2569718930@qq.com
1557 lines
55 KiB
TypeScript
1557 lines
55 KiB
TypeScript
import { Locale } from "@/lib/i18n";
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import {
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AiAnalysisStructured,
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CityDetail,
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HistoryPoint,
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NearbyStation,
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} from "@/lib/dashboard-types";
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const METAR_WX_MAP: Record<
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string,
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{ en: string; icon: string; zh: string }
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> = {
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RA: { en: "Rain", icon: "🌧️", zh: "降雨" },
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"-RA": { en: "Light rain", icon: "🌦️", zh: "小雨" },
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"+RA": { en: "Heavy rain", icon: "⛈️", zh: "强降雨" },
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SN: { en: "Snow", icon: "❄️", zh: "降雪" },
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"-SN": { en: "Light snow", icon: "🌨️", zh: "小雪" },
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"+SN": { en: "Heavy snow", icon: "🌨️", zh: "大雪" },
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DZ: { en: "Drizzle", icon: "🌦️", zh: "毛毛雨" },
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FG: { en: "Fog", icon: "🌫️", zh: "雾" },
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BR: { en: "Mist", icon: "🌫️", zh: "薄雾" },
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HZ: { en: "Haze", icon: "🌫️", zh: "霾" },
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TS: { en: "Thunderstorm", icon: "⛈️", zh: "雷暴" },
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VCTS: { en: "Nearby thunderstorm", icon: "⛈️", zh: "附近雷暴" },
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SQ: { en: "Squall", icon: "💨", zh: "飑线" },
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GS: { en: "Hail", icon: "🌨️", zh: "冰雹" },
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};
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function isEnglish(locale: Locale) {
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return locale === "en-US";
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}
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function getObservationSourceCode(detail: CityDetail): string {
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const source = String(detail.current?.settlement_source || "")
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.trim()
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.toLowerCase();
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if (source) return source;
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const city = String(detail.name || detail.display_name || "")
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.trim()
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.toLowerCase();
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if (city === "hong kong") return "hko";
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if (city === "taipei") return "cwa";
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return "metar";
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}
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function getObservationSourceTag(detail: CityDetail): string {
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const label = String(detail.current?.settlement_source_label || "")
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.trim()
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.toUpperCase();
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if (label) return label;
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const code = getObservationSourceCode(detail);
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if (code === "hko") return "HKO";
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if (code === "cwa") return "CWA";
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if (code === "mgm") return "MGM";
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return "METAR";
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}
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function normalizeCloudSummary(
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cloudDesc: string | null | undefined,
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locale: Locale,
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): { icon: string; text: string } {
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const raw = String(cloudDesc || "").trim();
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if (!raw) {
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return { icon: "🔍", text: isEnglish(locale) ? "Unknown" : "未知" };
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}
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const lower = raw.toLowerCase();
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if (
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raw.includes("晴") ||
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raw.includes("晴朗") ||
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lower.includes("clear") ||
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lower.includes("sunny")
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) {
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return { icon: "☀️", text: isEnglish(locale) ? "Clear" : "晴朗" };
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}
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if (raw.includes("阴") || lower.includes("overcast")) {
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return { icon: "☁️", text: isEnglish(locale) ? "Overcast" : "阴天" };
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}
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if (raw.includes("多云") || lower.includes("cloud")) {
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return { icon: "☁️", text: isEnglish(locale) ? "Cloudy" : "多云" };
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}
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if (raw.includes("少云") || lower.includes("few")) {
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return { icon: "🌤️", text: isEnglish(locale) ? "Mostly clear" : "少云" };
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}
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if (raw.includes("散云") || lower.includes("scattered")) {
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return { icon: "⛅", text: isEnglish(locale) ? "Partly cloudy" : "散云" };
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}
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return { icon: "🔍", text: raw };
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}
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export function translateMetar(code?: string | null, locale: Locale = "zh-CN") {
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if (!code) return null;
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const metarCode = String(code);
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for (const [key, value] of Object.entries(METAR_WX_MAP)) {
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if (metarCode.includes(key)) {
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return {
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icon: value.icon,
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label: isEnglish(locale) ? value.en : value.zh,
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};
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}
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}
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return { icon: "🔍", label: metarCode };
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}
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export function getRiskBadgeLabel(
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level?: string | null,
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locale: Locale = "zh-CN",
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) {
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if (isEnglish(locale)) {
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return (
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{
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high: "🔴 High Risk",
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low: "🟢 Low Risk",
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medium: "🟠 Medium Risk",
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}[String(level || "low")] || "Unknown Risk"
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);
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}
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return (
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{
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high: "🔴 高风险",
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low: "🟢 低风险",
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medium: "🟠 中风险",
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}[String(level || "low")] || "未知风险"
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);
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}
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export function getWeatherSummary(detail: CityDetail, locale: Locale = "zh-CN") {
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const current = detail.current || {};
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const cloud = normalizeCloudSummary(current.cloud_desc, locale);
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let weatherText = cloud.text;
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let weatherIcon = cloud.icon;
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if (current.wx_desc) {
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const translated = translateMetar(current.wx_desc, locale);
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if (translated) {
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weatherText = translated.label;
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weatherIcon = translated.icon;
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}
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}
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return { weatherIcon, weatherText };
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}
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export function getHeroMetaItems(detail: CityDetail, locale: Locale = "zh-CN") {
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const current = detail.current || {};
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const parts: string[] = [];
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const sourceTag = getObservationSourceTag(detail);
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if (current.obs_time) {
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const ageText =
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current.obs_age_min != null && current.obs_age_min >= 30
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? isEnglish(locale)
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? ` (${current.obs_age_min} min ago)`
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: `（${current.obs_age_min} 分钟前）`
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: "";
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parts.push(`✈️ ${sourceTag} ${current.obs_time}${ageText}`);
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}
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if (current.wx_desc) {
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const translated = translateMetar(current.wx_desc, locale);
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if (translated) {
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parts.push(`${translated.icon} ${translated.label}`);
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}
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} else if (current.cloud_desc) {
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const cloud = normalizeCloudSummary(current.cloud_desc, locale);
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parts.push(`${cloud.icon} ${cloud.text}`);
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}
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if (current.wind_speed_kt != null) {
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parts.push(`💨 ${current.wind_speed_kt}kt`);
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}
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if (current.visibility_mi != null) {
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parts.push(`👁️ ${current.visibility_mi}mi`);
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}
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if (detail.mgm?.temp != null) {
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const timeMatch = detail.mgm.time?.match(/T?(\d{2}:\d{2})/);
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const timeText = timeMatch ? ` @${timeMatch[1]}` : "";
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parts.push(
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isEnglish(locale)
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? `🛰 MGM Obs: ${detail.mgm.temp}${detail.temp_symbol}${timeText}`
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: `🛰 MGM 实测: ${detail.mgm.temp}${detail.temp_symbol}${timeText}`,
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);
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}
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const trend = detail.trend || {};
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if (trend.is_dead_market) {
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parts.push(isEnglish(locale) ? "☠️ Flat market" : "☠️ 死盘");
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} else if (trend.direction && trend.direction !== "unknown") {
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const labels: Record<string, string> = isEnglish(locale)
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? {
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falling: "📉 Cooling",
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mixed: "📊 Choppy",
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rising: "📈 Warming",
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stagnant: "⏸ Flat",
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}
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: {
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falling: "📉 降温中",
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mixed: "📊 波动中",
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rising: "📈 升温中",
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stagnant: "⏸ 持平",
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};
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parts.push(labels[trend.direction] || trend.direction);
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}
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return parts;
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}
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export function getTemperatureChartData(
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detail: CityDetail,
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locale: Locale = "zh-CN",
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) {
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const hourly = detail.hourly || {};
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const times = hourly.times || [];
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const temps = hourly.temps || [];
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if (!times.length) return null;
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const currentHour = detail.local_time
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? `${detail.local_time.split(":")[0]}:00`
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: null;
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const currentIndex = currentHour ? times.indexOf(currentHour) : -1;
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const omMax = detail.forecast?.today_high;
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const debMax = detail.deb?.prediction;
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const offset =
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debMax != null && omMax != null ? Number(debMax) - Number(omMax) : 0;
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const debTemps = temps.map((temp) =>
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temp != null ? Number((temp + offset).toFixed(1)) : null,
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);
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const debPast = debTemps.map((temp, index) =>
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currentIndex >= 0 && index <= currentIndex ? temp : null,
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);
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const debFuture = debTemps.map((temp, index) =>
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currentIndex < 0 || index >= currentIndex ? temp : null,
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);
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const observationTag = getObservationSourceTag(detail);
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const observationCode = getObservationSourceCode(detail);
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const settlementSource =
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observationCode === "hko" || observationCode === "cwa";
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const observationSource = settlementSource
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? detail.settlement_today_obs?.length
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? detail.settlement_today_obs
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: detail.current?.obs_time && detail.current?.temp != null
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? [{ time: detail.current.obs_time, temp: detail.current.temp }]
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: []
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: detail.metar_today_obs?.length
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? detail.metar_today_obs
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: detail.trend?.recent || [];
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const metarPoints = new Array(times.length).fill(null);
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observationSource.forEach((item) => {
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const parts = String(item.time || "").split(":");
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let hour = Number.parseInt(parts[0], 10);
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const minute = Number.parseInt(parts[1] || "0", 10);
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if (Number.isNaN(hour)) return;
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if (minute >= 30) hour = (hour + 1) % 24;
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const key = `${String(hour).padStart(2, "0")}:00`;
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const index = times.indexOf(key);
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if (index >= 0 && metarPoints[index] === null) {
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metarPoints[index] = item.temp ?? null;
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}
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});
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const mgmPoints = new Array(times.length).fill(null);
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if (detail.mgm?.temp != null && detail.mgm?.time) {
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const match = detail.mgm.time.match(/T?(\d{2}):(\d{2})/);
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if (match) {
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let hour = Number.parseInt(match[1], 10);
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const minute = Number.parseInt(match[2], 10);
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if (minute >= 30) hour = (hour + 1) % 24;
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const key = `${String(hour).padStart(2, "0")}:00`;
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const index = times.indexOf(key);
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if (index >= 0) {
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mgmPoints[index] = detail.mgm.temp;
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}
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}
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}
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const mgmHourlyPoints = new Array(times.length).fill(null);
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let hasMgmHourly = false;
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detail.mgm?.hourly?.forEach((item) => {
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const match = String(item.time || "").match(/T?(\d{2}):(\d{2})/);
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if (!match) return;
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const key = `${match[1]}:00`;
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const index = times.indexOf(key);
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if (index >= 0) {
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mgmHourlyPoints[index] = item.temp ?? null;
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hasMgmHourly = true;
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}
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});
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const allValues = [
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...debTemps.filter((value) => value != null),
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...metarPoints.filter((value) => value != null),
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...mgmPoints.filter((value) => value != null),
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...mgmHourlyPoints.filter((value) => value != null),
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] as number[];
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if (!allValues.length) return null;
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const min = Math.floor(Math.min(...allValues)) - 1;
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const max = Math.ceil(Math.max(...allValues)) + 1;
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const legendParts: string[] = [];
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if (detail.mgm?.temp != null) {
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legendParts.push(`MGM: ${detail.mgm.temp}${detail.temp_symbol}`);
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}
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if (!hasMgmHourly && debMax != null && omMax != null && Math.abs(offset) > 0.3) {
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const sign = offset > 0 ? "+" : "";
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legendParts.push(
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isEnglish(locale)
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? `DEB offset ${sign}${offset.toFixed(1)}${detail.temp_symbol} vs OM`
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: `DEB 偏移 ${sign}${offset.toFixed(1)}${detail.temp_symbol} vs OM`,
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);
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}
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if (hasMgmHourly) {
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legendParts.push(
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isEnglish(locale)
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? "Using MGM hourly forecast to replace DEB curve"
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: "已使用 MGM 小时预报替代 DEB 曲线",
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);
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}
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if ((detail.trend?.recent?.length || 0) > 0 || observationSource.length > 0) {
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const recentData =
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observationSource.length > 0
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? [...observationSource]
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: [...(detail.trend?.recent || [])];
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const recentText = recentData
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.slice(0, 4)
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.reverse()
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.map((item) => `${item.temp}${detail.temp_symbol}@${item.time}`)
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.join(" -> ");
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legendParts.push(`${observationTag}: ${recentText}`);
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}
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return {
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datasets: {
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debFuture,
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debPast,
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hasMgmHourly,
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metarPoints,
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mgmHourlyPoints,
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mgmPoints,
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offset,
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temps,
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},
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observationLabel: isEnglish(locale)
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? `${observationTag} Observation`
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: `${observationTag} 实况`,
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legendText: legendParts.join(" | "),
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max,
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min,
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times,
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};
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}
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export function getProbabilityView(detail: CityDetail, targetDate?: string | null) {
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const date = targetDate || detail.local_date;
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if (date === detail.local_date) {
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return {
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mu: detail.probabilities?.mu ?? null,
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probabilities: detail.probabilities?.distribution || [],
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};
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}
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const daily = detail.multi_model_daily?.[date];
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return {
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mu: daily?.deb?.prediction ?? null,
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probabilities: daily?.probabilities || [],
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};
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}
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export function getModelView(detail: CityDetail, targetDate?: string | null) {
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const date = targetDate || detail.local_date;
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const daily = detail.multi_model_daily?.[date];
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if (daily) {
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return {
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deb: daily.deb?.prediction ?? null,
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models: daily.models || {},
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};
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}
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return {
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deb: detail.deb?.prediction ?? null,
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models: detail.multi_model || {},
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};
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}
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export function parseAiAnalysis(analysis: CityDetail["ai_analysis"]) {
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const fallback = {
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bullets: [] as string[],
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summary: "",
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};
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if (!analysis) return fallback;
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if (typeof analysis === "string") {
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return {
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bullets: [],
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summary: analysis.trim(),
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};
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}
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const structured = analysis as AiAnalysisStructured;
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return {
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bullets: Array.isArray(structured.highlights)
|
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? structured.highlights
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: Array.isArray(structured.points)
|
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? structured.points
|
||
: [],
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summary: structured.summary || structured.text || structured.message || "",
|
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};
|
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}
|
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export function pickAnkaraNearbyStations(stations: NearbyStation[]) {
|
||
const preferredNames = [
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"Airport (MGM/17128)",
|
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"Ankara (Bölge/Center)",
|
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"Ankara (Bolge/Center)",
|
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"Etimesgut",
|
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"Pursaklar",
|
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"Cubuk",
|
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"Çubuk",
|
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"Kalecik",
|
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];
|
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|
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const picks = preferredNames
|
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.map((name) => stations.find((station) => station?.name === name))
|
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.filter(Boolean) as NearbyStation[];
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return picks.length ? picks : stations;
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}
|
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export function getFutureSlice(detail: CityDetail, dateStr: string) {
|
||
const hourly = detail.hourly_next_48h || {};
|
||
const times = hourly.times || [];
|
||
const slice: Array<{
|
||
cloudCover: number | null;
|
||
dewPoint: number | null;
|
||
label: string;
|
||
precipProb: number | null;
|
||
pressure: number | null;
|
||
radiation: number | null;
|
||
temp: number | null;
|
||
time: string;
|
||
windDir: number | null;
|
||
windSpeed: number | null;
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||
}> = [];
|
||
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||
for (let index = 0; index < times.length; index += 1) {
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||
const timestamp = times[index];
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||
if (!timestamp || !String(timestamp).startsWith(dateStr)) continue;
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||
|
||
slice.push({
|
||
cloudCover: hourly.cloud_cover?.[index] ?? null,
|
||
dewPoint: hourly.dew_point?.[index] ?? null,
|
||
label: String(timestamp).split("T")[1]?.slice(0, 5) || timestamp,
|
||
precipProb: hourly.precipitation_probability?.[index] ?? null,
|
||
pressure: hourly.pressure_msl?.[index] ?? null,
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||
radiation: hourly.radiation?.[index] ?? null,
|
||
temp: hourly.temps?.[index] ?? null,
|
||
time: timestamp,
|
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windDir: hourly.wind_direction_10m?.[index] ?? null,
|
||
windSpeed: hourly.wind_speed_10m?.[index] ?? null,
|
||
});
|
||
}
|
||
|
||
return slice;
|
||
}
|
||
|
||
function trendBucketFromDir(direction?: number | null) {
|
||
const value = Number(direction);
|
||
if (!Number.isFinite(value)) return null;
|
||
if (value >= 135 && value <= 240) return "southerly";
|
||
if (value >= 290 || value <= 45) return "northerly";
|
||
if (value > 45 && value < 135) return "easterly";
|
||
return "westerly";
|
||
}
|
||
|
||
function bucketLabel(bucket: string | null, locale: Locale = "zh-CN") {
|
||
if (isEnglish(locale)) {
|
||
return (
|
||
{
|
||
southerly: "S / SW wind",
|
||
northerly: "N / NW wind",
|
||
easterly: "E wind",
|
||
westerly: "W wind",
|
||
}[bucket || ""] || "Unknown wind direction"
|
||
);
|
||
}
|
||
return (
|
||
{
|
||
southerly: "南 / 西南风",
|
||
northerly: "北 / 西北风",
|
||
easterly: "东风",
|
||
westerly: "西风",
|
||
}[bucket || ""] || "风向不明"
|
||
);
|
||
}
|
||
|
||
export function wuRound(value: number | null | undefined) {
|
||
const numeric = Number(value);
|
||
if (!Number.isFinite(numeric)) return null;
|
||
return numeric >= 0
|
||
? Math.floor(numeric + 0.5)
|
||
: Math.ceil(numeric - 0.5);
|
||
}
|
||
|
||
export function formatDelta(value: number | null | undefined, suffix = "") {
|
||
const numeric = Number(value);
|
||
if (!Number.isFinite(numeric)) return "--";
|
||
const sign = numeric > 0 ? "+" : "";
|
||
return `${sign}${numeric.toFixed(1)}${suffix}`;
|
||
}
|
||
|
||
function getForecastTextForDate(detail: CityDetail, dateStr: string) {
|
||
const periods = detail.source_forecasts?.weather_gov?.forecast_periods || [];
|
||
return periods.filter((period) =>
|
||
String(period.start_time || "").startsWith(dateStr),
|
||
);
|
||
}
|
||
|
||
export function computeFrontTrendSignal(
|
||
detail: CityDetail,
|
||
dateStr: string,
|
||
locale: Locale = "zh-CN",
|
||
) {
|
||
const backendSummary =
|
||
dateStr === detail.local_date
|
||
? String(detail.dynamic_commentary?.summary || "").trim()
|
||
: "";
|
||
const backendNotes = Array.isArray(detail.dynamic_commentary?.notes)
|
||
? detail.dynamic_commentary?.notes
|
||
?.map((item) => String(item || "").trim())
|
||
.filter(Boolean) || []
|
||
: [];
|
||
const slice = getFutureSlice(detail, dateStr);
|
||
const currentTemp = Number(detail.current?.temp);
|
||
const currentDew = Number(detail.current?.dewpoint);
|
||
|
||
if (!slice.length) {
|
||
return {
|
||
confidence: "low",
|
||
label: isEnglish(locale) ? "Monitoring" : "监控中",
|
||
metrics: [] as Array<{
|
||
label: string;
|
||
note: string;
|
||
tone?: string;
|
||
value: string;
|
||
}>,
|
||
precipMax: 0,
|
||
score: 0,
|
||
summary:
|
||
backendSummary ||
|
||
(isEnglish(locale)
|
||
? "Insufficient intraday structured data. Keep baseline monitoring."
|
||
: "当日日内结构化数据不足，暂时只保留基础监控。"),
|
||
weatherGovPeriods: [] as ReturnType<typeof getForecastTextForDate>,
|
||
};
|
||
}
|
||
|
||
const normalizeHm = (value: unknown): string | null => {
|
||
const match = String(value || "").match(/(\d{1,2}):(\d{2})/);
|
||
if (!match) return null;
|
||
const hour = Number.parseInt(match[1], 10);
|
||
const minute = Number.parseInt(match[2], 10);
|
||
if (
|
||
!Number.isFinite(hour) ||
|
||
!Number.isFinite(minute) ||
|
||
hour < 0 ||
|
||
hour > 23 ||
|
||
minute < 0 ||
|
||
minute > 59
|
||
) {
|
||
return null;
|
||
}
|
||
return `${String(hour).padStart(2, "0")}:${String(minute).padStart(2, "0")}`;
|
||
};
|
||
const hmToMinutes = (value: string | null) => {
|
||
if (!value) return null;
|
||
const [hourPart, minutePart] = value.split(":");
|
||
const hour = Number.parseInt(hourPart || "", 10);
|
||
const minute = Number.parseInt(minutePart || "", 10);
|
||
if (
|
||
!Number.isFinite(hour) ||
|
||
!Number.isFinite(minute) ||
|
||
hour < 0 ||
|
||
hour > 23 ||
|
||
minute < 0 ||
|
||
minute > 59
|
||
) {
|
||
return null;
|
||
}
|
||
return hour * 60 + minute;
|
||
};
|
||
const pointMinutes = (point: { label?: string }) =>
|
||
hmToMinutes(normalizeHm(point.label));
|
||
|
||
const isTargetToday = dateStr === detail.local_date;
|
||
const currentHm = normalizeHm(detail.local_time);
|
||
const sunsetHm = normalizeHm(detail.forecast?.sunset);
|
||
const currentMinutes = hmToMinutes(currentHm);
|
||
const sunsetMinutes = hmToMinutes(sunsetHm);
|
||
const canUseSunsetWindow =
|
||
isTargetToday &&
|
||
currentMinutes !== null &&
|
||
sunsetMinutes !== null &&
|
||
sunsetMinutes > currentMinutes;
|
||
|
||
const futureSlice =
|
||
isTargetToday && currentMinutes !== null
|
||
? slice.filter((point) => {
|
||
const minutes = pointMinutes(point);
|
||
return minutes === null ? true : minutes >= currentMinutes;
|
||
})
|
||
: slice;
|
||
const untilSunsetSlice =
|
||
canUseSunsetWindow && sunsetMinutes !== null
|
||
? futureSlice.filter((point) => {
|
||
const minutes = pointMinutes(point);
|
||
return minutes === null ? false : minutes <= sunsetMinutes;
|
||
})
|
||
: futureSlice;
|
||
const workingSlice =
|
||
untilSunsetSlice.length >= 2
|
||
? untilSunsetSlice
|
||
: futureSlice.length >= 2
|
||
? futureSlice
|
||
: slice;
|
||
const usingSunsetWindow = canUseSunsetWindow && untilSunsetSlice.length >= 2;
|
||
const first = workingSlice[0] || slice[0];
|
||
const last = workingSlice[workingSlice.length - 1] || slice[slice.length - 1];
|
||
const effectiveHours = Math.max(1, workingSlice.length);
|
||
const windowLabel = `${first?.label || "--"}-${last?.label || "--"}`;
|
||
const windowText = isEnglish(locale)
|
||
? usingSunsetWindow
|
||
? `today ${windowLabel} (~${effectiveHours}h, now -> sunset)`
|
||
: isTargetToday
|
||
? `today ${windowLabel} (~${effectiveHours}h)`
|
||
: `daily ${windowLabel} (~${effectiveHours}h)`
|
||
: usingSunsetWindow
|
||
? `今日 ${windowLabel}（约 ${effectiveHours} 小时，当前至日落）`
|
||
: isTargetToday
|
||
? `今日 ${windowLabel}（约 ${effectiveHours} 小时）`
|
||
: `当日日内 ${windowLabel}（约 ${effectiveHours} 小时）`;
|
||
const firstTemp = Number.isFinite(Number(first.temp)) ? Number(first.temp) : currentTemp;
|
||
const lastTemp = Number.isFinite(Number(last.temp)) ? Number(last.temp) : firstTemp;
|
||
const tempDelta =
|
||
Number.isFinite(firstTemp) && Number.isFinite(lastTemp) ? lastTemp - firstTemp : 0;
|
||
const firstDew = Number.isFinite(Number(first.dewPoint))
|
||
? Number(first.dewPoint)
|
||
: currentDew;
|
||
const lastDew = Number.isFinite(Number(last.dewPoint))
|
||
? Number(last.dewPoint)
|
||
: firstDew;
|
||
const dewDelta =
|
||
Number.isFinite(firstDew) && Number.isFinite(lastDew) ? lastDew - firstDew : 0;
|
||
const firstPressure = Number.isFinite(Number(first.pressure))
|
||
? Number(first.pressure)
|
||
: null;
|
||
const lastPressure = Number.isFinite(Number(last.pressure))
|
||
? Number(last.pressure)
|
||
: firstPressure;
|
||
const pressureDelta =
|
||
Number.isFinite(Number(firstPressure)) && Number.isFinite(Number(lastPressure))
|
||
? Number(lastPressure) - Number(firstPressure)
|
||
: 0;
|
||
const firstCloud = Number.isFinite(Number(first.cloudCover))
|
||
? Number(first.cloudCover)
|
||
: null;
|
||
const lastCloud = Number.isFinite(Number(last.cloudCover))
|
||
? Number(last.cloudCover)
|
||
: firstCloud;
|
||
const cloudDelta =
|
||
Number.isFinite(Number(firstCloud)) && Number.isFinite(Number(lastCloud))
|
||
? Number(lastCloud) - Number(firstCloud)
|
||
: 0;
|
||
const precipMax = slice.reduce(
|
||
(max, point) => Math.max(max, Number(point.precipProb) || 0),
|
||
0,
|
||
);
|
||
const firstBucket = trendBucketFromDir(first.windDir);
|
||
const lastBucket = trendBucketFromDir(last.windDir);
|
||
const weatherGovPeriods = getForecastTextForDate(detail, dateStr);
|
||
const weatherGovText = weatherGovPeriods
|
||
.map(
|
||
(period) =>
|
||
`${period.short_forecast || ""} ${period.detailed_forecast || ""}`.toLowerCase(),
|
||
)
|
||
.join(" ");
|
||
|
||
let warmScore = 0;
|
||
let coldScore = 0;
|
||
if (tempDelta >= 2) warmScore += 24;
|
||
else if (tempDelta >= 0.8) warmScore += 12;
|
||
if (tempDelta <= -2) coldScore += 24;
|
||
else if (tempDelta <= -0.8) coldScore += 12;
|
||
if (dewDelta >= 1.2) warmScore += 14;
|
||
if (dewDelta <= -1.2) coldScore += 10;
|
||
if (pressureDelta >= 1.2) coldScore += 16;
|
||
if (pressureDelta <= -1.0) warmScore += 8;
|
||
if (lastBucket === "southerly") warmScore += 14;
|
||
if (firstBucket !== lastBucket && lastBucket === "southerly") warmScore += 10;
|
||
if (lastBucket === "northerly") coldScore += 14;
|
||
if (firstBucket !== lastBucket && lastBucket === "northerly") coldScore += 10;
|
||
if (cloudDelta >= 15 && tempDelta >= 0) warmScore += 6;
|
||
if (cloudDelta >= 15 && tempDelta < 0) coldScore += 8;
|
||
if (precipMax >= 40) coldScore += 8;
|
||
if (
|
||
weatherGovText.includes("cold front") ||
|
||
weatherGovText.includes("temperatures falling")
|
||
) {
|
||
coldScore += 18;
|
||
}
|
||
if (weatherGovText.includes("warm front") || weatherGovText.includes("warmer")) {
|
||
warmScore += 18;
|
||
}
|
||
if (weatherGovText.includes("thunder") || weatherGovText.includes("snow")) {
|
||
coldScore += 8;
|
||
}
|
||
|
||
const score = Math.max(-100, Math.min(100, warmScore - coldScore));
|
||
const warmLabel = isEnglish(locale)
|
||
? "Warm advection / warm-front tendency"
|
||
: "暖平流 / 暖锋倾向";
|
||
const coldLabel = isEnglish(locale)
|
||
? "Cold advection / cold-front tendency"
|
||
: "冷平流 / 冷锋倾向";
|
||
const monitorLabel = isEnglish(locale) ? "Monitoring" : "监控中";
|
||
const label = score >= 18 ? warmLabel : score <= -18 ? coldLabel : monitorLabel;
|
||
const confidence =
|
||
Math.abs(score) >= 45 ? "high" : Math.abs(score) >= 22 ? "medium" : "low";
|
||
const summary = (() => {
|
||
const parts: string[] = [];
|
||
|
||
if (isEnglish(locale)) {
|
||
if (lastBucket === "southerly" && firstBucket !== "southerly") {
|
||
parts.push("Low-level wind turns more southerly.");
|
||
} else if (lastBucket === "northerly" && firstBucket !== "northerly") {
|
||
parts.push("Low-level wind shifts toward a northerly regime.");
|
||
}
|
||
|
||
if (tempDelta >= 0.8) {
|
||
parts.push(`Temperature rises by ${formatDelta(tempDelta, detail.temp_symbol)}.`);
|
||
} else if (tempDelta <= -0.8) {
|
||
parts.push(`Temperature eases by ${formatDelta(tempDelta, detail.temp_symbol)}.`);
|
||
}
|
||
|
||
if (dewDelta >= 0.8) {
|
||
parts.push("Dew point is lifting, suggesting moisture transport is strengthening.");
|
||
} else if (dewDelta <= -0.8) {
|
||
parts.push("Dew point is falling, so low-level air is turning drier.");
|
||
}
|
||
|
||
if (cloudDelta >= 15) {
|
||
parts.push("Cloud cover is building.");
|
||
} else if (cloudDelta <= -15) {
|
||
parts.push("Cloud cover is easing.");
|
||
}
|
||
|
||
if (pressureDelta >= 1) {
|
||
parts.push("Pressure rebound argues for a cooler push.");
|
||
} else if (pressureDelta <= -1) {
|
||
parts.push("Pressure is softening, which is less hostile to warming.");
|
||
}
|
||
|
||
if (precipMax >= 50) {
|
||
parts.push("Precipitation risk is high enough to watch for cloud/rain suppression.");
|
||
}
|
||
|
||
if (!parts.length) {
|
||
parts.push(`Structured trend is mixed, so the core judgement still centers on ${windowText}.`);
|
||
} else {
|
||
parts.push(`Core judgement remains focused on ${windowText}.`);
|
||
}
|
||
} else {
|
||
if (lastBucket === "southerly" && firstBucket !== "southerly") {
|
||
parts.push("低层风向更偏南，暖空气输送权重上升。");
|
||
} else if (lastBucket === "northerly" && firstBucket !== "northerly") {
|
||
parts.push("低层风向转偏北，冷空气影响权重上升。");
|
||
}
|
||
|
||
if (tempDelta >= 0.8) {
|
||
parts.push(`气温抬升 ${formatDelta(tempDelta, detail.temp_symbol)}。`);
|
||
} else if (tempDelta <= -0.8) {
|
||
parts.push(`气温回落 ${formatDelta(tempDelta, detail.temp_symbol)}。`);
|
||
}
|
||
|
||
if (dewDelta >= 0.8) {
|
||
parts.push("露点同步上升，说明暖湿输送在增强。");
|
||
} else if (dewDelta <= -0.8) {
|
||
parts.push("露点回落，低层空气在转干。");
|
||
}
|
||
|
||
if (cloudDelta >= 15) {
|
||
parts.push("云量正在增多。");
|
||
} else if (cloudDelta <= -15) {
|
||
parts.push("云量正在回落。");
|
||
}
|
||
|
||
if (pressureDelta >= 1) {
|
||
parts.push("气压回升，更偏向冷空气压入。");
|
||
} else if (pressureDelta <= -1) {
|
||
parts.push("气压走低，对增温压制减弱。");
|
||
}
|
||
|
||
if (precipMax >= 50) {
|
||
parts.push("降水概率已足以关注云雨压温。");
|
||
}
|
||
|
||
if (!parts.length) {
|
||
parts.push(`结构信号分化较大，核心仍围绕${windowText}观察。`);
|
||
} else {
|
||
parts.push(`核心判断窗口仍以${windowText}为主。`);
|
||
}
|
||
}
|
||
|
||
return parts.join(isEnglish(locale) ? " " : "");
|
||
})();
|
||
const cloudNote = (() => {
|
||
if (cloudDelta >= 15 && tempDelta >= 0.8 && dewDelta >= 0.8) {
|
||
return isEnglish(locale)
|
||
? "Clouds are increasing while temperature and dew point still rise; this usually fits ongoing warm-moist transport rather than immediate cooling."
|
||
: "云量上升时温度和露点仍在抬升，更像暖湿输送持续中，而不是立刻转凉。";
|
||
}
|
||
if (cloudDelta >= 15 && tempDelta >= 0 && lastBucket === "southerly") {
|
||
return isEnglish(locale)
|
||
? "Clouds are building without clear cooling, and the low-level wind still leans southerly; watch for warm advection to continue."
|
||
: "云量增多但未明显降温，且低层风仍偏南，需继续关注暖平流是否延续。";
|
||
}
|
||
if (cloudDelta >= 15 && tempDelta < 0 && precipMax >= 40) {
|
||
return isEnglish(locale)
|
||
? "Clouds are thickening while temperature eases and precipitation risk is elevated; cloud/rain suppression is becoming more likely."
|
||
: "云量增厚且气温回落，同时降水概率偏高，更像云雨压温开始生效。";
|
||
}
|
||
if (cloudDelta >= 15 && tempDelta < 0 && pressureDelta >= 1) {
|
||
return isEnglish(locale)
|
||
? "Clouds are increasing while temperature softens and pressure rebounds; watch for cold-air push or frontal suppression."
|
||
: "云量上升同时气温走弱、气压回升，需留意冷空气压入或锋面压温。";
|
||
}
|
||
if (cloudDelta <= -15 && tempDelta >= 0.8) {
|
||
return isEnglish(locale)
|
||
? "Cloud cover is easing while temperature rises; daytime heating efficiency is improving."
|
||
: "云量回落且温度抬升，白天增温效率在改善。";
|
||
}
|
||
return isEnglish(locale)
|
||
? "Read cloud-cover change together with temperature, dew point, wind, and precipitation; cloud change alone does not define the regime."
|
||
: "云量变化需要结合温度、露点、风向和降水一起看，不能单独决定天气形势。";
|
||
})();
|
||
const dewNote = (() => {
|
||
if (dewDelta >= 1.2 && tempDelta >= 0.8) {
|
||
return isEnglish(locale)
|
||
? "Dew point and temperature rise together, which usually supports strengthening warm-moist transport."
|
||
: "露点和温度同步抬升，更偏向暖湿输送增强。";
|
||
}
|
||
if (dewDelta >= 1.2 && precipMax >= 40) {
|
||
return isEnglish(locale)
|
||
? "Moisture is building while precipitation risk is already notable; watch for showers to cap daytime heating."
|
||
: "水汽在累积且降水风险已抬升，需关注阵雨对午后增温的压制。";
|
||
}
|
||
if (dewDelta <= -1.2 && tempDelta <= 0) {
|
||
return isEnglish(locale)
|
||
? "Drier low-level air is arriving together with softer temperature, which leans away from warm-moist support."
|
||
: "低层空气在转干且温度偏弱，暖湿支撑正在减弱。";
|
||
}
|
||
return isEnglish(locale)
|
||
? "Use dew-point change to judge whether low-level warm-moist transport is strengthening or fading."
|
||
: "露点变化主要用于判断低层暖湿输送是在增强还是减弱。";
|
||
})();
|
||
const pressureNote = (() => {
|
||
if (pressureDelta >= 1.2 && tempDelta <= -0.8) {
|
||
return isEnglish(locale)
|
||
? "Pressure rebound with cooling usually points to a cooler push or frontal suppression."
|
||
: "气压回升且温度走弱，更像冷空气压入或锋面压温。";
|
||
}
|
||
if (pressureDelta <= -1.0 && tempDelta >= 0.8) {
|
||
return isEnglish(locale)
|
||
? "Pressure is softening while temperature rises, a setup less hostile to warming."
|
||
: "气压走低同时温度抬升，对增温的压制相对减弱。";
|
||
}
|
||
return isEnglish(locale)
|
||
? "Pressure change is used as a supporting signal for cold-air push versus warming resilience."
|
||
: "气压变化更适合作为冷空气压入或增温韧性的辅助判断。";
|
||
})();
|
||
const windNote = (() => {
|
||
if (firstBucket !== lastBucket && lastBucket === "southerly") {
|
||
return isEnglish(locale)
|
||
? "Wind turns toward a southerly regime, which is more favorable for warming."
|
||
: "风向转偏南，更有利于增温。";
|
||
}
|
||
if (firstBucket !== lastBucket && lastBucket === "northerly") {
|
||
return isEnglish(locale)
|
||
? "Wind turns toward a northerly regime, which is more favorable for cooling."
|
||
: "风向转偏北，更有利于降温。";
|
||
}
|
||
if (lastBucket === "southerly") {
|
||
return isEnglish(locale)
|
||
? "Low-level flow remains southerly, so warm advection has not been disrupted."
|
||
: "低层风维持偏南，暖平流支撑尚未被破坏。";
|
||
}
|
||
if (lastBucket === "northerly") {
|
||
return isEnglish(locale)
|
||
? "Low-level flow remains northerly, so cooling-side support is still present."
|
||
: "低层风维持偏北，降温侧支撑仍在。";
|
||
}
|
||
return isEnglish(locale)
|
||
? "Wind-direction change matters most when it crosses into southerly or northerly buckets."
|
||
: "风向变化最关键的是是否跨入偏南或偏北风桶。";
|
||
})();
|
||
const precipNote = (() => {
|
||
if (precipMax >= 60) {
|
||
return isEnglish(locale)
|
||
? "Precipitation risk is high enough that cloud/rain suppression can materially change the peak outcome."
|
||
: "降水概率已高到足以显著改变峰值兑现结果，需要重点防压温。";
|
||
}
|
||
if (precipMax >= 40) {
|
||
return isEnglish(locale)
|
||
? "Precipitation risk is meaningful; watch whether cloud and showers interrupt daytime heating."
|
||
: "降水概率已有存在感，需要关注云系和阵雨是否打断白天增温。";
|
||
}
|
||
return isEnglish(locale)
|
||
? "Precipitation risk remains limited and is used mainly as a suppression check."
|
||
: "降水风险暂时有限，主要作为压温风险校验项。";
|
||
})();
|
||
|
||
const metrics = [
|
||
{
|
||
label: isEnglish(locale) ? "Temperature delta" : "温度变化",
|
||
note: isEnglish(locale)
|
||
? `Official Open-Meteo hourly data; window: ${windowText}`
|
||
: `官方 Open-Meteo 小时数据；计算窗口：${windowText}`,
|
||
tone: tempDelta >= 0.8 ? "warm" : tempDelta <= -0.8 ? "cold" : "",
|
||
value: formatDelta(tempDelta, detail.temp_symbol),
|
||
},
|
||
{
|
||
label: isEnglish(locale) ? "Dew point delta" : "露点变化",
|
||
note: dewNote,
|
||
tone: dewDelta >= 0.8 ? "warm" : dewDelta <= -0.8 ? "cold" : "",
|
||
value: formatDelta(dewDelta, detail.temp_symbol),
|
||
},
|
||
{
|
||
label: isEnglish(locale) ? "Pressure delta" : "气压变化",
|
||
note: pressureNote,
|
||
tone: pressureDelta >= 1 ? "cold" : pressureDelta <= -1 ? "warm" : "",
|
||
value: formatDelta(pressureDelta, " hPa"),
|
||
},
|
||
{
|
||
label: isEnglish(locale) ? "Wind-direction evolution" : "风向演变",
|
||
note: windNote,
|
||
value: `${bucketLabel(firstBucket, locale)} -> ${bucketLabel(lastBucket, locale)}`,
|
||
},
|
||
{
|
||
label: isEnglish(locale) ? "Precip probability" : "降水概率",
|
||
note: precipNote,
|
||
tone: precipMax >= 50 ? "cold" : "",
|
||
value: `${Math.round(precipMax)}%`,
|
||
},
|
||
{
|
||
label: isEnglish(locale) ? "Cloud-cover delta" : "云量变化",
|
||
note: cloudNote,
|
||
tone:
|
||
cloudDelta >= 15 && tempDelta >= 0
|
||
? "warm"
|
||
: cloudDelta >= 15 && tempDelta < 0
|
||
? "cold"
|
||
: "",
|
||
value: formatDelta(cloudDelta, "%"),
|
||
},
|
||
];
|
||
|
||
if (backendNotes.length) {
|
||
const normalizedSummary = backendSummary.trim();
|
||
const alignedNotes = backendNotes.filter(
|
||
(note) => String(note || "").trim() !== normalizedSummary,
|
||
);
|
||
alignedNotes.slice(0, metrics.length).forEach((note, index) => {
|
||
if (!note) return;
|
||
metrics[index] = {
|
||
...metrics[index],
|
||
note,
|
||
};
|
||
});
|
||
}
|
||
|
||
return {
|
||
confidence,
|
||
label,
|
||
metrics,
|
||
precipMax,
|
||
score,
|
||
summary: backendSummary || summary,
|
||
weatherGovPeriods,
|
||
};
|
||
}
|
||
|
||
export function getFutureModalView(
|
||
detail: CityDetail,
|
||
dateStr: string,
|
||
locale: Locale = "zh-CN",
|
||
) {
|
||
const forecastEntry =
|
||
detail.forecast?.daily?.find((item) => item.date === dateStr) || null;
|
||
const dailyModel = detail.multi_model_daily?.[dateStr] || {};
|
||
const probabilities = dailyModel.probabilities || [];
|
||
const totalProbability = probabilities.reduce((sum, item) => {
|
||
const probability = Number(item.probability);
|
||
return Number.isFinite(probability) ? sum + probability : sum;
|
||
}, 0);
|
||
const weightedProbability = probabilities.reduce((sum, item) => {
|
||
const value = Number(item.value);
|
||
const probability = Number(item.probability);
|
||
if (!Number.isFinite(value) || !Number.isFinite(probability)) {
|
||
return sum;
|
||
}
|
||
return sum + value * probability;
|
||
}, 0);
|
||
const mu = totalProbability > 0 ? weightedProbability / totalProbability : null;
|
||
const deb = dailyModel.deb?.prediction ?? forecastEntry?.max_temp ?? null;
|
||
|
||
return {
|
||
deb,
|
||
forecastEntry,
|
||
front: computeFrontTrendSignal(detail, dateStr, locale),
|
||
models: dailyModel.models || {},
|
||
mu: Number.isFinite(Number(mu)) ? Number(mu) : null,
|
||
probabilities,
|
||
slice: getFutureSlice(detail, dateStr),
|
||
};
|
||
}
|
||
|
||
export function getShortTermNowcastLines(
|
||
detail: CityDetail,
|
||
dateStr: string,
|
||
locale: Locale = "zh-CN",
|
||
) {
|
||
const slice = getFutureSlice(detail, dateStr);
|
||
if (dateStr !== detail.local_date) {
|
||
const afternoon = slice.filter((point) => {
|
||
const hour = Number.parseInt(String(point.label).split(":")[0], 10);
|
||
return Number.isFinite(hour) && hour >= 12 && hour <= 18;
|
||
});
|
||
const target = afternoon.length ? afternoon : slice;
|
||
if (!target.length) {
|
||
return [
|
||
[isEnglish(locale) ? "Target date" : "目标日期", dateStr],
|
||
[
|
||
isEnglish(locale) ? "Peak window" : "峰值窗口",
|
||
isEnglish(locale)
|
||
? "No sufficient hourly forecast data for target-day peak-window diagnostics."
|
||
: "暂无足够的小时级 forecast 数据，无法生成目标日午后峰值窗口判断。",
|
||
],
|
||
] as const;
|
||
}
|
||
|
||
const maxIndex = target.reduce((bestIndex, point, index, array) => {
|
||
const temp = Number(point.temp);
|
||
const bestTemp = Number(array[bestIndex]?.temp);
|
||
if (!Number.isFinite(temp)) return bestIndex;
|
||
if (!Number.isFinite(bestTemp) || temp > bestTemp) return index;
|
||
return bestIndex;
|
||
}, 0);
|
||
|
||
const peakSlice = target.slice(
|
||
Math.max(0, maxIndex - 1),
|
||
Math.min(target.length, maxIndex + 2),
|
||
);
|
||
const start = peakSlice[0];
|
||
const end = peakSlice[peakSlice.length - 1];
|
||
const peakPoint = target[maxIndex] || end;
|
||
const startTemp = Number(start.temp);
|
||
const endTemp = Number(end.temp);
|
||
const startDew = Number(start.dewPoint);
|
||
const endDew = Number(end.dewPoint);
|
||
const startPressure = Number(start.pressure);
|
||
const endPressure = Number(end.pressure);
|
||
const precipValues = peakSlice
|
||
.map((point) => Number(point.precipProb))
|
||
.filter(Number.isFinite);
|
||
const cloudValues = peakSlice
|
||
.map((point) => Number(point.cloudCover))
|
||
.filter(Number.isFinite);
|
||
const maxPrecip = precipValues.length ? Math.max(...precipValues) : 0;
|
||
const maxCloud = cloudValues.length ? Math.max(...cloudValues) : 0;
|
||
|
||
return [
|
||
[isEnglish(locale) ? "Target date" : "目标日期", dateStr],
|
||
[
|
||
isEnglish(locale) ? "Peak window" : "峰值窗口",
|
||
isEnglish(locale)
|
||
? `${start.label} - ${end.label} (prefer 12:00-18:00)`
|
||
: `${start.label} - ${end.label}（优先取 12:00-18:00）`,
|
||
],
|
||
[
|
||
isEnglish(locale) ? "Peak estimate" : "峰值预估",
|
||
`${Number.isFinite(Number(peakPoint.temp)) ? Number(peakPoint.temp).toFixed(1) : "--"}${detail.temp_symbol} @ ${peakPoint.label || "--"}`,
|
||
],
|
||
[
|
||
isEnglish(locale) ? "Window temperature" : "窗口温度",
|
||
`${Number.isFinite(startTemp) ? startTemp.toFixed(1) : "--"}${detail.temp_symbol} -> ${Number.isFinite(endTemp) ? endTemp.toFixed(1) : "--"}${detail.temp_symbol} (${formatDelta(endTemp - startTemp, detail.temp_symbol)})`,
|
||
],
|
||
[
|
||
isEnglish(locale) ? "Dew-point delta" : "露点变化",
|
||
isEnglish(locale)
|
||
? `${formatDelta(endDew - startDew, detail.temp_symbol)} for diagnosing warm/wet transport in afternoon.`
|
||
: `${formatDelta(endDew - startDew, detail.temp_symbol)}，用于判断午后暖湿输送是否增强。`,
|
||
],
|
||
[
|
||
isEnglish(locale) ? "Wind shift" : "风向演变",
|
||
isEnglish(locale)
|
||
? `${bucketLabel(trendBucketFromDir(start.windDir), locale)} -> ${bucketLabel(trendBucketFromDir(end.windDir), locale)} around peak window.`
|
||
: `${bucketLabel(trendBucketFromDir(start.windDir), locale)} -> ${bucketLabel(trendBucketFromDir(end.windDir), locale)}，关注峰值前后是否转南风或回摆北风。`,
|
||
],
|
||
[
|
||
isEnglish(locale) ? "Pressure delta" : "气压变化",
|
||
isEnglish(locale)
|
||
? `${formatDelta(endPressure - startPressure, " hPa")} (higher pressure usually favors cold-air push).`
|
||
: `${formatDelta(endPressure - startPressure, " hPa")}，上升更偏向冷空气压入。`,
|
||
],
|
||
[
|
||
isEnglish(locale) ? "Precip / cloud" : "降水 / 云量",
|
||
isEnglish(locale)
|
||
? `${Math.round(maxPrecip)}% / ${Math.round(maxCloud)}% for cloud-suppression judgement around peak hours.`
|
||
: `${Math.round(maxPrecip)}% / ${Math.round(maxCloud)}%，用于判断峰值时段是否受云系压制。`,
|
||
],
|
||
] as const;
|
||
}
|
||
|
||
const recent = Array.isArray(detail.metar_recent_obs)
|
||
? detail.metar_recent_obs.slice(-4)
|
||
: [];
|
||
const nearby = Array.isArray(detail.mgm_nearby) ? detail.mgm_nearby : [];
|
||
const sourceLabel =
|
||
detail.name === "ankara"
|
||
? isEnglish(locale)
|
||
? "MGM nearby stations"
|
||
: "MGM 周边站"
|
||
: isEnglish(locale)
|
||
? "METAR nearby stations"
|
||
: "METAR 周边站";
|
||
const currentTemp = Number(detail.current?.temp);
|
||
const recentTemps = recent
|
||
.map((point) => Number(point.temp))
|
||
.filter((value) => Number.isFinite(value));
|
||
const baseline = recentTemps.length ? recentTemps[0] : currentTemp;
|
||
const shortDelta =
|
||
Number.isFinite(currentTemp) && Number.isFinite(baseline)
|
||
? currentTemp - baseline
|
||
: 0;
|
||
let nearbyLead: { diff: number; name: string; temp: number } | null = null;
|
||
|
||
for (const station of nearby) {
|
||
const temp = Number(station.temp);
|
||
if (!Number.isFinite(temp) || !Number.isFinite(currentTemp)) continue;
|
||
const diff = temp - currentTemp;
|
||
if (!nearbyLead || Math.abs(diff) > Math.abs(nearbyLead.diff)) {
|
||
nearbyLead = {
|
||
diff,
|
||
name:
|
||
station.name ||
|
||
station.icao ||
|
||
(isEnglish(locale) ? "Nearby station" : "周边站"),
|
||
temp,
|
||
};
|
||
}
|
||
}
|
||
|
||
const rows: Array<readonly [string, string]> = [
|
||
[
|
||
isEnglish(locale) ? "Primary station" : "当前主站",
|
||
`${detail.current?.temp ?? "--"}${detail.temp_symbol} @ ${detail.current?.obs_time || "--"}`,
|
||
],
|
||
[
|
||
isEnglish(locale) ? "Raw METAR" : "原始 METAR",
|
||
detail.current?.raw_metar || (isEnglish(locale) ? "N/A" : "暂无"),
|
||
],
|
||
[
|
||
isEnglish(locale) ? "Next 0-2h" : "近 0-2 小时",
|
||
isEnglish(locale)
|
||
? `${formatDelta(shortDelta, detail.temp_symbol)} based on latest METAR sequence short-term momentum.`
|
||
: `${formatDelta(shortDelta, detail.temp_symbol)}，依据最近 METAR 序列判断短时动量。`,
|
||
],
|
||
[
|
||
sourceLabel,
|
||
isEnglish(locale)
|
||
? `${nearby.length} stations joined the nearby scan.`
|
||
: `${nearby.length} 个站点参与邻近监控。`,
|
||
],
|
||
];
|
||
|
||
if (nearbyLead) {
|
||
const tone = isEnglish(locale)
|
||
? nearbyLead.diff > 0
|
||
? "warmer"
|
||
: nearbyLead.diff < 0
|
||
? "cooler"
|
||
: "flat"
|
||
: nearbyLead.diff > 0
|
||
? "偏暖"
|
||
: nearbyLead.diff < 0
|
||
? "偏冷"
|
||
: "持平";
|
||
rows.push([
|
||
isEnglish(locale) ? "Leading station" : "领先站",
|
||
isEnglish(locale)
|
||
? `${nearbyLead.name} ${nearbyLead.temp}${detail.temp_symbol}, relative to primary station ${formatDelta(nearbyLead.diff, detail.temp_symbol)} (${tone}).`
|
||
: `${nearbyLead.name} ${nearbyLead.temp}${detail.temp_symbol}，相对主站 ${formatDelta(nearbyLead.diff, detail.temp_symbol)}（${tone}）。`,
|
||
]);
|
||
}
|
||
|
||
return rows;
|
||
}
|
||
|
||
export function getHistorySummary(
|
||
history: HistoryPoint[],
|
||
cityLocalDate?: string | null,
|
||
) {
|
||
const toFinite = (value: unknown): number | null => {
|
||
const numeric = Number(value);
|
||
return Number.isFinite(numeric) ? numeric : null;
|
||
};
|
||
const isExcludedModel = (name: string) =>
|
||
String(name || "").toLowerCase().includes("meteoblue");
|
||
|
||
const cutoff = new Date();
|
||
cutoff.setHours(0, 0, 0, 0);
|
||
cutoff.setDate(cutoff.getDate() - 14);
|
||
|
||
const recentData = history.filter((row) => {
|
||
if (!row?.date) return false;
|
||
const rowDate = new Date(`${row.date}T00:00:00`);
|
||
return !Number.isNaN(rowDate.getTime()) && rowDate >= cutoff;
|
||
});
|
||
|
||
const settledData = recentData.filter((row) => {
|
||
if (!row?.date) return false;
|
||
return cityLocalDate
|
||
? row.date < cityLocalDate
|
||
: row.date < new Date().toISOString().slice(0, 10);
|
||
});
|
||
|
||
let hits = 0;
|
||
const debErrors: number[] = [];
|
||
const modelErrors: Record<string, number[]> = {};
|
||
|
||
settledData.forEach((row) => {
|
||
const actual = toFinite(row.actual);
|
||
const deb = toFinite(row.deb);
|
||
if (actual != null && deb != null) {
|
||
debErrors.push(Math.abs(actual - deb));
|
||
if (wuRound(actual) === wuRound(deb)) {
|
||
hits += 1;
|
||
}
|
||
}
|
||
|
||
const forecasts = row.forecasts || {};
|
||
Object.entries(forecasts).forEach(([modelName, modelValue]) => {
|
||
if (isExcludedModel(modelName)) return;
|
||
const mv = toFinite(modelValue);
|
||
if (actual == null || mv == null) return;
|
||
if (!modelErrors[modelName]) {
|
||
modelErrors[modelName] = [];
|
||
}
|
||
modelErrors[modelName].push(Math.abs(actual - mv));
|
||
});
|
||
});
|
||
|
||
const modelMaeList = Object.entries(modelErrors)
|
||
.map(([name, errors]) => ({
|
||
mae:
|
||
errors.length > 0
|
||
? errors.reduce((sum, value) => sum + value, 0) / errors.length
|
||
: Number.POSITIVE_INFINITY,
|
||
model: name,
|
||
sampleCount: errors.length,
|
||
}))
|
||
.filter((row) => Number.isFinite(row.mae) && row.sampleCount > 0)
|
||
.sort((a, b) => a.mae - b.mae);
|
||
|
||
const primaryModelMaeList = modelMaeList.filter((row) => row.sampleCount >= 2);
|
||
const bestModel = (primaryModelMaeList[0] || modelMaeList[0]) ?? null;
|
||
const bestModelName = bestModel?.model || null;
|
||
const bestModelMae = bestModel ? Number(bestModel.mae.toFixed(1)) : null;
|
||
const bestModelSeries = recentData.map((row) =>
|
||
bestModelName ? toFinite(row.forecasts?.[bestModelName]) : null,
|
||
);
|
||
|
||
let debWinDaysVsBest = 0;
|
||
let debVsBestComparableDays = 0;
|
||
if (bestModelName) {
|
||
settledData.forEach((row) => {
|
||
const actual = toFinite(row.actual);
|
||
const deb = toFinite(row.deb);
|
||
const bestModelVal = toFinite(row.forecasts?.[bestModelName]);
|
||
if (actual == null || deb == null || bestModelVal == null) return;
|
||
debVsBestComparableDays += 1;
|
||
if (Math.abs(deb - actual) <= Math.abs(bestModelVal - actual)) {
|
||
debWinDaysVsBest += 1;
|
||
}
|
||
});
|
||
}
|
||
|
||
return {
|
||
dates: recentData.map((row) => row.date),
|
||
debMae: debErrors.length
|
||
? Number(
|
||
(
|
||
debErrors.reduce((sum, value) => sum + value, 0) / debErrors.length
|
||
).toFixed(1),
|
||
)
|
||
: null,
|
||
debs: recentData.map((row) => row.deb),
|
||
bestModelName,
|
||
bestModelMae,
|
||
bestModelSeries,
|
||
modelMaeRanks: modelMaeList.map((row) => ({
|
||
model: row.model,
|
||
mae: Number(row.mae.toFixed(1)),
|
||
sampleCount: row.sampleCount,
|
||
})),
|
||
debWinDaysVsBest,
|
||
debVsBestComparableDays,
|
||
debWinRateVsBest:
|
||
debVsBestComparableDays > 0
|
||
? Number(((debWinDaysVsBest / debVsBestComparableDays) * 100).toFixed(0))
|
||
: null,
|
||
hitRate: debErrors.length
|
||
? Number(((hits / debErrors.length) * 100).toFixed(0))
|
||
: null,
|
||
mgms: recentData.map((row) => row.mgm ?? null),
|
||
recentData,
|
||
settledCount: settledData.length,
|
||
actuals: recentData.map((row) => row.actual),
|
||
};
|
||
}
|
||
|
||
export function getCityProfileStats(detail: CityDetail, locale: Locale = "zh-CN") {
|
||
const risk = detail.risk || {};
|
||
const current = detail.current || {};
|
||
const nearbyCount = Array.isArray(detail.mgm_nearby) ? detail.mgm_nearby.length : 0;
|
||
const sourceCode = getObservationSourceCode(detail);
|
||
const isOfficialSource = sourceCode === "hko" || sourceCode === "cwa";
|
||
|
||
const sourceDisplay = (() => {
|
||
if (sourceCode === "hko") {
|
||
return isEnglish(locale)
|
||
? "Hong Kong Observatory (HKO)"
|
||
: "香港天文台 (HKO)";
|
||
}
|
||
if (sourceCode === "cwa") {
|
||
return isEnglish(locale)
|
||
? "Central Weather Administration (CWA)"
|
||
: "交通部中央气象署 (CWA)";
|
||
}
|
||
const tag = getObservationSourceTag(detail);
|
||
if (sourceCode === "mgm") {
|
||
return isEnglish(locale) ? `MGM (${tag})` : `MGM (${tag})`;
|
||
}
|
||
if (risk.airport && risk.icao) return `${risk.airport} (${risk.icao})`;
|
||
if (risk.airport) return String(risk.airport);
|
||
return isEnglish(locale) ? "No profile" : "暂无档案";
|
||
})();
|
||
|
||
return [
|
||
{
|
||
label: isOfficialSource
|
||
? isEnglish(locale)
|
||
? "Settlement source"
|
||
: "结算源"
|
||
: isEnglish(locale)
|
||
? "Settlement airport"
|
||
: "结算机场",
|
||
value: sourceDisplay,
|
||
},
|
||
{
|
||
label: isOfficialSource
|
||
? isEnglish(locale)
|
||
? "Reference distance"
|
||
: "参考距离"
|
||
: isEnglish(locale)
|
||
? "Station distance"
|
||
: "站点距离",
|
||
value:
|
||
risk.distance_km != null && Number.isFinite(Number(risk.distance_km))
|
||
? `${risk.distance_km} km`
|
||
: isEnglish(locale)
|
||
? "Not marked"
|
||
: "未标注",
|
||
},
|
||
{
|
||
label: isEnglish(locale) ? "Observation update" : "观测更新",
|
||
value:
|
||
current.obs_time ||
|
||
detail.updated_at ||
|
||
(isEnglish(locale) ? "Unavailable" : "未提供"),
|
||
},
|
||
{
|
||
label: isEnglish(locale) ? "Nearby stations" : "周边站点",
|
||
value:
|
||
nearbyCount > 0
|
||
? isEnglish(locale)
|
||
? `${nearbyCount} participating stations`
|
||
: `${nearbyCount} 个参与监控`
|
||
: isEnglish(locale)
|
||
? "No nearby stations"
|
||
: "暂无周边站",
|
||
},
|
||
];
|
||
}
|
||
|
||
export function getSettlementRiskNarrative(
|
||
detail: CityDetail,
|
||
locale: Locale = "zh-CN",
|
||
) {
|
||
const risk = detail.risk || {};
|
||
const sourceCode = getObservationSourceCode(detail);
|
||
const stationTerm = sourceCode === "hko" || sourceCode === "cwa"
|
||
? isEnglish(locale)
|
||
? "settlement reference station"
|
||
: "结算参考站"
|
||
: isEnglish(locale)
|
||
? "settlement airport"
|
||
: "结算机场";
|
||
const lines: string[] = [];
|
||
|
||
if (risk.warning) {
|
||
lines.push(
|
||
isEnglish(locale)
|
||
? `Current key risk: ${risk.warning}`
|
||
: `当前主要风险是：${risk.warning}`,
|
||
);
|
||
}
|
||
|
||
if (risk.distance_km != null) {
|
||
if (risk.distance_km >= 60) {
|
||
lines.push(
|
||
isEnglish(locale)
|
||
? `The ${stationTerm} is far from urban core; market feel and settlement value may diverge significantly.`
|
||
: `${stationTerm}与城市核心区域距离偏大，盘面温度与结算值可能出现明显背离。`,
|
||
);
|
||
} else if (risk.distance_km >= 25) {
|
||
lines.push(
|
||
isEnglish(locale)
|
||
? `The ${stationTerm} has material distance from downtown; peak/overnight rhythm should prioritize the settlement station.`
|
||
: `${stationTerm}与城区存在可感知距离，午后峰值和夜间降温节奏需要优先看结算站。`,
|
||
);
|
||
} else {
|
||
lines.push(
|
||
isEnglish(locale)
|
||
? `The ${stationTerm} is close enough; city feel and settlement temperature are usually more synchronized.`
|
||
: `${stationTerm}距离较近，城市体感与结算温度通常更同步。`,
|
||
);
|
||
}
|
||
}
|
||
|
||
if (detail.name === "ankara") {
|
||
lines.push(
|
||
isEnglish(locale)
|
||
? "For Ankara, focus on LTAC / Esenboğa plus MGM nearby-station linkage, not urban sensation alone."
|
||
: "Ankara 需要重点看 LTAC / Esenboğa 与 MGM 周边站联动，不能只看城区体感。",
|
||
);
|
||
}
|
||
|
||
if (detail.current?.obs_age_min != null) {
|
||
if (detail.current.obs_age_min >= 45) {
|
||
lines.push(
|
||
isEnglish(locale)
|
||
? `Current METAR is ${detail.current.obs_age_min} minutes old. Blend nearby stations for nowcast instead of single-station snapshot.`
|
||
: `当前 METAR 已有 ${detail.current.obs_age_min} 分钟时滞，临近判断要结合周边站而不是只看主站快照。`,
|
||
);
|
||
} else {
|
||
lines.push(
|
||
isEnglish(locale)
|
||
? "Primary station observation is fresh enough; short-term judgement can anchor on it."
|
||
: "当前主站观测较新，短时判断可以把主站温度作为主要锚点。",
|
||
);
|
||
}
|
||
}
|
||
|
||
return lines;
|
||
}
|
||
|
||
export function getClimateDrivers(detail: CityDetail, locale: Locale = "zh-CN") {
|
||
const drivers: Array<{ label: string; text: string }> = [];
|
||
const lat = Math.abs(Number(detail.lat));
|
||
const nearbyCount = Array.isArray(detail.mgm_nearby)
|
||
? detail.mgm_nearby.length
|
||
: 0;
|
||
const distanceKm = Number(detail.risk?.distance_km);
|
||
|
||
if (lat >= 50) {
|
||
drivers.push({
|
||
label: isEnglish(locale) ? "High-latitude cold air" : "高纬冷空气",
|
||
text: isEnglish(locale)
|
||
? "At higher latitude, temperature rhythm is more affected by cold-air surges, trough passage, and seasonal radiation angle."
|
||
: "该城市位于较高纬度，温度变化更容易受到冷空气南下、短波槽和日照角度变化影响。",
|
||
});
|
||
} else if (lat >= 35) {
|
||
drivers.push({
|
||
label: isEnglish(locale) ? "Mid-latitude westerlies" : "中纬西风带",
|
||
text: isEnglish(locale)
|
||
? "Temperature shifts are often controlled by frontal transitions rather than pure daytime radiation."
|
||
: "该城市主要受中纬西风带和锋面活动控制，升降温常来自气团切换，而不是单一日照变化。",
|
||
});
|
||
} else if (lat >= 20) {
|
||
drivers.push({
|
||
label: isEnglish(locale) ? "Subtropical highs" : "副热带高压",
|
||
text: isEnglish(locale)
|
||
? "Subtropical ridge, clear-sky radiation and low-level warm advection often dominate warming efficiency."
|
||
: "该城市更容易受副热带高压、晴空辐射和低层暖平流影响，午后增温能力通常更强。",
|
||
});
|
||
} else {
|
||
drivers.push({
|
||
label: isEnglish(locale) ? "Tropical moisture & convection" : "热带水汽与对流",
|
||
text: isEnglish(locale)
|
||
? "Temperature and feels-like are often modulated by moisture transport, cloud convection and showers."
|
||
: "该城市偏热带环境，温度与体感常受水汽输送、云对流和阵雨触发影响。",
|
||
});
|
||
}
|
||
|
||
drivers.push({
|
||
label: isEnglish(locale) ? "Dry-wet boundary layer" : "干湿边界层",
|
||
text: isEnglish(locale)
|
||
? "Boundary-layer humidity controls daytime warming efficiency; dry boundary warms faster, wet boundary is more cloud/precip-sensitive."
|
||
: "低层干湿状态会决定午后升温效率。干空气通常升温更快，湿空气更容易受云量和降水过程抑制。",
|
||
});
|
||
|
||
drivers.push({
|
||
label: isEnglish(locale) ? "Advection transport" : "平流输送",
|
||
text: isEnglish(locale)
|
||
? "Short-term trend is usually driven by low-level air-mass transport. Persistent wind origin tends to sustain thermal direction."
|
||
: "短时趋势常由低层气团输送控制。若风向持续来自同一侧，温度通常更容易沿该方向延续。",
|
||
});
|
||
|
||
if (Number.isFinite(distanceKm) && distanceKm >= 25) {
|
||
drivers.push({
|
||
label: isEnglish(locale) ? "Station representativeness" : "站点代表性",
|
||
text: isEnglish(locale)
|
||
? "When settlement station is not near city core, perceived temperature and settlement value may diverge."
|
||
: "结算站与城市核心区存在一定距离时，体感温度和结算温度可能分离，评估时应优先以结算站观测为准。",
|
||
});
|
||
}
|
||
|
||
if (nearbyCount >= 4) {
|
||
drivers.push({
|
||
label: isEnglish(locale) ? "Local heterogeneity" : "局地差异",
|
||
text: isEnglish(locale)
|
||
? "More nearby stations suggest terrain/urban-heat heterogeneity; settlement station and downtown sensation should be evaluated separately."
|
||
: "周边可用站点较多，说明地形、城区热岛或下垫面差异可能明显，结算站与城区体感需要分开评估。",
|
||
});
|
||
}
|
||
|
||
return drivers;
|
||
}
|