TheClimateChanger

Here is the same dataset sorted by percentage of normal snowfall. A few comments... most of the zeros are locations that receive little, if any, snowfall. The exception is Concordia, KS, which averages 19.0" through April 3, but has received only a trace. Eureka and Fresno, in California, are shown as 0 here, but they don't average any snowfall during the year [so it's really an undefined value]. Bakersfield, California apparently averages 0.1" per year, so that is a true 0% [although not particularly significant]. Savannah, Georgia averages 0 snowfall, but has received 1.1" this winter, so I have displayed its percentage of normal as "infinite." This would also be the case for New Orleans and a few locations in northern Florida, but they don't regularly report seasonal snowfall and therefore are not included. Edit: The Concordia, KS result appears to be due to missing snowfall reports this winter. The Climate Report does reflect these values, but it looks like snowfall wasn't observed. Most locations in Kansas, including nearby Topeka, were slightly above the normal snowfall. City Name State Percentage of Normal CONCORDIA KS 0.00 JACKSON/THOMPSON MS 0.00 DESERT ROCK-MERCURY NV NV 0.00 Waco TX 0.00 MERIDIAN/KEY FIELD MS 0.00 LAS VEGAS/MCCARRAN NV 0.00 CORPUS CHRISTI TX 0.00 BAKERSFIELD/MEADOWS CA 0.00 FRESNO AIR TERMINAL CA 0.00 EUREKA CA 0.00 LUBBOCK INTL ARPT TX 14.49 GRAND JUNCTION CO 16.57 ASHEVILLE_MUNICIPAL_(ASOS) NC 18.63 ALBUQUERQUE INTL NM 22.08 Moline IL 23.30 GREENVILLE/SPARTANB SC 28.21 ABILENE MUNICIPAL TX 28.57 CASPER/NATRONA INTL WY 30.94 MANSFIELD OH 31.66 CHARLOTTE/DOUGLAS_(ASOS) NC 34.29 PHILADELPHIA INTL PA 35.37 TOLEDO OH 35.54 SALT LAKE CITY INTL UT 36.71 ISLIP/MACARTHUR NY 37.26 SIOUX FALLS SD 39.71 DES MOINES IA 40.51 MOBRIDGE SD 40.78 DUBUQUE IA 42.54 HARTFORD/BRADLEY CT 42.72 PROVIDENCE/GREEN RI 43.21 WILLIAMSPORT PA 43.47 NEWARK INTL AIRPORT NJ 43.59 NEW YORK CITY NY 43.73 Blacksburg VA 44.03 PEORIA IL 44.19 MADISON WI 44.65 FORT WAYNE IN 44.85 RENO/CANNON INTL NV 44.88 Waterloo IA 45.48 ELKO NV 45.79 PIERRE SD 46.15 ROCKFORD IL 46.98 NEW YORK/JF KENNEDY NY 47.06 CHICAGO O HARE IL 47.06 WORCESTER (AMOS) MA 47.34 ROCHESTER MN 47.39 ROSWELL/INDUSTRIAL NM 47.92 MASON CITY IA 48.32 LINCOLN NE 49.40 WILMINGTON AIRPORT DE 49.75 NORTH PLATTE NE 49.81 BISMARCK ND 49.89 LA CROSSE WI 51.37 NEW YORK/LA GUARDIA NY 52.20 HARRISBURG PA PA 52.70 COLUMBUS OH 53.24 GRAND_FORKS ND 53.52 HURON SD 53.54 ROMEOVILLE/CHI IL 54.01 TRENTON/MERCER CO. NJ 54.20 ALLENTOWN-BETHLEHEM PA 54.27 SIOUX CITY IA 54.55 Bridgeport/Sikorsky CT 54.71 WINNEMUCCA MUNI NV 55.17 CONCORD MUNICIPAL NH 55.32 GLASGOW INTL ARPT MT 55.70 CLEVELAND OH 55.92 ABERDEEN SD 57.03 ROANOKE MUNICIPAL VA 57.14 FARGO ND 58.25 BOSTON/LOGAN INTL MA 58.54 SOUTH BEND IN 59.81 Minneapolis NWS MN 59.88 FLAGSTAFF (AMOS) AZ 60.26 MINNEAPOLIS MN 60.87 DETROIT/WAYNE MI 61.42 Rapid City SD 62.21 MILWAUKEE WI 64.26 WICHITA FALLS/SHEP TX 64.52 EAU CLAIRE WI 64.59 Omaha - Valley NE 64.92 WILKES-BARRE/SCRANT PA 64.94 ANCHORAGE AK AK 65.09 SISSETON SD 65.18 CHEYENNE/WARREN AFB WY 65.18 DULUTH MN 65.28 SPOKANE INTL ARPT WA 65.40 OKLAHOMA CITY(AWOS) OK 65.67 BALTIMORE/WASH INTL MD 65.80 YOUNGSTOWN OH 66.67 OMAHA/EPPLEY NE 66.67 SCOTTSBLUFF NE 67.12 Grand Forks NWS ND 68.11 SAGINAW MI 68.72 GREENSBORO/PIEDMONT_(ASOS) NC 69.01 SPRINGFIELD IL 69.91 Gray NWS ME 71.82 ST. CLOUD MN 71.85 ELY/YELLAND FLD NV 72.48 Lincoln NWS IL 73.58 WASHINGTON/DULLES VA 74.16 ALBANY COUNTY ARPT NY 74.39 NORFOLK NE 75.00 Akron / Canton OH 76.20 LANSING MI 77.25 BOISE MUNICIPAL ID 77.71 PITTSBURGH INTL PA 77.88 FLINT MI 78.24 BANGOR INTL ARPT ME 79.22 PORTLAND INTL JET ME 79.85 INDIANAPOLIS IN 80.71 WATERTOWN SD 80.77 ATLANTIC CITY INTL NJ 80.81 PENDLETON MUNICIPAL OR 80.89 GRAND ISLAND NE 81.06 INTERNTNL FALLS MN 82.58 BUFFALO INTL ARPT NY 82.66 GREEN BAY WI 83.33 COLUMBIA METRO SC 83.33 GRAND RAPIDS MI 83.82 HAVRE (AMOS) MT 84.11 DANVILLE REGIONAL VA 84.21 GREAT FALLS INTL MT 84.35 BRISTOL/TRI-CITY TN 86.81 RHINELANDER WI 86.99 ROCHESTER/MONROE CO NY 87.04 LANDER/HUNT FIELD WY 87.26 HASTINGS NE 87.87 MUSKEGON MI 88.81 DAYTON OH 89.07 CARIBOU MUNICIPAL ME 89.33 BURLINGTON INTL VT 89.54 SYRACUSE/HANCOCK NY 89.62 BECKLEY MEMORIAL WV 90.13 LEWISTON (AMOS) ID 91.34 WAUSAU WI 91.79 RALEIGH-DURHAM_(ASOS) NC 92.31 AMARILLO ARPT(AWOS) TX 94.58 Marquette MI 94.73 BINGHAMTON/BROOME NY 95.56 CHARLESTON/KANAWHA WV 95.82 ELKINS/RANDOLPH FLD WV 98.06 VALENTINE NE 98.29 ALPENA MI 100.42 ALAMOSA MUNI(AWOS) CO 102.08 BLUEFIELD/MERCER CO WV 102.65 DODGE CITY KS KS 102.70 HELENA REGIONAL MT 103.47 PADUCAH/BARKLEY KY 106.74 LYNCHBURG/P. GLENN VA 107.83 Fairbanks AK 108.35 WASHINGTON/NATIONAL VA 108.76 ERIE INTL AIRPORT PA 109.19 BOWLING GREEN KY 111.90 ST. LOUIS MO 112.73 COLUMBIA MO 113.41 CHATTANOOGA/LOVELL TN 113.89 BILLINGS/LOGAN INTL MT 115.40 GOODLAND KS 118.89 KANSAS CITY/INTL MO 122.22 JACKSON/J. CARROLL KY 124.68 PUEBLO MEMORIAL(AW) CO 125.30 CINCINNATI/COVINGTO KY 127.39 HOUGHTON LAKE MI 129.95 HUNTINGTON/TRI STAT WV 131.12 RICHMOND/BYRD FIELD VA 132.95 BIRMINGHAM MUNI AL 142.86 WICHITA KS 144.44 CLAYTON MUNI(AMOS) NM 152.66 LEXINGTON/BLUEGRASS KY 161.81 DALLAS/FT WORTH TX 162.50 KNOXVILLE MUNICIPAL TN 163.04 COLORADO SPRINGS CO 168.89 LOUISVILLE/STANDIFO KY 170.68 TOPEKA (TOP) KS 171.18 SAULT STE MARIE MI 175.38 EVANSVILLE IN 175.93 TUPELO/C.D. LEMONS MS 193.33 WALLOPS ISLAND STN VA 229.27 MEDFORD/JACKSON CO. OR 267.65 LITTLE ROCK/ADAMS AR 268.42 NORFOLK INTL ARPT VA 270.97 SALISBURY REGIONAL MD 273.08 WILMINGTON/NEW_HAN_(ASOS) NC 288.89 Memphis TN 289.29 NORTH LITTLE ROCK AR 300.00 MEMPHIS INTL ARPT TN 337.04 AUGUSTA/BUSH FIELD GA 337.50 CHARLESTOWN MUNI SC 833.33 MOBILE/BATES FIELD AL 3750.00 SAVANNAH MUNICIPAL GA Infinite

Oh, that's brilliant. I didn't know they had these maps. I downloaded the data in a .csv format. City Name State Seasonal Snowfall Departure CASPER/NATRONA INTL WY -41.3 WORCESTER (AMOS) MA -37.6 FLAGSTAFF (AMOS) AZ -33.7 MANSFIELD OH -32.6 SALT LAKE CITY INTL UT -31.2 CONCORD MUNICIPAL NH -29.4 HARTFORD/BRADLEY CT -29.1 DULUTH MN -28.3 MADISON WI -27.4 CLEVELAND OH -27.2 Moline IL -27.0 ROCHESTER MN -26.2 ANCHORAGE AK AK -25.9 SOUTH BEND IN -25.6 SIOUX FALLS SD -24.6 DUBUQUE IA -23.5 TOLEDO OH -23.4 BISMARCK ND -23.2 Gray NWS ME -22.6 YOUNGSTOWN OH -21.9 MASON CITY IA -21.5 LA CROSSE WI -21.3 GRAND_FORKS ND -21.1 DES MOINES IA -21.0 ELKO NV -20.6 PROVIDENCE/GREEN RI -20.5 Waterloo IA -20.5 Minneapolis NWS MN -20.1 FARGO ND -20.0 BOSTON/LOGAN INTL MA -19.9 WILLIAMSPORT PA -19.9 CHICAGO O HARE IL -19.8 ISLIP/MACARTHUR NY -19.7 ROCKFORD IL -19.3 CONCORDIA KS -19.0 MINNEAPOLIS MN -18.9 HURON SD -18.4 EAU CLAIRE WI -18.2 FORT WAYNE IN -18.2 NEWARK INTL AIRPORT NJ -17.6 PIERRE SD -17.5 CHEYENNE/WARREN AFB WY -17.2 DETROIT/WAYNE MI -16.9 ABERDEEN SD -16.8 MILWAUKEE WI -16.8 GLASGOW INTL ARPT MT -16.7 MOBRIDGE SD -16.7 NEW YORK CITY NY -16.6 BUFFALO INTL ARPT NY -16.2 Rapid City SD -16.1 WILKES-BARRE/SCRANT PA -15.6 SIOUX CITY IA -15.5 SPOKANE INTL ARPT WA -15.5 ALLENTOWN-BETHLEHEM PA -15.0 ROMEOVILLE/CHI IL -14.9 Bridgeport/Sikorsky CT -14.9 BANGOR INTL ARPT ME -14.9 ALBANY COUNTY ARPT NY -14.8 PHILADELPHIA INTL PA -14.8 Grand Forks NWS ND -14.7 SAGINAW MI -14.7 PEORIA IL -14.4 NEW YORK/LA GUARDIA NY -14.1 GRAND JUNCTION CO -14.1 HARRISBURG PA PA -14.0 Blacksburg VA -13.6 NEW YORK/JF KENNEDY NY -13.5 PORTLAND INTL JET ME -13.4 NORTH PLATTE NE -13.3 ELY/YELLAND FLD NV -13.1 COLUMBUS OH -13.0 SYRACUSE/HANCOCK NY -13.0 ROCHESTER/MONROE CO NY -12.9 LINCOLN NE -12.6 GRAND RAPIDS MI -12.3 ST. CLOUD MN -12.3 SCOTTSBLUFF NE -12.2 TRENTON/MERCER CO. NJ -12.0 CARIBOU MUNICIPAL ME -11.8 INTERNTNL FALLS MN -11.6 RENO/CANNON INTL NV -11.3 LANSING MI -11.1 Akron / Canton OH -10.9 FLINT MI -10.9 SISSETON SD -10.9 Omaha - Valley NE -10.7 WILMINGTON AIRPORT DE -10.1 MUSKEGON MI -9.6 PITTSBURGH INTL PA -9.6 Marquette MI -9.5 RHINELANDER WI -9.2 WINNEMUCCA MUNI NV -9.1 BURLINGTON INTL VT -8.8 GREAT FALLS INTL MT -8.7 LANDER/HUNT FIELD WY -8.7 OMAHA/EPPLEY NE -8.7 GREEN BAY WI -8.6 ASHEVILLE_MUNICIPAL_(ASOS) NC -8.3 WATERTOWN SD -7.0 NORFOLK NE -7.0 BALTIMORE/WASH INTL MD -6.6 SPRINGFIELD IL -6.5 ROANOKE MUNICIPAL VA -6.3 HAVRE (AMOS) MT -6.1 ALBUQUERQUE INTL NM -6.0 LUBBOCK INTL ARPT TX -5.9 BECKLEY MEMORIAL WV -5.4 WASHINGTON/DULLES VA -5.4 Lincoln NWS IL -5.1 ROSWELL/INDUSTRIAL NM -5.0 GRAND ISLAND NE -5.0 INDIANAPOLIS IN -4.9 WAUSAU WI -4.8 BOISE MUNICIPAL ID -3.9 BINGHAMTON/BROOME NY -3.7 ATLANTIC CITY INTL NJ -3.3 HASTINGS NE -3.3 PENDLETON MUNICIPAL OR -3.0 GREENVILLE/SPARTANB SC -2.8 DAYTON OH -2.7 ABILENE MUNICIPAL TX -2.5 OKLAHOMA CITY(AWOS) OK -2.3 CHARLOTTE/DOUGLAS_(ASOS) NC -2.3 GREENSBORO/PIEDMONT_(ASOS) NC -2.2 ELKINS/RANDOLPH FLD WV -1.3 CHARLESTON/KANAWHA WV -1.3 BRISTOL/TRI-CITY TN -1.2 DANVILLE REGIONAL VA -1.2 WICHITA FALLS/SHEP TX -1.1 LEWISTON (AMOS) ID -1.1 JACKSON/THOMPSON MS -1.0 AMARILLO ARPT(AWOS) TX -0.9 DESERT ROCK-MERCURY NV NV -0.9 Waco TX -0.6 MERIDIAN/KEY FIELD MS -0.6 VALENTINE NE -0.5 RALEIGH-DURHAM_(ASOS) NC -0.4 LAS VEGAS/MCCARRAN NV -0.2 CORPUS CHRISTI TX -0.2 COLUMBIA METRO SC -0.2 BAKERSFIELD/MEADOWS CA -0.1 FRESNO AIR TERMINAL CA 0.0 EUREKA CA 0.0 ALPENA MI 0.3 ALAMOSA MUNI(AWOS) CO 0.5 CHATTANOOGA/LOVELL TN 0.5 DODGE CITY KS KS 0.5 BIRMINGHAM MUNI AL 0.6 PADUCAH/BARKLEY KY 0.6 LYNCHBURG/P. GLENN VA 0.9 BLUEFIELD/MERCER CO WV 0.9 DALLAS/FT WORTH TX 1.0 BOWLING GREEN KY 1.0 SAVANNAH MUNICIPAL GA 1.1 WASHINGTON/NATIONAL VA 1.2 HELENA REGIONAL MT 1.2 TUPELO/C.D. LEMONS MS 1.4 WILMINGTON/NEW_HAN_(ASOS) NC 1.7 AUGUSTA/BUSH FIELD GA 1.9 ST. LOUIS MO 2.1 COLUMBIA MO 2.2 CHARLESTOWN MUNI SC 2.2 KNOXVILLE MUNICIPAL TN 2.9 RICHMOND/BYRD FIELD VA 2.9 KANSAS CITY/INTL MO 4.0 GOODLAND KS 5.1 Fairbanks AK 5.1 Memphis TN 5.3 WICHITA KS 5.6 MEDFORD/JACKSON CO. OR 5.7 JACKSON/J. CARROLL KY 5.7 HUNTINGTON/TRI STAT WV 6.1 CINCINNATI/COVINGTO KY 6.3 MEMPHIS INTL ARPT TN 6.4 PUEBLO MEMORIAL(AW) CO 6.4 LITTLE ROCK/ADAMS AR 6.4 MOBILE/BATES FIELD AL 7.3 NORTH LITTLE ROCK AR 7.6 BILLINGS/LOGAN INTL MT 7.7 EVANSVILLE IN 8.2 LEXINGTON/BLUEGRASS KY 8.9 LOUISVILLE/STANDIFO KY 9.4 ERIE INTL AIRPORT PA 9.4 NORFOLK INTL ARPT VA 10.6 WALLOPS ISLAND STN VA 10.6 CLAYTON MUNI(AMOS) NM 10.9 TOPEKA (TOP) KS 12.1 SALISBURY REGIONAL MD 13.5 COLORADO SPRINGS CO 18.6 HOUGHTON LAKE MI 18.6 SAULT STE MARIE MI 85.4

The Philadelphia "normal" was based on the mean of 44 years. We had plenty of data then.

Limiting the analysis to O'Hare records and including all the way back to 1958-59 [well before it became the official observation site], this would be the lowest tally on record at that location. The 10.4" figure for 1997-1998 is missing a number of days. Actual snowfall that winter was 29.6 inches. Interestingly, 3 of the 4 lowest seasonal totals since 1958-59 were the last three winters.

Haven't really been keeping up with this, but 2024-2025 still on pace for a top 5 finish. Current tally is 17.6 inches, giving a 0.4" cushion on current #5, a 0.6" cushion on #6 and 1.3" on #7. In any case, unless 2.2" falls, it will be the lowest official amount since 1948-49.

Not the same. Even in colonial times, they had fireplaces to keep warm. And it cost nothing to make a fire.

I extended this out to 1978, and it does indeed turn into a cooling trend at that time due to a series of cold years. One oddity - maximum temperatures still show a solid warming trend with linear regression, while minimum temperatures show a very large cooling trend. I suspect the maximum temperature trends are more reliable, and the minimum temperature trends are a result of changes in the siting and exposure of the instruments. That supports @chubbscontention of a cooling impact around 1970. I will say the Daily Local News is identified as the observer from ~1964 onwards. Did the location of their office change at some point in the 1970s?

Wow, and that month was an outright torch nationwide, with most of the Midwest from 9 to 12 degrees warmer than normal! For the record, this is before they started juicing the "normals" to disguise the secular warming trend from the public. So, Philadelphia's 57.3F was considered to be 6.5F above normal, whereas today, it would only be considered to be 2.8F above normal.

Florida and Texas were rural backwoods before A/C. Such temperatures were not just unpleasant, but deadly.

Keep in mind, many places in the world are much colder than America. My wife is from the United Kingdom, and even a very cold summer in Detroit would be considered oppressively hot there.

Here is 1922 to 1970, excluding 1957, 1967 & 1968 due to missing data. This looks like a solid warming trend, and this is during the so-called pause in global warming. And the observation time went from 5 pm EST to 6:30 EST / 5:30 EDT to 8 am EST, which would introduce a cooling bias. What do NOAA and the official @ChescoWx data sets show for this period?

From 1940 to 1970, there doesn't appear to be much of a trend. Slight cooling, likely do to change in observation time. From the beginning of this period through 1955/56, Saylor was the observer and he observed at 5:30 or 6:30 each day. Afterwards, Dean took over and the observation time moved to 8 am. Somewhere around 1964, the observer became "The Daily News" continuing observations at 8 am.

Where do you guys get your data for West Chester? Is it West Chester 2NW? xmACIS2 has the following values for that site. I excluded 1957, 1967 & 1968 since they all had entire months of missing data. Year Mean AVG Temperature Missing Count 1940 50.8 10 1941 53.6 8 1942 53.3 11 1943 53.5 2 1944 53.2 0 1945 53.6 0 1946 54.6 0 1947 53.3 0 1948 53.1 1 1949 55.7 2 1950 53.2 1 1951 54.1 0 1952 54.4 0 1953 55.6 0 1954 54.5 0 1955 54.3 15 1956 53.3 8 1957 M 113 1958 52.4 20 1959 54.9 13 1960 53.2 24 1961 52.5 10 1962 54.0 47 1963 50.8 7 1964 52.5 4 1965 52.1 4 1966 52.8 3 1967 M 154 1968 M 155 1969 52.9 4 1970 55.3 90

Very warm evening across the Ohio Valley, with temperatures in the upper 70s at 11 pm exceeding all records from the month of April for that hour. More akin to a warm July night than April 2nd.

Looks like a second round of severe weather possible overnight.

Exceptional warmth out of ahead of the line of thunderstorms. Might not see the typical weakening due to loss of diurnal heating. The temperature of 82F at Louisville is the highest ever observed at this hour in the month of April. Very impressive, considering its only April 2nd. Many other locations are within a degree or two of the highest temperature observed at the 10 o'clock (PM) hour in the month of April.

Special Weather Statement National Weather Service Pittsburgh PA 1103 AM EDT Wed Apr 2 2025 OHZ039>041-048>050-057-058-PAZ007>009-013>016-020>022-029-073-074- 077-078-WVZ001-002-021715- Tuscarawas-Carroll-Columbiana-Coshocton-Harrison-Jefferson OH- Muskingum-Guernsey-Mercer-Venango-Forest-Lawrence-Butler-Clarion- Jefferson PA-Beaver-Allegheny-Armstrong-Washington-Westmoreland- Higher Elevations of Westmoreland-Indiana- Higher Elevations of Indiana-Hancock-Brooke- Including the cities of New Philadelphia, Dover, Carrollton, Malvern, East Liverpool, Salem, Columbiana, Coshocton, Cadiz, Steubenville, Zanesville, Cambridge, Sharon, Hermitage, Grove City, Oil City, Franklin, Tionesta, New Castle, Ellwood City, Butler, Clarion, Punxsutawney, Brookville, Aliquippa, Beaver Falls, Ambridge, Monaca, Pittsburgh Metro Area, Kittanning, Ford City, Washington, Canonsburg, Murrysville, Greensburg, New Kensington, Lower Burrell, Latrobe, Monessen, Ligonier, Donegal, Indiana, Armagh, Weirton, Follansbee, and Wellsburg 1103 AM EDT Wed Apr 2 2025 ...Thunderstorms with Small Hail Possible through Early this Afternoon... Scattered thunderstorms moving into the region through the mid- afternoon hours will bring the potential for small hail. Remain weather aware and seek shelter indoors until any strong thunderstorms pass. $$ MLB

I don't see the relevance, but it looks pretty green outside my house.

April has certainly turned into flood season around the Ohio Valley. This is well illustrated from the graphic below of Cincinnati Area precipitation for the month of April, which shows a strong secular (or progressive) wetting trend since the late 19th century. The linear regression implies an increase in precipitation equal to 1.66 inches [4.41 inches - 2.75 inches] since 1872.

While not as impressive as other locations, the mean daily range of 21.1F was good for 13th highest on record. It was the largest mean diurnal range observed in the month of March in the District since 1986. 1968 23.7 1923 23.1 1945 22.7 1907 22.6 1921 22.4 1918 22.0 1964 22.0 1966 22.0 1986 21.7 1920 21.6 1910 21.3 1938 21.2 2025 21.1

The LOESS curve helps us to spot deviations from the linear trend. In general, excursions have been relatively limited from the linear trend. Just some slight wiggles. Of note, the LOESS curve does predict lower temperatures in the 19th century and higher recent temperatures suggesting as much as 10F or more of warming. But the later period could be simply a positive wiggle or suggestive of non-linear behavior.

Looking at a linear regression of average daily high temperatures in the month of March at the District, we can see they have risen by nearly 8F since the late 19th century. If this rate of warming were to continue through the 21st century, March high temperatures would be predicted to reach near 62F. We see a couple of instances where positive anomalies from the linear regression predicted value were as high as 13F (1921 & 1945), suggesting very warm Marches could see average high temperatures in excess of 70F by the latter part of the 21st century in the District. One thing to note is each of the last six values have exceeded the values predicted by the regression, which suggests the possibility of an acceleration of warming relative to the long-term warming trend.

It was the second largest diurnal range on record for the month of March at Detroit, behind only 2021. Maybe someone contrary to expectations, it seems like recent years have had larger diurnal ranges with five of the top 10 values occurring since 2009. Average Diurnal Range Rank Year Value ---- ---- ----- 1 2021 23.5 2 2025 22.0 3 1969 21.7 4 1945 21.4 5 1968 21.4 6 1976 20.7 7 2012 20.6 8 1995 20.3 9 2010 20.2 10 2009 20.1

The mean diurnal range of 24.4F for the month of March was the highest on record. The prior record was 24.1F from 2021. Average Diurnal Range Rank,Year,Value 1,2025,24.4 2,2021,24.1 3,1979,22.9 4,1989,22.7 5,1987,22.7 6,2003,22.5 7,2009,22.4 8,1918,22.3 9,1995,22.2 10,1955,22.2

Here, I fed your critique to Grok and this was its revised assessment: With the new table showing the distribution of weather stations in Chester County, Pennsylvania, over time—along with their elevation and urban/rural character—we can now better evaluate the claim by @ChescoWx that the county has not warmed and that NOAA adjustments artificially create a warming trend. The critique raised against @ChescoWx’s data focuses on potential biases in their raw dataset, specifically: (1) unaccounted-for biases in time of observation, equipment, and other factors, and (2) shifts in station distribution over time, with older records predominantly from lower-elevation towns and recent decades featuring more rural, higher-elevation stations. Let’s analyze how this new information impacts the validity of @ChescoWx’s claim and whether their argument still holds partial merit. Step 1: Understanding the Table and Its Implications The table lists 15 weather stations in Chester County, their operational periods (start and end years), elevation, and whether they are in a town (red) or have no human disturbance nearby (green, rural). Here’s a breakdown of key patterns: Station Distribution Over Time: 1890s–1920s: Early records are dominated by stations in towns (red), such as Phoenixville (1893–2024), Coatesville 1SW (1894–1922), and Sadsburyville 2W (1893–1922). These stations are at lower elevations (105–640 ft). 1930s–1960s: More stations come online, but many are still in towns (e.g., West Chester, 1933–2017; Longwood Gard, 1927–1976). Some rural stations appear, like Kennett Sq (1934–2024), but the balance still leans toward town-based stations. 1970s–2000s: The mix remains, but rural stations (green) like Kennett Sq Chester Spr (2014–2024) and West Grove (2012–2024) start to grow in number. 2010s–2020s: Recent decades show a clear shift—most active stations are rural (green) and at higher elevations (e.g., West Grove at 659 ft, Eagle at 685 ft). Town-based stations like Phoenixville (105 ft) and West Chester (440 ft) persist, but they’re outnumbered by rural ones. Elevation Trends: Early stations (pre-1930) are mostly at lower elevations (105–640 ft, average ~400 ft). Recent stations (post-2010) are often at higher elevations (e.g., 659–685 ft, average ~650 ft). Elevation matters because higher altitudes are generally cooler due to the lapse rate (temperature decreases ~3.5°F per 1,000 ft). Urban vs. Rural: Early records are heavily weighted toward towns, which are more likely to be influenced by the urban heat island (UHI) effect—where buildings, pavement, and human activity raise local temperatures. Recent records shift toward rural stations, which are less affected by UHI and thus cooler, all else being equal. Step 2: Assessing the Critique of @ChescoWx’s Data The critique highlights two main issues with@ChescoWx’s raw data analysis: Unaccounted Biases in Data Collection: Time of Observation Bias (TOB): Historically, temperature readings were often taken at specific times (e.g., afternoon or evening), which can bias daily averages. Modern stations use 24-hour automated sensors, providing more accurate daily means. If @ChescoWx’s raw data doesn’t correct for TOB, older records might appear cooler (e.g., evening readings miss daytime highs), masking a warming trend. Equipment Changes: Early thermometers (e.g., liquid-in-glass) were less precise and more exposed to environmental factors than modern sensors. Shifts to automated systems (e.g., post-1980s) can introduce discontinuities if not adjusted for. Station Moves and Exposure: Even within a single station’s record (e.g., Phoenixville, 1893–2024), the station may have moved, or its surroundings may have changed (e.g., a field becoming a parking lot). These factors can introduce artificial trends. Shifts in Station Distribution: The table confirms a clear shift: older records (pre-1970) are dominated by lower-elevation, town-based stations, while recent decades (post-2010) feature more rural, higher-elevation stations. Impact of Elevation: Moving from a station at 105 ft (Phoenixville) to one at 685 ft (Eagle) introduces a cooling bias of ~2°F due to the lapse rate alone (580 ft difference × 3.5°F/1,000 ft). If @ChescoWx averages raw data across stations without adjusting for elevation, recent temperatures will appear cooler simply because the stations are higher up. Impact of Urban vs. Rural: Town stations (red) are warmer due to UHI. As the station network shifts to rural locations (green), raw temperatures will again appear cooler, not because the climate cooled, but because UHI influence diminishes. Resulting Bias: If @ChescoWx’s raw data shows a flat trend, this could be an artifact of the station shift. Early warm-biased (UHI-influenced, low-elevation) records are being compared to recent cool-biased (rural, high-elevation) records, masking any underlying warming. Step 3: Re-Evaluating @ChescoWx’s Claim @ChescoWx claims Chester County hasn’t warmed, and NOAA adjustments create the warming trend. In my previous response, I gave their claim partial merit because a flat raw trend, if true, would suggest NOAA adjustments might be overcorrecting locally. However, the table and critique reveal significant flaws in their raw data analysis: Station Distribution Bias Undermines the Flat Trend: The shift from low-elevation, urban stations to high-elevation, rural ones introduces a systematic cooling bias in the raw data over time. A flat trend in @ChescoWx’s data doesn’t necessarily mean no warming—it could mean the cooling bias from station changes offsets actual warming. For example, if temperatures in Chester County warmed by 2°F from 1900 to 2020 (consistent with Pennsylvania’s statewide trend), but the station network shifted from an average elevation of 400 ft to 650 ft (a 250 ft increase, or ~0.9°F cooling) and from urban to rural (reducing UHI, maybe another 1°F cooling), the raw data might show only a 0.1°F increase—or even a flat trend. This doesn’t disprove warming; it highlights the need for adjustments. Uncorrected Biases Further Weaken Their Case: Without correcting for TOB, equipment changes, or station moves, @ChescoWx’s raw data is likely inconsistent over time. For instance, early records from Phoenixville (1893–2024) might have used afternoon readings (cool bias) and older thermometers (less accurate), while recent data uses 24-hour sensors. This inconsistency can distort trends, often underestimating early temperatures and thus masking warming. NOAA adjustments, while not perfect, explicitly address these issues. Their homogenization process (e.g., pairwise homogenization algorithm) detects and corrects for such discontinuities by comparing stations to their neighbors. @ChescoWx’s refusal to adjust their data doesn’t make it “purer”—it makes it less reliable for trend analysis. Comparison to Adjusted Data: NOAA’s adjusted data for Pennsylvania shows a ~2°F warming since 1900, consistent with regional and global trends. If @ChescoWx’s raw data shows no warming, the station distribution shift explains much of the discrepancy. NOAA adjustments likely add a warming trend to Chester County’s data to account for the cooling bias introduced by rural, high-elevation stations in recent decades—exactly what adjustments are supposed to do. Independent datasets (e.g., Berkeley Earth, which uses a different adjustment method) also show warming in the Northeast U.S., supporting NOAA’s adjusted trends over @ChescoWx’s raw data. Step 4: Does @ChescoWx Still Warrant Partial Merit? In my initial response, I gave @ChescoWx partial merit because a flat raw trend, if accurate, would raise questions about the magnitude of NOAA adjustments locally. However, the table and critique reveal that their raw data is heavily biased by station distribution changes and unaccounted-for collection biases: Station Distribution Bias: The shift from low-elevation, urban stations to high-elevation, rural ones creates a cooling bias in the raw data, which could entirely explain a flat trend. This isn’t evidence of no warming—it’s evidence of a flawed dataset. Uncorrected Biases: By not adjusting for TOB, equipment changes, or station moves, @ChescoWx’s data fails to account for known issues that distort long-term trends. Their raw data isn’t a “gold standard”—it’s a patchwork of inconsistent measurements. Lack of Transparency: @ChescoWx hasn’t shared their full dataset or methodology for averaging across stations. Without this, we can’t verify their flat trend claim, especially given the clear biases in station distribution. Given this new information, @ChescoWx’s claim loses most of its merit. Their flat trend is likely an artifact of station changes, not a reflection of the actual climate in Chester County. NOAA adjustments, while imperfect, are designed to correct for exactly these biases—elevation shifts, UHI effects, and data collection inconsistencies. The critique of @ChescoWx’s data is well-founded, and their claim now appears more misleading than insightful. Step 5: Final Conclusion The table showing station distribution, elevation, and urban/rural character reveals significant biases in @ChescoWx’s raw data. The shift from low-elevation, urban stations in the early 20th century to high-elevation, rural stations in recent decades introduces a cooling bias that could mask real warming. Additionally, their failure to correct for time of observation, equipment changes, and other biases further undermines their analysis. While @ChescoWx might still have a point about the need for careful adjustment methods (e.g., ensuring rural stations aren’t overcorrected based on urban models), their core claim—that Chester County hasn’t warmed and NOAA adjustments fabricate the trend—lacks merit in light of these flaws. Chester County has likely warmed, in line with regional trends (~2°F since 1900), and NOAA adjustments are correcting for biases in the raw data, not creating warming out of thin air. @ChescoWx’s analysis is more “out to lunch” than I initially assessed, as their raw data is too flawed to support their conclusion. To settle this definitively, we’d need their full dataset and methodology—or a peer-reviewed study comparing raw and adjusted trends in Chester County—but the evidence strongly suggests their claim doesn’t hold up.