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Weather forecasting for astronomers

As astronomers and astrophotographers (amateur or professional), our field requires a caliber of weather data that exceeds a basic civilian weather forecast. It also requires a better understanding of weather models on our part in order to be successful. The following tries to clear some of the confusion around weather forecasts and help you get the best info to optimize a night of observing.

The Weather App Overload

Type "weather" into any search engine or app store and thousands of results will come back, each claiming to provide more accurate forecasts than the next with up to the minute data. Often, amateur astronomers will download many of these apps, consult them all and try to make an informed decision. This can lead to a lot of confusion which stems from the simple fact that the vast majority of these weather apps are showing the same weather data. In fact there are only a few weather models for North America that offer the resolution and variables necessary to generate an astronomy forecast, and they update four times a day.

Given there are only a handful of forecast models that can be used, how is it that there are thousands of weather apps all advertising the best forecast? The answer is publicly available weather APIs that allow any developer to include weather in their app without having to know a thing about weather. You'll know these services because they'll say something like "Powered by Meteosource Weather API" or "Powered by Dark Skies" somewhere on the page. This has led to a flood of weather apps on the market all showing the same data in different ways. These tend to hide (or simply not know) which weather model the data is coming from.

What's the problem with this? If you don't know what model you're looking at then it's easy to form confirmation bias about upcoming weather. For example, if you check 5 apps or websites and they all say it's going to be clear tonight, you may feel positive it will be a great night. However, if all of those 5 sources used the same weather API, then you really haven't looked at different models, you've checked one (and wasted a lot of time).

Getting good weather data

Luckily, there are a few basic principles we can follow to help get the best weather data

  1. Look at the most up to date data from multiple weather models
    1. Over time, knowing the model will help you determine which may be overly optimistic or overly pessimistic for your observing location. If you're lucky, one model will be just right. Because models prioritize different aspects of forecasting, expect different results between models.
    2. The full version of Astrospheric Professional bring in multiple high quality models, removing all the guess work.
  2. Make sure the data is from the most recent model run.
    1. It shouldn't be surprising, but looking at the latest data leads to more accurate results. Surprisingly, may weather websites don't stay up today.
    2. Astrospheric shows the latest data and keeps you informed on when that data was updated.
  3. Look at the data on a map
    1. As astronomers and astrophotographers, a simple point forecast will never show the full picture. Viewing the data on a map is the only way to view trends necessary to make the best decision.
    2. Another benefit of viewing data on a map is that it weeds out lazy repackaged weather websites. Creating map overlays requires processing entire variable(s) from a model, which is expensive and not easily done by services simply repackaging weather data from other sources.

Astrospheric's Approach

Astrospheric follows these basic principles to ensure accurate representation of the model data

  1. Provide graphical access to raw model data from multiple models in the form of map overlays and point forecasts
  2. Update as frequently as the models update
  3. Build the forecast dynamically, exactly at the requested location

Astrospheric uses the Canadian RDPS model for its primary variables. For additional variables (Aerosol optical depth, long range cloud cover and jet stream) Astrospheric uses the GFS model. Smoke is brought in from NOAAs RAP model. The RDPS model covers Canada, USA, and portions of Mexico, which is why Astrospheric works in these regions only.

Seeing and Transparency data are upgrades based on Allan Rahill's incredible astronomy variables, which are derived from the RDPS model. Astrospheric has introducing updates to these variables that are also derived off of RDPS and are based on the latest scientific research and tuned through input from observers like you.

Astrospheric is the only astronomy service that provides an Ensemble cloud forecast for astronomers, combining the RDPS, GFS, NBM and NAM cloud models into one simple view and removing the need to check multiple other weather websites.

When you request a forecast using Astrospheric, the server parses through hundreds of millions of raw data points to build a specialized astronomy forecast for your exact location. Sun, Moon, and ISS data is also built for the exact location. This is why it can take a few seconds to generate a forecast which is considered an acceptable trade off to ensure that the most relevant data is being returned.

Because of the way Astrospheric approaches weather forecasts, it stands apart from the myriad of other options available. It is the reason it continues to be used by professional observatories, universities, professional astronomers, and amateurs alike.

We hope you enjoy it too!

Clear Skies,

Astrospheric LLC