In today's world, ice formation poses a serious risk to road users, pedestrians, infrastructure and goods. Rapid changes in temperature and humidity can cause ice to form unexpectedly, often before it is visible. Our IoT sensors for ice warning provide real time insight into atmospheric and surface conditions to detect icing risks early and support timely preventive action.
Our IoT sensors continuously measure the environmental parameters that indicate ice formation risk.
Mass Concentration
Mass concentration measurements help identify airborne particles that can influence condensation and freezing behavior under certain conditions.
PM4 / PM10
Particulate matter data provides additional insight into air conditions that may affect moisture behavior and surface freezing.
Air Temperature
Air temperature is a primary indicator of freezing risk. Continuous monitoring helps identify when conditions approach or cross critical thresholds.
Relative Humidity
Relative humidity influences moisture availability in the air. High humidity combined with low temperatures significantly increases icing risk.
Barometric Pressure
Barometric pressure changes provide context for weather transitions that may lead to sudden icing conditions.
Air Humidity
Air humidity measurements support accurate assessment of moisture levels that can condense and freeze on surfaces.
Surface Temperature
Surface temperature directly indicates whether ice can form on roads, bridges, or walkways. This is one of the most critical metrics for ice warning systems.
Dew Point Temperature
Dew point temperature shows when moisture in the air will condense or freeze on surfaces. Monitoring dew point enables prediction of ice formation before it occurs.
By combining air conditions, surface temperature, and dew point measurements, ice warning systems provide a comprehensive view of icing risk. This enables faster responses, safer environments, and more efficient winter operations.
Infrared Thermometer Range: From -20 to 80 °C for ambient temperature From -40 to 1030 °C for object temperature Accuracy: ±1.5 % or ±1.5 °C Optical Resolution: 15:1 Repeatability: ±0.75 % or ±0.75 °C Spectral Range: 8 ... 14 μm Environmental rating: IP 63 Length cable: 1m (langer cable on request) LoRaWAN® class A 2 C alkaline batteries
Winter Road Maintenance Range: From -40 to 1030 °C (target temperature) From -20 to 80 °C (sensor head temperature) Resolution: 0.1 °C Accuracy: ±1.5 % or ±1.5 °C Repeatability: ±0.75 % or ±0.75 °C Spectral range: 8...14μm Optical Resolution: 15:1 Environmental rating: IP 63 Air Temperature Range: From -40 to 125 °C Resolution: 0.01 °C Accuracy: ±0.1 °C (20 to 60 °C) ±0.2 °C (-40 to 90 °C) Air Humidity Range: From 0 to 100%RH Resolution: 0.01 % RH Accuracy: ±1.5%RH(0 to 80%RH) ±2.0 % RH (80 to 100 % RH) LoRaWAN™ class A 2 C alkaline batteries
High-Precision Winter Road Maintenance Sensor Range: From -40 to 70°C (-40 to 160°F) Accuracy: From -40 to 60°C (±0.5°C) Note: Accuracy is temperature dependent. The quoted accuracy is against a blackbody source within the ambient temperature range of -20 to 50°C and object temperature range of -40 to 60°C. Resolution: ±0.01°C Air Temperature Range: From -40 to 70°C (-40 to 160°F) Accuracy: ±0.4°C Note: Inaccuracy can be higher under moderate-to-high solar radiation. Resolution: ±0.01°C Relative Humidity Range: From 0 to 100 % Resolution: 0.1 % Accuracy: < ±3 % or better Dew Point Temperature (Calculated) Range: From -40 to 70°C (-40 to 160°F) Accuracy: ±1.0 °C (-35 to 50 °C) Note: The calculated dew point temperature is found from Tetens' equation solved for dew point with coefficients optimized for the temperature range -35 to 50°C. Resolution: 0.1°C LoRaWAN® class A 2 C alkaline batteries
Improved Safety
Early detection of icing conditions helps reduce accidents and slip hazards for vehicles and pedestrians.
Proactive Response
Real time data enables timely warnings, de icing, or traffic management actions before conditions become dangerous.
Reduced Operational Costs
Condition based interventions reduce unnecessary salting, gritting, and maintenance activities.
Higher Situational Awareness
Continuous monitoring provides clear insight into rapidly changing weather and surface conditions.
Support for Smart Infrastructure
Ice warning systems form a critical component of smart city and intelligent transportation strategies.
Data Driven Decision Making
Reliable sensor data supports accurate forecasting, reporting, and long term safety planning.
Ice does not need to be visible to be dangerous. Detect icing risks early and act before conditions turn hazardous.
