That's it.

No NOAA homogenization adjustments were applied.

What does this actually tell us?

This certainly does not demonstrate that every NOAA adjustment is perfect, nor does our simple experiment precisely reproduce NOAA’s methodology. NOAA uses a larger station network, homogenization, and substantially more sophisticated spatial interpolation.

But it does demonstrate something important.

Most of the apparent disagreement between a naïve “raw USHCN” calculation and NOAA/NCEI can be reproduced without changing a single thermometer observation.

Two mundane methodological problems explain an enormous portion of it:

1. Changing station composition
2. Unequal geographic station density

The raw USHCN network has lost hundreds of reporting stations in recent decades, and the stations disappearing have, on average, been climatologically warmer than those remaining. That artificially suppresses recent temperatures if one simply averages absolute station readings.

Meanwhile, equal station weighting gives densely sampled portions of the country far too much influence. Correcting that spatial bias pushes recent hot years upward and, in some cases, early hot years downward.

The transformation of 1901 is particularly instructive:

Naïve raw USHCN: +3.25°F
Geographically weighted raw USHCN: +2.55°F
NCEI: +2.60°F

And for 2026:

Naïve raw USHCN: +2.67°F
Geographically weighted raw USHCN: +3.15°F
NCEI: +3.26°F

So when someone produces a graph of “raw USHCN temperatures” and argues that the difference from NOAA must therefore have been manufactured through data adjustments, there is a very large omitted variable: how the raw data are aggregated in the first place. A simple station-anomaly calculation plus basic geographic weighting — using the unaltered raw observations themselves — gets astonishingly close to the official NCEI record.

Nice analysis. Zeke Hausfather had a blog recently on how the 1930s warmth gets overestimated. Here's one of his plots. US stations were concentrated in the Great Plains in the 1930s, exactly where the warmth of the 1930s was focused. That is why it is important to grid the station data as you did. 

https://www.theclimatebrink.com/p/raw-data-wrong-answers

13 hours ago, ChescoWx said:

 As you know homogenization remains nothing more then a hypothesis-driven statistical correction, it is of course not a measurement.

Nope.  Homogenization is accepted science. Thoroughly tested over decades. Proven to obtain the correct climate information from raw temperature when station changes have occurred. The raw temperature data in Chester County is contaminated by myriad station moves and changes. Assuming that Coatesville and other Chesco COOPs stayed in exactly the same spot with zero equipment and method changes over decades will give you the wrong climate answer every time.

The cooling of Coatesville between 1945 and 1949 is well documented in this thread. The regional raw temperature data is 100% conclusive on this point. Coatesville was much cooler in 1949 than it was in 1945 relative to all stable regional stations. Comparison of Coatesville to Phoenixville over the period 1941-75, is immaterial; because, as the bottom chart shows, Phoenixville had a station break in 1948. The post-war  Phoenixville and Coatesville station breaks occurred at different times proving that they are not weather-related.

Mike's analysis and the NCEI bias adjustments are completely independent, derived from 2 different sources of information. Mike uses the station records. Bias adjustments use raw temperature data. The fact that they agree so well shows how powerful and accurate homogenization is in Chester County.

Your AI slop is wasting our time if it doesn't focus on the station breaks themselves. Regression over 35 years is not the right tool to identify station breaks. You need much more focused analyses to look at station breaks. The 1945-49 period for the Coatesville post-war move/changes for instance. 

Good quick explainer. 

July was a record for GISS 1.23 vs 1.20 in 2024. NOAA's July tied 2024. First of many monthly records over the next year. 

3 hours ago, Mike Cycle said:

Interestingly, there is pre-1894 data available. From 1873 forward there is daily temperature.  I am somewhat awed by the level of scientific competency shown in these (and other) old records.  

Observer is hard to read, but not Jesse Green.  Almost certainly there are usable archives at West Chester that would provide more information, including photos.  Tracking this stuff down and building a story line would be a perfect cross-disciplinary project for a college intern.

Would you mind if I had Claude run your method above on Chester County and nearby stations?  The output would be a complete human-readable timeline for how reference stations are used to arrive at adjusted records for these stations--something the OP has requested numerous times.  As I understand it the actual NCEI process involves matrix math, too complex for easy communication if you ask me; a manual method using your approach will be easy for anyone to understand.  

 

No I don't mind running Claude as you describe. Should be useful as a learning tool. 

Berkeley Earth has a West Chester 1 station with data back to Dec 1845, link for West Chester 1 below. The Berkeley Earth write-up for West Chester 2NW says that West Chester 1 is an alternative name for the station. So West Chester 1  may be the older data for West Chester 2NW. Worth checking anyway.

https://data.berkeleyearth.org/stations/35128