IMS4 ARWIS provides the airport authorities with an essential runway surface condition data. Using the real-time collected measurements from the field sensors as well as forecasts from the integrated model the system detects and predicts the runway conditions such as ice, freezing rain, accumulation of snow and thaw, thus helping to prevent runway excursions and to optimize the runway maintenance.
Therefore, the ARWIS system is mainly suitable for airports and locations where winter temperatures drop below the freezing point. However, recently the system has been more and more demanded also in the tropical regions where the seasonal monsoons cause the water layer formation on the runways resulting in worsened conditions for pilots to navigate through the airport.
Recent improvements
Being involved in the SESAR (Single European Sky ATM Research) program, MicroStep-MIS has significantly improved and upgraded the IMS4 ARWIS within the SESAR Project PJ.03b, Solution Pj03b-06 Safety support tools for runway excursions. As the test / validation site, the Poprad – Tatry Airport (ICAO: LZTT, IATA: TAT), an airport with one of the highest elevations in Central Europe at 718 m, was chosen.
The IMS4 ARWIS has been presented at the Global Airport Safety Net Forum, on 4th - 5th of April, Toulouse, France and MicroStep-MIS became one of the funding members of the EUROCAE WG-109 Runway Weather Information Systems.
The IMS4 ARWIS is now fully compliant with the new ICAO Annex 14, 7th Edition, Amendment 13b, ICAO Annex 15, 15th Edition, Amendment 39b and ICAO Doc 9981, 2nd Edition introducing new Global Reporting Format, all applicable from 5th of November 2020.
Malta Luqa aquaplaning assessment and nowcasting
The newly featured IMS4 ARWIS has recently been deployed and integrated at Malta Luqa Airport (ICAO: LMML, IATA: MLA). Integration of aquaplaning into the system, compliant with current ICAO regulations (implementation of Runway Condition Report) and additional integration with radar-based nowcasting enable the system to provide and generate more precise RWY condition assessment. Moreover, implementation of prediction model is beneficial in order to access better quality of consolidated data necessary for winter runway maintenance. Graphical user interface has been customized according to the particular requirements of the customer.