A prediction of future weather using supercomputer models, satellite observations and radar. Accurate to 90%+ for next 24 hours, 60-70% for next week.
forecastingWhat is a Weather Forecast?
A weather forecast is a prediction of future atmospheric conditions — temperature, precipitation, wind, humidity, cloud cover and so on — for a specific location and time. Modern forecasts combine three pillars:
- Observations of current weather (satellites, ground stations, radar, balloons, aircraft, ships)
- Numerical weather prediction (NWP) models that simulate the atmosphere forward in time using physics equations
- Human meteorologists who interpret model output and apply local knowledge
For South Asia, weather forecasting is essential because the region’s weather is uniquely consequential — the monsoon affects 1.8 billion people, tropical cyclones can kill thousands, heatwaves affect crops and energy demand, and air quality dominates winter public health.
How modern forecasting works
Step 1: Data collection (continuous)
- Satellites (geostationary INSAT-3D, polar-orbiting): cloud, temperature, moisture profiles
- Weather stations (~10,000 worldwide; ~700 IMD AWS in India): surface temp, humidity, wind
- Radiosondes (weather balloons twice daily from ~800 sites): vertical atmospheric profile
- Doppler radar (38+ in India): precipitation, winds, storm structure
- Aircraft (AMDAR): in-flight observations
- Ocean buoys: sea-surface temperature, waves, marine winds
Step 2: Data assimilation (continuous)
- All observations fed into NWP models every 6 hours
- Variational analysis blends observations with model state
- 4D-Var (ECMWF) uses time-history to improve initial conditions
- AI-based data assimilation emerging as future direction
Step 3: Numerical integration
- ECMWF/GFS/JMA/UKMet/IMD models solve fluid dynamics + thermodynamics
- 137 vertical levels × ~25 million horizontal cells globally
- Time-stepped forward (typically 1-hour increments)
- Output for next 10-14 days deterministic, 30-46 days ensemble
Step 4: Post-processing
- Statistical bias correction at local stations
- Probabilistic outputs (e.g., “70% chance of rain”)
- Multi-model blending for ensemble products
- Regional model nesting for high resolution
Step 5: Human review
- Meteorologists check model output against current observations
- Severe weather forecasts manually verified
- Cyclone tracks/intensities estimated with multiple models
Step 6: Dissemination
- Public broadcasts (TV, radio, newspaper)
- Mobile apps (IMD Sachet, PMD app, BMD)
- Specialized briefings (aviation, military, marine, agriculture)
- Mausam Online provides hourly/daily forecasts via Open-Meteo
Forecast accuracy by lead time
| Lead Time | Temperature | Precipitation | Severe Events |
|---|---|---|---|
| 6 hours | 98% | 90% | 95% |
| 1 day | 95% | 80% | 85% |
| 3 days | 85% | 65% | 70% |
| 5 days | 75% | 50% | 55% |
| 7 days | 60% | 35% | 40% |
| 10 days | 50% | 25% | 25% |
| Beyond 10 days | Trend only | Trend only | Trend only |
These are rough global averages. For South Asia specifically:
- Monsoon onset/withdrawal within 1-3 days
- Cyclone track 5-day forecast within 50-100 km
- Cyclone intensity still poorly predicted at 3-day lead
- Heatwave alerts 3-day reliable
South Asian forecasting agencies
India Meteorological Department (IMD):
- Founded 1875
- Operates 38+ Doppler radars
- Issues 0-3 hour nowcasts plus 5-day forecasts
- Sachet app for severe weather alerts
- Monsoon Mission, Mission Mausam for skill improvement
Pakistan Meteorological Department (PMD):
- Operates national radar + weather station network
- Issues cyclone warnings, flood alerts
- Cooperation with IMD on Indus basin
Bangladesh Meteorological Department (BMD):
- Pioneered cyclone preparedness program
- 14,000+ cyclone shelters
- Effective evacuation system has saved hundreds of thousands of lives
Nepal Department of Hydrology and Meteorology (DHM):
- Monsoon onset/withdrawal monitoring
- Glacial lake outburst warnings
Bhutan, Sri Lanka, Maldives maintain their own national services.
Mausam Online forecasting approach
Mausam Online uses Open-Meteo’s ECMWF integration to provide:
- Current conditions updated every 1 hour
- Hourly forecast for next 24 hours
- Daily forecast for next 7 days
- AQI and air quality alongside weather
- Heatwave + cyclone + storm alerts when triggered
The site is client-side rendered for speed — your browser fetches data directly from Open-Meteo, ensuring forecasts are as fresh as possible. No server delays.
Limitations and uncertainties
Inherent atmospheric chaos:
- The “butterfly effect” — small initial errors grow exponentially
- After 2-3 weeks, individual weather features become unpredictable
- Only general atmospheric trends forecastable at seasonal timescale
Specific challenges:
- Cyclone intensity harder than track
- Cloudbursts and convective rainfall difficult to place precisely
- Mountain weather (Himalayan, Western Ghats) — terrain effects at small scale
- Sea breeze cycles — small-scale circulations
- Local microclimate effects — urban heat island, valley pooling
Tropical-specific:
- MJO (Madden-Julian Oscillation) affects monsoon, hard to model
- Convective initiation timing difficult
- Saharan dust in Arabian Sea cyclone forecasts
Forecasters are improving these all the time. Mission Mausam (India’s 2024-2030 program) aims for 5°×5° km resolution by 2030.
How to use weather forecasts well
Daily decisions:
- Hourly forecast for “Will it rain on my commute?”
- Day-of forecast for outdoor plans
- Trust 1-2 day forecasts highly
Weekly planning:
- Use 5-day forecast for travel, sports, events
- Add buffers for outdoor weddings, festivals
- Don’t trust day-7 details — only trends
Seasonal planning:
- IMD monsoon forecast (April-May) for agriculture
- Cyclone outlook for coastal businesses
- Seasonal temperature for energy procurement
Severe weather:
- Always heed official cyclone, flood, heatwave warnings
- IMD Sachet, PMD app, BMD app — install all
- Don’t ignore “watch” vs “warning” distinction — warning = imminent
Frequently asked questions
Why are forecasts wrong sometimes? The atmosphere is fundamentally chaotic — tiny initial errors grow over time. Model resolution limits what can be predicted at small scale (thunderstorms, mountain effects). And data assimilation has gaps in remote regions. Modern forecasts achieve 80-95% short-term accuracy; perfect prediction is impossible.
Which weather app is most accurate? For ECMWF-based forecasts (most accurate): Mausam Online, Windy, AccuWeather, IBM Weather. For pure data: Open-Meteo, NOAA. For Indian specifics: IMD Sachet. For Pakistan: PMD. For Bangladesh: BMD. Most accurate depends on location and forecast type.
How far ahead can weather be forecast? Deterministic individual weather: 7-10 days. General trends: 4-6 weeks via ECMWF subseasonal. Seasonal averages: 6-12 months via dynamical seasonal models. Climate decades: not weather, but climate models project trends.
Can AI improve weather forecasts? Yes — and rapidly. Google GraphCast, Huawei Pangu-Weather, ECMWF AIFS demonstrate AI models trained on past data can match or exceed physics-based models on many metrics. The future is likely hybrid AI/physics.
Where can I see forecasts for my city? Mausam Online provides hourly/daily forecasts on every city page. See Delhi, Mumbai, Chennai, Kolkata, Dhaka, Karachi, Bengaluru.