A prediction of future weather using supercomputer models, satellite observations and radar. Accurate about 90% of the time for a five-day forecast and about 80% for a seven-day forecast.
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 the whole region, 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; 1,008 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 × 6,599,680 horizontal grid points globally in ECMWF’s 9 km model
- Time-stepped forward (450-second steps in ECMWF’s 9 km model)
- 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 (NDMA’s Sachet, PMD app, BMD)
- Specialized briefings (aviation, military, marine, agriculture)
- Mausam Online provides hourly/daily forecasts via Open-Meteo
Forecast accuracy by lead time
For South Asia:
- Monsoon onset over Kerala within ±4 days
- Cyclone track 5-day forecast average error 224.8 km (IMD, 2023)
- 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
- Severe weather alerts carried on NDMA’s Sachet app
- Monsoon Mission, Mission Mausam for skill improvement
Pakistan Meteorological Department (PMD):
- Operates national radar + weather station network
- Issues cyclone warnings, flood alerts
Bangladesh Meteorological Department (BMD):
- Issues the cyclone warning signals relayed by the Cyclone Preparedness Programme
- Bangladesh prepared 14,636 shelter centres for Cyclone Amphan in 2020
- 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 about two 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
Forecasters are improving these all the time. Mission Mausam (India’s program, Phase I 2024-26 and Phase II 2026-31) aims to improve NWP model resolution from 12 km to 6 km.
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
- NDMA’s 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. A five-day forecast is accurate about 90% of the time and a seven-day forecast about 80%; perfect prediction is impossible.
Which weather app is most accurate? For ECMWF-based forecasts (most accurate): Mausam Online, Windy. For pure data: Open-Meteo, NOAA. For Indian specifics: NDMA’s Sachet, which carries IMD alerts. 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.