Schuylkill County’s
Hidden Climate

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Page 5

A Landscape Built to Complicate Weather

The Landscape Is Part of the Climate System

Before precipitation, temperature, solar radiation, or atmospheric dryness can be interpreted across Schuylkill County, the terrain has to be understood on its own terms.

The county is not arranged as a broad, gently tilted surface. It is built from repeated bands of ridge, upper slope, midslope, narrow valley, lowland, gap, and upland. These features often change over distances shorter than the width of a conventional four-kilometer climate cell.

That matters because terrain does not merely sit beneath the atmosphere. It changes how the lower atmosphere moves, cools, warms, drains, mixes, and receives sunlight.

A ridge can expose a location to winds that are partly blocked a few miles away. A valley can collect cold air during clear, calm nights. Opposing slopes receive different amounts and timing of solar energy. Moist air crossing a ridge may be lifted, while nearby descending air may warm and dry. A narrow gap may channel flow that would appear weak or poorly organized in a countywide average.

None of those processes guarantees a particular climate outcome by itself. Elevation is not a magic dial that simply turns precipitation up and temperature down. Terrain effects depend on wind direction, atmospheric stability, moisture depth, season, storm type, vegetation, land cover, and the position of each location within the larger Appalachian ridge-and-valley system.

The terrain data nevertheless shows why a single county average is structurally inadequate.

Across the 3,288 fine-grid cells, mean elevation ranges from approximately 456 to 1,951 feet. The median cell sits near 1,021 feet, while the countywide mean is about 1,067 feet. The underlying 30-meter terrain pixels extend from roughly 383 to 2,091 feet.

The difference is not confined to opposite ends of the county.

The largest elevation contrast found within a three-mile search radius drops approximately 1,080 feet in 2.90 miles, from a mean cell elevation near 1,700 feet to one near 620 feet. Within five miles, the largest identified contrast reaches approximately 1,107 feet.

Those are not isolated pixel-level cliffs. They are contrasts between mean elevations of entire 800-meter climate cells. The actual terrain within each cell is finer and often more varied still.

The median 800-meter cell contains about 300 feet of internal elevation range. One quarter of the cells exceed approximately 471 feet of internal range, and the most topographically varied cell spans nearly 959 feet from its lowest to highest underlying terrain pixels.

An 800-meter climate value can therefore summarize a surprisingly complicated piece of ground.

A four-kilometer value can summarize several different landscapes at once.

Pull quote

In Schuylkill County, terrain is not background scenery. It is part of the machinery that produces local climate.

Page 6

Six Useful Ways to Describe the Ground

Ridge, Valley, Slope, and Everything Between

To make the terrain interpretable, each 800-meter cell was assigned to one of six broad terrain-position classes. These classes combine elevation, slope, topographic position, and related terrain measures. They are analytical categories rather than official named landforms, because the Earth has once again declined to organize itself according to our spreadsheet.

Midslope or plain

1,492 cells — 45.4 percent

This is the largest category. It includes cells that are neither strongly ridge-like nor strongly valley-like at the scales used in the analysis. Some are genuine midslopes. Others are comparatively broad or subdued surfaces embedded within a more complicated landscape.

The category should not be interpreted as flat. A cell can occupy an intermediate topographic position while still containing substantial slope or internal relief.

Valley or low topographic position

562 cells — 17.1 percent

These cells sit lower than their surrounding terrain according to the topographic-position measures. They include many enclosed or elongated valley settings but are not necessarily the county’s lowest elevations.

A high-elevation hollow can be valley-like relative to its surroundings, just as a low ridge can remain ridge-like despite having a modest absolute elevation.

Ridge or high topographic position

536 cells — 16.3 percent

These cells occupy locally elevated positions relative to surrounding terrain. They include ridge crests, broad upland spines, and high shoulders.

Ridge position can influence exposure, wind, cloud interaction, snow behavior, and the ability of cold air to drain away during favorable nights.

Steep ridge or upper slope

318 cells — 9.7 percent

These cells combine elevated topographic position with comparatively steep terrain. They are among the clearest examples of places where a single gridded climate value must represent strong internal variation.

Steep midslope

202 cells — 6.1 percent

These cells are steep without meeting the ridge-position criteria. Many occur along valley walls and transitional terrain between upland and lowland settings.

Valley lowland

178 cells — 5.4 percent

These cells combine low elevation with valley-like position. They are especially relevant when considering cold-air drainage, fog, frost, humidity, and nighttime temperature behavior.

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Terrain complexity

The terrain was also summarized by a separate complexity score based on elevation range, slope, relief, roughness, and related measures.

• 453 cells are classified as flat or low relief.
• 805 cells are gentle.
• 992 cells are moderate.
• 746 cells are complex.
• 292 cells are very complex.

The complex and very complex categories together contain 1,038 cells, or approximately 31.6 percent of the county grid.

The median cell has a mean slope of approximately 7.7 degrees. One quarter of the cells exceed about 10.3 degrees, and the steepest cell-scale mean reaches approximately 20.5 degrees.

At the three-kilometer neighborhood scale, median local relief is about 720 feet. Five percent of cells occupy neighborhoods with mean local relief exceeding approximately 1,002 feet.

This is the terrain regime beneath the climate analysis: not uniformly mountainous, not uniformly lowland, and rarely simple for long.

Page 7

What Fits Inside a Four-Kilometer Cell

The Drama Box Problem

The four-kilometer PRISM grid is extremely useful for regional climate analysis. It is also large enough to contain several distinct Schuylkill County landscapes at once.

The county’s 3,288 fine-grid cells are nested within 160 nominal four-kilometer parent footprints. A complete parent cell can contain approximately 25 of the 800-meter children, although edge cells contain fewer because the county boundary cuts through them.

For each parent cell, the analysis measured the elevation range among its children, the number of ridge-like and valley-like children, the prevalence of steep terrain, and the average terrain complexity. The resulting “parent drama score” identifies places where a single coarse cell contains an especially diverse collection of terrain.

The highest-ranked parent box, R224_C1176, contains only nine county-intersecting child cells, yet their mean elevations range from approximately 456 to 1,338 feet. That is an 882-foot spread inside one nominal four-kilometer climate cell. Five children are ridge-like, two are valley-like, and eight are classified as steep.

The second-ranked box, R220_C1165, contains 15 children ranging from approximately 789 to 1,635 feet. Six are ridge-like, five are valley-like, and 11 are steep.

The third-ranked box, R220_C1166, contains 23 children with mean elevations ranging from approximately 797 to 1,712 feet. Its 915-foot child-elevation range is larger than the vertical relief many regional climate summaries would imply for an entire county.

Another box, R224_C1162, contains 16 children spanning approximately 752 to 1,700 feet, a range of about 948 feet.

These examples explain why the fine-grid and parent-grid values do not always move together perfectly. The parent value is not necessarily wrong. It is performing the job it was designed to perform: representing a broader area.

The problem appears when that broader value is treated as though it precisely describes every ridge, slope, hollow, and borough inside it.

A coarse cell can be regionally representative and locally misleading at the same time.

That distinction will become increasingly important in later modules. Some towns repeatedly differ from the larger parent-cell climate signal. Some 800-meter cells change their precipitation rank strongly with the season. Others occupy persistent wet, dry, warm, cool, exposed, or sheltered positions.

The terrain framework does not prove the cause of every departure. It tells us where departures are physically plausible, where averaging is most aggressive, and where the report should look more closely.

Terrain lessons carried into the climate analysis

1. Absolute elevation is only one variable. Local position relative to nearby ridges and valleys can matter as much as the elevation number itself.

2. Scale changes the answer. A terrain feature visible at 800 meters may disappear inside a four-kilometer average.

3. Cells are not internally uniform. Even an 800-meter cell can contain hundreds of feet of elevation range.

4. Terrain effects are conditional. The same ridge can enhance, suppress, redirect, or barely affect a weather event depending on the atmospheric setup.

5. Persistent climate anomalies deserve physical inspection. When a town or cell repeatedly departs from its parent-footprint grouping, terrain is one of the first suspects, not the automatic verdict.

Closing line

The terrain does not tell us exactly what the climate must be. It tells us why the county has so many opportunities to be different.

Module B Data Notes

Terrain-position classes

The terrain-position classes are derived analytical categories based on topographic metrics. They are not official geomorphic mapping units and should not be interpreted as field-verified landform boundaries.

Elevation conversion

Terrain elevations were supplied in meters and converted to feet for report presentation.

Cell summaries

Values described as cell elevation, slope, or relief summarize many underlying terrain pixels. They do not represent one surveyed point at the center of each grid cell.

Parent drama score

The parent drama score is a project-specific ranking designed to identify four-kilometer cells containing unusually diverse child terrain. It is useful for exploration and communication but is not a standard published geomorphometric index.

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Page 11

One County, Twelve Versions of the Rainfall Map

The Annual Pattern Is a Composite

The annual precipitation map is real, persistent, and useful.

It is also the sum of twelve monthly maps that do not agree perfectly with one another.

Schuylkill County’s long-period monthly precipitation cycle reaches its minimum in February, when the mean across all 3,288 cells is approximately 2.95 inches. The countywide mean rises through spring, exceeds four inches from May through September, and peaks in July at approximately 4.59 inches.

The monthly means describe the seasonal water cycle:

• January: 3.30 inches
• February: 2.95 inches
• March: 3.72 inches
• April: 3.94 inches
• May: 4.36 inches
• June: 4.53 inches
• July: 4.59 inches
• August: 4.46 inches
• September: 4.29 inches
• October: 3.90 inches
• November: 3.72 inches
• December: 3.72 inches

Those values explain when the county is wettest on average. They do not explain where each month is wettest.

The location of the maximum changes repeatedly. The wettest January cell is not the wettest February cell. Different cells lead March, April, June, July, August, September, and October. The long-term annual winner, cell SCHU_003062, leads May and December but does not dominate the entire calendar.

The strength of the spatial contrast also changes.

The narrowest monthly county spread occurs in April, when the wettest and driest cells differ by approximately 0.65 inch. The largest spread occurs in June, at approximately 1.32 inches. January follows closely at 1.27 inches, with July at 1.20 inches.

A one-inch monthly climatological difference is substantial. It is not the result of one storm. It is the average geographic separation accumulated across more than a century of that calendar month.

The monthly maps share an annual backbone. The northern and northwestern dry sector remains evident in many months, while wetter eastern and southeastern sectors recur.

But the strength, shape, and local ranking of those zones shift with the season.

This is why an annual map should not be treated as though it applies identically to every event or month. The annual map is the accumulated vote of twelve related but nonidentical precipitation regimes.

Page 12

Similar, but Not the Same

Measuring the Relationship Among Months

Visual comparison suggests that the monthly maps are related. Rank correlation measures how strongly they preserve the same wet-to-dry ordering across all 3,288 cells.

The mean off-diagonal Spearman correlation among monthly wetness ranks is 0.786.

That is high enough to confirm a persistent spatial backbone. Places that are relatively wet in one month are often relatively wet in another. The county does not invent a completely new precipitation geography every thirty days, which is considerate of it.

The correlations are not uniformly high.

The strongest monthly pairing is February and December, with a rank correlation of approximately 0.969. Their fine-grid wet-to-dry patterns are remarkably similar despite occurring at opposite ends of the calendar year.

April and May are also strongly aligned, with a correlation near 0.948.

The weakest pairing is January and October, with a correlation of approximately 0.524. July and October follow at about 0.540, while January and August correlate at about 0.551.

A correlation near 0.52 is still positive. It means that some broad geographic memory remains. But it also permits thousands of individual cells to move substantially in rank.

These weaker pairings make physical sense as candidates for different spatial behavior.

Winter precipitation can be shaped by synoptic storm tracks, snow and mixed precipitation, terrain interactions within stable air, and cold-season moisture transport. Summer precipitation includes a larger convective contribution, with localized storms and warm-season flow patterns. Autumn introduces its own mix of frontal systems, tropical moisture, coastal influences, and changing atmospheric stability.

The climatology cannot assign one mechanism to every pattern. It can show that the spatial rankings produced by those seasonal mixtures differ measurably.

The correlation matrix therefore gives the annual map a useful middle ground.

The monthly patterns are neither independent nor interchangeable.

Interpretation note

Rank correlation evaluates relative position, not absolute precipitation. Two months can have very different countywide amounts while retaining a similar wet-to-dry ordering.

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Page 13

The Stable Zones and the Shape-Shifters

Some Cells Know Their Place

Each 800-meter cell was classified according to how much its countywide wetness rank varies among the twelve months.

The results divide the county into four groups:

• 336 cells, or 10.2 percent, are highly consistent;
• 1,055 cells, or 32.1 percent, are generally consistent;
• 1,533 cells, or 46.6 percent, are seasonal rank shifters;
• 364 cells, or 11.1 percent, are highly seasonal rank shifters.

The two shifting classes contain 1,897 cells, representing 57.7 percent of the county grid.

Most of Schuylkill County therefore changes its relative wetness position meaningfully during the year.

The most stable cells occur at both ends of the annual distribution. Several of the county’s driest cells remain near the dry end in nearly every month. Cell SCHU_000111, for example, ranks 3,287th annually and has a monthly-rank standard deviation of only about 54 positions among 3,288 cells.

Persistent wet zones also exist. Cell SCHU_003111 ranks fifth annually and is classified as highly consistent.

The highly seasonal cells behave very differently.

The most extreme rank range belongs to SCHU_003008. It ranks only 1,276th in the annual climatology, but rises to 13th wettest in November and falls to 3,209th in August. Its rank range across the year is 3,196 positions.

A neighboring group of cells shows a similar November-versus-August reversal:

• SCHU_003009 moves from eighth in November to 3,180th in August;
• SCHU_003010 moves from fifth in November to 3,133rd in August;
• SCHU_003063 becomes the single wettest cell in November but falls to 3,124th in August.

Another cluster behaves differently, reaching its best relative rank in August and its worst in June.

These are not small fluctuations around an annual rank. They are near-complete reversals of the county hierarchy.

The seasonal-consistency map reveals that the rank shifters are geographically organized. They form broad clusters rather than random salt-and-pepper noise. That coherence suggests changing relationships among terrain orientation, moisture flow, storm type, and regional precipitation structure.

The classification does not by itself identify the mechanism. It identifies where the annual climatology is least capable of describing the calendar.

Data callout

The county’s seasonal population

• Stable or generally stable: 1,391 cells
• Seasonal or highly seasonal: 1,897 cells
• Seasonal share of county grid: 57.7 percent

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Page 14

Towns Move Through the Ranking Too

Municipal Climate Is Seasonal Climate

Area-weighted town values preserve the same seasonal rearrangement seen in the individual cells.

Only four of the 31 incorporated places are classified as highly or generally consistent:

• McAdoo
• Ringtown
• Tamaqua
• Coaldale

McAdoo remains exceptionally stable at the wet end. It ranks first annually, never falls below fifth in any month, and has a best-to-worst monthly range of only four positions.

Ringtown shows the opposite form of stability. It ranks 31st annually and remains between 27th and 31st throughout the year.

Coaldale ranges from first to ninth. Tamaqua ranges from first to eighth. Their precise positions change, but both remain within the wet group.

The remaining 27 towns are seasonal or highly seasonal rank shifters.

Auburn provides the most dramatic municipal example. It ranks 13th annually but rises to third wettest in January and falls to 29th in October, a range of 26 positions.

Deer Lake also spans 26 places, from second in January to 28th in October.

Other large shifts include:

• Landingville: fifth in July to 30th in October;
• Pine Grove: sixth in June to 31st in April;
• Port Carbon: sixth in November to 30th in July;
• St. Clair: sixth in February to 28th in July;
• Tremont: fifth in October to 27th in February;
• Shenandoah: eighth in August to 29th in March.

These shifts complicate any attempt to assign a town one permanent label such as wet, dry, favored, or sheltered.

Pine Grove is the fourth-driest incorporated place annually. Yet it becomes the sixth-wettest town in June. Port Carbon sits near the middle of the annual ranking but reaches sixth in November.

A town’s annual mean remains important for water balance, vegetation, runoff, infrastructure, and long-term comparison. Its monthly rank reveals when that annual identity holds and when the seasonal circulation temporarily rewrites it.

The town heatmap is therefore less a ranking table than a calendar of changing geographic advantage.

Module D Interpretation Notes

What the rank analysis measures

Monthly rank describes each cell or town’s position relative to the rest of Schuylkill County during the same calendar month. It does not compare January precipitation directly with July precipitation.

A cell can receive less precipitation in January than in July while ranking higher within the county in January.

Why rank is useful

Rank isolates spatial organization from the countywide seasonal cycle. It allows wetness geography to be compared even when the absolute monthly totals differ greatly.

Why rank can exaggerate small differences

In tightly clustered months, small precipitation differences can move a cell through many ranks. April has the narrowest spatial spread, so rank changes during April should be interpreted alongside the underlying precipitation amounts.

Mechanistic caution

The patterns are consistent with seasonal changes in storm tracks, moisture transport, convection, snow processes, atmospheric stability, and terrain interaction. This analysis does not isolate the causal contribution of each process.

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Data Notes

• Monthly climatology covers 1895 through May 2026.
• The rank-consistency summaries use the twelve calendar-month climatologies.
• All cell ranks run from 1, wettest, to 3,288, driest.
• Town ranks run from 1, wettest, to 31, driest.
• Monthly town values are area-weighted from intersecting 800-meter cells.
• Spearman correlation measures similarity in relative spatial ordering.

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Page 19

There Is More Than One Kind of Extreme

The Record Book Needs Several Shelves

A county can experience its wettest day, hottest cell, coldest valley, widest temperature gradient, strongest atmospheric drying demand, and most spatially complicated day at entirely different times.

Trying to reduce all of that to one record would produce a ranking with the intellectual clarity of a junk drawer.

The extreme hunt therefore uses separate metrics.

For each day from 1981 through 2025, it summarizes conditions across all 3,288 cells:

• county mean, minimum, maximum, range, standard deviation, and sum;
• precipitation contrasts;
• temperature contrasts;
• dew-point and vapor-pressure-deficit contrasts;
• total solar-radiation contrasts;
• high-versus-low elevation differences;
• ridge-versus-valley differences.

It also creates a composite raccoon-day score.

The score converts 18 available metrics into percentile ranks and averages those percentiles. A day scores highly when several kinds of spatial oddity occur at once.

It is not a hazard index.

The highest composite score belongs to August 24, 1989, at approximately 85.55. That day combines an 8.6-degree-Celsius minimum-temperature range, a 5.3-degree maximum-temperature range, an 11.61-hPa maximum-VPD range, a 6.52-MJ-per-square-meter solar range, and several additional contrasts.

Its county-average rainfall is only 3.33 millimeters.

The score therefore identifies a multi-variable spatial puzzle, not the day on which residents necessarily faced the greatest danger.

The next leading dates are June 2, 1988; July 31, 2007; August 18, 1988; and July 17, 1988.

All twenty highest-scoring days occur in June, July, or August:

• six in June;
• eight in July;
• six in August.

Among the top 50, 46 occur from June through August. The remaining four consist of three May dates and one September date.

Warm-season dominance is not surprising. Summer allows large vapor-pressure deficits, strong solar contrasts, convective precipitation gradients, and substantial temperature differences to occur simultaneously.

But it is partly a property of the score itself. A metric suite containing VPD, solar radiation, precipitation spread, and temperature spread naturally gives summer more ways to become unusual.

The composite score is best used as a discovery tool.

It finds days when many atmospheric dimensions deserve inspection together. It should never be mistaken for a final ranking of human impact, flood severity, heat danger, or meteorological importance.

Page 20

When the Water Took Over

Daily and Monthly Precipitation Records

The largest modeled one-day precipitation total at any 800-meter cell is 216.22 millimeters, or 8.51 inches, on September 18, 2004.

That date also produces the second-highest county-average daily precipitation: 134.77 millimeters, or 5.31 inches.

The wettest county-average day occurs on September 2, 2021, at 137.36 millimeters, or 5.41 inches across all 3,288 cells.

The difference between those records is instructive.

September 18, 2004 contains the higher local maximum. September 2, 2021 produces the slightly higher countywide mean. One event is marginally more extreme at the wettest cell; the other is more uniformly wet over the county.

The largest one-day spatial precipitation spread occurs on April 20, 1983. The wettest and driest cells differ by 182.36 millimeters, or 7.18 inches.

The same date contains the second-highest single-cell daily total, 195.91 millimeters.

Inside one nominal four-kilometer parent footprint, the largest child-cell precipitation range occurs on June 28, 2018. The 25 children of R224_C1167 differ by 99.33 millimeters, or 3.91 inches.

That is the local-scale version of the county problem: one regional value attempting to summarize a storm that did not distribute itself regionally.

The monthly archive extends back to 1895 and reveals a separate hierarchy.

September 2011 is the wettest county-average month, with 390.28 millimeters, or 15.37 inches. It also contains the wettest monthly single cell, at 566.04 millimeters, or 22.28 inches.

The next-wettest county-average months are:

• June 1972: 14.36 inches;
• June 2006: 14.16 inches;
• August 1955: 13.89 inches;
• July 2018: 11.85 inches.

June 1972 appears only in the monthly hierarchy because the daily archive begins in 1981. This is why the daily and monthly record books should remain connected but not forcibly merged. Different record lengths are performing different jobs.

Page 21

Heat, Cold, and the Nights When Elevation Reversed

Absolute Temperature Is Only Half the Story

The highest modeled daily maximum temperature at any cell is 39.6 degrees Celsius, or 103.3 degrees Fahrenheit, on July 23, 2011.

The lowest modeled daily minimum is minus 32.7 degrees Celsius, or minus 26.9 degrees Fahrenheit, on January 22, 1994.

Those are the absolute ends of the daily temperature record.

The spatial ranges reveal a different kind of extremity.

On February 19, 2008, maximum temperature varies by 15.9 degrees Celsius, equivalent to 28.6 Fahrenheit degrees, across the county. The same day also has the largest county mean-temperature range, at 8.9 degrees Celsius.

The largest minimum-temperature range occurs on September 19, 1991. The warmest and coldest cells differ by 14.5 degrees Celsius, or 26.1 Fahrenheit degrees.

That date is especially interesting because the terrain contrasts point in the same direction.

Mean ridge minimum temperature is 2.15 degrees Celsius, or 3.87 Fahrenheit degrees, warmer than the mean valley minimum. This is the strongest ridge-warm versus valley-cold contrast in the archive.

The strongest high-elevation-warm versus low-elevation-cold contrast occurs on November 17, 2023, when the high-elevation group averages 3.19 degrees Celsius, or 5.74 Fahrenheit degrees, warmer at minimum temperature than the low-elevation group.

These values describe group means, not the full warmest-to-coldest cell range. Their significance lies in geographic organization.

A random collection of warm and cold cells could create a large county range. A positive high-minus-low or ridge-minus-valley contrast shows that terrain position itself is aligned with the temperature pattern.

That is the fingerprint of inversion-like behavior: lower terrain cools more strongly while elevated terrain remains warmer.

The analysis does not resolve the boundary layer directly. It does not measure wind, cloud cover, snow cover, or valley drainage. But dates such as September 19, 1991 deserve targeted reconstruction because the grid pattern is coherent with those processes.

Page 22

The Records Most Reports Forget

Drying Demand, Humidity, and Sunlight

Temperature and precipitation dominate conventional climate reports because humans enjoy variables they can discuss while waiting for an elevator.

Vapor-pressure deficit, dew point, and solar radiation often reveal more of the county’s fine-scale atmospheric structure.

The highest daily maximum VPD in the archive is 50.48 hectopascals on July 7, 2010.

Maximum VPD describes the atmospheric moisture deficit. Higher values correspond to stronger evaporative demand, assuming other surface controls are equal.

The largest countywide maximum-VPD spread occurs on July 10, 2016, when cells differ by 24.28 hectopascals.

This is not simply a hot-versus-cool contrast. VPD incorporates both temperature and moisture. A large range can indicate different combinations of heating, humidity, terrain exposure, and cloud or precipitation history across the county.

The largest county mean-dew-point spread occurs on April 7, 2021. Cells differ by 8.8 degrees Celsius, or 15.8 Fahrenheit degrees.

Within a single parent footprint, maximum VPD differs by as much as 13.71 hectopascals among 25 children on August 10, 2001.

The solar record is equally revealing.

The highest county-average daily total solar radiation is 30.41 megajoules per square meter per day on June 17, 2021. June 18 follows immediately behind.

The largest county solar-radiation spread occurs on March 10, 2009, at 16.43 megajoules per square meter per day.

A spread of that magnitude implies that the county was not sharing one simple sky. Cloud distribution, slope geometry, terrain shadowing, and the solar interpolation field combined to produce sharply different daily radiation environments.

These variables matter beyond climatological curiosity.

VPD influences plant water stress and evaporation. Dew point helps distinguish dry-air intrusions from purely temperature-driven contrasts. Solar radiation affects surface heating, snowmelt, evapotranspiration, agriculture, and the daily evolution of boundary-layer mixing.

They also help explain why two places with similar precipitation or elevation may behave differently.

The final record book is therefore not a list of winners. It is a catalog of atmospheric configurations that deserve further study.

Module F Interpretation Notes

The raccoon-day score is exploratory

The composite score averages percentile ranks across 18 available metrics. It is useful for discovering multi-variable days, but it has no direct unit and no calibrated relationship with damage, risk, or rarity outside this dataset.

County range is not measurement uncertainty

A large range indicates modeled spatial contrast among cells. It should not be interpreted as the error bar of the county mean.

Group contrasts are conditional

High-minus-low elevation and ridge-minus-valley differences use terrain-defined cell groups. They summarize geographic organization but do not independently diagnose inversions, downslope flow, cloud patterns, or storm mechanisms.

Daily and monthly records have different lengths

Daily analysis covers 1981–2025. Monthly analysis begins in 1895 and extends through May 2026. A monthly record before 1981 cannot be reconstructed from the daily archive supplied here.

PRISM values are modeled estimates

A record in this analysis is a record within the supplied PRISM grid archive. It is not automatically the official observed county record from weather stations.

Solar-variable provenance deserves explicit documentation

The supplied archive includes daily and monthly solar variables. Final methods should identify the precise source and processing chain used to create those time series, especially where it differs from standard downloadable PRISM time-series elements.

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Data and Unit Notes

• Precipitation: millimeters.
• Temperature and dew point: degrees Celsius.
• Vapor-pressure deficit: hectopascals.
• Solar radiation: megajoules per square meter per day.
• Daily grid analysis: 54,041,568 joined records, 1981–2025.
• Monthly grid analysis: 5,185,176 joined records, 1895–May 2026.

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Page 28

How the Report Was Built

From Grid Cells to a County Climate Portrait

This report began with a geometric problem.

The climate files contained 3,288 coordinate locations associated with Schuylkill County. The national PRISM 800-meter mesh contained millions of polygons. The first task was to select the county-intersecting polygons, match every downloader coordinate to its exact mesh cell, and preserve a stable identifier through every later stage.

The match was exact.

All 3,288 coordinate rows were assigned to 3,288 unique polygons. There were no unmatched rows, duplicate location identifiers, duplicate mesh assignments, or nonzero match distances.

The terrain analysis then summarized 15 raster layers inside every climate cell. These included elevation, slope, aspect, topographic-position indices at several scales, relief, roughness, terrain-ruggedness measures, and cartographic relief products.

The zonal-statistics table contains 198 columns. The derived terrain-feature table contains 217.

Terrain position was classified into six analytical groups. Terrain complexity was calculated from 17 standardized measures of elevation variability, slope, relief, roughness, and topographic structure.

Nominal four-kilometer parent footprints were derived from the original PRISM row and column indices by grouping cells in five-by-five blocks. A complete footprint contains 25 fine-grid children. Of the county’s 160 footprints, 104 are complete and 56 are partial because the county boundary intersects only part of the nominal block.

The climate archive contains nine variables:

• precipitation;
• minimum temperature;
• mean temperature;
• maximum temperature;
• mean dew point;
• minimum vapor-pressure deficit;
• maximum vapor-pressure deficit;
• total solar radiation;
• solar slope.

The daily archive covers January 1, 1981 through December 31, 2025. The monthly archive extends from January 1895 through May 2026.

All 315 daily source files and 70 monthly source files were normalized successfully.

The climate–terrain join contains 54,041,568 daily rows and 5,185,176 monthly rows. Every row joined to a terrain cell.

The project then produced five related families of analysis:

1. terrain structure and relief;
2. annual and monthly precipitation climatology;
3. seasonal wetness-rank consistency;
4. within-footprint cell and town departures;
5. daily and monthly extreme-event rankings.

Municipal geography was added from 2025 Census place and county-subdivision boundaries. The resulting link tables connect the fine-grid climate cells to 67 municipalities, including 31 incorporated cities and boroughs and 36 townships.

The final report is therefore not one model or one ranking.

It is a linked spatial system in which climate, terrain, nominal parent footprints, and municipal boundaries retain their own identities.

Page 29

What Passed Quality Control

Internal Consistency Before Interpretation

Quality control cannot make a modeled climate field true.

It can establish that the analysis did not lose rows, duplicate cells, misassign identifiers, silently fail during normalization, or join only the convenient portion of the archive.

The mesh build passed its expected-count check:

• expected cells: 3,288;
• selected cells: 3,288;
• coordinate rows matched: 3,288;
• unmatched rows: zero;
• duplicate location IDs: zero;
• duplicate mesh IDs: zero.

The terrain pipeline preserved the same 3,288 rows through the master mesh, zonal statistics, derived features, and parent-footprint assignment.

The final terrain manifest checked 16 expected files. None were missing.

All required terrain-feature columns were present. No nulls occurred in the terrain complexity score, terrain classes, ridge and valley flags, elevation, slope, aspect components, topographic position, relief, or roughness fields used by the report.

The parent-footprint structure also passed its internal checks:

• 160 total footprints;
• 104 complete 25-child footprints;
• 56 partial edge footprints;
• zero footprints with more than 25 children;
• all 3,288 cells assigned exactly once.

The climate archive completed a similarly blunt but useful test.

All 315 daily files and 70 monthly files normalized without failure. Every daily and monthly row joined to the terrain table.

The daily join contains 54,041,568 rows and 3,288 unique locations. The monthly join contains 5,185,176 rows and the same 3,288 locations.

Town geography passed its link build:

• 406 incorporated-town-to-cell links;
• 4,441 municipality-to-cell links;
• 803 Census-place-to-cell links.

These checks provide confidence that the report describes the supplied archive consistently.

They do not independently validate the observational accuracy of PRISM, the correctness of every source raster, the physical interpretation of every pattern, or the provenance of each upstream file.

A pipeline can be internally immaculate and scientifically misguided. The point of QA is to eliminate the avoidable failures before arguing about the interesting ones.

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The Boundary Between Finding and Claim

What This Report Can Support

The report provides strong descriptive evidence that Schuylkill County contains persistent, organized fine-scale climate structure.

It can support the following conclusions:

• modeled annual precipitation varies substantially within the county;
• the monthly wet-to-dry ranking changes with the season;
• terrain and climate structure are related but cannot be reduced to elevation alone;
• some cells and towns repeatedly depart from their local grouped-child mean;
• different variables reveal different forms of local distinctiveness;
• large daily spatial contrasts occur in precipitation, temperature, dew point, VPD, and solar radiation;
• the same county can contain several meaningfully different climate environments on the same day.

The report cannot establish every physical cause behind those patterns.

PRISM is a modeled grid informed by observations, terrain, and interpolation. A fine-grid difference can reflect physical climate structure, the model’s terrain relationships, station distribution, or combinations of those influences.

The 3,288 cells are not independent weather stations.

The long-period annual precipitation means use complete years from 1895 through 2025. They are project climatologies, not official 30-year normals.

The monthly archive is longer than the daily archive. Comparisons across record books must retain that difference.

The parent-footprint analysis requires especially precise language.

Each child is compared with the contemporaneous mean of the 800-meter children sharing its nominal four-kilometer footprint. That grouped-child mean is not an independently downloaded native four-kilometer PRISM value.

The analysis therefore measures internal heterogeneity and local representativeness. It does not yet measure resolution-to-resolution disagreement between the official PRISM products.

Wetness ranks also require restraint. When a month has a narrow countywide precipitation spread, a small difference can move a cell through many rank positions.

The correlations are descriptive. A precipitation–elevation correlation does not prove how much precipitation was caused by elevation. Terrain classes and the parent drama score are project-specific analytical tools rather than official geomorphic standards.

Town values are area-weighted grid summaries. They do not replace observations within the town, and they can smooth large internal gradients.

Finally, the extreme-event rankings are records within the supplied gridded archive. They should not be promoted as official observed county records without station-based confirmation.

The report becomes stronger when those boundaries are visible.

Scientific caution is not an apology inserted after the interesting claims. It is the structure that keeps the claims interesting after someone else checks them.

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What Schuylkill County Taught Us

One Boundary, Many Local Climates

The county began as 3,288 grid cells and several folders of climate and terrain output.

It ends as a coherent climate portrait.

The terrain changes quickly enough that one nominal four-kilometer footprint can contain ridges, valleys, steep slopes, and nearly one thousand feet of difference among child-cell mean elevations.

Annual precipitation changes enough that the wettest and driest fine-grid cells differ by 10.57 inches per year.

Elevation helps organize that map, but it does not dictate it. Similar elevations can occupy opposite ends of the wetness hierarchy.

The annual map is not repeated twelve times. More than half of the county grid changes its relative precipitation position meaningfully through the calendar.

Municipal summaries preserve those contrasts. McAdoo and Ringtown remain stable at opposite ends. Auburn looks ordinary annually and becomes extraordinary only when the monthly ranking is examined. Tamaqua remains wet despite crossing complex terrain. Pottsville sits near the county mean without becoming climatically featureless.

The within-footprint analysis adds another dimension.

Most cells do not depart persistently across many variables. A substantial minority does. Some differences appear only during events. Others recur across temperature, dew point, VPD, and solar variables with enough consistency to form recognizable local fingerprints.

Mahanoy City is the clearest municipal example. Its broad departure is not random. The town repeatedly runs lower in minimum temperature and dew point and higher in maximum VPD relative to its surrounding footprint mean.

The record book reinforces the same lesson.

The wettest cell, wettest countywide day, greatest precipitation spread, strongest inversion-like temperature contrast, highest VPD, largest solar contrast, and highest composite weirdness score all occur on different dates.

There is no single winner because there is no single kind of extreme.

The report’s central conclusion is therefore not that Schuylkill County is unusually wet, unusually mountainous, or unusually strange.

It is that scale matters.

At county scale, the averages are useful.

At town scale, stable wet and dry sectors emerge.

At four-kilometer-footprint scale, internal terrain and precipitation ranges become difficult to ignore.

At 800-meter scale, seasonal shifts and recurring multivariable departures become visible.

None of those scales is the one correct climate.

Each answers a different question.

Final pull quote

Schuylkill County does not have one climate at finer scales. It has a structured family of local climates sharing one county boundary.

Future Research Priorities

Direct native-resolution comparison

Download the independent native four-kilometer PRISM archive and compare it directly with the 800-meter product by cell, date, variable, season, and terrain class.

Station validation

Compare the modeled fine-grid signals with COOP, ASOS, mesonet, CoCoRaHS, and other station observations. Prioritize persistent outliers such as Mahanoy City and the leading cell clusters.

Event classification

Stratify extreme and within-footprint departures by synoptic flow, storm track, season, precipitation type, convection, snow cover, cloud cover, and boundary-layer regime.

Trend analysis

The current report emphasizes climatology and spatial structure. A separate trend study should evaluate how precipitation, temperature, VPD, solar radiation, and spatial gradients changed through time.

Solar-data provenance

Document the precise upstream source and processing chain for the solar variables used in the supplied archive.

Field-scale town studies

Select paired ridge, slope, and valley locations for temporary sensors. Test whether the modeled nighttime temperature, dew-point, VPD, and solar fingerprints appear in direct observations.

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Final Data Note

Every value in this report should be read as a result from the supplied project archive and the documented processing chain. The report is strongest when it remains reproducible, specific about scale, and honest about the difference between modeled structure and direct observation.

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