## 1 Introduction
Although existing ASTM tests provide relevant information regarding the texture of the runway, which is important for the braking procedure of aircraft, more information is still needed to confirm the nature of contaminants on the surface. Efforts have been made to use weather and flight data to estimate surface conditions at landing using data analysis, machine learning and fusion and model correlation [15; 16; 17]. A few sensors exist in the market that are mounted in the runway to predict the type of contaminant present [18; 19; 20]. [PERSON] et al. developed a new sensor which operates using light scattering principles to accurately measure the snow cover properties [18; 19]. The concept was proven using different snow samples having various grain size distribution, liquid water content, density and thickness. The sensors are still in the development stage and could be adapted to other types of precipitation or contaminants. [PERSON] et al. demonstrated that Raman spectroscopy could be used to differentiate the fluids (aircraft and runway products), to identify the evolution of freezing temperatures with water dilution and the concentrations of active compounds in each fluid [20]. The presented methodology could be mounted with a surface texture sensor to accurately determine the nature of the contaminant and its effect on the runway surface. Despite the existence of such tools being constantly developed and improved, the only recognized method to-date of evaluating surface conditions remains the human eye, as specified in the Global Reporting Format document [21]. Since November 2021, the International Civil Aviation Organization (ICAO, Montreal, QC, Canada) has implemented the New Global Reporting Format (GRF) for runway surface conditions at all airports. The main idea is to mitigate the risk of runway excursions by enabling a harmonized assessment and reporting of runway surface conditions, and by an improved flight crew assessment of takeoff and landing performance. Among the work carried out to implement this global standard, Transport Canada published the Advisory Circular (AC, Ottawa, ON, Canada) No. 300-019 [22]. The GRF principle implies that at any time when water, snow, slush, ice, or frost are present on an operating runway, the airport operator evaluates the runway surface conditions. This evaluation yields a RunWaY Condition Code (RWYCC) and a description of the runway surface, which the flight crew may use to calculate airplane performance. The airport operator's best evaluation of the runway surface quality is based on this format, which is based on the type, depth, and coverage of contaminants. However, other relevant factors should be considered as well, and the changes in circumstances should be notified as soon as possible. The Runway Condition Assessment Matrix (RCAM) is the technique by which an airport or aerodrome operator generates a preliminary RWYCC for each runway-third if there is water, snow, slush, ice, or frost present on the runway surface. Descriptions of the winter conditions are included directly in this matrix and allow their reproduction in controlled laboratory conditions. Airport maintenance teams, as well as runway de-icer product manufacturers need procedures to evaluate the impact of RDPs when used in those conditions. The idea is to gather relevant information on the skid conditions of the surface to know whether the product is being used in an anti-icing mode (before the precipitation) or in the de-icing mode (after the precipitation). The most relevant method used to evaluate the skid resistance of pavements and runways is based on their microtexture, and hence their frictional properties can be assessed using the BPT [23; 24; 25; 26; 27]. Thanks to its light weight, the BPT is portable and does not necessitate surface preparation or external energy [23]. The BPT is well-documented and plenty of research proves that this method gives sufficient information to accurately evaluate the state of the surface.