Locations Management - Tagging and Alphanumeric Sorting
In our continued efforts to make location management as seamless and efficient as possible for users, we have two new releases designed to do so: location tagging and alphanumeric sorting. With location tagging, users can now add “tags” to locations both in the user interface and programmatically through the API. Tags allow users to add tags to any location and filter them in several different areas of the platform - on the Monitoring Map View, on the Insights Dashboard, and within the Locations Manager itself.
Watch this video on adding location tags to start seeing efficiencies:
1. In this tutorial, you will learn how to create a location tag, As you follow the scenario, you should recreate the steps with your business data.
Tip! Change the language, or the mode of these steps to at the top of this step panel.
2. The first step is to sign in to Tomorrow.io and click "Locations" from the menu bar.
3. To find a location, search for the location by address or name in the "Filter locations" search query bar.
4. When found, click Edit on the location line.
5. To add tags, click "Tags"
6. Type in your desired tags and press the enter key on your keyboard. Pressing enter ensures the tags stick.
7. When done, click "Save Changes."
Now, use those tags to search for locations in other workflows of the platform.
8. To search for tags in the Maps workflow, click Maps from the menu bar.
9. From within the "monitoring" view, search for monitored locations by Location Tags.
Tip! Remember, monitors are locations with an associated insight.
10. You can also leverage locations tags from an Insight dashboard. Click "Insights Dashboard" from the menu bar.
11. Click "Location Tags" to search for tags, in the appropriate dashboard.
Thats it! You have added tags to your locations. These tags are now accessible in the Maps and Insight Dashboard screens.
Here's an interactive tutorial
** Best experienced in Full Screen (click the icon in the top right corner before you begin) **
Modeling Improvements through additional weather station observations
We never stop striving to improve our models and forecast accuracy, and one way we do that is by constantly adding ever-more weather stations into our real-time weather analysis and air quality product and CBAM models. October was a huge month in that regard, with thousands of new automated weather station observations assimilated, to strengthen our model data quality in areas and regions of the world that are historically data-sparse, including in Latin America, Australia, and East, and Central Asia. The more robust our observation network continues to grow, the more our models - and forecasting accuracy - will benefit, with a clearer picture of the past and present state of the atmosphere.
Data product and meteorology improvements
We are continually making iterative improvements to our global geo sat precipitation algorithm, especially in areas or regions where we see specific trends. In October, we addressed an issue with the over-reduction of rainfall specifically from shallow cloud top events. There are many challenges presented by using passive satellite data/cloud top temperatures to detect where and when precipitation is coming, and this tweak to our algorithm will help address those challenges.
Similarly, an additional processing step was implemented to address a “bright banding” issue that occurs due to a regularly timed cooling system issue during specific periods on either side of the spring and autumnal equinoxes. With some of the best meteorological talent building our platform, our mission to revolutionize weather intelligence and forecasting are continuously progressing.