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Number(maxMatch[1]) : undefined;\n\n return { productDate, maxF };\n}\n\n// Find settlement high for a local date using NWS Products API (CLI type).\n// We search recent products and match by “product DATE minus 1 day”.\nasync function getSettlementHighForDate(\n cliLocation: string,\n targetLocalISO: string,\n) {\n const listUrl =\n `https://api.weather.gov/products/types/CLI/locations/${cliLocation}?limit=20`;\n const listing = await fetchJson(listUrl);\n const items: Array<{ id: string }> = listing[\"@graph\"] ?? [];\n\n for (const it of items) {\n const prod = await fetchJson(`https://api.weather.gov/products/${it.id}`);\n const text: string = prod?.productText;\n if (!text) continue;\n\n const parsed = parseCliYesterdayHigh(text);\n if (!parsed.productDate || parsed.maxF === undefined) continue;\n\n // CLI “DATE” is the issuance day; it reports “YESTERDAY” as the prior day.\n const issuance = DateTime.fromFormat(parsed.productDate, \"MM/dd/yyyy\", {\n zone: \"America/Los_Angeles\",\n });\n const yesterday = issuance.minus({ days: 1 }).toISODate();\n\n if (yesterday === targetLocalISO) {\n return {\n maxF: parsed.maxF,\n productId: it.id,\n issuanceDate: issuance.toISODate(),\n };\n }\n }\n return null;\n}\n\nasync function getCalib(site: string) {\n const obj = (await kv.get(`calib:${site}`)) as any;\n return { errors: (obj?.errors ?? []) as number[] };\n}\n\nasync function putCalib(site: string, errors: number[]) {\n await kv.set(`calib:${site}`, {\n errors: errors.slice(-60),\n updatedAtUTC: DateTime.utc().toISO(),\n });\n}\n\nexport default async function settleAndCalibrate() {\n // settle “yesterday” in the settlement timezone.\n const target = DateTime.now().setZone(\"America/Los_Angeles\").minus({\n days: 1,\n }).toISODate()!;\n\n for (const site of SITES) {\n const predKey = `pred:${site}:${target}`;\n const pred = (await kv.get(predKey)) as any;\n if (!pred) continue; // no prediction stored for that day\n\n // already settled?\n const settledKey = `settled:${site}:${target}`;\n if (await kv.get(settledKey)) continue;\n\n const settlement = await getSettlementHighForDate(site, target);\n if (!settlement) continue; // CLI not posted/found yet\n\n const settledHigh = settlement.maxF;\n const forecastMean = Number(pred.meanHigh);\n\n const error = settledHigh - forecastMean; // positive means we under-forecast\n\n const { errors } = await getCalib(site);\n errors.push(error);\n await putCalib(site, errors);\n\n await kv.set(settledKey, {\n site,\n dateLocal: target,\n settledHighF: settledHigh,\n forecastMeanF: forecastMean,\n errorF: error,\n productId: settlement.productId,\n issuanceDateLocal: settlement.issuanceDate,\n settledAtUTC: DateTime.utc().toISO(),\n });\n }\n\n return { ok: true, targetDate: target };\n}","createdAt",["D",1766712812014],"branchVersion","branchId","0990179a-e1f4-11f0-8dec-42dde27851f2","id","098fe1ee-e1f4-11f0-8dec-42dde27851f2","get-high-temp",["D",1766709869715],"deletedAt","legacyEnvVars","privacy","public","httpPrivacy","description","imageUrl","019aeb5a-8601-72dc-933c-5116c9b9ad52","pinnedFileId","pinPreview","pinMigrated","screenshotVersion","likeCount",0,"orgId","019aeb5a-8601-72dc-933c-5699b88cb012","tursoName","tursoHostname","tursoMeasuredAt","pinned","userPerm","r","valPerm","blobPerm","rw","emailPerm","sqlitePerm","projectPerm","telemetryPerm","user",{"_95":94,"_102":98},"org",{"_95":94,"_96":-5,"_97":98,"_18":55,"_99":100,"_101":27},"branch",{"_13":84,"_33":85,"_44":43,"_86":45,"_87":-5,"_88":-5,"_89":-5,"_90":-5,"_91":-5,"_92":46,"_93":94},"isOwned",40,"main","projectId","forkedBranchId","forkedBranchVersion","forkedProjectPrivacy","forkedProjectId","forkedProjectOrgId","projectName","userHandle","jvt012","handle","displayName","avatar","https://img.clerk.com/eyJ0eXBlIjoiZGVmYXVsdCIsImlpZCI6Imluc18yTm10cGhQUklwU0tqZHNiOHNnNGd2ZjJURFUiLCJyaWQiOiJ1c2VyXzM2T2tsUkc5c3FHYlNtOG1TamQxZ0pSZzdWWiJ9","tier","free","canRestrictVals","profileImageUrl","branchName","adoptOrgId",{"_44":45,"_33":46,"_39":175,"_48":-5,"_49":27,"_50":51,"_52":51,"_53":-5,"_54":-5,"_18":55,"_56":-5,"_57":27,"_58":-5,"_59":-5,"_60":61,"_62":63,"_64":-5,"_65":-5,"_66":-5,"_67":-5,"_68":69,"_70":69,"_71":72,"_73":72,"_74":72,"_75":69,"_76":69,"_77":176,"_79":177,"_81":178,"_83":27},"tree",[162,163,164],"files",[162,163,164],"counts",{"_151":61,"_152":153,"_154":61,"_155":156,"_157":158},"contents",[124,125,126],"upstreamProject","upstreamBranch","emptyEnvVars","flags",[],"hasHttpFiles","grants","orgs","customDomains",{},{"_44":147,"_33":29,"_31":32,"_130":-5,"_42":43,"_41":14,"_132":27,"_39":148,"_13":149,"_34":35,"_36":38,"_24":27,"_137":-5,"_28":150},{"_44":139,"_33":140,"_31":141,"_130":-5,"_42":43,"_41":142,"_132":27,"_39":143,"_13":144,"_34":135,"_36":145,"_24":27,"_137":-5,"_28":146},{"_44":127,"_33":128,"_31":129,"_130":-5,"_42":43,"_41":131,"_132":27,"_39":133,"_13":134,"_34":135,"_36":136,"_24":27,"_137":-5,"_28":138},"99c584fc-e1fa-11f0-a938-42dde27851f2","second.ts","807939b2-e1fa-11f0-a4cb-42dde27851f2","parentId",12,"latest",["D",1766712688633],4,"http","import { DateTime } from \"https://esm.sh/luxon@3.5.0\";\nimport { kv } from \"https://esm.town/v/std/kv\";\n\nconst USER_AGENT = \"your-valtown-hightemp (jwa012@ciceroisback.com)\"; // NWS requires contact\n\n// “site” here is the NWS CLI climate location code used for settlement.\nconst SITES: Record =\n {\n SFO: { cliLocation: \"SFO\", lat: 37.6188056, lon: -122.3754167 }, // KSFO\n // Add your other Robinhood-settled sites if needed\n };\n\nconst OPEN_METEO_MODELS = [\n \"hrrr\", // often available for US short-range\n \"gfs_global\", // usually available\n // you can add more if Open-Meteo supports them for your point\n];\n\nfunction clamp(x: number, a: number, b: number) {\n return Math.max(a, Math.min(b, x));\n}\n\nfunction mean(xs: number[]) {\n return xs.reduce((a, b) => a + b, 0) / Math.max(1, xs.length);\n}\n\nfunction stddev(xs: number[]) {\n if (xs.length < 2) return NaN;\n const m = mean(xs);\n const v = xs.reduce((acc, x) => acc + (x - m) ** 2, 0) / (xs.length - 1);\n return Math.sqrt(v);\n}\n\n// Normal CDF approximation (good enough for this use).\nfunction normCdf(z: number) {\n // Abramowitz-Stegun approximation\n const t = 1 / (1 + 0.2316419 * Math.abs(z));\n const d = 0.3989423 * Math.exp(-z * z / 2);\n let p = d * t *\n (0.3193815 +\n t * (-0.3565638 +\n t * (1.781478 +\n t * (-1.821256 +\n t * 1.330274))));\n if (z > 0) p = 1 - p;\n return p;\n}\n\nasync function fetchJson(url: string, accept = \"application/geo+json\") {\n const r = await fetch(url, {\n headers: { \"User-Agent\": USER_AGENT, \"Accept\": accept },\n });\n if (!r.ok) throw new Error(`Fetch failed ${r.status} for ${url}`);\n return r.json();\n}\n\nasync function getNwsHourly(lat: number, lon: number) {\n const points = await fetchJson(\n `https://api.weather.gov/points/${lat},${lon}`,\n );\n const tz = points.properties.timeZone as string;\n const hourlyUrl = points.properties.forecastHourly as string;\n\n const hourly = await fetchJson(hourlyUrl);\n const periods = hourly.properties.periods as Array<\n { startTime: string; temperature: number; temperatureUnit: string }\n >;\n\n return {\n tz,\n hours: periods.map((p) => ({\n t: DateTime.fromISO(p.startTime),\n tempF: p.temperatureUnit === \"F\"\n ? p.temperature\n : (p.temperature * 9) / 5 + 32,\n })),\n };\n}\n\nasync function getLatestNwsObservation(stationId: string) {\n const url =\n `https://api.weather.gov/stations/${stationId}/observations/latest`;\n const j = await fetchJson(url);\n\n const ts = j.properties.timestamp as string;\n const tempC = j.properties.temperature?.value;\n if (!ts || tempC === null || tempC === undefined) return null;\n\n return {\n obsTimeUTC: DateTime.fromISO(ts).toUTC(),\n tempF: (tempC * 9) / 5 + 32,\n };\n}\n\nfunction highForLocalDate(\n hours: Array<{ t: DateTime; tempF: number }>,\n tz: string,\n localISODate: string,\n) {\n const todays = hours\n .map((h) => ({ ...h, local: h.t.setZone(tz) }))\n .filter((h) => h.local.toISODate() === localISODate);\n if (!todays.length) return null;\n return Math.max(...todays.map((h) => h.tempF));\n}\n\nfunction nearestForecastTemp(\n hours: Array<{ t: DateTime; tempF: number }>,\n whenUTC: DateTime,\n) {\n let best = hours[0];\n let bestAbs = Infinity;\n for (const h of hours) {\n const d = Math.abs(h.t.toUTC().diff(whenUTC).as(\"minutes\"));\n if (d < bestAbs) {\n bestAbs = d;\n best = h;\n }\n }\n return best.tempF;\n}\n\n// Bias-nudge with exponential decay\nfunction adjustedHighForToday(\n hours: Array<{ t: DateTime; tempF: number }>,\n tz: string,\n todayISO: string,\n obsUTC: DateTime,\n biasF: number,\n tauHours = 6,\n) {\n const obs = obsUTC.toUTC();\n const todays = hours\n .map((h) => ({ ...h, local: h.t.setZone(tz) }))\n .filter((h) => h.local.toISODate() === todayISO);\n\n let maxAdj = -Infinity;\n for (const h of todays) {\n const leadHours = Math.max(0, h.t.toUTC().diff(obs).as(\"hours\"));\n const w = Math.exp(-leadHours / tauHours);\n const tadj = h.tempF + biasF * w;\n if (tadj > maxAdj) maxAdj = tadj;\n }\n return maxAdj;\n}\n\nasync function getOpenMeteoHourly(\n lat: number,\n lon: number,\n tz: string,\n model: string,\n) {\n const url = new URL(\"https://api.open-meteo.com/v1/forecast\");\n url.searchParams.set(\"latitude\", String(lat));\n url.searchParams.set(\"longitude\", String(lon));\n url.searchParams.set(\"hourly\", \"temperature_2m\");\n url.searchParams.set(\"temperature_unit\", \"fahrenheit\");\n url.searchParams.set(\"timezone\", tz);\n url.searchParams.set(\"forecast_days\", \"2\");\n url.searchParams.set(\"models\", model);\n\n const r = await fetch(url.toString(), {\n headers: { \"User-Agent\": USER_AGENT },\n });\n if (!r.ok) {\n throw new Error(`Open-Meteo failed ${r.status} for model=${model}`);\n }\n const j = await r.json();\n\n const times: string[] = j.hourly?.time ?? [];\n const temps: number[] = j.hourly?.temperature_2m ?? [];\n return times.map((t, i) => ({\n t: DateTime.fromISO(t, { zone: tz }),\n tempF: temps[i],\n }));\n}\n\nasync function modelHighsForToday(\n lat: number,\n lon: number,\n tz: string,\n todayISO: string,\n) {\n const highs: Record = {};\n for (const model of OPEN_METEO_MODELS) {\n try {\n const hrs = await getOpenMeteoHourly(lat, lon, tz, model);\n const todays = hrs.filter((h) => h.t.toISODate() === todayISO);\n if (todays.length) highs[model] = Math.max(...todays.map((h) => h.tempF));\n } catch {\n // Skip unavailable models; keep going\n }\n }\n return highs;\n}\n\n// --- Calibration storage ---\n// We store recent errors: error = (settlementHigh - ourForecastMeanAtIssue)\nasync function getCalib(site: string) {\n const key = `calib:${site}`;\n const obj = (await kv.get(key)) as any;\n const errors: number[] = obj?.errors ?? [];\n return { key, errors };\n}\n\nasync function putCalib(site: string, errors: number[]) {\n const key = `calib:${site}`;\n // keep last 60\n const trimmed = errors.slice(-60);\n await kv.set(key, { errors: trimmed, updatedAt: DateTime.utc().toISO() });\n}\n\nfunction calibratedParams(errors: number[]) {\n // Priors to avoid overconfidence with small samples\n const priorSigma = 3.5; // °F\n const minSigma = 2.0;\n\n if (errors.length < 5) {\n return { muErr: 0, sigma: priorSigma, n: errors.length };\n }\n const mu = mean(errors);\n const sd = stddev(errors);\n const sigma = clamp(\n isNaN(sd) ? priorSigma : Math.sqrt(sd * sd + priorSigma * priorSigma * 0.2),\n minSigma,\n 8,\n );\n // mild shrinkage toward 0 bias\n const muErr = mu * 0.7;\n return { muErr, sigma, n: errors.length };\n}\n\nexport default async function (req: Request): Promise {\n try {\n const url = new URL(req.url);\n const site = (url.searchParams.get(\"site\") || \"SFO\").toUpperCase();\n const strike = Number(url.searchParams.get(\"strike\") || \"0\");\n\n const cfg = SITES[site];\n if (!cfg) {\n return new Response(\n JSON.stringify({ error: `Unknown site ${site}` }, null, 2),\n { status: 400 },\n );\n }\n\n // Forecast basis from NWS\n const { tz, hours } = await getNwsHourly(cfg.lat, cfg.lon);\n const todayISO = DateTime.now().setZone(tz).toISODate()!;\n\n const nwsHigh = highForLocalDate(hours, tz, todayISO);\n if (nwsHigh === null) throw new Error(\"No NWS hourly points for today.\");\n\n // Observation bias using NWS station obs (for SFO, stationId usually KSFO)\n const stationId = site === \"SFO\" ? \"KSFO\" : site; // adjust if you add other sites\n const obs = await getLatestNwsObservation(stationId);\n\n let bias = 0;\n let highNwsAdj = nwsHigh;\n if (obs) {\n const tNwsNow = nearestForecastTemp(hours, obs.obsTimeUTC);\n bias = obs.tempF - tNwsNow;\n highNwsAdj = adjustedHighForToday(\n hours,\n tz,\n todayISO,\n obs.obsTimeUTC,\n bias,\n 6,\n );\n }\n\n // Independent model signal(s)\n const modelHighs = await modelHighsForToday(cfg.lat, cfg.lon, tz, todayISO);\n const modelValues = Object.values(modelHighs);\n const modelMedian = modelValues.length\n ? modelValues.slice().sort((a, b) =>\n a - b\n )[Math.floor(modelValues.length / 2)]\n : null;\n\n // Blend (simple starting weights)\n const blended = modelMedian === null\n ? highNwsAdj\n : (0.65 * highNwsAdj + 0.35 * modelMedian);\n\n // Calibration parameters learned from past settlement errors\n const { errors } = await getCalib(site);\n const { muErr, sigma, n } = calibratedParams(errors);\n\n const meanHigh = blended + muErr; // adjust mean toward observed settlement truth\n\n // Probability high > strike (integer high; continuity correction)\n // Contract “> X” is equivalent to “>= X+1”; continuity approx uses X+0.5.\n const z = ((strike + 0.5) - meanHigh) / sigma;\n const pYes = 1 - normCdf(z);\n\n // Store today’s prediction so the settler can score it later\n await kv.set(`pred:${site}:${todayISO}`, {\n site,\n dateLocal: todayISO,\n tz,\n issuedAtUTC: DateTime.utc().toISO(),\n meanHigh,\n sigma,\n components: {\n nwsHigh,\n highNwsAdj,\n bias,\n modelHighs,\n modelMedian,\n blended,\n muErr,\n n,\n },\n });\n\n const out = {\n site,\n dateLocal: todayISO,\n timeZone: tz,\n\n meanHighF: Math.round(meanHigh * 10) / 10,\n sigmaF: Math.round(sigma * 10) / 10,\n calibN: n,\n\n strikeF: strike,\n pHighGreaterThanStrike: Math.round(pYes * 10000) / 10000,\n fairPrice: Math.round(pYes * 10000) / 10000, // $1 payout => fair price ≈ probability\n\n debug: {\n nwsHighF: Math.round(nwsHigh * 10) / 10,\n nwsAdjHighF: Math.round(highNwsAdj * 10) / 10,\n obsBiasF: Math.round(bias * 10) / 10,\n modelHighs,\n modelMedianF: modelMedian ? Math.round(modelMedian * 10) / 10 : null,\n },\n };\n\n return new Response(JSON.stringify(out, null, 2), {\n headers: { \"content-type\": \"application/json\" },\n });\n } catch (e: any) {\n return new Response(\n JSON.stringify({ error: String(e?.message ?? e) }, null, 2),\n {\n status: 500,\n headers: { \"content-type\": \"application/json\" },\n },\n );\n }\n}","label",[128],"faa82b6c-e1f5-11f0-9aea-42dde27851f2","main.ts","09916244-e1f4-11f0-8dec-42dde27851f2",7,["D",1766710703671],6,"import { DateTime } from \"https://esm.sh/luxon@3.5.0\";\n\nconst USER_AGENT = \"airport-high-forecast (jwa012@ciceroisback.com)\"; // required by NWS\n\n// Keep this list to ~10 airports. Hardcode lat/lon for simplicity & reliability.\nconst AIRPORTS: Record<\n string,\n { icao: string; iata?: string; lat: number; lon: number }\n> = {\n KLAX: { icao: \"KLAX\", iata: \"LAX\", lat: 33.9425, lon: -118.4081 },\n KHOU: { icao: \"KHOU\", iata: \"HOU\", lat: 29.6454, lon: -95.2789 },\n KSEA: { icao: \"KSEA\", iata: \"SEA\", lat: 47.4472, lon: -122.31361 },\n KMIA: { icao: \"KMIA\", iata: \"MIA\", lat: 25.79, lon: -80.32 },\n KMDW: { icao: \"KMDW\", iata: \"MDW\", lat: 41.78, lon: -87.65 },\n KAUS: { icao: \"KAUS\", iata: \"AUS\", lat: 30.18, lon: -97.68 },\n KDEN: { icao: \"KDEN\", iata: \"DEN\", lat: 39.85, lon: -104.66 },\n KSFO: { icao: \"KSFO\", iata: \"SFO\", lat: 37.61961, lon: -122.36558 },\n KDFW: { icao: \"KDFW\", iata: \"DFW\", lat: 32.9, lon: -97.02 },\n};\n\nfunction resolveStation(params: URLSearchParams) {\n const icaoParam = (params.get(\"icao\") || \"\").toUpperCase();\n const iataParam = (params.get(\"iata\") || \"\").toUpperCase();\n\n if (icaoParam && AIRPORTS[icaoParam]) return AIRPORTS[icaoParam];\n\n if (iataParam) {\n const match = Object.values(AIRPORTS).find((a) => a.iata === iataParam);\n if (match) return match;\n }\n return null;\n}\n\nasync function fetchJson(url: string) {\n const r = await fetch(url, {\n headers: { \"User-Agent\": USER_AGENT, \"Accept\": \"application/geo+json\" },\n });\n if (!r.ok) throw new Error(`Fetch failed ${r.status} for ${url}`);\n return r.json();\n}\n\nasync function getNwsHourly(lat: number, lon: number) {\n const points = await fetchJson(\n `https://api.weather.gov/points/${lat},${lon}`,\n );\n const tz = points.properties.timeZone as string;\n const hourlyUrl = points.properties.forecastHourly as string;\n\n const hourly = await fetchJson(hourlyUrl);\n const periods = hourly.properties.periods as Array<\n { startTime: string; temperature: number; temperatureUnit: string }\n >;\n\n // NWS hourly startTime is ISO with offset; Luxon will parse correctly.\n return {\n tz,\n hours: periods.map((p) => ({\n t: DateTime.fromISO(p.startTime),\n tempF: p.temperatureUnit === \"F\"\n ? p.temperature\n : (p.temperature * 9) / 5 + 32,\n })),\n };\n}\n\n// function parseAddsCsv(text: string): Array> {\n// // ADDS returns comment lines starting with #, then a header row.\n// const lines = text.split(\"\\n\").map((l) => l.trim()).filter((l) =>\n// l && !l.startsWith(\"#\")\n// );\n// if (lines.length < 2) return [];\n// const header = lines[0].split(\",\").map((s) => s.trim());\n// return lines.slice(1).map((line) => {\n// const cols = line.split(\",\");\n// const row: Record = {};\n// for (let i = 0; i < header.length; i++) {\n// row[header[i]] = (cols[i] ?? \"\").trim();\n// }\n// return row;\n// });\n// }\n\nasync function getLatestNwsObservation(stationId: string) {\n const url =\n `https://api.weather.gov/stations/${stationId}/observations/latest`;\n\n const r = await fetch(url, {\n headers: {\n \"User-Agent\": USER_AGENT,\n \"Accept\": \"application/geo+json\",\n },\n });\n if (!r.ok) {\n throw new Error(`NWS obs fetch failed ${r.status} for ${stationId}`);\n }\n const j = await r.json();\n\n const ts = j.properties.timestamp as string; // ISO time\n const tempC = j.properties.temperature?.value; // degC, may be null\n\n if (!ts || tempC === null || tempC === undefined) return null;\n\n return {\n obsTime: DateTime.fromISO(ts).toUTC(),\n tempF: (tempC * 9) / 5 + 32,\n raw: j.properties,\n };\n}\n\nfunction todayHighForLocalDate(\n hours: Array<{ t: DateTime; tempF: number }>,\n tz: string,\n localDateISO: string,\n) {\n const todays = hours\n .map((h) => ({ ...h, local: h.t.setZone(tz) }))\n .filter((h) => h.local.toISODate() === localDateISO);\n\n if (!todays.length) return null;\n\n let max = -Infinity;\n let maxTime: DateTime | null = null;\n for (const h of todays) {\n if (h.tempF > max) {\n max = h.tempF;\n maxTime = h.local;\n }\n }\n return { highF: max, atLocal: maxTime! };\n}\n\nfunction nearestForecastTemp(\n hours: Array<{ t: DateTime; tempF: number }>,\n whenUTC: DateTime,\n) {\n let best = hours[0];\n let bestAbs = Infinity;\n for (const h of hours) {\n const d = Math.abs(h.t.toUTC().diff(whenUTC).as(\"minutes\"));\n if (d < bestAbs) {\n bestAbs = d;\n best = h;\n }\n }\n return best.tempF;\n}\n\nfunction adjustedHigh(\n hours: Array<{ t: DateTime; tempF: number }>,\n tz: string,\n localDateISO: string,\n obsUTC: DateTime,\n biasF: number,\n tauHours = 6,\n) {\n const obs = obsUTC.toUTC();\n const todays = hours\n .map((h) => ({ ...h, local: h.t.setZone(tz) }))\n .filter((h) => h.local.toISODate() === localDateISO);\n\n let maxAdj = -Infinity;\n for (const h of todays) {\n const leadHours = Math.max(0, h.t.toUTC().diff(obs).as(\"hours\"));\n const weight = Math.exp(-leadHours / tauHours);\n const tadj = h.tempF + biasF * weight;\n if (tadj > maxAdj) maxAdj = tadj;\n }\n return maxAdj;\n}\n\nfunction clamp(x: number, a: number, b: number) {\n return Math.max(a, Math.min(b, x));\n}\n\nexport default async function (req: Request): Promise {\n try {\n const url = new URL(req.url);\n const airport = resolveStation(url.searchParams);\n if (!airport) {\n return new Response(\n JSON.stringify(\n {\n error: \"Pass eg ?icao=KLAX or ?iata=LAX.\",\n },\n null,\n 2,\n ),\n {\n status: 400,\n headers: { \"content-type\": \"application/json\" },\n },\n );\n }\n\n const { tz, hours } = await getNwsHourly(airport.lat, airport.lon);\n const nowLocal = DateTime.now().setZone(tz);\n const todayISO = nowLocal.toISODate()!;\n\n const base = todayHighForLocalDate(hours, tz, todayISO);\n if (!base) throw new Error(\"No NWS hourly data for today.\");\n\n const metar = await getLatestNwsObservation(airport.icao);\n\n let highAdj = base.highF;\n let bias = 0;\n\n if (metar) {\n const tNwsNow = nearestForecastTemp(hours, metar.obsTime);\n bias = metar.tempF - tNwsNow;\n highAdj = adjustedHigh(hours, tz, todayISO, metar.obsTime, bias, 6);\n }\n\n const range = clamp(\n 2 + 0.6 * Math.abs(highAdj - base.highF) + 0.3 * Math.abs(bias),\n 2,\n 8,\n );\n\n const out = {\n station: airport.icao,\n iata: airport.iata ?? null,\n dateLocal: todayISO,\n timeZone: tz,\n\n forecastHighF: Math.round(highAdj),\n confidencePlusMinusF: Math.round(range),\n\n debug: {\n nwsHighF: Math.round(base.highF),\n nwsHighTimeLocal: base.atLocal.toISO(),\n metarUsed: Boolean(metar),\n biasF: Math.round(bias * 10) / 10,\n metarObsTimeUTC: metar?.obsTime.toISO() ?? null,\n metarTempF: metar ? Math.round(metar.tempF * 10) / 10 : null,\n },\n };\n\n return new Response(JSON.stringify(out, null, 2), {\n headers: { \"content-type\": \"application/json\" },\n });\n } catch (e: any) {\n return new Response(\n JSON.stringify({ error: String(e?.message ?? e) }, null, 2),\n {\n status: 500,\n headers: { \"content-type\": \"application/json\" },\n },\n );\n }\n}",[140],"e34fbeee-e1fa-11f0-bc63-42dde27851f2",["D",1766712812014],2,[29],"pulls","branches",1,"forks","envVars",{"_159":61,"_161":61},"connections",{"_159":61,"_160":61},"count","unfilledCount","emptyCount",{"_44":147,"_33":29,"_31":32,"_130":-5,"_42":43,"_41":14,"_132":27,"_39":172,"_13":149,"_34":35,"_36":38,"_24":27,"_137":-5,"_166":173,"_28":174},{"_44":139,"_33":140,"_31":141,"_130":-5,"_42":43,"_41":142,"_132":27,"_39":169,"_13":144,"_34":135,"_36":145,"_24":27,"_137":-5,"_166":170,"_28":171},{"_44":127,"_33":128,"_31":129,"_130":-5,"_42":43,"_41":131,"_132":27,"_39":165,"_13":134,"_34":135,"_36":136,"_24":27,"_137":-5,"_166":167,"_28":168},["D",1766712688633],"children",[],[128],["D",1766710703671],[],[140],["D",1766712812014],[],[29],["D",1766709869715],{"_95":94,"_102":98},{"_95":94,"_96":-5,"_97":98,"_18":55,"_99":100,"_101":27},{"_13":84,"_33":85,"_44":43,"_86":45,"_87":-5,"_88":-5,"_89":-5,"_90":-5,"_91":-5,"_92":46,"_93":94},"ctx",{"_77":-5,"_121":212,"_213":214,"_215":216,"_217":27,"_218":27,"_219":27,"_220":25,"_221":-5,"_222":-7,"_223":-7,"_224":61,"_225":27,"_226":27,"_227":-5,"_228":27,"_229":27,"_230":27,"_231":-5},"ENV",{"_183":184,"_185":186,"_187":85,"_188":189,"_190":191,"_192":193,"_194":195,"_196":197,"_198":199,"_200":201,"_202":203,"_204":205,"_206":207,"_208":209,"_210":211},"REACT_APP_SENTRY_DSN","https://586020d86f05489db63a554690acf1e9@o1398359.ingest.sentry.io/6724499","RENDER_GIT_COMMIT","e87eb04e707cf64e7e532c8e8ec395f209e17f15","RENDER_GIT_BRANCH","POSTHOG_API_TOKEN","phc_nmoyuAkStEZuUp69rYlg1DZgrIt19Gx0Y4Hwf8tGgJP","POSTHOG_API_HOST","https://wart.val.town","WEB_API_TEMPLATE","https://{val}.web.val.run","APP_URL","https://www.val.town","MODULE_URL","https://esm.town","VANITY_WEB_API_TEMPLATE","https://{label}.val.run/","DENOLS_URL","https://lsp.val.town","NODE_ENV","production","CLOUDFLARE_TURNSTILE_SITE_KEY","0x4AAAAAACtaIZJH7s9rBMZ6","GOOGLE_CLIENT_ID","875989696899-jtsqkhlbdv5jrlsk8631p7ipjg3c6o62.apps.googleusercontent.com","STRIPE_PUBLISHABLE_KEY","pk_live_51N2zvWEpZTTLipIkVEwXrUG9AT0yeOOEYxszarZT1jpsZV58Ly6wlg3SltZpHN26CUalOBmUSY7adQRQiSkCZz2500qSXNkf8Y","API_URL","https://api.val.town",[],"preferencesKeybindings","default","preferencesTheme","light","preferencesFixers","preferencesCopilot","preferencesSelectionAi","preferencesCloseBrackets","preferencesChatPanelState","preferencesDprint","preferencesDenofmt","notificationCount","flagDenols","isBusinessOrgMember","businessOrgHandle","isBusinessOrgAdmin","isHigherTownieTier","navCollapsed","currentOrgId"]

Diff · v15