Somerset Weather · Hyper-local Forecast

Brian's charge (October 16, 2026): do novel research on every part of the forecast, from the ground to the upper atmosphere. Chase any connection that looks real. Research can run across many sessions and wait for data to build up. The goal is a more accurate model, and where possible a narrower +/- range, even if only for the nearest days.

This folder is where that research lives. Each thread is one text file that grows over time: what we know, what the literature says, what was explored, what is waiting for data, and what to try next. The Lab Notebook entries (L-00N) are the formal tests. A thread produces ideas; only a pre-registered test can turn an idea into a change.

How research becomes a forecast change

1. Explore on the discovery years (walk-forward residuals for March 2024 to September 2025). Look freely. Nothing found here counts as evidence by itself, because looking at many ideas on the same days will always turn up some that work by luck.
2. Pre-register the most promising idea as a Lab Notebook entry: question, plan and pass/fail bar written and published before the test.
3. Test walk-forward on the test year (October 2025 onward), against today's model on identical days.
4. Confirm on months nobody has looked at (from October 2026 on). The test year has now been used by many experiments, so a pass there is necessary but not enough for promotion.
5. Promote only after Brian and Claude agree in a session.

The honest ceiling

Our day-1 errors are already small: about 1.6 F for lows and 1.8 F for highs on average (discovery years). Typical national guidance runs 2.5 to 3 F. What is left is partly weather nobody can see coming a day ahead, and partly the station's own noise. Gains from here will be tenths of a degree, hard won. Narrower ranges are more within reach than big accuracy jumps, because a range can shrink on the days we can tell are easy.

Data on hand (Tower: /lab/data, Mac: hyperlocal/data/research)

python /lab/research_data.py runs adds the newest model runs (the morning watcher does this daily); era5, asos and upper top up the others.

Threads

Library (literature behind the threads)

• Gneiting, Raftery, Westveld and Goldman (2005), EMOS / nonhomogeneous Gaussian regression: the predictive spread depends on the ensemble spread. Basis of R-001. Overview with later work: https://arxiv.org/pdf/1407.0058
• Spread and skill, heteroscedastic postprocessing: https://arxiv.org/pdf/1903.06739
• Radiation-night minimum temperature from evening temperature and dew point (UC Davis biometeorology): https://biomet.ucdavis.edu/Forecasting_Temperature_Trends_during_a_Radiation_Frost_Night.html
• NWS Western Region TM 102 (dew point floor, wind and cloud limits on cooling): https://www.weather.gov/media/wrh/online_publications/TMs/TM-102.pdf
• NWS Western Region TM 106 (MOS minimum with fog and dew adjustment): https://www.weather.gov/media/wrh/online_publications/TMs/TM-106.pdf
• Night decoupling, wind thresholds and model warm/cold biases on stable nights: https://link.springer.com/10.1007/s10546-024-00886-6 and https://ecmwf.net/sites/default/files/elibrary/2011/14930-radiation-and-turbulence-sbl.pdf
• Mueller et al. (2016), cropland intensification cooled Midwest summer heat extremes through evapotranspiration: https://phuybers.sites.fas.harvard.edu/Doc/Mueller_NatureCC2015.pdf
• Soil moisture and 2 m temperature forecasts: https://doaj.org/article/e5261d809e674492bbb700873f5af3f4
• 850 mb mix-down for highs and when it fails: https://theweatherprediction.com/habyhints/296/
• Sandu et al. (2013), why stable night air is hard for models (extra mixing kept on purpose): https://www.ecmwf.int/sites/default/files/elibrary/2012/12086-why-it-so-difficult-represent-stably-stratified-conditions-nwp-models.pdf
• Kalman filter bias correction of station temperatures (Libonati, Trigo, DaCamara): https://idlcc.fc.ul.pt/publications.php?id=220 ; NOAA decaying-average bias code: https://downloads.psl.noaa.gov/Projects/GEFSv12/NBM/code/python_backup/control_decayavg_bias_corr.py
• MOS rain chance predictors (mean layer humidity, 850/700 mb humidity, water vapour, vertical motion): https://weather.gov/media/erh/ta/ta99-1.PDF and https://www.nws.noaa.gov/mdl/synop/tpb/487body.htm
• Tipping-bucket undercatch (wind, splash, evaporation, light rain): https://eprints.ncl.ac.uk/306060

Questions for the library

The unattended lab has no web browsing (a safety choice: see the brief). When a session needs literature, it adds a question here and Claude looks it up in the next session with Brian.

• (none yet)

The question

Every forecast carries an 80% range (the +/-). Today it widens only when the four models disagree (expected miss = a + b x model spread). Can we tell easy days from hard days better than that, so the range is narrow when the weather is predictable and honestly wide when it is not? Brian asked for a smaller +/-; this is the most direct route.

Why it might work (physics and literature)

• The spread of an ensemble carries information about the size of the error (the spread-skill relationship). EMOS (Gneiting et al. 2005) builds the forecast spread from a constant plus the ensemble variance and gives better calibrated ranges than a fixed width. We use 4 models; the 122-run ensembles we now save are a richer signal.
• Some weather is simply easier: a sunny afternoon with a steady wind mixes the air predictably; an overcast day whose clouds may or may not break, a front arriving mid-day, or a snow-covered night are hard.
• Our discovery data already shows it for highs (below).

What has been explored

2026-10-18, cold and warm spells (from Brian's review of a long-running physics forecaster's history). That program found its forecasts erred differently in unusually cold or warm spells, and warned that measuring it against what HAPPENED instead of what was FORECAST manufactures a false pattern. Checked on our forecast, discovery years, out-of-sample predictions, leads 1 to 7, with 1991-2020 normals for daily highs and lows (ERA5 at the station, shifted to the station by month; research/normals.py, research/spells.py).

No spell bias: our forecast is neither too timid nor too bold in big cold or warm spells, at any lead, and a cross-validated correction made every lead slightly worse (0.02 to 0.06 F). Spells are HARDER (day-1 lows 2.0 F off when forecast 12+ F below normal, 1.4 F near normal) but not biased. The false pattern is real and grows with lead: calibrating on observed departures would have pushed our day-7 forecasts a third of a degree further from normal per degree, the wrong way. Every correction we test is built on what is known at issue time, which avoids it. One thing to watch: day-1 lows forecast 12+ F below normal ran 0.7 F too warm (36 nights, within noise). The deep cold this winter will tell.

2026-10-16, discovery days (Mar 2024 to Sep 2025), leave-one-month-out. Expected miss from model spread alone vs. spread plus weather regime (clear-and-calm index, model cloud and wind, season, recent error size, snow on the ground, cold nights). Both scaled so 80% of misses fall inside.

(CRPS: the standard score for a whole forecast distribution, lower is better.)

What it means so far: the regime version spreads the ranges out (easy days get much narrower ranges, hard days wider) while keeping 80% coverage, but the overall score barely moves. Sunny light-wind days have smaller misses in the highs (1.7 F vs 2.4 F on day 1-2). Snow on the ground makes lows harder (2.4 F vs 1.9 F, 40 nights). A lead, not a finding.

Scripts: hyperlocal/research/r001_explore.py, results r001_explore.json.

2026-10-18, the miss review look-back (Brian asked what we do with big misses, after the October 18 high came in 68.4 F against 63.3 F). Our day-1 high and low as forecast walk-forward (each day from data before it), with the conditions of each day: the models' forecast (disagreement, cloud, sunshine, dryness) and what happened (pyranometer sunshine, wind, dew point swing, rain). Patterns looked for on March 2024 to July 15, 2026 only; July 16 to October 12, 2026 held back to check them. Script: hyperlocal/research/miss_lookback.py (output miss_lookback.out).

What it shows:

• The +/- for the high is honest on ordinary days and too narrow on cloudy and wet days. That is idea 3 below, answered for highs: the range is right on average but wrong on the days that matter most. Lows are close to 80% in every group checked.
• Big misses are mostly misses the four models made too: on 23 of the 31 highs off by 5 F or more, the models' average was off by 4 F or more as well. Our correction cannot fix what every model gets wrong. 42% of the big misses came on days whose sunshine differed from the forecast by 15% of clear sky or more (21% of all days): cloud timing again (R-010).
• Days like October 18 (September to November, 80%+ sunshine, dry air): 76 days, typical miss 1.36 F, came in 0.41 F warmer than forecast on average, 18% outside the range. No sign of a systematic problem; October 18 was a rare day (the held-back 12 such days: +0.76 F, 33% outside, too few to say more).
• Lows ran 0.9 F too cold on nights the dew point held steady (306 nights; held back +1.27 F, 14 nights) and 0.9 F too warm when it swung 12 F or more (an air-mass change; held back -0.58 F). The swing is measured after the fact, so it cannot be used as it is; the question is whether the models' FORECAST of the swing carries the same signal.

Ideas to chase

0b. Lows: does the models' forecast overnight dew point change predict the warm/cold lean above? Explore on

1. Wider ranges for the high on days forecast cloudy (80%+) or wet (rain chance high): fit the extra width on the exploration days, test coverage on fresh days (the held-back months are too few cloudy days to judge). discovery days first (R-002).

1. The 122-run ensemble spread as the confidence signal (needs about 6 months saved: spring 2027).
2. Upper-air stability and mixing as a confidence signal for highs: strong inversions and shallow mixing make highs hard (R-003 data).
3. Check that the narrower ranges are honest within each regime (80% coverage on sunny days and on cloudy days separately), not just overall. A range that is right on average but wrong on the days it shrinks is worse.
4. Different shapes: our misses may not be bell-shaped near freezing or on snow nights (see R-007).

Next step

When the run archive covers a winter, pre-register: "regime-aware ranges, highs days 1-2: mean +/- at least 5% narrower at 80% +/- 2% coverage on the test year, and coverage within 75-85% in each regime tercile." Then confirm on fresh months.

Log

• 2026-10-16: thread opened by Claude with Brian. First exploration above.
• 2026-10-18: cold and warm spells checked: no spell bias at any lead; the observed-departure false pattern confirmed.
• 2026-10-18: miss review look-back: highs' range too narrow on cloudy and wet days; big misses are shared with the models; the live miss review (hyperlocal/misses.py) now records every finished period's conditions.

The question

On a clear calm night the ground radiates heat to space and the air near it cools fast; wind stirs warmer air down; clouds send heat back; moisture puts a floor under the temperature because dew and frost release heat as they form. Our lows are already good (1.6 F average miss on day 1), but about 7% of nights miss by 7-9 F even when every model agreed. What are we missing, and can anything we can measure warn us?

Literature

• Radiation-night minimum from the temperature and dew point about two hours after sunset (UC Davis), valid only on clear nights with wind under about 5 mph.
• NWS TM 102: the dew point acts as a floor because condensation offsets radiative cooling; clouds or wind of any duration usually keep minima well above the radiative value.
• NWS TM 106: raise a MOS minimum when it is 5 F or more below the afternoon dew point (fog and dew limit cooling).
• The boundary layer can "decouple" on calm nights (wind under about 1 m/s near the ground): the surface layer cools on its own. Models handle this poorly and the sign of their error varies by model and surface scheme.

What has been explored

2026-10-16, discovery nights (Mar 2024 to Sep 2025, 560 nights, day-1 forecast). At 8-9 PM: temperature vs our low, dew point vs our low (the condensation floor), dew point depression, wind, the 2-hour cooling rate, pressure change. Leave-one-month-out correction of our low:

Nothing helps. The strongest single signal is the evening cooling rate (correlation 0.14 with the miss) and the dew point floor (0.11), both tiny. The models (through our corrections, which already include a clear-and-calm index and the dew point depression) already know the evening state. What causes the big misses happens later in the night (the next entry looks at what).

Scripts: hyperlocal/research/r002_explore.py, results r002_explore.json.

2026-10-16, bust night anatomy (same 560 discovery nights, station minute data). Nights grouped by our miss:

The clearest signal so far: how much of the night goes calm. Nights that ended colder than forecast were calm half the time; nights that ended warmer were calm a quarter of the time (correlation with the miss -0.22, the strongest of anything tried). Within these groups the models' wind barely differs (7.2 to 8.4 mph), so the extra calm on bust nights is beyond what the wind forecast shows (across all nights the models' wind does track calm nights well; see R-003). This is boundary layer decoupling, as the literature describes. Air masses arriving overnight (temperature rising 3 F or more after 10 PM, or the dew point dropping 5 F or more) explain only 2 of 14 warm busts and 2 of 17 cold busts. Cloud surprises explain little (see R-006).

Script: hyperlocal/research/r002_bust_anatomy.py, results r002_bust_anatomy.json.

Ideas to chase

1. Predict decoupling (top priority, with R-003). What could tell us in advance that a night will go calm at the ground? Candidates: the forecast wind at 925 mb (about 2,500 ft; a weak flow aloft lets the surface decouple, a low-level jet keeps it stirred), the strength of the evening inversion (925 mb minus 2 m temperature), the pressure gradient. Explore on upper_hist (perfect prog), then test on the real runs. A decoupling chance could also feed R-001: calm-prone nights deserve a wider range on the cold side.
2. Soil and ground heat. Wet, warm soil in autumn releases heat at night (R-004 data: ERA5 soil temperature and moisture). Does it explain warm misses in fall?
3. Done 2026-10-16: evening snapshot (null), upstream clouds (weak, R-006), bust anatomy (decoupling).
4. Decoupling, measured (new 2026-10-17). The ground sensor (R-007, "Ground sensor data") gives the grass-minus-air gap and the soil heat profile every 5 minutes. Ideas: (a) an evening nowcast: does the gap at, say, 9 PM tell how far below our forecast the night's low will land? (b) does the 8-inch minus 2-inch soil difference (heat flowing up) explain warm misses on autumn nights (idea 2, now with our own soil instead of ERA5)? Exploration on the nights as they come; any correction is tested only on months after it is written down.

Log

• 2026-10-16: thread opened by Claude with Brian (from the backlog idea "evening frost nowcast"). Null result above closes the simple version of that idea.
• 2026-10-16: bust anatomy: decoupling (calm share) is the leading mechanism. Next: predict it from the air above.
• 2026-10-16: R-003 perfect prog: the inversion strength lines up with the miss (strong inversion = colder than forecast), matching the known too-much-mixing bias of models on stable nights. Formal test planned in R-003.
• 2026-10-17: our ground sensor went in (R-007). It measures decoupling directly; idea 4 added.

The question

Everything our model uses today is at the surface (2 m temperature, dew point, cloud, wind, sunshine, CAPE, pressure). The weather at the ground is decided partly aloft: how deep the afternoon mixing goes, how warm the air is at 850 mb (about 5,000 ft), whether a temperature inversion traps cold air, how moist the column is. Does the upper air explain misses that the surface fields cannot?

Physics and literature

• Mix-down for highs: on a sunny day the boundary layer mixes, and the high approaches the 850 mb temperature brought down dry-adiabatically (about +13 C from 850 mb to the surface). It fails with cloud, rain, fronts or strong temperature advection, and in winter inversions (Haby hints 296; OU METR 2413).
• Inversions and snow: in winter, a shallow cold layer under warm air aloft keeps highs below model values, especially with snow cover or low cloud.
• Wind aloft at night: a strong 925 mb wind (low-level jet) keeps nights stirred and warm; a weak one lets the surface decouple (R-002).
• Freezing level and the column's moisture matter for rain type and amount (R-005).
• Models mix stable night air too much, on purpose. Operational models (ECMWF documented it) keep extra turbulent mixing in stable conditions because it helps the large-scale forecast, though it degrades near-surface temperatures on stable nights (Sandu, Beljaars, Bechtold, Mauritsen and Balsamo 2013, JAMES 5:117-133: https://www.ecmwf.int/sites/default/files/elibrary/2012/12086-why-it-so-difficult-represent-stably-stratified-conditions-nwp-models.pdf). Prediction: under strong inversions the models' nights run too warm.

Data

• upper_hist_<model>.parquet: 2023 on, stitched first hours of each run (close to an analysis). Learning only.
• runs_<model>.parquet: real runs from April 2, 2026 at true lead times. The only fair data for testing a forecast input. Grows by two runs a day per model.
• Observed soundings (ILN Wilmington OH, ILX Lincoln IL, DTX White Lake MI) are available from the IGRA archive if needed to check the models aloft.

What has been explored

2026-10-16, decoupling and inversions, PERFECT PROG (GFS near-analysis aloft, 565 discovery nights, day-1 lows). An upper bound only: these values are known in hindsight.

What predicts a calm night at the station (correlation with the share of calm minutes, 11 PM to 7 AM): the models' 10 m night wind -0.75, our clear-and-calm index 0.79, the 925 mb wind -0.67, the inversion 0.44. So the models do know which nights will be calm in general; the 925 mb wind adds little to that.

But the inversion strength (925 mb temperature minus 2 m temperature, overnight) lines up with our miss, steadily:

Under strong inversions the night ends colder than we forecast; with none it ends warmer. Exactly what the too-much-mixing bias predicts. Leave-one-month-out correction of our low: 1.618 -> 1.601 F with 925 mb wind and inversion, 1.587 F adding wind x clear sky and the evening inversion (2% better, an upper bound).

Caution: the inversion here uses the near-analysis's own 2 m temperature, which may partly stand in for knowing the night's outcome. Only the real runs (forecast inversion at true lead times) can say whether this survives.

Script: hyperlocal/research/r003_decoupling_pp.py, results r003_decoupling_pp.json.

2026-10-17, L-004 on the real runs (pre-registered). 177 nights, April to September 2026, ECMWF and ICON 00Z runs. Lows days 1-2: 1.541 -> 1.528 F (0.013 better; bar 0.02), positive in 74% of resamples (bar 90%). Strong forecast inversions (3 F or more, 72 forecasts): correction -0.42 F on average, error 1.181 -> 1.170 F (physics check passed). Day 1 +0.031 better, day 2 0.005 worse. Not passed. Winter confirmation (October 2026 to March 2027) still scheduled with the frozen correction; a pass there would justify a new pre-registered version, not promotion.

Next step (as pre-registered, now run: see L-004 in the notebook)

Formal test, written into the notebook before any run-period data is examined: forecast inversion strength (925 mb minus 2 m, 11 PM to 5 AM) and 925 mb wind from the real runs, the run issued closest to our issue time for each lead, as a correction to our day 1-2 lows. Perfect-prog design: learn the correction on upper_hist (2023 to September 2025), then apply it unchanged to the real runs' forecast values (the runs start too late to train on). Test months April to September 2026 (test-year months never used with these inputs), confirm on October 2026 to March 2027 (winter, when inversions are strongest). Draft bar: lows days 1-2 MAE at least 0.02 F better on both periods, and the gain coming from the strong-inversion nights (physics, not luck).

Ideas to chase

1. Mix-down potential for highs: 850 and 925 mb temperatures brought down dry-adiabatically, compared with our forecast high, on sunny days. Does the gap predict our miss?
2. Inversion strength (925 mb minus 2 m temperature; 850 minus 925) as a confidence signal (R-001) and as a correction in winter.
3. Low-level jet at night: 925 mb wind speed after midnight vs our low's miss.
4. Thickness and air mass: 1000-500 mb thickness (from geopotential heights) as an air-mass signal when the surface models disagree.
5. Perfect-prog route: learn relationships on upper_hist (2023 on), then apply them to the real runs and test on the run period only.
6. Aircraft profiles (Brian's question 2026-10-16). Airliners report temperature and wind (some also humidity) on every climb and descent; Indianapolis (FedEx hub, busy overnight) gives many evening and night profiles. They observe the low-level inversion directly, hours before the night plays out. Access: NOAA MADIS keeps aircraft data since 2001; most is public after 48 hours, real time only for the humidity-sensor aircraft (mainly UPS and Southwest) or with an approved MADIS application (https://amdar.noaa.gov, https://madis-data.cprk.ncep.noaa.gov/wmo_gdc.shtml). Research test with the delayed data: does the evening inversion measured by aircraft at IND explain our lows' miss better than the models' inversion? If clearly yes, that justifies applying for real-time access.

Log

• 2026-10-16: thread opened. Backfill of upper-air history (4 models, 2023 on) and real runs (April 2026 on) started.
• 2026-10-16: perfect-prog inversion signal found (above). Next: pre-register the real-run test.
• 2026-10-17: L-004 pre-registered and run: right direction, too small in spring and summer. Not passed.

The question

The station sits in north-central Indiana farm country. The ground changes through the year: bare fields in spring, corn and soybeans pumping water into the air in July, harvest in October, leaves on and off, snow cover. Wet soil spends the sun's energy evaporating water (cooler highs, higher dew points); dry soil heats the air (hotter highs, bigger day-night swings). Do our forecast misses follow the state of the land?

Physics and literature

• Mueller et al. (2016): cropland intensification across the Midwest cooled the hottest summer days through evapotranspiration; no cooling during droughts, because transpiration needs soil water.
• Soil moisture errors in models shift 2 m temperature forecasts by up to about 2 C (Geophysical Research Letters 2024), with the biggest effects when soils are dry.
• "Corn sweat": peak-season corn raises dew points in July, a known Corn Belt effect on heat index forecasts.

Data

• era5.parquet: soil moisture at 0-7, 7-28 and 28-100 cm; soil temperature; evapotranspiration; vapour pressure deficit. Known with about a 6-day delay, so a live version would use the station's own rain history or a model's soil analysis instead.
• The station's rain history (minutes.parquet) gives antecedent rain for free, live.
• USDA weekly crop progress for Indiana (planting, silking, harvest dates) would date the crop calendar each year.

What has been explored

2026-10-16, soil moisture vs. our day-1 highs (561 discovery days). ERA5 soil moisture anomaly (vs. the same time of year) at 7 AM; the station's rain over the previous 7 and 14 days.

Correlation of our miss (observed minus forecast) with the soil moisture anomaly, sunny days in brackets: spring +0.13 [+0.24], summer +0.15 [+0.14], fall +0.17 [+0.27 at 0-7 cm, +0.37 at 7-28 cm], winter -0.11 [-0.15].

Spring and summer highs by soil moisture (7-28 cm): driest fifth of days -0.89 F (we ran too warm), dry -0.16, normal -0.57, wet -0.18, wettest fifth -0.11.

The sign is the opposite of the textbook. Dry soil should make the real high hotter than a model expects; here, on dry-soil days, the day came in cooler than our forecast. Possible readings: (1) the models already over-react to dry topsoil (their soil schemes dry the surface fast, while deep-rooted corn and soybeans keep transpiring from water a metre down); (2) a confound: the dry spells of 2024 and 2025 fell in particular weather patterns. As a correction it is worthless: leave-one-month-out MAE 1.805 -> 1.805 (soil), 1.817 (station rain, worse).

Script: hyperlocal/research/r004_soil_explore.py, results r004_soil_explore.json.

Ideas to chase

1. Untangle the dry-soil sign: does the models' own forecast high on dry days run hotter than ours (i.e. are the models over-reacting and our correction only part-way)? Compare raw model highs vs. truth by soil anomaly.
2. July-August dew point miss vs. crop stage: do models under-forecast dew points at peak corn?
3. Leaf-off (late October) and harvest: does the low's bias shift in the weeks after?
4. Snow cover: lows are harder with snow on the ground (R-001 found 2.4 F vs 1.9 F, 40 nights); is the miss mostly one sign (models too warm over snow)?
5. Done 2026-10-16: soil anomaly by season and station antecedent rain (above).

Log

• 2026-10-17: the station mast stands a few feet north of a corn field (Brian). The crop cycle (planting, canopy, harvest, corn/soybean rotation) may leave a seasonal mark on the station's own readings; log crop and dates yearly.
• 2026-10-17: siting details (Brian): the Davis station is on the same mast; air sensor about 5 ft (below the top of standing corn), anemometer about 12 ft. The 2026 corn is standing and dried down, harvest date to come. 12 ft is only about 4 ft above the canopy, so winds from the south (over the corn) likely read slower and gustier than north winds until harvest; check wind speed by direction before vs after harvest in the minute data.
• 2026-10-16: thread opened; ERA5 soil and boundary layer history (2023 on) saved.
• 2026-10-16: first exploration: soil moisture lines up with our highs' miss with the unexpected sign; no gain.

The question

Our rain chance is a calibrated blend of the models' rain amounts, the ensemble and the NWS discussion. It uses surface fields only. Rain needs moisture through a deep layer and something to lift it; models are often right about rain but wrong about when, or get showers right in general but not over one gauge. Where do our rain chances go wrong, and which of those causes can we measure?

Physics and literature

• Classic MOS rain chance equations lean on the mean relative humidity of the column, 850 and 700 mb humidity and the model's rain amount; precipitable water, K index and vertical motion are secondary (NWS ER TA 99-1; MDL TPB 487).
• Convective showers are hit or miss at a single point: a 60% chance of storms means scattered cells, not 60% of the area wet everywhere. Stratiform rain from a large system is far more predictable.
• Tipping-bucket gauges undercatch, most in wind and in light rain (splash, evaporation, wetting loss). A 0.01 in tip threshold means drizzle can fall without counting (Dunn et al. 2025 review).

Data

• Real runs (April 2026 on): 850/700/500 mb humidity, vertical velocity, water vapour, CAPE, lifted index, freezing level at true lead times.
• upper_hist (2023 on) for learning.
• Airports (asos.parquet): hourly precipitation and weather codes (RA, DZ, TS) to separate drizzle from rain and to see whether rain fell nearby but missed the gauge.

Ideas to chase

1. Miss anatomy: sort discovery-year misses (wet when we said under 30%, dry when we said over 70%) by storm type (CAPE, lifted index), season and timing.
2. Deep moisture: mean 850-500 mb humidity from the models as an extra input (perfect-prog first, then on runs).
3. Timing: for missed rain, did it fall within 3 hours of a period boundary? If so the miss is timing, not occurrence, and a probability spread across the boundary might help.
4. Gauge check: days when two or more neighbouring airports had measurable rain and our gauge had none (or 0.01 in). How often, and in what wind? An honest estimate of what the gauge misses.
5. Showers vs. steady rain: separate calibration for convective and stratiform regimes.

Log

• 2026-10-16: thread opened.

The question

Weather moves. Most of ours arrives from the west and southwest. Eight airports within about 70 miles report every hour: temperature, dew point, wind, gusts, pressure, cloud layers with their heights, visibility and present weather. They measure what is happening while the models can only predict it, and weather from the west reaches them hours before it reaches us. Can they correct our nearest forecasts (tonight, tomorrow) and the live track?

Stations

Marion (MZZ, closest, about 15 miles), Kokomo (OKK, west), Grissom AFB (GUS, northwest), Logansport (GGP, northwest), Muncie (MIE, southeast), Anderson (AID, south), Huntington (HHG, northeast), Fort Wayne (FWA, northeast), Rochester (RCR, north), Warsaw (ASW, north). Outer ring (50 to 105 miles): TYQ, LAF, IND, SBN, AOH, DNV, DAY, GYY.

Ideas to chase

1. Upstream cloud for bust nights (with R-002): at 8 PM, cloud cover and ceiling at the stations upwind (chosen by the 850 mb wind direction) vs. what the models have for our sky after midnight.
2. Neighbour consensus: the average of their evening temperature anomalies vs. ours: is our station running unusually cold or warm, a siting signal?
3. Siting: compare our station with Marion on clear calm nights. A consistent difference measures our cold-air pooling, and its size may depend on wind and season.
4. Live track input: neighbour temperatures as an extra signal in Today's Track.
5. Rain nearby (with R-005): did rain fall at neighbours when our gauge recorded none?
6. Aviation forecasts (TAFs, Brian's idea 2026-10-16). Forecasters write hour-by-hour cloud, visibility, wind and rain timing for Fort Wayne (KFWA), Indianapolis (KIND) and Lafayette (KLAF), 24-30 hours out. The Iowa Environmental Mesonet archives them, already parsed (cloud layers and heights, weather, timing). Questions: do TAF cloud groups for tonight separate the clear-calm bust nights better than the models' cloud? Does TAF rain timing sharpen our 12-hour rain chances near period boundaries? Note: the forecast discussion we already read includes an aviation section that summarises the same TAFs, so the new information is the exact timing and structure. Pilot reports (PIREPs) and AIRMETs: too sparse or too broad for a point forecast.

What has been explored

2026-10-16, upstream clouds and bust nights (560 discovery nights, day-1 lows). Cloud cover from airport reports (the highest of the two lowest layers) at 8-9 PM, and through the night at all eight airports, compared with the models' night cloud.

Cloud surprises lean the expected way (clearer than forecast = colder) but weakly (correlation 0.13). The west airports' evening cloud does not predict the miss (correlation 0.06; leave-one-month-out MAE 1.620 vs 1.612 for our low, no help). Caveat: airport ceilometers only see clouds below 12,000 ft, so high cloud is invisible here. Conclusion: upstream low cloud is not the bust mechanism at our station; decoupling is (R-002).

Script: hyperlocal/research/r006_upstream_clouds.py, results r006_upstream_clouds.json.

2026-10-18, the outer ring (Brian: should the ring cover 12 to 24 hours of advection?). Eight more airports about 50 to 105 miles out: Indianapolis Executive TYQ (49 mi, SSW), Lafayette LAF (60, WSW), Indianapolis IND (71, SSW), South Bend SBN (76, NNW), Lima AOH (94, E), Danville DNV (98, WSW), Dayton DAY (99, ESE), Gary GYY (106, NW). History from 2023 (backfill_outer.py). Same question as the inner ring: does the outer upwind airports' model error now predict the station's error hours later, beyond its own error and the inner upwind airports? Downwind is the control.

On windy hours (12 mph or more) the outer upwind side does beat the downwind side by 0.004 to 0.009 F at 4 to 9 hours, so the direction effect is genuine, but it is a hundredth of a degree. Conclusion: no. Beyond about 6 hours the forecast runs themselves have already taken in the air mass that is upwind (they are rerun every 6 to 12 hours from the same observations), and what is left of the station's error is local (siting, the ground, the corn) rather than carried in. The useful upwind reach is the inner ring and the first few hours (L-013). Not pre-registered: nothing worth testing. Scripts: physmodel/outer.py, outer_error_hours.parquet.

Data

asos.parquet, 2023 on (Iowa Environmental Mesonet). Live use would need the collector to fetch the same hourly reports (public, free).

2026-10-18, the ring goes live (ring_now.py). Brian pointed out the station sits inside a ring of automated airports (Marion 14.6 mi SSE, Kokomo 15.5 SW, Grissom 17.7 W, Huntington 23.2 ENE, Logansport 28.6 W, Rochester 33.1 NW, Muncie 37.6 SE, Fort Wayne 38.7 ENE, Anderson 40.3 SSE, Warsaw 41.7 N; Wabash has no automated reports). The Tower now reads their latest reports every 15 minutes (aviationweather.gov, table ring_obs) and pushes an "around us" summary to the Briefing: sky and low cloud bases, rain or snow and where, fog, wind, the station against the ring, and the air arriving from upwind (the upwind airports' temperature and dew point against ours, and the travel time at the current wind). Rochester and Warsaw were backfilled into the archive (asos.parquet) to close the ring north. New idea (Brian, after WxSim's upwind METAR advection): OBSERVED ADVECTION OF MODEL ERROR. If the upwind airports are colder or drier right now than the models think, that error should drift here within an hour or two, before the next model run corrects it. Test: for each hour, the upwind ring stations' (observed minus model) temperature and dew point, carried by the wind, against the station's next 1 to 3 hours' (observed minus model). Needs the models' values at each ring airport (the Open-Meteo previous-runs archive by point) for discovery, then fresh months live.

2026-10-18, observed advection of model error: first look (physmodel/advect_error.py, fetch_ring_models.py). Model values (GFS + ICON stitched first hours, the freshest run's view of each hour) fetched at all ten ring airports (data/research/ring_models.parquet). Each hour: every airport's error (observed minus models) and the station's; the ring wind (1/distance vector mean); upwind = airports within 50 degrees of where the wind comes from, downwind = within 50 degrees of where it goes (the control). Discovery hours (March 2024 to September 2025), wind 5 mph or more, about 8,200 hours, cross-validated by 6 blocks, uncertainty by resampling whole days. Temperature error 1/2/3 h ahead, mean absolute error, own error only -> + upwind: 0.667 -> 0.656, 0.852 -> 0.836, 0.945 -> 0.934 F; gains 0.011 (95% 0.005 to 0.017), 0.016 (0.009 to 0.024), 0.011 (0.005 to 0.016) F; downwind gain 0.000 every time; the whole ring in between. The gain grows with wind (2 h: 0.016 at 5-10 mph, 0.018 at 10-15, 0.024 at 15+). That is the signature of air actually arriving: real, but small (1.5 to 2.5%). Dew point: no upwind edge (moisture errors look regional). Caveat: the "model" here is the freshest run's first hours, so the models have little error left to advect; the same idea against our issued forecast (hours old, less fresh) could matter more, and is the natural next step (a 1 to 6 hour nowcast of the station's temperature).

2026-10-18, L-013 PASSED: a 1 to 6 hour nowcast. From a real forecast (ECMWF + ICON, the newest run out at least 6 hours before), nudged by the station's own error and the upwind airports' error: test hours July 16 to October 11, 2026, averaged over 1 to 6 h: forecast 2.13 F, + own 1.76 F, + upwind 1.68 F (gain 0.083 F, 95% 0.056 to 0.111), + downwind instead 1.73 F. Against hours-old forecasts the upwind effect is about ten times what it is against the freshest runs. See the lab notebook, L-013.

Log

• 2026-10-16: thread opened; eight airports' hourly history (2023 on) saved.
• 2026-10-16: upstream clouds explored: weak. Siting and neighbour-consensus ideas still open.
• 2026-10-18: the ring nowcast went live; idea added: observed advection of model error from upwind airports.
• 2026-10-18: observed advection of model error: real (upwind beats downwind, grows with wind) but small (about 0.01-0.02 F).
• 2026-10-18: L-013 passed: the upwind airports improve a 1 to 6 hour nowcast by 0.08 F on top of the station's own error.
• 2026-10-18: outer ring (8 airports 50 to 105 miles out) explored: adds about 0.00 to 0.01 F at 4 to 12 hours; not worth carrying.

The question

Our frost (36 F or lower) and freeze (32 F or lower) chances come from a bell curve centred on our forecast low, as wide as the +/- range. That assumes our misses are the same size and shape on cold nights as on any night. Near freezing, physics says they may not be: dew and frost forming release heat and slow the last few degrees of cooling; wet ground gives up heat; clear calm nights can plunge.

What has been explored

2026-10-16, discovery nights (Mar 2024 to Sep 2025), forecasts 1-3 days ahead (1,695 forecasts).

Frost (36 F or lower), 408 occurred. Brier score 0.028 vs 0.183 for climatology (much better).

Freeze (32 F or lower), 309 occurred. Brier 0.018 vs 0.149.

Nights forecast within 6 F of freezing ended on average 0.55 F warmer than forecast (frost: 0.42 F warmer).

What it means so far: broadly reliable. Two hints, both from small samples: low freeze chances (10-30%) came true less often than we said, and high ones (70-90%) for both frost and freeze also fell short. Both fit a slight warm tendency near the threshold, as the condensation-floor physics predicts. A lead, not a finding: the bins hold only 24 to 59 nights each.

Script: hyperlocal/research/r007_frost_check.py, results r007_frost_check.json.

Ideas to chase

1. Is the near-freezing warm tendency tied to the dew point (nights where the dew point is near or above the forecast low)? That would be a physical, testable correction.
2. Ground heat: soil temperature (ERA5) on early-season frost nights.
3. A skewed curve instead of a bell curve for cold nights.
4. Test on this autumn and winter (fresh months) before anything changes: there will be many frost nights.
5. Our own ground sensor: BUILT 2026-10-17. See "Ground sensor data" below. It measures real grass frost, so frost (grass at or below 32 F) can now be scored directly, not just freeze (station air). Once a season of nights is in: forecast the grass low as our air low minus a predicted grass-below-air gap (from clouds, wind, dew point). Exploration only until then; the first fair test is autumn 2027.

Ground sensor data (from 2026-10-17)

• Table ground_obs in /hl/hyperlocal.db: t_utc, seq, probe, position, temp_f, vbat, rssi, snr; one row per probe every 5 minutes. Positions: grass (2 in above short grass), soil_2in, soil_4in, soil_8in, all at the station mast, beside the station's own air sensor (about 5 ft up). Rows with position test are readings taken while testing the probes (hand-warmed) and must never be used.
• Site: a corn field begins a few feet SOUTH of the mast base (Brian, 2026-10-17). In October the sun is low (about 40 degrees at noon), so standing corn about 8 ft tall would shade a spot within roughly 10 ft north of it for much of the day: the likely reason the grass read 8-10 F below the air an hour before sunset. The corn also blocks part of the southern sky and breaks the wind at grass height. HARVEST IS A BREAK IN THE SERIES (full sun, more open sky, more wind): log its date, and the crop each year (corn and soybeans usually alternate).
• Daily summaries and records are pushed to the site as fc/hl/ground.json (hyperlocal/ground.py).
• Probe history, which changes the readings: installed 2026-10-17 about 5 PM EDT with a bare stainless grass probe (low emissivity, so it likely reads a little WARM on clear nights). Brian painted it white on 2026-10-18 at 2:03 PM EDT (18:03 UTC). Expect clear-night grass readings a degree or two colder after that, and sunny-afternoon readings lower too (the bare probe soaked up sun). Treat 18:03 UTC as a break in the series: grass records and the grass-minus-air fits use before and after separately. The 14:02 and 14:07 EDT readings (69 F, against 57 F just before) are the probe being handled, not weather. At the same visit Brian found the probe had FALLEN and was lying on the ground; it is now held in the printed cradle mount 2 in above the grass. When it fell is unknown: the 5-minute readings show no clear step (the only candidate is 1:16 AM EDT on 2026-10-18, +1.3 F), and the first evening (grass 12 F under the air, then held at the dew point) looks like a probe in the grass. Today's daytime readings (5 to 8 F BELOW the sunny air) fit Brian's report: the old spot was in the mast's shade (and on the ground). The new spot is in the sun, so daytime grass readings will now run ABOVE the air (sun on the probe and the grass top, normal for a grass-minimum probe; only its NIGHT readings are the grass temperature that matters for frost). So: the bare-probe stretch (2026-10-17 5 PM to 2026-10-18 2 PM EDT) is "first mount, position uncertain"; the clean series starts with the mount, 18:03 UTC. Battery/solar move into a weatherproof box around the same time.
• First evening (2026-10-17, clear and calm): the grass ran 12 F below the station's air by 7:10 PM, then its fall stalled at 35.5 F, within 0.1 F of the station's dew point (35.4 F): the dew-formation brake, seen directly. Ground truth: Brian touched the grass about 7:45 PM and found dew had formed (grass 34.6 F, station dew point 35.4 F). The stall marks dew onset.

Log

• 2026-10-18: grass probe painted white at 2:03 PM EDT and put in the printed mount 2 in above the grass (it had fallen to the ground at some point); series break.
• 2026-10-17: the detailed-run page (fc/detail.php) shows a night grass ESTIMATE from a starting rule: grass = air - 12 F x clear x exp(-wind/8 mph), held no lower than 1.5 F under the dew point (insight.py GRASS_*). Fit these on the ground sensor's nights; the rule is labelled experimental on the page.
• 2026-10-17: our ground sensor went in (grass + soil 2/4/8 in at the mast); data notes above. First evening: grass 12 F below the air, stalled at the dew point.
• 2026-10-17: Freeze Warning night, case study C-001 (outcome pending at 2 AM; station cooling pace pointed to a low above our 31 F forecast). Score it next session as the first fresh-month night.
• 2026-10-16: thread opened by Claude with Brian. Reliability check above.

The question

Our tested forecast works in 12-hour periods (the day's high, the night's low, rain chance per period), refreshed every hour. Everything finer on the pages is the raw model blend: the 3-hour strip's sky, wind and rain come straight from the models, and its temperatures follow a curve shaped to our high and low. Can we make our own hour-by-hour forecast for the first two days, corrected to the station the same way the highs and lows are? Brian asked for this on October 16, 2026.

Why it might work

• The models have station-specific habits by hour, not only by day: how fast the evening cools, when the afternoon peaks, when the wind drops after sunset, how the dew point behaves at dawn. A daily correction can't fix timing.
• The station's 1-minute history (2023 on) gives about 30,000 hourly truths to learn from.
• The NWS does this operationally: its hourly guidance (LAMP, the Localized Aviation MOS Program, and the hourly National Blend) corrects models hour by hour and, for the first hours, leans on the latest observations. That last part is what our live track already does for the high and low.

Data

• archive_<model>.parquet (/trainer/data): hourly model temperature, dew point, rain, cloud and wind at lead days 1-7, 2023 on. This is the training data: true lead times.
• runs_<model>.parquet (/lab/data): real runs at exact lead hours, April 2026 on, for the honest recent test.
• minutes.parquet: the station, every minute. Truth for every hour.

Plan (draft, to be pre-registered before testing)

1. Targets: hourly temperature, dew point and wind speed for lead hours 1-48.
2. Method: for each lead hour, correct the model blend with a ridge regression on the models' own values, the hour of day, season, and our daily high/low forecast (so the hourly path agrees with the daily numbers). For the first few hours, blend in the latest station reading (persistence), as LAMP does.
3. Compare with what the page shows today (the curve plus the raw blend) on identical hours, walk-forward.
4. Measures: hourly error by lead hour; the timing of the day's peak and the evening drop; wind speed error; how often the hourly path stays inside its range.
5. Draft bar: at least 10% lower hourly temperature error than today's strip over lead hours 1-24, with the daily high and low no worse.

What has been explored

2026-10-16, discovery issues (March 2024 to September 2025, 415 issues scored, walk-forward). Mean hourly temperature error (F):

Hour 1: 2.17 (blend), 1.82 (strip), 0.86 (correction): knowing how far off the blend is at 6 PM matters most early. Side finding: the strip is slightly worse than the raw blend away from the peak and the low, because it shifts every hour of a window by the high/low correction, which mostly belongs near the extreme. Formal test pre-registered as L-003 before any test-year run. Script: hyperlocal/research/r008_hourly.py.

2026-10-16, L-003 on the test year (358 issues). Hours 1-24: strip 1.946 F, correction 1.760 F, 9.57% better (bar 10%: fail). Hours 25-48: 2.326 vs 2.196 (pass). Better in 13 of 13 months (pass). 90% interval of the gain 7.7% to 11.4%. Hours 1-6 21% better, hours 7-24 5% better. Not passed; see the notebook entry for next steps.

Ideas to chase

1. The evening drop: does a station-corrected cooling curve catch calm-night decoupling earlier (with R-002/R-003)?
2. Hourly rain timing from the ensemble, calibrated to the gauge (with R-005).
3. If it works, the briefing's 24-hour strip and Today's Track would both use it.
4. When the high and low happen (added 2026-10-17, Brian). Our daily forecasts give how warm or cold, never when; the hourly curves borrow the models' timing unchecked. Score the models' time of the day's high and low against the station (1-minute history), by season and cloud, and on the odd days: (a) a strong cold front late morning or early afternoon (the high comes early, then the temperature falls like a rock); (b) the calendar-day high at 4 AM ahead of a front or with warm air overnight; (c) warm-air nights where the low comes in the evening and the temperature rises toward dawn. Fronts: WPC passages (data/fronts/passages.csv) and the station's own frontal signature (fronts.py / frontlib.py). Questions: are the models early or late on the front's arrival here, by how many hours, and does a timing fix make the hourly track (and the calendar-day high vs the 8 AM-7 PM high) more accurate? First look below (station climatology only, no forecasts scored).

2026-10-17, timing first look (station only, Feb 2023 to Oct 2026, 1,271 days). Script: hyperlocal/research/r008_timing_count.py, results r008_timing_count.json. Calendar-day high: 1-5 PM on 54% of days, 5-8 PM 28%, but midnight-6 AM on 10% (6-11 AM only 1.5%). Off-hours highs (before 11 AM or after 8 PM) run 35-38% of days in December and January, 22% in November and February, under 4% June to September. On the 128 days whose high came before 9 AM, the calendar high averaged 4.8 F above the 8 AM-7 PM high. Lows: 4-9 AM on 59% of days, but 9 PM-midnight on 22% (the low at the end of the day: cold air arriving), 46% of December days away from dawn. Daytime cold fronts (WPC passage 9 AM-6 PM, 60 days): the high came before 1 PM on 25%; 3-hour drop median only 2.3 F (many fronts are weak or mistimed by the 3-hourly analyses), but 7 dropped 10 F or more, up to 21.7 F (2026-03-11). Takeaway: off-hours timing is a cold-season matter, and big front drops are rare but real; the scoring of model timing is the next step.

Log

• 2026-10-16: thread opened at Brian's request.
• 2026-10-16: discovery exploration (above); L-003 pre-registered and published.
• 2026-10-16: L-003 result: 9.57% vs a 10% bar. Not passed.
• 2026-10-17: idea 4 (timing of the high and low, front days) added at Brian's request; station-only first look.

The question

Brian runs WxSim, a local physics model of his site. How accurate have its forecasts been against the station, and could it add something our forecast lacks?

Data

• WxSim forecasts: 5,651 runs, January 2018 to March 2023 (gaps mid-2019 to mid-2020, late 2020, April 2021 to January 2022), six runs a day, about 7 days ahead in half-hour steps. WorkingDrive "4. Archives/WxSim/wxsim/f*.txt".
• Station truth for those years: Weather Display's NOAA monthly reports, February 2018 to June 2020 (calendar-day high and low). WorkingDrive "4. Archives/Meteorology". No station record for 2021-2022 has turned up.
• Recent WxSim: the forecast-vs-actual database on Hostinger, hourly since June 4, 2026 (needs an export).

What has been explored

2026-10-17, WxSim graded against the station, 424 days (February 2018 to June 2020). The 6 PM run each day (10 PM if missing); calendar-day highs and lows. Benchmarks on the same days: persistence (tomorrow like today) and the NWS GFS guidance for Marion (MOS, the run out around 6 PM). Mean error (F), with average bias (+ = too warm):

• WxSim beat persistence easily but trailed the NWS guidance by about 1 F on day-1 highs and lows, less for lows at days 2-3.
• Within 3 F of the actual: about 47% of day-1 highs and lows. Misses of 8 F or more: 61 day-1 highs (14%) and 46 day-1 lows (11%).
• Season pattern, day 1: in summer WxSim ran 3.2 F too cool by day and 3.8 F too warm at night: its day-night swing was about 7 F too small. In winter its lows ran 1.5 F too cold. A damped summer swing is what a model does when it treats the ground as too moist or the air too humid or cloudy, so it is worth checking WxSim's soil moisture and ground settings for summer.
• Day-1 lows (4.00) were worse than day-2 lows (3.58), which is unusual. Unexplained; one possibility is how the 6 PM run handles its first night.
• Caveats: 2018-2020 settings and equipment may differ from today; WxSim keeps standard time year round while Weather Display uses the PC clock (one hour at midnight in summer); MOS's day and night windows differ slightly from calendar days. For rough context only (different years and day definitions): our forecast today misses by about 1.6 F (lows) and 1.8 F (highs) on day 1.

Script: hyperlocal/research/wxsim_grade.py, results wxsim_grade.json.

2026-10-17, WxSim's saved forecasts decoded. The current WxSim (Weather PC) keeps every run as f<YYMMDDHH>.wxf: 4,010 readable runs, June 2023 to now. Format: each half-hour step starts with a line "Mon DD",hours (standard time), then the air temperature in Kelvin, converted as (K - 273.0) x 1.8 + 32 (matches latest.csv exactly). Script: hyperlocal/research/wxsim_extract.py.

2026-10-17, WxSim as an input, discovery years (March 2024 to September 2025). WxSim value = newest run finished by our issue time, max/min over our windows. Mean error (F):

WxSim improved since 2018-2020 (highs 4.5 -> 3.2, lows 4.0 -> 3.4) and its errors only partly overlap ours. Leave- one-month-out, WxSim's information alone moved our error by: highs day 1 +0.009 (worse), day 2 -0.021; lows day 1 -0.009, day 2 -0.012, day 3 -0.007. A fixed bias term looked helpful for day 2-3 highs but that was our own forecast's bias on those days, not WxSim's information, so it was left out. The test year had WxSim on only about 70 days (it was down most of the year), too few to detect 0.01-0.02 F, so the formal test (L-006) uses fresh months. Script: hyperlocal/research/wxsim_input_test.py.

Ideas to chase

1. WxSim as an input: L-006 (fresh months). WxSim's own WXSIM-Lite blend has not been fed since August 21, 2026 (fdata.txt stale; its loop is being fixed); if Brian turns the blend on, WxSim changes, so log the date.
2. Grade the live WxSim (June 2026 on) the same way, to see whether today's setup does better than 2018-2020.
3. If Brian adjusts WxSim's summer ground settings, the live grading would show the effect.

Log

• 2026-10-17: thread opened at Brian's request; 2018-2020 grading above.
• 2026-10-17: saved forecasts decoded; discovery exploration; L-006 pre-registered on fresh months.

The question

Our forecast is statistical: it corrects the global models with years of the station's record. Its biggest misses are nights that go calm and cool on their own (R-002), linked to the inversion near the ground (R-003), which the global models are known to mix too much (Sandu et al. 2013). That is a physical process with well-understood ingredients. Can a small physics model of the night at this one spot, calibrated on the station's own minute data, predict those nights better than statistics alone, and feed our forecast as one more input? Brian asked for this on October 17, 2026: build our own physics of the station's air and ground, piece by piece, each piece earning its place against the station.

Physics (version 1: sunset to sunrise)

• The ground radiates to the sky. Outgoing longwave from the surface minus what the sky sends back. Clear-sky sky emission from air temperature and humidity (Brunt, Swinbank, Brutsaert-type formulas); cloud adds back radiation depending on cloud height and cover.
• Wind mixes warmer air down. Turbulent heat exchange between the ground and the air, scaled by wind speed and cut off as the air near the ground becomes stable. Below a minimum wind the layer decouples and the ground cools on its own (van de Wiel et al. 2012, minimum wind speed for sustainable turbulence).
• The ground gives back stored heat. A simple two-layer soil (the force-restore method, Deardorff 1978) started from the soil temperature: warm wet autumn soil slows cooling.
• Moisture puts a floor under the temperature. Dew and frost forming release heat near the dew point (the condensation floor, NWS TM 102).
• The air aloft caps it. The 925 mb temperature and wind bound how far mixing can reach.

Version 1 design

• Start: the station's own 6 PM readings (temperature, dew point, wind). Soil: ERA5 for history; the models' soil temperature live.
• Drive each hour overnight with the forecast at issue time: cloud (low, mid, high), wind at 10 m and 925 mb, humidity, 925 mb temperature (from the real runs since April 2026; near-analysis upper air for learning).
• A handful of free parameters (exchange strength, soil heat capacity, cloud back-radiation, decoupling wind), calibrated on discovery nights (March 2024 to September 2025) against the station's minute-by-minute cooling curves.
• Output: a physics low and a physics cooling curve for tonight. Use: (1) "physics low minus our low" as an input to the statistical model, (2) a decoupling indicator for the +/- range (R-001), (3) the evening path for the hourly forecast (R-008).
• Test like every other input: pre-registered plan and bar, the test year, then a winter confirmation on fresh months.

What has been explored

2026-10-18, version 1 built (hyperlocal/physmodel/: scm.py the model, nights.py inputs, calibrate.py, crossval.py). Brian asked to start building our own physics, to run alongside the statistical model, never replacing it. Version 1 is the night model designed above: a grass/soil skin radiating to the sky (Brutsaert clear sky, cloud by layer), turbulent exchange with the 2 m air (cut back by stability), dew and frost latent heat, force-restore soil, mixing toward the 925 mb air brought down dry-adiabatically, plus (added after the first fit) a share BETA of the air-mass change aloft (advection). 13 constants, fitted by optimisation on 474 dry DISCOVERY nights (March 2024 to September 2025), sunset to sunrise in 5-minute steps, against the station's minute record. Inputs: the models' stitched first hours (upper_hist, 4-model mean) and ERA5 soil: perfect prog, so learning only.

Results (low of the night, F, mean absolute error and bias):

• In-sample fit: physics 2.01 (-0.01); clear and calm nights 1.94 (-0.18). The models' own near-analysis 2 m temperature: 1.46 (+0.76); clear and calm 1.93 (+1.61). Adding BETA (advection) cut the curve error from 2.44 to 2.18 F and removed biases of about +/-1.2 F on nights when the air aloft cooled or warmed.
• Cross-validated (6 blocks of months, constants refit without each block): physics alone 2.13; a blend with the models 1.40 vs 1.46; physics only on clear, calm nights 1.33 overall, 1.56 vs 1.93 on clear, calm nights.
• BUT the fair rival is a plain statistical correction of the models on clear, calm nights (what our forecast already does with its clear-and-calm feature): 1.51. Adding physics to that rival made it slightly WORSE (-0.09 F, 95% interval -0.13 to -0.06). So version 1's apparent gain is only the calm-night warm bias, which statistics already removes. Version 1 adds no new information. Results: physmodel/crossval_v1.json, constants physmodel/params_v1.json.

Reading the fitted constants: the optimiser chose a deep cooling layer (Z_EFF about 160 m), strong coupling and almost no stability cut-off, i.e. it gave up on the shallow, decoupled 2 m layer and modelled the night as one bulk layer. That is the physics that matters most on our worst nights (R-002), so version 1 cannot capture it by construction. And the comparison is harsh: the baseline here is the models' near-analysis, which has already seen the night.

2026-10-18, version 2 (two air layers: a shallow 2 m layer that can decouple under a bulk night layer; physmodel/scm2.py, calibrate2.py). Fitted freely it reached 1.98 F in-sample (version 1: 2.01) by abandoning the shallow layer again (a 180 m "surface layer", a wind floor that erases calm). Held to physical ranges it was worse (2.15 F) and pushed against every limit. Driven by the STATION's measured wind instead of the models' it improved only to 1.92 F and still would not decouple. So the second layer is not where version 1 loses; shelved for now.

2026-10-18, the fair comparison and L-007. Against OUR day-ahead low (walk-forward, discovery nights), adding the cross-validated version 1 physics low (started at sunset) cut the error 1.63 to 1.56 F, while the same sunset readings used statistically added nothing (1.66). Pre-registered as L-007 and run on real forecast runs (ECMWF and ICON 12Z, April to October 2026, 149 dry nights): ours 1.52 F, ours + 0.25 x (physics - ours) 1.38 F, control with the fresh runs' own 2 m low 1.54 F, gain positive in 99.9% of resamples: PASS. See the lab notebook, L-007.

2026-10-18, the DAY model (physmodel/days.py, dayscm.py, calibrate_day.py; live in physlive.py). Brian asked for daytime physics with the same rules (published, runs on the Tower). Sunrise to sunset: clear-sky sunshine from the sun's height dimmed by low, mid and high cloud; net longwave; a share into the soil; an evaporative fraction from topsoil moisture and the growing season; the rest heats the air, which first burns off the night's inversion and then builds a mixed layer by encroachment (Carson 1973) through the morning's 925/850/700 mb profile; the 2 m air follows the mixed layer plus a hot layer at the ground on sunny, calm days; advection from the 850 mb change. Fitted on 455 dry DISCOVERY days. With all constants free the fit went unphysical (a negative albedo), so albedo 0.20, soil share 0.15 and entrainment 0.2 are fixed and the rest bounded. Version 1.1 added snow albedo (from 5 cm of snow), an uneven growing season (crops dry down in the fall), a calm-day hot layer and a morning inversion depth; that took the in-sample error on the high from 2.22 to 2.03 F (sunny days 1.75 F, better than the models' near-analysis 1.87 F; cloudy days 2.43 F are its weak spot). Fitted constants: low cloud blocks 79% of the sun, mid 62%, high 19%; evaporative fraction 0.49 + 0.52 x (moisture - 0.30) + 0.36 x (season - 0.5); inversion 334 m. Cross-validated against OUR walk-forward high: ours 1.68 F, ours + sunrise readings (statistical) 1.67, + day physics 1.63: gain 0.05 F, 95% interval -0.02 to 0.11. Not proven, so no real-run test yet and nothing blended; the day run is shown on the Physics page for interest. Driving it with the models' own sunshine instead of our cloud dimming was worse (stuck at a +2.8 F bias). Its highs come about 40 minutes late on average (mixing keeps going until the sun is low).

2026-10-18, the station's solar sensor (physmodel/solar.py, cloud_skill.py). The Davis pyranometer, logged by Weather Display every minute since January 2023, as 5-minute sunshine (370,076 values to October 14, 2026; data/research/solar5.parquet). Calibration is steady: the clearest middays read 0.98 to 1.10 of the clear-sky formula in summer and about 1.10 to 1.15 in winter (the formula runs low at low sun), with no drift year to year. (Earlier uses: tested as a statistical temperature input on October 14, no gain; shipped as front evidence, the sky clearing behind fronts, on October 15.)

• Cloud forecasts against the sensor, next-day daytime clearness (8 AM to 5 PM), 12Z runs, April 3 to October 13, 2026, 183 days (error on a 0 to 1 scale / correlation): ECMWF + ICON own radiation averaged 0.097 / 0.86; ICON 0.101 / 0.84; ECMWF 0.116 / 0.82; our cloud-layer dimming of their clouds 0.136 / 0.81 (ICON's clouds through our dimming: -0.19 bias); yesterday's clearness 0.226 / 0.18; the month's average 0.198 / 0.05. The models' radiation schemes know cloud thickness; three cloud layers do not. For sunshine, use the models' radiation.
• CORRECTION (same evening): the day fits with a switch on (measured sunshine, the models' sunshine) were wrong. A bug in calibrate_day.py scored every candidate with the DEFAULT model during fitting, so those constants were tuned for cloud dimming and only the report used the switch. The night model (no switches; L-007) and the cloud-forecast scores above (no fitting) are unaffected. Rerun correctly (cross-validated high, gain over ours + sunrise info): measured sunshine 2.24 F, gain 0.05 F (95% -0.04 to 0.13); the models' sunshine 2.42 F, gain 0.00. So with the old advection, even perfect sunshine barely helped: the bottleneck was advection after all.

2026-10-18, day ADVECTION version 2. The models' 925 mb, 850 mb and 2 m temperatures change through the day for two reasons: air moving in (advection, what we want) and the models' own daily heating (which our physics does itself). Version 2 removes the typical daily cycle for the month and UTC hour (from the stitched archive, discovery years only) and adds the remaining "weather" change since sunrise, weighted per level (fitted: 925 mb 0.71, 850 mb 0.02, 2 m 0.16; the low-level flow about 2,500 ft up carries it). Cross-validated high, discovery days:

Our walk-forward high is 1.68 F on the same days. Rule on discovery (v2, cloud dimming): high = ours + 0.408 + 0.498 x (physics - ours). Perfect clouds would add more on top: cloud forecasting pays once advection is right. Pre-registered as L-009 (real 00Z runs, April to October 2026). Files: params_day_v1_adv2.json, rerun_*.txt.

2026-10-18, L-009 FAILED (day physics on real 00Z runs, 135 dry days). Ours 1.46 F, rule 1.55 F, fresh-run control 1.35 F, physics alone 2.08 F (bias -1.23). Diagnostic on the same days and runs with the MEASURED sunshine: physics alone 1.48 F, level with ours. The physics and advection hold up; the cloud-to-sunshine step failed (right on average, 96% of measured, wrong on individual cloudy days). Next: the models' own radiation as the sunshine input, explored on discovery, tested on fresh days only.

2026-10-18, the day model's sunshine rebuilt on the models' radiation (step 1 after L-009), discovery only. Sensor calibration on discovery daytimes: measured / models' sunshine 0.907. (a) The physics fitted on MEASURED sunshine, then driven by 0.907 x the models' sunshine, cross-validated (blocks refit): physics alone 1.63 F (ours 1.68), gain over ours + sunrise information 0.10 F (95% -0.01 to 0.21); rule ours + 0.30 + 0.54 x (physics - ours). (b) Everything refit with the models' sunshine: 1.73 F, gain 0.08 F (-0.02 to 0.19), and the fit ran to its limits (it wanted 1.3 x the models' sunshine while the sensor says 0.9 x: constants trading off, not physics). (a) is anchored to the sensor at both ends and is the version frozen for L-010 (fresh days from October 19) and put live. L-008 (the night model live, against our issued low at sunset) was pre-registered at the same time.

2026-10-18, PRECIPITATION TYPE (physmodel/ptype.py, ptype_eval.py, fetch_levels.py, l011.py). The energy method (Bourgouin 2000) on the profile above the station, verified against what 8 nearby airports reported (Marion, Kokomo, Grissom, Muncie, Huntington, Fort Wayne, Logansport, Anderson; freezing rain only where sensors can report it: Fort Wayne, Grissom, Muncie). With only 925/850/700 mb: rain vs snow worse than a 33 F surface rule (marginal hours 70.8% vs 80.9% at Marion); freezing rain caught 32% with 83% of ice calls right. Added 10 levels (1000 to 600 mb, GFS and ICON stitched first hours, cold seasons 2023 to 2026, data/research/levels_*.parquet): freezing rain caught 41% (temperature) to 50% (wet-bulb profile), calls about 80% right; rain vs snow still worse than the rules (too snowy just above freezing: rain hours it called snow had the station at 35.5 F). Hybrid (physics decides ice, station wet-bulb 32.5 F decides rain or snow) on the two exploration winters: 95.6% vs 95.1%. L-011 on the unseen winter 2025-26: hybrid = wet-bulb rule (97.0%, marginal 76.2%), both beating the 33 F rule (marginal 71.3%); ice: 18 reported hours only, 1 caught, 16 of 18 ice calls during snow. Lesson: the wet-bulb rule is the solid gain; our ice detection is not shown to work.

2026-10-18, FOG, first look (discovery nights; physmodel/fog_nights.csv). Truth: visibility under 1 mile at Marion or Huntington during the night (about 1 night in 15 at any one airport; the two agree on only 25 of their 82 fog nights: fog is very local), and the station's own saturation: its Davis humidity sensor tops out at 98% (night maxima, 99th percentile), so 97% or more for 3 hours stands for "saturated". Station 97%+ for 5 hours or more came with regional fog on 50% of nights (base rate 9%). Ranking skill (AUC) on 474 dry discovery nights, regional fog / station saturation: night model's closest air-to-dew-point gap 0.63 / 0.67; its saturated calm hours 0.60 / 0.72; crossover (physics low minus the sunset dew point) 0.66 / 0.70; the models' near-analysis 2 m humidity through the night 0.90 / 0.90 (flattered, but the gap is far beyond that). The night model was fitted for temperature; its moisture (dew deposition plus the models' drift) is too crude for fog, which needs temperature and dew point right to within a degree at the same hour. A fog-capable night model needs its own moisture budget (deposition, evaporation from wet ground, mixing of drier air from above), calibrated on the station's humidity and the grass.

2026-10-18, night version 3: a MOISTURE BUDGET (physmodel/scm3.py, calibrate3.py; live physlive.integrate_v3). Version 1's temperature physics (frozen) plus water: dew deposits on the grass (drying the air), dew and then a share of the soil evaporate, fog forms beyond saturation (latent heat; fog sends longwave back like low cloud). Version 3a mixed the 2 m air's moisture toward the 925 mb air, which is about 9 F drier in dew point at sunset: the dew point collapsed (7.1 F error) and the lows went too cold (4.5 F). Version 3b keeps the models' own 2 m moisture and lets our physics add only the LOCAL departure (dew, evaporation) that fades back over TAU_Q hours. Fitted (discovery dry nights, temperature constants frozen): moisture layer 86 m, TAU_Q 3.1 h, soil evaporation almost nil at night, fog longwave 0.49 of low cloud. Cross-validated: dew point curve error 1.67 F (version 1 1.77), low 2.03 F (2.01); fog ranking skill, regional / station saturation: version 1 0.63 / 0.67, version 3 0.67 / 0.73, the models' near-analysis humidity 0.90 / 0.90 (flattered). Our ~2 F night temperature error is too large for fog, which needs air and dew point within a degree. The fair test against the models' FORECAST humidity at sunset is pre-registered as L-012 on fresh fall-to-spring nights; the Tower logs both signals from October 18.

2026-10-18, the 24-HOUR CYCLE (physlive.chain). Day and night joined: from the most recent sunrise or sunset (the station's reading then) each half runs to its end and the next starts where it finished, day (the L-010 version) into night (version 1) and on, four halves (about two days), with ECMWF + ICON clouds, wind and air mass. Shown on the Physics page against our forecast and the station; never blended. Each start's predicted extremes are logged (phys_runs kind 'chain') so a later test can judge the physics' own next-day high and low; first live chain (from the October 18 sunrise): today 62.4 F (ours 63.2), tonight 33.4 (34.5), Monday 65.8 (67.8), Monday night 36.1 (38.6): cooler than ours, more so further out.

2026-10-18, FAIR-WEATHER CUMULUS (physmodel/cumulus.py, ring.py). Cumulus forms when the mixed layer's top reaches the condensation level of the mixed-layer air (125 m per deg C of temperature minus dew point; mixed-layer moisture from the models' 2 m, 925 and 850 mb humidity averaged over the depth mixed). Truth: days with a clear morning and no weather, cumulus (FEW/SCT/BKN, base 1,500 to 9,000 ft) 10 AM to 5 PM. First against Marion alone (242 such days, cumulus on 41%, onset mostly 10 AM to noon, first base median 4,600 ft): the L-010 day model reached the condensation level on only 2 of 160 exploration days (ranking skill 0.76). Its afternoon mixed layer is too shallow: median 3,370 to 3,500 ft vs ERA5's boundary layer 4,810 ft (day-to-day correlation 0.73); fitted to temperature only, it got its warmth partly from the hot layer at the ground instead of depth. Refit with ERA5's depth in the loss (params_day_v1_obs_adv2_blh.json): 3,960 ft, temperature skill unchanged (high 1.51 F cross-validated, gain over ours 0.21 F). THE RING (Brian's idea): the six airports within 35 miles (Marion 14.6 mi SE, Kokomo 15.5 SW, Grissom 17.0 W, Huntington 23.2 NE, Logansport 28.6 W, Rochester 33.0 NNW; Wabash has no automated reports), weighted by 1/distance, as a consensus for the station (ring.py; Rochester and Warsaw were backfilled into the airport archive to close the ring to the north). 255 clear-morning days, cumulus on 35%. Ranking skill: physics 0.84 (Marion-only truth: 0.76), the models' near-analysis low cloud 0.92 (flattered). As yes/no it needs a 300 to 600 m allowance (the depth bias): 300 m: 81% of calls right, 38% caught; 600 m: 58% right, 88% caught. Onset off by 2.2 to 2.6 h; base off by 1,500 to 1,900 ft, worse than the forecaster's 227 ft per F rule. Conclusion: the physics knows which days lean to cumulus, not when or how high; the mixed layer is too crude (too shallow, three upper levels).

A pattern across the session: our physics adds real information where the models carry systematic LOCAL biases in temperature (the night low, L-007; the day high with measured or model sunshine, L-010 pending); for clouds, fog and precipitation type the models' own fields are stronger, and the useful physics there is simple and local (the wet-bulb rule; the station's own humidity ceiling for fog).

Road map toward Brian's goal (anticipating clouds, rain, snow and storms ourselves, which drive the temperatures):

1. Afternoon cumulus: when the mixed layer reaches its condensation level (from the dew point), clouds form; that would let the day model make its own fair-weather clouds and cap its own heating.
2. Storms: lift a parcel from the mixed layer through the models' upper air (instability, a lid) for our own thunderstorm potential and timing.
3. Rain and evaporative cooling, then precipitation type from the column (freezing level, a warm layer aloft).
4. Fog and dew at night from the night model's dew point and the grass.
5. Join night and day into one continuous 24-hour cycle, each half starting from where the other ended.

Do the small effects add up? (2026-10-18, L-014)

Brian's question: many tested effects were each worth about 0.02 F and set aside; together, do they become something big? Every small candidate (the other forecast program, the inversion at issue time, soil moisture, the ring's error and the upwind airports' error at issue time) was put next to our forecast in one penalised regression, with and without the physics, cross-validated by 6 blocks of months on discovery days (research/combined.py).

They do not add up. Two reasons: they overlap (the other program and the inversion carry part of what the physics already knows, correlation 0.2 to 0.3), and the ring and upwind errors fade within hours, so at the next day's low or high they are noise (they belong to the 1 to 6 hour nowcast, where they PASSED in L-013). The only small piece left on top of the physics was the 6 PM inversion for lows. L-014 tested it on the 149 real-run nights: 1.38 -> 1.37 F, gain 0.014 F, positive in 72% of resamples: FAIL, the size discovery predicted. The physics is the one large piece.

Wet weather (2026-10-18, L-015 and L-016)

Version 1 of the night physics ran only on dry nights. On wet nights it ran far too warm: +1.7 F on average and +4.1 F with a quarter inch of rain or more (discovery, perfect prog). The cause was physics, not tuning: it brings the 925 mb air down to the ground along the DRY adiabat (9.8 K per km). In rain the air below the cloud is saturated and the air follows the MOIST adiabat (about 6 K per km): 2.5 K (4.5 F) colder at the ground on heavy-rain nights, the size of the bias. physmodel/scmw.py adds, on top of version 1's dry constants (identical when it is dry):

• the moist adiabat in rain (full at 0.15 mm/h smoothed over 2 hours: fitted);
• falling rain cooling the air toward its wet-bulb (the fit made it small once the moist adiabat was in);
• a wet lawn evaporating after rain (fitted: about 4 mm counts as fully wet);
• the rain's own heat, snow melting in air above freezing (holding it at 32 F), and freezing rain giving up the heat of fusion on a sub-freezing lawn, with a "drip" share that runs off, ice tracked and melted later. The discovery years have about 30 hours of snow and 1 hour of freezing rain on our nights, so these keep their physical starting values; this winter's nights check them.

The wet physics makes the night model right on rainy nights, but it brings our forecast nothing there: rain-night lows are set by the rain and the air mass, which the statistical forecast already carries.

The day model needed no add-on. A wet-weather version (rain cooling toward the wet-bulb, a wet-ground store) made highs worse (bias -2 F): the day model already sees a rain day through the runs' dark sunshine and the soil moisture, and the add-on counted the water twice. Unchanged, the day physics is worth MORE on rainy days than on dry ones:

Our forecast misses rain-day highs by more than dry-day highs (2.1 vs about 1.5 F), and the physics, started from the station's sunrise reading and fed the runs' sunshine, catches a third of the miss. Scripts: physmodel/scmw.py, daywet.py, calibrate_wet.py, calibrate_daywet.py, l015.py, l016.py.

Snow on the ground (2026-10-18, L-017)

Version 1 ignored snow cover and ran far too warm over snow: +5.8 F on last winter's nights with 2 inches or more. Snow conducts heat a fraction as well as soil, so the soil's warmth reaches the surface through the snow in series, k = 1 / (1/k_soil + depth/k_snow), and a snow surface cannot warm past 32 F (physmodel/scmw.py, item 7). Fitted on the learning winters (40 dry snow nights): snow conductivity 0.118 W/m/K, the textbook range for settled snow; the fit did not support a thin fast-chilling surface layer. Held-out winter 2025-26 (perfect prog, a physics check: no real runs exist for that winter):

A large step (L-017 passed), not the end: deep snow is still about 2 F too warm, likely because the learning winters had only 3 nights that deep. The ground probes will sit under real snow this winter and measure the insulation directly. Snow depth live comes from Brian's reports (fc/snow.php), carried forward by the Tower (snowpack.py), with the grass probe as a burial detector and the models' snow depth as the fallback.

The calibration run (2026-10-18, L-018)

Idea from the notes review: start each run from a state tuned to the station's recent past. For the day model the uncertain state is how wet the ground behaves. For every past day, the evaporative share that best reproduces the station's warming curve with the measured sunshine was inferred (calib_run.py). One day's value is mostly that day's noise (correlation with the day before 0.13); a two-week average holds a slow signal (0.23), and calm clear days carry it best (0.30, half the noise). On discovery days, half the two-week estimate improved the day physics (1.465 -> 1.417 F) and the L-010 rule (gain 0.04 F). With real forecast runs on 178 test-year days it added nothing (gain -0.004 F, L-018 FAILED): the forecast sunshine and air mass carry far larger errors than the ground's slow wetness. The grid's soil moisture stays as the day model's input. By-product: the L-010 rule, unchanged, beat ours on all 178 days (1.61 -> 1.53 F, dry and wet); its live test (L-010, dry days) continues.

The physics ensemble (2026-10-18, L-019)

Running the night physics once per forecast model and averaging the results (as forecast ensembles do), instead of once on the averaged forcing, did not help on discovery nights (2.12 vs 2.08 F, four models), even on nights when the models disagreed about low cloud; the constants were fitted on averaged forcing, which favours it. Adding GFS to the averaged forcing looked better on discovery nights (ECMWF + ICON 2.14, + GFS 2.06 F) but FAILED with real forecasts (1.79 -> 1.85 F, L-019): its help came from its first hours, which are close to an analysis. Real-run members alone: GFS 2.43, ECMWF 2.18, ICON 1.86 F; the ECMWF + ICON average beats each. Lesson for this program: an input's value in the stitched first hours does not transfer to forecasts; test every input on real runs. The spread of the members is a modest confidence signal (tighter half of nights 1.6 F, wider half 1.95 F).

The day model's cold bias and the crop calendar (2026-10-18, L-020)

With real forecasts the day physics ran about 1 F too cold. Swapping inputs on the exploration months (April 2 to July 15, 2026) split it: forecast inputs about 0.6 F (sunshine timing more than amount: forecast sunshine runs 11.7% over the pyranometer, close to the 0.907 calibration; plus the morning upper air and the air moving in), the model itself the rest. And the model's own error repeats every year by season: too cold in spring and fall, too warm in July-August and winter. Its evaporation followed a smooth season peaking in mid-July; real fields here are bare in spring, a closed transpiring canopy in July-August, and drying in the fall. A second seasonal harmonic (a crop calendar, refitted on discovery days with measured sunshine) fixed much of it (discovery 1.28 -> 1.19 F, 2023 1.59 -> 1.41 F), and a +0.84 F offset for real forecasts (fitted on the exploration months) the rest. On held-back months (L-020 passed): 1.79 -> 1.59 F, bias -1.22 -> -0.37 F, now better than ours alone (1.71 F). Midsummer (August) is still about 1.4 F cold: the next piece, probably the corn canopy's peak transpiration or the hot layer.

Clouds: what they cost, and the midday sunshine update (2026-10-18, L-021)

Neither model forecasts clouds itself; both inherit ECMWF and ICON's. With real forecasts (2026), the sunshine reaching the station differs from the forecast by 10% of clear-sky on a typical day; on the 10 much-darker days our high missed by 2.5 F (1.3 F too warm), on days the sunshine was about right 1.4 F. At night, against the cloud the airports reported, the low missed by 1.3 F when the cloud forecast was right and 1.4 to 1.7 F when not. Cloud surprises explain about 10% of our high's error and 20% of the day physics'; with the measured sunshine the day physics would improve from 1.63 to 1.43 F. The morning's sunshine surprise alone says little about the afternoon (correlation 0.09: morning clouds burn off, afternoon cumulus is its own). Restarting the day physics with the sunshine measured so far (and the last two hours' measured/forecast ratio, fading) and blending it with ours and the station's error improved the high on held-back months at 11 AM, noon and 1 PM (gains 0.07 to 0.11 F over the station-error update), but at the pre-registered 1 PM not consistently enough (86% of resamples): L-021 failed narrowly. A live trial would settle it. The night version (inferring the real cloudiness from the first hours of cooling after sunset, with the grass probe) waits for a few weeks of the mounted probe.

What would make it better (version 2)

• Two air layers: the 2 m layer that decouples and a bulk layer above it (the "air mass"), so a calm night can run away at the ground while the layer above cools slowly. Calibrate the 2 m layer on the ground sensor's grass and station curves, and the stability constants on the nights the grass and air separate.
• Start from the station's evening state (sunset temperature, dew point, the first hour's cooling rate) and the ground sensor's soil temperatures, then judge it where it could actually help: a 6 PM forecast of tonight's low with the 12Z runs' forecast clouds and winds, against our own issued low at the same hour. That needs cloud layers and 10 m wind from the real runs (to add to research_data.py), and a pre-registered plan before any test-year night.
• Rain nights (evaporative cooling, wet ground), then daytime (sun, mixing depth: highs).

• A grass-level temperature sensor at the station mast (and a soil probe a few inches down): the ground's actual temperature to start and check the model, and the ground-to-air difference in real time as decoupling begins. Tabled by Brian (see R-007); valuable here once it has a season of data.
• An infrared sensor looking at the grass measures the radiating surface directly (check Meshtastic support first).

Later pieces

1. Afternoon mixing for highs (with R-003): how deep the sun mixes the air, from the 850/925 mb temperatures.
2. The shape of the hourly temperature curve (with R-008).

Questions for the library

• Simple published nocturnal boundary-layer or surface energy-balance models tuned to single stations: which formulations worked best for minimum temperature?

Log

• 2026-10-17: thread opened at Brian's request; version 1 designed.
• 2026-10-17: the ground sensor went in the same day (R-007). Version 1 can now start from MEASURED soil temperature (2, 4, 8 in) instead of model soil, and each night's grass curve checks the radiation and exchange parameters directly (first night: grass 12 F under the air at dusk, then held at the dew point). Brian asked again for our own physics model; next step is building version 1 on the station's minute history.
• 2026-10-18: version 1 built, fitted and cross-validated (above): matches the models on calm nights but adds nothing beyond a statistical correction. Version 2 plan: two air layers, evening start, real-run inputs.
• 2026-10-18: version 2 explored and shelved; L-007 pre-registered and PASSED with version 1. Next: run live on the Tower and test against our live-adjusted low (L-008).
• 2026-10-18: day model v1.1 built, fitted, cross-validated (gain 0.05 F, not proven) and put live for display; road map to clouds, storms and precipitation added.
• 2026-10-18: solar sensor brought in; models' own radiation forecasts beat our cloud dimming.
• 2026-10-18 evening: fitting bug found and corrected (switched day variants); advection v2 makes the day model add to our high (0.14 F cross-validated); L-009 pre-registered.
• 2026-10-18: L-009 failed; the cause is the cloud-to-sunshine step (with measured sunshine the physics matches ours on the same days).
• 2026-10-18: day sunshine rebuilt on the models' radiation (sensor-calibrated); L-008 and L-010 pre-registered; L-010 version live.
• 2026-10-18: precipitation type explored and tested (L-011): the wet-bulb rule beats the 33 F rule; physics ice detection not confirmed.
• 2026-10-18: fog first look: the night model as built is a poor fog forecaster (AUC 0.6-0.7 vs the models' humidity 0.9); needs a moisture budget.
• 2026-10-18: night moisture budget (version 3) built: dew point slightly better, fog still weak; fog log live; L-012 pre-registered.
• 2026-10-18: 24-hour cycle built and put live (display; extremes logged for a future test).

• 2026-10-18: fair-weather cumulus explored with the ring of airports as truth: ranking yes, timing and base no; mixed layer refit with ERA5 depth.
• 2026-10-18: the small effects combined (L-014): they overlap or fade by the next day; the physics is the one large piece.
• 2026-10-18: wet weather: the moist adiabat fixes the night physics in rain (L-016 failed vs ours); the day physics passes on wet days (L-015).
• 2026-10-18: snow cover added to the night physics (L-017 passed on the held-out winter); snow reports live.
• 2026-10-18: the calibration run (ground wetness from the station's own curves) explored and tested: L-018 failed.
• 2026-10-18: physics ensemble explored and tested: L-019 failed; live forcing stays ECMWF + ICON.
• 2026-10-18: day cold bias diagnosed and fixed (crop calendar + real-forecast offset): L-020 passed.
• 2026-10-18: cloud cost measured; midday sunshine update tested (L-021 failed narrowly, consistent direction).
• 2026-10-18: observed clouds tried two ways at night (the ring airports' reports, L-023; MOS and National Blend sky cover at Marion, exploration): neither helps the physics; its cloud constants are tuned to the models' layered cloud. The night restart from the station's reading is what helped (L-023, narrow fail).