LoRaWAN Weather Station Systems

System Components

Basic station:

A basic LoRaWAN weather station starts with the BME280 sensor measuring temperature, humidity, and barometric pressure with ±0.5°C and ±3% RH accuracy—sufficient for most monitoring applications. A tipping bucket rain gauge with 0.2mm resolution tracks precipitation accurately enough for agricultural and flood prediction use cases. Solar panels paired with battery storage provide long-term autonomous operation without grid power. The LoRaWAN transmitter handles communication, with range depending on terrain and gateway placement.

Advanced station:

Adding wind monitoring requires a cup anemometer measuring wind speed with 0.5 m/s resolution. A wind vane provides 16-point compass direction data, revealing wind patterns critical for spray timing or site assessment. UV sensors track solar radiation for agricultural applications. Soil moisture probes at 30cm depth monitor root zone conditions, closing the loop between weather data and plant water needs.

Temperature, Humidity, Pressure

Sensor selection:

Match sensor accuracy to application requirements. The DHT22 with ±2°C accuracy works fine for irrigation timing decisions where exact temperature matters less than trends. BME280 sensors with ±0.5°C precision suit HVAC control applications where half-degree differences affect system efficiency. Vaisala and similar industrial sensors provide traceable calibration certificates when regulatory compliance or insurance claims require documented accuracy.

Installation requirements:

Radiation shields are mandatory—direct sunlight hitting the sensor creates false readings 10-20°C above actual air temperature. Use white, ventilated shields that allow airflow while blocking solar radiation. Avoid all heat sources: rooftops absorb and radiate heat, concrete creates thermal mass effects, exhaust vents blow warm air. Install sensors at 1.5-2m height following meteorological standards, ensuring measurements represent ambient conditions rather than ground-level or rooftop microclimates.

Rain Measurement

How tipping buckets work: Rain fills bucket until it accumulates 0.2mm or 0.5mm, then tips, empties, triggers count. No calibration drift, works for years.

Resolution matters:

Choose 0.2mm resolution for agriculture and flood prediction applications where detecting light rain and measuring accurate intensity matter. The finer resolution catches drizzle that 0.5mm gauges miss, providing data for irrigation decisions and early flood warnings. Use 0.5mm resolution for general weather monitoring where precise intensity measurement isn't critical—it's simpler and costs less.

Hardware:

Davis, Onset, and Pessl Instruments manufacture tipping bucket gauges compatible with LoRaWAN pulse counters. Transmission intervals of 10-30 minutes balance data freshness with battery life, providing adequate temporal resolution for most applications while enabling multi-year autonomous operation.

Placement requirements:

Install in open areas with no obstructions within 4x the gauge height—trees and buildings create wind turbulence that throws rain away from the funnel. Level the gauge within 2 degrees, absolutely critical since tilting causes systematic undercounting. Mount at 1-1.5m standard height, maintaining clear line-of-sight to the LoRaWAN gateway. Ensure accessibility for maintenance—clogged funnels require monthly cleaning.

Wrong location undercounts by 30% due to wind effects. Obstructions create turbulence.

Data processing:

Calculate running totals across multiple timeframes—1 hour, 24 hours, 7 days, and 30 days—revealing both immediate storms and seasonal patterns. Rainfall intensity in mm/hour classifies storm severity for operational decisions. The distinction matters: 20mm spread over 6 hours allows soil absorption (beneficial), while 20mm in 30 minutes runs off causing erosion and flooding.

Applications:

Frost protection systems activate sprinklers intelligently—only when rainfall drops below 2mm/hour, achieving 60-70% water savings compared to manual operation. Urban flood prediction uses intensity thresholds: readings exceeding 15mm/hour trigger pump activation 20-30 minutes faster than rainfall radar alone. Insurance claims benefit from calibrated gauges providing GPS-timestamped, verifiable precipitation records that resolve disputes.

Wind Monitoring

Sensor types:

Cup anemometers: Standard for long-term monitoring. 3-cup design, 0.5 m/s threshold, ±0.3 m/s accuracy. Brands: NRG, Thies, Vector Instruments. Bearings wear out every 2-3 years in harsh conditions.

Sonic anemometers: No moving parts. Ultrasonic time-of-flight measurement. Measures 3D wind vector. More expensive but maintenance-free except cleaning. Better for icing conditions.

Wind vanes: Potentiometer or magnetic encoder. Mount above anemometer to avoid wake effects.

Installation height:

Agricultural applications mount sensors at 2-10m height where crops interact with wind. Wind energy assessments require measurements at turbine hub height—typically 40-120m—since wind speed increases with altitude. Environmental monitoring uses 10m as the standard reference height. Building-mounted installations place sensors at least 2x the building height above the roofline to clear turbulent air created by the structure itself.

Placement rules:

Open terrain with no obstructions within 10x sensor height ensures representative measurements. Building edges, tree lines, and terrain features create turbulence that corrupts data—you'll measure local air disturbance rather than actual wind conditions.

Applications:

Wind turbine site assessment demands 1-2 years of data collected at hub height. Regional wind estimates can overestimate by 30% due to local terrain effects—costing hundreds of thousands in miscalculated ROI. Agricultural spray operations require wind speeds below 5 m/s to prevent drift. Frost protection systems trigger based on combined wind and temperature thresholds. Construction crane operation limits typically fall at 12-15 m/s for safety. Aviation weather stations detect wind shear conditions affecting takeoff and landing.

Data outputs:

Wind speed data includes both average values and peak gusts. Direction in degrees from north reveals prevailing wind patterns. Standard deviation indicates turbulence intensity—high values signal gusty, variable conditions. Wind roses display data on 16 or 36-point compass diagrams, visualizing directional distribution. Statistical analysis fits Weibull distributions and calculates power density for energy applications.

Maintenance:

Cup anemometer bearings wear fastest in coastal or dusty conditions, requiring replacement every 18-24 months to maintain accuracy. Sonic anemometers need annual cleaning—spider webs between sensors cause measurement errors. Agricultural applications benefit from annual field calibration checks. Wind energy installations require certified laboratory calibration every 1-2 years to maintain measurement standards.

Data Pipeline

The standard data flow runs LoRaWAN → ChirpStack → InfluxDB → Grafana. Transmission intervals from 1-10 minutes depend on application requirements—irrigation control needs frequent updates, seasonal monitoring tolerates longer intervals. Local alerting catches out-of-range values immediately, triggering notifications before problems escalate. Historical comparison charts current conditions against the same period last year, revealing anomalies and long-term trends. API access enables automated systems—irrigation controllers, HVAC systems, and frost protection equipment—to query current conditions and adjust operations. The database calculates derived metrics like cumulative rainfall totals, intensity values, and wind rose distributions automatically.

Real Applications

Agricultural monitoring: Multiple stations across hectares map microclimates. Temperature variations affect planting density and yield optimization.

Ski resort operations: Stations at different altitudes track temperature and snow depth. Feed snowmaking systems - run compressors only when optimal (<-2°C, <60% humidity).

Urban planning: Map temperature variations across neighborhoods. Identify heat islands, inform urban greening decisions.

Renewable energy: Site-specific wind data over months beats regional estimates. ROI calculations depend on accurate wind speed distribution.

Hardware Components

Component sourcing:

You source all hardware components directly—BME280 or equivalent temperature/humidity/pressure sensors, tipping bucket rain gauges from manufacturers like Davis, Onset, or Pessl Instruments. Wind monitoring requires cup anemometers or sonic anemometers depending on maintenance preferences and budget. Wind vanes add directional data when applications require it. LoRaWAN transmitters need analog and pulse inputs to interface with sensors. Solar panels paired with battery systems provide autonomous power in remote locations. Mounting masts, hardware, and lightning protection (for tall installations) complete the physical setup. LoRaWAN gateways may be needed depending on existing coverage.

System specifications:

LoRaWAN range varies with terrain, antenna height, and gateway placement. Battery life depends heavily on transmission frequency and environmental conditions. Solar power system sizing depends on latitude, seasonal sun availability, sensor power draw, and desired autonomy during cloudy periods.

I specify what you need based on accuracy requirements, environmental conditions, and application demands. I don't sell hardware—just help you source the right components and integrate them into working systems.

What I Provide

Services:

• Sensor selection and specification for accuracy requirements
• Site assessment and sensor placement planning
• LoRaWAN network design and coverage analysis
• Data pipeline architecture and implementation
• Dashboard design and alert configuration
• Statistical analysis and data processing algorithms
• System integration with existing automation

You own everything:

• Complete system source code and integration scripts
• Self-hosted infrastructure (database, visualization, network server)
• All configuration and installation documentation
• Calibration procedures and quality control methods
• No ongoing licensing fees or vendor dependencies

I don't push specific hardware brands or software platforms. If you prefer different sensors, alternative databases, or custom visualization tools, I work with what suits your requirements and budget.