Company
We founded Weathersight to improve the utility of a large number of publicly available weather and climate datasets. As we discuss in our blog, government-produced climate reports based on these datasets fall short in effectively communicating regional shifts in weather patterns. We also found that both mainstream and social media often provide unreliable context when covering extreme weather events. Key local data is lacking, with an overreliance on global trends to frame the discussion. For instance, the frequency and severity of extreme events affecting specific areas are frequently underreported, leaving gaps in understanding their impact on things like local infrastructure reliability. We believe a consistent, data-driven approach to reporting extreme weather will help the public better grasp these connections. To support this goal, we've begun developing tools to generate publicly accessible, high-quality weather insights that serve as a shared record of the planet’s extreme weather events.
Mission
To create and sustain a reliable, comprehensive, and user-friendly resource for global weather patterns.
Terms
Attribution
Unless otherwise noted, all content on this website is the property of Weathersight LLC and is protected by United States and international copyright laws. You are welcome to use our content for personal, non-commercial purposes, but we require that you attribute the content to us by including a hyperlink to our website or the specific page from which the content originated. You may not use our content for commercial purposes without our express written consent.
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Email-addresses that you have shared with us through our contact page will not be shared with third parties without your explicit consent.
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Data
Historical Data
All historical data used for daily (FULL_DAY), weekly (WEEK), monthly (MONTH), yearly (YEAR) anomalies and timeseries are derived from NOAA GSOD , NOAA GHCND and NOAA ISD datasets.
We also use historical ERA5 gridded data made available by Open Meteo
Recent Data
METAR
All recent data interprets METAR data and is made consistent with local timezone meteorological day starting
and
ending 0000 hrs.
The DAY anomalies use this data to compare against the corresponding week's
historical
distribution. The Tmax, Tmin are estimates based on hourly or half-hourly readings from the stations and
preciptiation totals are sums of readings.
Precipitation data is missing from Global METAR data. So, rainfall and snowfall anomalies of type DAY are restricted to US administered locations.
SYNOP
For fast turn-around of Tmax, Tmin and precipiation anomalies of global stations, SYNOP reports via ogimet are used. Anomalies of type FULL_DAY intend to reflect true Tmax, Tmin and 24hr precipitation totals. These are available within few hours of end of day and are eventually reconciled with NOAA GSOD dataset, usually after 2-7 days.
Reanalysis Datasets
We have used ERA5 dataset made available by open-meteo.com to cover gaps in observed precipiation and temperature data in several metropolitan areas. Since it is a separate dataset, analyses derived from it can be viewed by altering SourceType available with most offerings.
Climate Projections We have used downscaled CMIP6 datasets made available by open-meteo.com to understand estimated climate across various metropolitan areas through 2050. Since it is a separate dataset, analyses derived from it can be viewed by altering SourceType available with most offerings.
Metrics
Most metrics (see below) for most analyses are defined for the UTC day. The exception are for analyses of type DAY , where metrics are defined for local day starting at and ending before 0000 hrs. Since the definitions are consistent across time periods, anomaly computations are unaffected. However, some absolute measures of counts (e.g. number of days where Tmax breached a threshold) might be different from those obtained by using country specific definitions of a meteorological day (e.g. 0830 to 0830 IST).
| Metric | Description | Period | Reference |
|---|---|---|---|
| Maximum Temperature (Tmax) | Highest temperature observed in a 24hr period | UTC | |
| Maximum Temperature (Tmin) | Lowest temperature observed in a 24hr period | UTC | |
| Average Temperature (Tavg) | Mean of hourly observations in a 24hr period if good coverage is available , otherwise an estimate (Tmax+Tmin)/2 | UTC | |
| Average Dewpoint (ADPT) | Mean of hourly observations if good coverage is available , otherwise undefined | UTC | Definition |
| Max Wet Bulb Temp (WBULB) | Highest wet bulb temperature observed in a 24hr period from hourly observations | Local | Definition |
| Max Dew Point Depression (DPD) | Highest dew point depression in a 24hr period from hourly observations | Local | Definition |
| Daily Rain Accumulation (RAIN) | Accumulated rainfall over a 24hr period | UTC | |
| Hourly Rain Accumulation (RAINH) | Accumulated rainfall over a 1hr period. | n/a | |
| Daily Snow Accumulation (SNOW) | Water equivalent of snow over a 24hr period | UTC | |
| Average Wind Speed (AWND) | Mean of wind speeds observed in a 24hr period if good coverage is available , otherwise undefined | UTC | |
| Maximum Wind Speed (WSF5) | Highest wind observed in a 24hr period | UTC | Fastest 5-second wind speed |
| Peak Wind Gust (WSFG) | Peak wind gust speed in a 24hr period | UTC |