Visual Weather has a number of advanced feature that will take the forecasting capabilities to the next level.
Calculate what you need
You can receive huge amounts of forecast data but sometimes you need more. With the powerful computation tool and over a thousand of ready-to-use functions you can calculate and display everything you need: differences between two forecast models, 24-hour temperature tendency, thermodynamic indices, vorticity and advection parameters, derivations, probabilistic fields (max, min, average) from different models, and so on.
Use your expertise to modify forecast data
NWP models are, well, just models. Only humans can understand the weather in all its complexity. With Visual Weather you can use the knowledge and expertise of your forecasters and correct NWP fields in a consistent manner. Modifications are carried over to related parameters and subsequent times, e.g. changing the position of MSLP low will affect the wind values. It is not a trick! It is mathematics (and physics).
Analyse observed data
Analyse the observed data (METAR, SYNOP, TEMP) and immediately compare the analysis to NWP model data. You can also easily correct or reject observations with immediate update of the analysis.
Python extensions
Visual Weather is not a black-box – you can tap into its resources by creating your own Python plugins and use Visual Weather as a development platform for building your own additional weather forecasting applications
Plug to Internet sources
The Internet can serve as a vast resource of useful meteorological and geographical data. that can enhance your operation. Visual Weather not only allows you to download data available online, but its visualisation options include a WMS client layer, so you can display data obtained from a WMS server.
Web Services
Visual Weather is equipped with significantly powerful OpenGIS compliant interfaces so you can provide web applications with meteorological maps, features or data coverage information (WMS, WFS, WCS) which are now also available as additional module
