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FA-70856 / Weather index computation / Open access

Heat index regression: humid adjustment window · case 01

Tropical 88 to 97 degF readings with RH above 85 are pulled down by a negative humid correction.

Verified by executionVariant 1 · 8 checks per implementationDownload source bundle ↓JSON ↗

ROOT CAUSE

The humid-correction temperature window extends to 97 degF, where (87 - T) becomes negative.

VERIFIED REPAIR

Apply the humid correction only for 80 <= T <= 87.

Unsuccessful approach: Widening the lower edge to 75 degF corrects hot air but adjusts sub-80 degF air that already uses the regression.

Case contract

Input dry-bulb temperature in degF and relative humidity in percent. Compute the simple Steadman estimate 0.5*(T+61+1.2*(T-68)+0.094*RH); if the mean of that estimate and T is below 80 return it, otherwise use the Rothfusz regression with the dry adjustment (RH<13, 80<=T<=112) or the humid adjustment (RH>85, 80<=T<=87). Round to 0.1 degF.

Why this case matters

Heat advisories are issued from this value; a wrong branch changes public warnings.

1 / The failure

Exit 1
"""Failure Map reference implementation. Python standard library only."""
import json
import math
N = 1
observations = []
def solve(t_f, rh):
    simple = 0.5 * (t_f + 61.0 + (t_f - 68.0) * 1.2 + rh * 0.094)
    if (simple + t_f) / 2 < 80:
        return round(simple, 1)
    hi = (-42.379 + 2.04901523 * t_f + 10.14333127 * rh - 0.22475541 * t_f * rh
          - 0.00683783 * t_f * t_f - 0.05481717 * rh * rh + 0.00122874 * t_f * t_f * rh
          + 0.00085282 * t_f * rh * rh - 0.00000199 * t_f * t_f * rh * rh)
    if rh < 13 and 80 <= t_f <= 112:
        hi -= ((13 - rh) / 4) * math.sqrt((17 - abs(t_f - 95)) / 17)
    elif rh > 85 and 80 <= t_f <= 97:
        hi += ((rh - 85) / 10) * ((87 - t_f) / 5)
    return round(hi, 1)
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[['70F at 0% RH', [70, 0], 66.7], ['70F at 5% RH', [70, 5], 66.9], ['70F at 10% RH', [70, 10], 67.2], ['70F at 12% RH', [70, 12], 67.3], ['70F at 13% RH', [70, 13], 67.3], ['70F at 40% RH', [70, 40], 68.6], ['79F at 100% RH', [79, 100], 83.8], ['88F at 90% RH', [88, 90], 113.2]], [['70F at 85% RH', [70, 85], 70.7], ['70F at 86% RH', [70, 86], 70.7], ['70F at 90% RH', [70, 90], 70.9], ['70F at 95% RH', [70, 95], 71.2], ['70F at 100% RH', [70, 100], 71.4], ['76F at 0% RH', [76, 0], 73.3], ['79F at 95% RH', [79, 95], 83.4], ['90F at 90% RH', [90, 90], 121.9]], [['76F at 10% RH', [76, 10], 73.8], ['76F at 12% RH', [76, 12], 73.9], ['76F at 13% RH', [76, 13], 73.9], ['76F at 40% RH', [76, 40], 75.2], ['76F at 60% RH', [76, 60], 76.1], ['76F at 85% RH', [76, 85], 77.3], ['79.5F at 100% RH', [79.5, 100], 85.5], ['94F at 86% RH', [94, 86], 136.4]], [['76F at 90% RH', [76, 90], 77.5], ['76F at 95% RH', [76, 95], 77.8], ['76F at 100% RH', [76, 100], 78.0], ['79F at 0% RH', [79, 0], 76.6], ['79F at 5% RH', [79, 5], 76.8], ['79F at 10% RH', [79, 10], 77.1], ['79.5F at 95% RH', [79.5, 95], 84.9], ['94F at 100% RH', [94, 100], 154.8]], [['79F at 13% RH', [79, 13], 77.2], ['79F at 40% RH', [79, 40], 78.5], ['79F at 60% RH', [79, 60], 79.4], ['79F at 85% RH', [79, 85], 80.6], ['79F at 86% RH', [79, 86], 80.6], ['79F at 90% RH', [79, 90], 80.8], ['79.5F at 90% RH', [79.5, 90], 84.3], ['95F at 95% RH', [95, 95], 153.6]]]
for label, args, expected in fixtures[N - 1]:
    check(label, solve(*args), expected)
print(json.dumps({"observations": observations, "passed": all(x["passed"] for x in observations)}, ensure_ascii=False))
raise SystemExit(0 if all(x["passed"] for x in observations) else 1)
Boundary fixtureActualExpectedOutcome
70F at 0% RH66.766.7Passed
70F at 5% RH66.966.9Passed
70F at 10% RH67.267.2Passed
70F at 12% RH67.367.3Passed
70F at 13% RH67.367.3Passed
70F at 40% RH68.668.6Passed
79F at 100% RH83.883.8Passed
88F at 90% RH113.1113.2Failed

SHA-256 / 193578981db0bba26e0adea0cfe57cb2a534284ebdf0ec057fd723a1b4dd60e9

2 / The unsuccessful fix

Exit 1
"""Failure Map reference implementation. Python standard library only."""
import json
import math
N = 1
observations = []
def solve(t_f, rh):
    simple = 0.5 * (t_f + 61.0 + (t_f - 68.0) * 1.2 + rh * 0.094)
    if (simple + t_f) / 2 < 80:
        return round(simple, 1)
    hi = (-42.379 + 2.04901523 * t_f + 10.14333127 * rh - 0.22475541 * t_f * rh
          - 0.00683783 * t_f * t_f - 0.05481717 * rh * rh + 0.00122874 * t_f * t_f * rh
          + 0.00085282 * t_f * rh * rh - 0.00000199 * t_f * t_f * rh * rh)
    if rh < 13 and 80 <= t_f <= 112:
        hi -= ((13 - rh) / 4) * math.sqrt((17 - abs(t_f - 95)) / 17)
    elif rh > 85 and 75 <= t_f <= 87:
        hi += ((rh - 85) / 10) * ((87 - t_f) / 5)
    return round(hi, 1)
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[['70F at 0% RH', [70, 0], 66.7], ['70F at 5% RH', [70, 5], 66.9], ['70F at 10% RH', [70, 10], 67.2], ['70F at 12% RH', [70, 12], 67.3], ['70F at 13% RH', [70, 13], 67.3], ['70F at 40% RH', [70, 40], 68.6], ['79F at 100% RH', [79, 100], 83.8], ['88F at 90% RH', [88, 90], 113.2]], [['70F at 85% RH', [70, 85], 70.7], ['70F at 86% RH', [70, 86], 70.7], ['70F at 90% RH', [70, 90], 70.9], ['70F at 95% RH', [70, 95], 71.2], ['70F at 100% RH', [70, 100], 71.4], ['76F at 0% RH', [76, 0], 73.3], ['79F at 95% RH', [79, 95], 83.4], ['90F at 90% RH', [90, 90], 121.9]], [['76F at 10% RH', [76, 10], 73.8], ['76F at 12% RH', [76, 12], 73.9], ['76F at 13% RH', [76, 13], 73.9], ['76F at 40% RH', [76, 40], 75.2], ['76F at 60% RH', [76, 60], 76.1], ['76F at 85% RH', [76, 85], 77.3], ['79.5F at 100% RH', [79.5, 100], 85.5], ['94F at 86% RH', [94, 86], 136.4]], [['76F at 90% RH', [76, 90], 77.5], ['76F at 95% RH', [76, 95], 77.8], ['76F at 100% RH', [76, 100], 78.0], ['79F at 0% RH', [79, 0], 76.6], ['79F at 5% RH', [79, 5], 76.8], ['79F at 10% RH', [79, 10], 77.1], ['79.5F at 95% RH', [79.5, 95], 84.9], ['94F at 100% RH', [94, 100], 154.8]], [['79F at 13% RH', [79, 13], 77.2], ['79F at 40% RH', [79, 40], 78.5], ['79F at 60% RH', [79, 60], 79.4], ['79F at 85% RH', [79, 85], 80.6], ['79F at 86% RH', [79, 86], 80.6], ['79F at 90% RH', [79, 90], 80.8], ['79.5F at 90% RH', [79.5, 90], 84.3], ['95F at 95% RH', [95, 95], 153.6]]]
for label, args, expected in fixtures[N - 1]:
    check(label, solve(*args), expected)
print(json.dumps({"observations": observations, "passed": all(x["passed"] for x in observations)}, ensure_ascii=False))
raise SystemExit(0 if all(x["passed"] for x in observations) else 1)
Boundary fixtureActualExpectedOutcome
70F at 0% RH66.766.7Passed
70F at 5% RH66.966.9Passed
70F at 10% RH67.267.2Passed
70F at 12% RH67.367.3Passed
70F at 13% RH67.367.3Passed
70F at 40% RH68.668.6Passed
79F at 100% RH86.283.8Failed
88F at 90% RH113.2113.2Passed

SHA-256 / 5314b9229245e21b0e3bdd2b72ba025af6e62199e45c6d895c6f35aae4bb6d2e

3 / The verified repair

Exit 0
"""Failure Map reference implementation. Python standard library only."""
import json
import math
N = 1
observations = []
def solve(t_f, rh):
    simple = 0.5 * (t_f + 61.0 + (t_f - 68.0) * 1.2 + rh * 0.094)
    if (simple + t_f) / 2 < 80:
        return round(simple, 1)
    hi = (-42.379 + 2.04901523 * t_f + 10.14333127 * rh - 0.22475541 * t_f * rh
          - 0.00683783 * t_f * t_f - 0.05481717 * rh * rh + 0.00122874 * t_f * t_f * rh
          + 0.00085282 * t_f * rh * rh - 0.00000199 * t_f * t_f * rh * rh)
    if rh < 13 and 80 <= t_f <= 112:
        hi -= ((13 - rh) / 4) * math.sqrt((17 - abs(t_f - 95)) / 17)
    elif rh > 85 and 80 <= t_f <= 87:
        hi += ((rh - 85) / 10) * ((87 - t_f) / 5)
    return round(hi, 1)
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[['70F at 0% RH', [70, 0], 66.7], ['70F at 5% RH', [70, 5], 66.9], ['70F at 10% RH', [70, 10], 67.2], ['70F at 12% RH', [70, 12], 67.3], ['70F at 13% RH', [70, 13], 67.3], ['70F at 40% RH', [70, 40], 68.6], ['79F at 100% RH', [79, 100], 83.8], ['88F at 90% RH', [88, 90], 113.2]], [['70F at 85% RH', [70, 85], 70.7], ['70F at 86% RH', [70, 86], 70.7], ['70F at 90% RH', [70, 90], 70.9], ['70F at 95% RH', [70, 95], 71.2], ['70F at 100% RH', [70, 100], 71.4], ['76F at 0% RH', [76, 0], 73.3], ['79F at 95% RH', [79, 95], 83.4], ['90F at 90% RH', [90, 90], 121.9]], [['76F at 10% RH', [76, 10], 73.8], ['76F at 12% RH', [76, 12], 73.9], ['76F at 13% RH', [76, 13], 73.9], ['76F at 40% RH', [76, 40], 75.2], ['76F at 60% RH', [76, 60], 76.1], ['76F at 85% RH', [76, 85], 77.3], ['79.5F at 100% RH', [79.5, 100], 85.5], ['94F at 86% RH', [94, 86], 136.4]], [['76F at 90% RH', [76, 90], 77.5], ['76F at 95% RH', [76, 95], 77.8], ['76F at 100% RH', [76, 100], 78.0], ['79F at 0% RH', [79, 0], 76.6], ['79F at 5% RH', [79, 5], 76.8], ['79F at 10% RH', [79, 10], 77.1], ['79.5F at 95% RH', [79.5, 95], 84.9], ['94F at 100% RH', [94, 100], 154.8]], [['79F at 13% RH', [79, 13], 77.2], ['79F at 40% RH', [79, 40], 78.5], ['79F at 60% RH', [79, 60], 79.4], ['79F at 85% RH', [79, 85], 80.6], ['79F at 86% RH', [79, 86], 80.6], ['79F at 90% RH', [79, 90], 80.8], ['79.5F at 90% RH', [79.5, 90], 84.3], ['95F at 95% RH', [95, 95], 153.6]]]
for label, args, expected in fixtures[N - 1]:
    check(label, solve(*args), expected)
print(json.dumps({"observations": observations, "passed": all(x["passed"] for x in observations)}, ensure_ascii=False))
raise SystemExit(0 if all(x["passed"] for x in observations) else 1)
Boundary fixtureActualExpectedOutcome
70F at 0% RH66.766.7Passed
70F at 5% RH66.966.9Passed
70F at 10% RH67.267.2Passed
70F at 12% RH67.367.3Passed
70F at 13% RH67.367.3Passed
70F at 40% RH68.668.6Passed
79F at 100% RH83.883.8Passed
88F at 90% RH113.2113.2Passed

SHA-256 / 740ae7939c9d176c3cba3a34e399dc4b785a2f4662abd06aefbe3b620cbfa24d

Verification & scope

Stipulated deterministic teaching model of an operational weather index; coefficients and thresholds are fixed by the contract and no claim of standards conformance is made. This reproducer isolates one failure mechanism. Results cover the supplied fixtures. Variants within a family share a test contract and should remain grouped when constructing evaluation splits. Related mechanisms with a shared evaluation_group must also remain together; these controlled models are not independent production incidents.

Observations recorded using Python 3.12.14 at 2026-09-29T14:48:24.513376+00:00.

Case digest / acac87d406f7a0a5af8afab32ddf34770646ae6530c65b1de6e9c50610c5ca57