VayuMet is a free, open-access meteorological data platform built for India β delivering district-level weather forecasts, seasonal monsoon outlooks, aviation weather, agriculture weather intelligence, renewable energy planning data and air quality visualisations, all powered by global numerical weather prediction (NWP) models.
Founded and led by an Indian Air Force veteran with over 34 years of operational experience in aviation meteorological services β bringing a depth of professional credibility that is rare among open weather data platforms.
6-hourly and cumulative precipitation maps at ~25 km resolution across India, updated every 6 hours.
Animated wind at 10 m, 100 m, 850 hPa, 500 hPa, 300 hPa and 200 hPa β from surface to jet stream level.
Month-by-month precipitation and temperature anomaly maps at district and state level for the current monsoon season, sourced from CFSv2.
Real-time METARs, TAFs, SIGMETs, flight category overlays, icing and turbulence indices across Indian airspace.
PM2.5, PM10, AOD, dust AOD from ECMWF-CAMS, plus two experimental visibility estimation methods: the AOD-based IGP formula (V = 4 Γ Heff / AOD Γ f(RH)) and the PMβ.β Koschmeider formula β both incorporating per-pixel humidity correction (f(RH)) derived from GFS 2 m temperature and dew point.
Solar irradiance (GHI/DNI) (To be Added in Future), wind power potential at 100 m hub height, and sunshine duration maps for wind and solar energy site planning and generation forecasting.
Sowing-season rainfall outlooks, cumulative precipitation tracking, soil temperature proxies, frost risk alerts and harvest-period forecast summaries to support India's farming and food-security planning at district level.
Himawari-9 imagery from the Japan Meteorological Agency β infrared cloud-top temperature and a sandwich IR/RGB product covering South and Central Asia, refreshed continuously.
Long-period normals and climate-change analysis built from IMD Pune daily gridded records for 1951β2025, plus NOAA PSL reanalysis for wind and cloud β rainfall and temperature distributions, warming stripes and district-scale trend maps.
District-level IMD colour-coded alerts (Green/Yellow/Orange/Red) for Day +1 through Day +5. Experimental pipeline classifies precipitation events as convective or non-convective (stratiform) and derives associated peak gust speeds at ~25β50 km district scale β providing operationally specific hazard information beyond plain rainfall amounts.
VayuMet does not generate its own forecasts or observations. We download, process and visualise output from publicly available, authoritative sources: numerical weather prediction models operated by NOAA (USA) and ECMWF (Europe), long-term reanalysis and climatology archives from the NOAA Physical Sciences Laboratory (PSL), observed gridded rainfall and temperature from the India Meteorological Department (IMD), Pune, and live satellite imagery from the Japan Meteorological Agency (JMA).
| Dataset | Operator | Use in VayuMet | Resolution | Update Cycle |
|---|---|---|---|---|
| GFS (Global Forecast System) | NOAA / NCEP | Short-range weather: rain, wind, temperature, clouds, humidity, CAPE/CIN, soil | 0.25Β° | 4Γ daily (00Z, 06Z, 12Z, 18Z) |
| CFSv2 (Climate Forecast System v2) | NOAA / NCEP | Seasonal forecasts: precipitation & temperature anomalies | 1.0Β° | 4Γ daily, monthly outlooks |
| ECMWF-CAMS Aerosols | ECMWF | Air quality: PM2.5, PM10, AOD, dust AOD; input to experimental visibility | 0.25Β° | Daily |
| Dataset | Operator | Use in VayuMet | Resolution | Update Cycle |
|---|---|---|---|---|
| AWC (Aviation Weather Center) | NOAA | METARs, TAFs, SIGMETs and significant-weather charts | Station / airspace | Real-time |
| Himawari-9 (TRM/B13 IR, Sandwich RGB) | JMA β Meteorological Satellite Center | Live satellite imagery over South & Central Asia | Satellite native | Continuous (~10 min) |
| IMD real-time gridded rainfall | IMD Pune | Observed rainfall for the Monsoon Monitor's forecast-vs-actual comparison | 0.25Β° | Daily, subject to IMD publishing lag |
The District Climatology and Climate Change pages are built from long-term observed and reanalysis archives rather than forecast model output.
| Dataset | Operator | Use in VayuMet | Resolution | Period |
|---|---|---|---|---|
| IMD daily gridded archives (rain, tmax, tmin) | IMD Pune, via imdlib | Rainfall and temperature day-count distributions, yearly and monthly anomaly series, warming-trend analysis, and the reference climatology used to bias-correct seasonal forecasts | 0.25Β° rain Β· 1Β° temperature | 1951β2025 |
| CPC gridded daily climatology | NOAA PSL | Average temperature and precipitation charts; daily climatology curves | ~0.5Β° | 1991β2020 normals |
| NCEP-DOE Reanalysis 2 (daily 10 m u/v wind) | NOAA PSL | Wind speed distributions, wind roses and monthly wind normals | ~1.9Β° | 1996β2025 |
| NCEP monthly cloud climatology | NOAA PSL | Cloud-cover percentages, sunny/overcast day estimates, yearly cloud series | ~2Β° | 1979β2025 |
| CPC Global Precipitation | NOAA PSL | Weekly rainfall analysis products | 0.5Β° | Ongoing |
Data Transparency: All data is obtained from the public servers of NOAA (NOMADS and PSL), ECMWF-CAMS, JMA and IMD, and processed under their respective open-data terms. VayuMet applies post-processing, regridding and visualisation. We do not modify or bias-correct the underlying NWP output, with one deliberate exception: seasonal CFSv2 forecasts are bias-corrected against IMD reference climatology, as described below.
Why both IMD and PSL appear in climatology: the day-count distributions and the Climate Change tab are built from real IMD daily records, year by year, because a smoothed 30-year daily climatology badly overstates wet-day counts. The average-conditions charts still use the PSL/CPC daily climatology. Every chart states its own source in its footer, so the provenance of any number on the page is always visible.
Raw GFS GRIB2 files are downloaded from NOAA's NOMADS server, regridded to a uniform India bounding box (30Β°Eβ130Β°E, 5Β°Sβ50Β°N), and exported as GeoTIFF files. The GeoTIFFs are served directly to the browser, where a custom WebGL renderer applies colour ramps and overlays the data on an interactive Leaflet map.
Monthly Climate Forecast System ensemble output is bias-corrected against IMD Pune reference climatology and then spatially joined to India's district and state boundaries. The result is a district-level anomaly map (precipitation in %, temperature in Β°C departure) updated monthly for the JuneβOctober monsoon season window.
The Monsoon Monitor places the seasonal forecast beside what has actually fallen. Observed rainfall comes from IMD's real-time 0.25Β° daily gridded rainfall, accumulated from 1 June to date and aggregated to district and state polygons, then expressed in the same IMD percentage-departure categories used in official monsoon reporting. Because IMD publishes with a short lag, the most recent day or two may be absent until the archive catches up.
Long-period statistics are computed from real daily records rather than model output. IMD Pune daily rainfall (0.25Β°) and tmax/tmin (1Β°) grids covering 1951β2025 are downloaded via imdlib, scanned year by year, and reduced to district-level distributions β rainfall-amount bins, wet-day counts, hot-day and cold-night counts β and to monthly and yearly anomaly series for the warming-trend analysis. Wind statistics come from NCEP-DOE Reanalysis 2 daily 10 m winds (1996β2025) held at NOAA PSL, and cloud statistics from PSL's NCEP monthly cloud series.
One caveat is handled explicitly: IMD's temperature grid is 1Β° (~111 km), which does not resolve Himalayan terrain β a naive read placed Shimla near 20 Β°C annual mean against a true value close to 13.6 Β°C. The Climate Change series is rebased onto a finer reference to correct this rather than presenting the raw grid value.
U and V wind components at each pressure level are extracted from GFS output and converted to Leaflet-Velocity JSON format, enabling smooth particle-flow animation in the browser without server-side rendering.
Two complementary methods derive surface visibility from ECMWF-CAMS aerosol fields, both corrected for humidity-driven aerosol swelling using a per-pixel f(RH) factor computed from GFS 2 m temperature and dew point via the Magnus formula:
V = 4 Γ Heff / (AOD Γ f(RH)) β integrates column aerosol optical depth over an effective mixing height (default 1,000 m; ERA5 boundary-layer height used when the --met-grib option is supplied for the full operational formula).Ξ²ext = (3Γf(RH) + 0.3)ΓPMβ.β
+ 10 then V = 3 912 000 / Ξ²ext β requires no mixing height, deriving visibility directly from surface fine-particle concentration. Particularly suited to urban and industrial pollution haze.These are experimental products intended for fog/haze situational awareness, not safety-critical navigation. Results should be cross-checked against METAR visibility reports.
The weather alert pipeline applies an experimental classification step that discriminates convective from non-convective (stratiform) precipitation events and derives an associated peak gust speed forecast for each district polygon (~25β50 km scale). Convective events are flagged for rapid-onset hazards (sudden gusts, hail, wind shear); stratiform events are flagged for persistent-rainfall and flooding risk. This event-type + gust pairing provides operationally specific guidance for aviation, district administration, agriculture, and renewable energy sectors that a single rainfall-amount alert cannot deliver.
Not for Safety-Critical Use: VayuMet is intended for situational awareness and planning. It should not be used as the sole basis for safety-critical decisions. Consult the India Meteorological Department (IMD) and official aviation authorities for operational use.
VayuMet is founded and led by an Indian Air Force veteran whose career spans over three decades of hands-on operational aviation meteorology, large-scale organisational leadership, policy formulation, technology adoption and professional education. This is not a hobbyist project β it is built on the kind of domain authority that comes only from sustained frontline service at the highest levels of India's defence meteorological establishment.
This operational depth β spanning frontline aviation weather services, strategic policy leadership, large-scale personnel management, sensor technology expertise, and meteorological education β directly shapes VayuMet's design philosophy: aviation-grade rigour, transparent data sourcing, and practical usability for Indian conditions.
Our processing pipeline references methodologies, datasets and standards published by NOAA, ECMWF, JMA, IMD, and the Indian Institute of Tropical Meteorology (IITM).
imdlib package β imdpune.gov.inVayuMet is an independent platform. It is not affiliated with, endorsed by, or operated on behalf of NOAA, ECMWF, JMA or the India Meteorological Department; their data is used under the open-data terms each publisher provides.
We welcome feedback, data correction reports, and collaboration enquiries from researchers, meteorologists and developers.
π§ Email: contact@vayumetweather.com
π Website: www.vayumetweather.com
Open VayuMet Live Maps β