Cómo Funciona TEMPO y Resolución Temporal

1interface TempoPixel {
2  no2_column: number;
3  cloud_fraction: number;
4  quality_flag: number; // 0 = alta, 1 = media, 2 = baja
5}
6
7function shouldUseData(pixel: TempoPixel): boolean {
8  // Filtrar baja calidad
9  if (pixel.quality_flag > 1) {
10    return false; // Descarta
11  }
12
13  // Filtrar alta cobertura de nubes
14  if (pixel.cloud_fraction > 0.7) {
15    return false; // Las nubes bloquean la vista
16  }
17
18  return true; // OK para usar
19}
20

1def simulate_no2_diurnal_cycle(hour_local):
2    """
3    Ciclo diurno típico de NO₂ en ciudad
4    Retorna: Columna NO₂ en 10¹⁵ molec/cm²
5    """
6    # Background base
7    background = 2.0
8
9    # Pico matutino (7-9 AM)
10    if 7 <= hour_local <= 9:
11        morning_peak = 6.0
12    else:
13        morning_peak = 0
14
15    # Pico vespertino (5-7 PM)
16    if 17 <= hour_local <= 19:
17        evening_peak = 7.0
18    else:
19        evening_peak = 0
20
21    # Mínimo diurno (mezcla vertical)
22    if 12 <= hour_local <= 15:
23        mixing_reduction = -1.5
24    else:
25        mixing_reduction = 0
26
27    return max(0.5, background + morning_peak + evening_peak + mixing_reduction)
28
29# Simular día completo
30for hour in range(8, 23):
31    no2 = simulate_no2_diurnal_cycle(hour)
32    print(f"{hour:02d}:00 - NO₂: {no2:.2f} × 10¹⁵ molec/cm²")
33

1interface PlumeObservation {
2  timestamp: Date;
3  centroid_lat: number;
4  centroid_lon: number;
5  hcho_column: number;
6}
7
8function trackPlumeMovement(observations: PlumeObservation[]) {
9  if (observations.length < 2) return null;
10
11  const first = observations[0];
12  const last = observations[observations.length - 1];
13
14  // Distancia simple (aproximación)
15  const lat_diff = last.centroid_lat - first.centroid_lat;
16  const lon_diff = last.centroid_lon - first.centroid_lon;
17  const distance_km = Math.sqrt(lat_diff**2 + lon_diff**2) * 111;
18
19  // Tiempo transcurrido
20  const hours = (last.timestamp.getTime() - first.timestamp.getTime()) / (1000 * 60 * 60);
21
22  return {
23    velocity_kmh: distance_km / hours,
24    direction: lon_diff > 0 ? 'Este' : 'Oeste',
25    distance_traveled: distance_km,
26    hours_elapsed: hours
27  };
28}
29
30// Ejemplo
31const observations = [
32  { timestamp: new Date('2025-10-02T14:00'), centroid_lat: 19.5, centroid_lon: -99.1, hcho_column: 3e16 },
33  { timestamp: new Date('2025-10-02T16:00'), centroid_lat: 19.6, centroid_lon: -98.9, hcho_column: 2.5e16 }
34];
35
36const movement = trackPlumeMovement(observations);
37console.log(`Pluma se mueve a ${movement.velocity_kmh} km/h hacia el ${movement.direction}`);
38

1// Actualizar cada hora automáticamente
2setInterval(async () => {
3  const latestData = await fetchTEMPOLatest();
4  updateDashboard(latestData);
5}, 3600000); // 1 hora
6

1function predictNextHour(last6hours: HourlyData[]): string {
2  const recent_no2 = last6hours.slice(-3).map(h => h.no2);
3  const trend = (recent_no2[2] - recent_no2[0]) / 2;
4
5  if (trend > 2e15) {
6    return "⚠️ NO₂ subiendo rápido - tráfico aumentando";
7  } else if (trend < -2e15) {
8    return "✅ NO₂ bajando - calidad mejorando";
9  }
10  return "➡️ NO₂ estable";
11}
12

1interface HourlyTrend {
2  hour: number;
3  no2: number;
4  o3: number;
5  trend: 'rising' | 'falling' | 'stable';
6}
7
8function analyzeTrend(data: HourlyTrend[]): string {
9  const last3hours = data.slice(-3);
10
11  const no2_trend = last3hours[2].no2 - last3hours[0].no2;
12
13  if (no2_trend > 3e15) {
14    return "🔴 NO₂ aumentando rápidamente - posible evento de tráfico o industrial";
15  } else if (no2_trend < -3e15) {
16    return "🟢 NO₂ disminuyendo - condiciones mejorando";
17  }
18
19  return "🟡 Condiciones estables";
20}
21