This photo shows the current design of the 3D-PAWS. This configuration consists of a 3-cup anemometer, wind vane, tipping bucket rain gauge, temperature, relative humidity, light, and pressure sensors. The system is designed to be adapted and installed on a variety of frames. The current design uses low-cost, easily attainable PVC pipe. However, aluminum, metal pipe, and/or wood framing can be used.

The data logger is housed in a water-proof housing unit mounted on the frame. The Power to the 3D-PAWS can be supplied using commercial power (5V is required as input) or through battery with solar power backup. The sizes of the battery and solar panel depend on location of the system.

The following sections include step-by-step instructions for assembling the 3D-PAWS instruments. A parts list is included for each instrument, along with a list of the required tools. The 3D printing files for printed parts along with the software can be found in the Downloads section.

A summary of the cost (USD) of implementing a 3D-PAWS system is shown in Table 1.

Table 1: Approximate costs of building a 3D-PAWS weather station network.

*Doesn’t include monthly service costs.

For a list of current materials and tools needed for a 3D-PAWS, follow this link:

The original 3D-PAWS design included 3D printed components for all the housings, cable connectors, and wire harnesses. To simplify assembly, the new design uses ready made cables, cable junctions and connector “hats”, manufactured by companies such as Seeed Studios and Sparkfun. If you would like to continue using the old connector system, please contact us for the 3D files.

The designs of the components were created using open-source computer-aided design (CAD) software. A project goal is to make these designs an open-source resource so other institutions and educational programs can use and adapt the designs to meet their needs for research, operations, and/or education and outreach.

The light sensor measures levels of visible light, infrared (IR), and ultraviolet (UV) radiation. It operates by detecting electromagnetic radiation through photodiodes sensitive to specific wavelengths of light. The Adafruit SI1145, based on the SiLabs sensor, is used for this purpose.

Tutorial Videos

This video playlist demonstrates the entire assembly of the instrument. You can toggle between videos using the list icon at the top right or by using the "fast forward" button at the bottom left.

Instruction Slides

The sensors have been field tested to evaluate reliability and calibration over a variety of atmospheric conditions. Before field testing, the sensors were calibrated in a controlled environment. The temperature, pressure, and relative humidity (RH) sensor calibration test results were all within the manufacturer’s specifications in the laboratory. The tipping bucket rain gauge had less than 5% error for simulated rainfall rates of 0.1 to 30 mm/h. The 3D-PAWS 3-cup wind speed anemometer and wind vane sensors were tested and calibrated in the NOAA wind tunnel at the NOAA Testbed facility. Stress testing was conducted to maximum speeds of 70 m/s. The 3-cup anemometer performed very well at “hurricane-like” wind speeds. The 3D-PAWS wind vane was also tested over the same range of wind speeds. The tests indicated the wind direction measurements were consistent through the entire range of wind speeds.

Field evaluation of sensors was conducted at the NCAR Marshall Research Facility in Boulder, CO and at the NOAA Testbed Center in Sterling, VA. Sensor observations were compared with calibrated commercial reference sensors. Table 2 shows the reference sensors that were used in the evaluation. For the analysis, reference observations were matched at 1-min resolution to compute error estimates of the 3D-PAWS sensors. Observations were evaluated for the period: June 2016 – March 2017. Evaluation periods varied depending on data availability of each sensor. Comparisons were made to determine any environmental dependencies on the results. For example, the sensors were stratified by day/night and season (e.g., warm season/cold season) to evaluate possible dependencies on measure error.

Table 2: Reference Sensors used in the evaluation.

Temperature observations were evaluated for both the NCAR Marshall Research Facility and the NOAA Testbed Site. Both sites showed similar errors in measurements. The results from the NCAR Marshall Research Facility for all observations, daytime observations and nighttime observations are shown (Fig. 3). The period of study was from June 2016 to March 2017. There are three temperature sensors (BMP180, HTU21D, and MCP9808) integrated on the 3D-PAWS system. All three sensors agree well with the calibrated reference sensor. The error for all measurements is ±0.57° C. The error is slightly larger for daytime observations and lower during nighttime. This is expected due to solar heating on the radiation shield during the day. Overall, the 3D-PAWS temperature sensors have good performance over the range of values (-25° to 37° C) observed in Colorado.

The barometric station pressure was evaluated at both sites with similar performance being observed. The observations from the NCAR Marshall Research Facility are shown as reference (Fig. 4). The 3D-PAWS pressure sensor (BMP180) is well-behaved. The observed error for all measurements during the June 2016 to March 2017 period was ±0.49 hPa. Observations stratified by time of day show slightly greater error (±0.53 hPa) during the day and less during the night (±0.37 hPa). This is likely due to the heating and larger temperature variations observed during daytime hours.

The relative humidity sensors were evaluated at both the NCAR Marshall Research Facility and at the NOAA Testbed Site (Fig. 5). Both sites observed similar behavior in sensor performance (the Marshall site results are shown below). The comparison for all measurements matched with the reference sensor for the period June 2016 – March 2017 is shown in the left panel. The mean error from the analysis ±5.7%. However, there is large scatter in the mid-humidity ranges (20-80%) and an observed low-bias at low (< 10%) and high (> 90%) relative humidities. Nighttime observations showed slightly less error (±4.87%) compared to daytime measurements (±5.98%). However, the shape of the scatterplot and errors are nearly the same. The plot shows an occasional odd behavior with either the 3D-PAWS and/or reference sensor, likely due to condensation on the sensor.

Wind speed and wind direction observations performance were evaluated (not shown). For wind speed, the measurement error was computed to be ±0.87 m/s. The nighttime observations had observation errors (±0.79 m/s) slightly less than daytime errors (±0.94 m/s). The results show that the 3D-PAWS sensor was in agreement with the reference sensor with the predominant wind observed. The measured error was less than ± 5°.

Rainfall from the 3D-PAWS tipping bucket rain gauge was compared to the NOAA Testbed weighing precipitation gauge (Fig. 6). Precipitation was compared for the period of October 2016 to March 2017. A total of ~200 mm of rainfall was recorded at the site. There is good agreement between the gauges in total accumulation. There are small differences observed for individual precipitation events. These differences are likely attributed to wind and precipitation rate errors.

Table 4 provides a summary of sensor measurement characteristics and performance during the evaluation. Overall, the sensors compared well with calibrated reference sensors. The only exception is the performance of the relative humidity sensor which has a bias at high and low humidities and larger then expected errors in the mid-range. A new relative humidity sensor is being evaluated.

Table 4: Summary of sensor evaluation.

The tipping bucket rain gauge measures precipitation by collecting rainwater in a small bucket that tips when a certain volume is reached. Each tip triggers an SS451A Hall effect sensor to record rainfall increments digitally, ensuring accurate and reliable data collection.

The tipping bucket rain gauge is calibrated to ensure each tip corresponds to a standardized rainfall depth (e.g., 0.2 mm per tip) using the volume of a cylinder to determine the required rim radius of the collector funnel.

Tutorial Videos

This video playlist demonstrates the entire assembly of the instrument. You can toggle between videos using the list icon at the top right or by using the "fast forward" button at the bottom left.

Instruction Slides

What is 3D-PAWS?

3D-PAWS (3D-Printed Automatic Weather Station) is an international initiative that enables the local construction of reliable, low-cost weather stations using 3D printing and commercially available sensors. Developed by the University Corporation for Atmospheric Research (UCAR) and the US National Weather Service International Activities Office (NWS IAO), with support from USAID Office of U.S. Foreign Disaster Assistance (OFDA), 3D-PAWS addresses the challenges of limited weather observations in remote, rural, and underserved regions.

Goals of the 3D-PAWS initiative:

• Expand Weather and Climate Observations:

  Increase the density of surface weather and environmental monitoring in rural, remote, and underserved regions by enabling the local construction and deployment of reliable, low-cost weather stations

• Reduce Weather-Related Risks:

  Provide timely and accurate weather and hydrometeorological data to support early warning systems, regional decision support, and disaster risk reduction, especially in areas vulnerable to extreme weather events such as floods, droughts, and storms

• Empower Local Communities and Build Capacity:

  Facilitate local ownership, assembly, and maintenance of observation networks, allowing communities, schools, and agencies to sustainably manage their own data collection infrastructure

• Promote Open Access and Innovation:

  Open-source robust designs, documentation, and software to encourage widespread adoption, adaptation, and innovation in environmental sensing and data collection

System Overview

A very high quality 3D-PAWS surface weather station can be manufactured in about a week, at a cost of only $300-500, using locally sourced materials, microsensor technology, low-cost micro-controllers or single board computers, and a 3D printer. 3D-PAWS sensors currently measure pressure, temperature, relative humidity, wind speed, wind direction, precipitation, and visible/infrared/UV light. A range of options are available for data acquisition, data processing, and communications, including Arduino and Raspberry Pi based systems.

Benefits of a low-cost 3D-PAWS system:

• Uses low-cost, reliable micro-sensors

• Can be assembled locally at Met Offices or other local agencies

• Components can be “re-printed” when systems fail

• Local agencies take ownership in building and maintaining observation networks

Sensor Evaluation

3D-PAWS is being assessed at the NCAR Marshall Field Site in Boulder, CO, the NOAA Testbed facility in Sterling, VA, and at selected international locations. The Boulder site provides sampling conditions in a high-altitude semi-arid environment with subfreezing temperatures and frozen precipitation (the latter is not measured). The NOAA site provides sampling for a more temperate and humid climate near sea-level. The international 3D-PAWS sites provide an assessment of sensor performance in a variety of tropical and sub-tropical climate regimes.

Station Pilot Networks

3D-PAWS systems have been deployed in the United States and in more than 17 other countries around the world. The primary focus in the United States is on testing and evaluation. The two major "success stories" are in Kenya and Barbados - in Kenya the stations are co-located with schools as part of the Globe program, while the Barbados Meteorological Service (BMS) has built and installed more than 60 stations on the island with a goal of eventually reaching 100 sites.

Data Access

Benefits, Impacts, and End Users

3D-PAWS observations can be used for a variety of hydrometeorological applications.

Example applications:

• Early alert and regional decision support systems. Real-time monitoring of precipitation in ungauged or minimally gauged river basins can provide input to flash flood guidance and early warning decision support systems to support delivery of flood alerts.

• Agricultural monitoring. 3D-PAWS can support water resource management tools to improve reservoir operation for fresh water supplies and the generation of hydroelectric power. Other applications include operation of irrigation systems (e.g., center pivots) and agricultural crop monitoring.

• Health monitoring. 3D-PAWS can help monitor conditions leading to outbreaks of diseases such as meningitis and malaria.

Contact Information

The rain gauge is calibrated through a multi-step process to ensure accuracy.

1. First, the tipping bucket mechanism is "bedded in" by cycling it approximately 1,000 times to reduce mechanical resistance.

2. Next, water is pumped into the funnel to generate around 500 tips, and the total volume of water that passes through is collected and weighed. By dividing the total water mass (grams) by the number of tips, the system determines the grams of water per tip.

3. Using the density of pure water (1 gram = 1,000 mm³), this mass is converted to volume (mm³).

4. The target rainfall depth per tip—0.2 mm is then applied to the volume of a cylinder formula (V=πr²h).

5. Rearranging the equation to solve for radius , the funnel’s rim size is calculated to ensure each tip corresponds precisely to the desired rainfall depth.

This process bridges empirical testing (weighing water) with geometric principles (cylinder math), ensuring the gauge meets standardized meteorological requirements.

Tutorial Videos

This video playlist demonstrates the entire assembly of the instrument. You can toggle between videos using the list icon at the top right or by using the "fast forward" button at the bottom left.

Instruction Slides

Note: for rain gauge calibration using the drip bottle method, please reference the 3D-PAWS Manual 2022 (Qwiic cables) under Previous Manual Versions

The wind vane uses a magnetic encoder (AS5600) to measure the angular position of the vane as it aligns with wind flow. Direction is sampled every second and combined with wind speed data to form vectors. These vectors are averaged over 60 seconds to calculate the mean wind direction, while the peak gust direction is determined by averaging the three vectors corresponding to the highest 3-second wind speed period.

Tutorial Videos

This video playlist demonstrates the entire assembly of the instrument. You can toggle between videos using the list icon at the top right or by using the "fast forward" button at the bottom left.

Instruction Slides

Tutorial Videos

This video playlist demonstrates the entire assembly of the instrument. You can toggle between videos using the list icon at the top right or by using the "fast forward" button at the bottom left.

Instruction Slides

The rain gauge screen serves as a protective barrier, preventing debris from entering the funnel and interfering with the tipping bucket mechanism. By filtering out leaves, twigs, and other particles, it ensures accurate and reliable rainfall measurements. This screen is crucial for maintaining the gauge's precision and longevity, especially in outdoor environments where debris accumulation is common.

Instruction Slides

Instruction Slides

The radiation shield houses integrated environmental sensors to ensure accurate readings:

• Temperature: Monitored via the SHT31D & MCP9808 which measures ambient air temperature using a precision thermistor.

• Relative Humidity: Detected by the SHT31D, which calculates moisture levels via capacitance changes in a polymer layer.

• Atmospheric Pressure: Measured by the BMP390, a piezoresistive sensor that converts pressure changes into digital signals.

Wiring: All three sensors connect via I2C (shared SDA/SCL lines) to minimize wiring complexity. The radiation shield’s passive ventilation design protects sensors from direct sunlight, precipitation, and debris while maintaining airflow for precision.

Instruction Slides

The three-cup anemometer measures wind speed using a Hall effect sensor (SS451A) that detects rotations. Two magnets on the anemometer generate 2 interrupts per revolution, enabling precise tracking of rotational speed. Wind speed is sampled every second by recording interrupt counts and millisecond durations. These 1-second samples are converted into instantaneous wind speeds using the anemometer’s calibration factor. Observations are logged every minute, with:

• Wind Speed: Average of 60 consecutive 1-second samples.

• Wind Gust: Highest 3-second average (three consecutive samples).

Wind speed is calculated using:

• Radius: 0.079 meters (distance from center to cup)

• Calibration factor: 2.64 (empirically determined from wind tunnel testing)

Tutorial Videos

This video playlist demonstrates the entire assembly of the Anemometer. It demonstrates the glue in version of the bearing housing and hub using Qwiic cables. Please follow the manual instructions if using the twist version of the bearing housing or M5 stack Grove cables.

You can toggle between videos using the list icon at the top right or by using the "fast forward" button at the bottom left.

Instruction Slides

Anemometer Tips and Tricks

This document is slightly outdated due to the new threaded/twist version of the hub but still has helpful tips and tricks.

The radiation shield is a passively ventilated enclosure that protects temperature, humidity, and pressure sensors from environmental interference while maintaining airflow for accurate measurements. Its multi-plate design minimizes exposure to direct sunlight, precipitation, and debris, while allowing ambient air circulation. The shield reduces radiative heating and thermal inertia, ensuring sensors measure true ambient air conditions rather than artificial microclimates.

Tutorial Videos

This video playlist demonstrates the entire assembly of the instrument. You can toggle between videos using the list icon at the top right or by using the "fast forward" button at the bottom left.

Instruction Slides

This video demonstrates the entire assembly of the instrument. There is only one video in this particular playlist.

Instruction Slides

Regular maintenance is crucial for ensuring the reliability and accuracy of weather station data. This includes routine checks on sensor cleanliness and calibration, as well as verifying that all connections are secure and free from corrosion. Inspect the station's mounting and structural integrity, especially after extreme weather events, to prevent damage or misalignment. Additionally, update software and firmware periodically to incorporate bug fixes and new features. By prioritizing these tasks, you can prevent data gaps, maintain sensor precision, and extend the lifespan of equipment, ultimately ensuring that the station continues to provide consistent and reliable weather data over time.

Instruction Slides

The Stream Gauge and Storm Surge Gauge in the 3D-PAWS system utilizes the MaxBotix MB7363 or MB7364 HRXL-MaxSonar-WRLS ultrasonic sensor to provide reliable, high-resolution water level measurements. These rugged, weather-resistant sensors are designed for outdoor environmental monitoring, featuring millimeter-level resolution, a long detection range (up to 10 meters for MB7363 and 5 meters for MB7364), and robust noise rejection algorithms. Their narrow beam pattern and real-time calibration ensure accurate distance readings even in challenging conditions, making them well suited for monitoring stream heights and storm surges in dynamic weather environments. Please use the extended horn for the Storm Surge Gauge.

Instruction Slides

For a sample assembly:

Building the weather station involves constructing a sturdy frame using PVC pipes, fittings, and 3D printed parts, which provide a lightweight yet durable structure for housing the sensors and electronics. The station is mounted on a steel pole that has been securely set in concrete, ensuring stability and resistance to wind forces. This setup allows for easy installation of sensors like anemometers, rain gauges, and radiation shields, while the PVC frame facilitates cable management and access for maintenance.

Tutorial Videos

This video is part of a playlist that demonstrates building the 3D-PAWS station. You can toggle between videos using the list icon at the top right or by using the "fast forward" button at the bottom left.

Instruction Slides

The Raspberry Pi is a flexible platform that can be used to test sensors before installation as well as serve as a reliable data logger. It supports connection to Wi-Fi networks out of the box, and you can also add an external cellular modem for remote deployments without Wi-Fi access.

Note

The Raspberry Pi 3B+ and 4 models require significantly more power than microcontroller-based options like the Particle Boron. While the Particle Boron can operate efficiently on the smaller Voltaic solar panels and batteries we recommended, the Raspberry Pi 3B+ and 4 typically draw between 3.4 and 15 watts depending on workload, which means you’ll need a much larger solar panel and battery setup to ensure reliable, continuous operation-especially in remote or off-grid deployments. For example, a minimum 12W–22W solar panel is recommended for the Pi 3B+, and even higher capacity is necessary for the Pi 4, along with a robust battery sized for overnight and cloudy-day operation. Always size your power system according to your location’s sunlight availability and your Pi’s expected power draw to avoid interruptions in data collection.

Sensors Supported

• Light sensor

• Rain Gauge

• Anemometer

• Wind Vane

• Radiation Shield (Temperature, Pressure, & Relative Humidity)

Download the 3D-PAWS software

The Black Globe temperature sensor in the 3D-PAWS system leverages the MCP9808 digital temperature sensor to determine globe temperature, a key component in calculating Wet Bulb Globe Temperature (WBGT) for heat stress assessment. This sensor provides high-accuracy temperature readings and is designed for robust environmental monitoring. When housed within a black globe, this sensor measures the combined effects of ambient temperature, solar radiation, and wind, enabling accurate WBGT calculations. This data is essential for evaluating heat stress risk in outdoor environments, supporting public health and safety initiatives.

Instruction Slides

Introduction

The Particle Boron and Argon are powerful development boards designed for rapid IoT prototyping and deployment, making them ideal choices for use as data loggers in the 3D-PAWS (3D-Printed Automatic Weather Station) system. The Boron offers cellular connectivity, while the Argon connects via Wi-Fi, allowing flexible deployment in a variety of environments. With built-in battery charging, a range of GPIO options, and seamless integration with the Particle Device Cloud, these devices can reliably collect and transmit environmental data for research and monitoring applications.

Sensors Supported

• Light sensor

• Rain Gauge

• Anemometer

• Wind Vane

• Radiation Shield (Temperature, Pressure, & Relative Humidity)

• Globe Temperature

• Air Quality (PM 1.0, 2.5, & 10)

• Distance Gauge (Stream, Storm Surge, & Snow Height)

• Soil Moisture and Temperature

• Leaf Wetness

Download the 3D-PAWS firmware

• Click the green Code button near the top of the repository page.

• Select Download ZIP from the dropdown menu to download the entire repository as a ZIP file.

Learn about Particle basics with these essential resources:

Integrate Particle Cloud data with the CHORDS data portal

Use a 3rd Party SIM with the Boron

Firmware Variants for Different Product Applications

• Storm Surge and Wind Product: This product uses a measurement interval and data processing approach that aligns with NOAA’s National Ocean Service Center for Operational Oceanographic Products and Services (CO-OPS) specifications. In accordance with CO-OPS standards, the firmware is configured to acquire and store water level measurements every six minutes, using an average of discrete samples centered about each six-minute mark. This interval and methodology ensure compatibility with national data networks and support high-quality, standardized data collection for coastal and oceanographic monitoring.

• Ultra Low Power Stream and Snow Gauge Product: Designed for remote locations where power efficiency is critical, this firmware minimizes energy consumption while reliably logging stream or snow depth data. The ultra low power mode is ideal for battery- or solar-powered deployments in difficult-to-access areas.

• Regular Power Distance (Stream and Snow) Product: For sites where power is less constrained, this firmware supports more frequent measurements and additional radiation shield sensors, making it suitable for continuous monitoring of stream or snow depth in less remote locations.

Introduction

In addition to using the Particle Boron or Argon and Raspberry Pi for data logging with 3D-PAWS, the Adafruit Feather M0 Adalogger is a versatile alternative. This compact all-in-one board combines a powerful ARM Cortex M0 processor with built-in USB, battery charging, and an onboard microSD card slot for reliable local data storage or LoRaWAN/Wifi connectivity.

It can be used with the same Grove Shield FeatherWing for Particle Mesh to integrate sensors easily, providing a flexible and portable solution for environmental data collection and logging

Sensors Supported

• Light sensor

• Rain Gauge

• Anemometer

• Wind Vane

• Radiation Shield (Temperature, Pressure, & Relative Humidity)

• Globe Temperature

• Air Quality (PM 1.0, 2.5, 10)

• Distance Gauge (Stream, Storm Surge, and Snow Height)

• Soil Moisture and Temperature

• Leaf Wetness

Download the 3D-PAWS firmware

The Adafruit Feather M0 Adalogger comes in a few different varieties depending on communication needs. Use the following software with the appropriate device:

For LoRaWAN and Wifi connectivity:

For a no network device that saves the data to the SD locally:

Firmware Variants for Different Product Applications

• Storm Surge and Wind Product: This product uses a measurement interval and data processing approach that aligns with NOAA’s National Ocean Service Center for Operational Oceanographic Products and Services (CO-OPS) specifications. In accordance with CO-OPS standards, the firmware is configured to acquire and store water level measurements every six minutes, using an average of discrete samples centered about each six-minute mark. This interval and methodology ensure compatibility with national data networks and support high-quality, standardized data collection for coastal and oceanographic monitoring.

• Ultra Low Power Stream and Snow Gauge Product: Designed for remote locations where power efficiency is critical, this firmware minimizes energy consumption while reliably logging stream or snow depth data. The ultra low power mode is ideal for battery- or solar-powered deployments in difficult-to-access areas.

• Feather LoRa Remote Units for Soil, Rain, and Distance Sensors

  We also support remote sensor units built with Adafruit Feather boards equipped with LoRa radios. These remote units are designed for low-power operation in the field and can be used with soil moisture, rain, and distance (stream or snow) sensors. Each remote unit transmits its sensor data wirelessly over LoRa to a central “Full Station.” The Full Station, typically a Particle Boron, acts as a gateway: it receives LoRa data from multiple remote units and then relays that data to the Particle Cloud using its cellular connection. This architecture enables reliable data collection from widely distributed sensors, even in remote locations without Wi-Fi or direct cellular coverage at each sensor site.

Accurate wind vane alignment to true north (geographic north) is critical for reliable meteorological and wind turbine data. Misalignment introduces errors in wind direction measurements.

Instruction Slides

Use the following link to go to our GitHub repository for the .stl files. The README file contains recommended filament and printer settings for printing the 3D-PAWS parts.

Each research team can operate their own portal, which is created simply by running a copy of the CHORDS appliance on Amazon cloud services. This provides a web server and database. CHORDS can both ingest and deliver data via simple http: requests, just like any web browser. Configuration and management of your private CHORDS server is also done through a web interface. Any user on the Internet can attach to the data streams using tools of their choice, such as Matlab, Excel, Python, web browsers; i.e. anything that is able to issue an http: request.

CHORDS portals can also forward real-time data streams to higher level CHORDS services, which might provide functions such as OGC compliant formatting, mapping services, federation, and just about anything else you can think of.

The goals of CHORDS are:

• To lower the barrier for disseminating real-time geoscience observations via the Internet.

• To provide a simple, "shrink-wrapped" and inexpensive service that will be especially attractive to teams that do not have IT expertise and budgets.

• To provide standards-based interoperability with next level processing systems.

Below are our training slides for using CHORDS with your 3D-PAWS system

And here is a detailed introduction to CHORDS

Grafana is an open-source visualization tool that allows users to create dynamic dashboards for real-time data analysis. In the context of CHORDS (Cloud-Hosted Realtime Data Services for the Geosciences), Grafana provides a powerful platform to visualize data streams collected by instruments like 3D-PAWS.

How Grafana Works with CHORDS

1. Integration with CHORDS: Grafana is implemented as a separate web server running as a Docker container within the CHORDS infrastructure. It connects directly to the CHORDS InfluxDB database, enabling responsive and customizable visualizations.

2. Creating Dashboards:

   • Users can configure Grafana to query data from the CHORDS database.

   • Dashboards are built using panels, which graphically represent query results. Panels can display data as time-series graphs, gauges, heatmaps, tables, and more.

   • Grafana offers extensive customization options, such as applying colors based on thresholds or using specific visualization types to suit different datasets.

3. Visualization Process:

   • Access the Grafana interface via the "Visualization" link on the CHORDS portal.

   • Configure queries to fetch data from specific instruments or sites in CHORDS.

   • Design dashboards to display real-time weather data or other geoscience metrics with tools like time navigation and alerts.

Grafana's integration with CHORDS enables scientists to decode complex datasets and monitor geophysical phenomena effectively, making it an essential tool for real-time geoscience research.

Solar Power Setup for Raspberry Pi and Particle Data Loggers The system uses two distinct solar configurations tailored to each device’s power requirements. For the Particle Boron data logger, a 5W 6V Voltaic solar panel (ETFE-coated, IP67-rated) paired with a V50 USB battery pack provides reliable off-grid power. This setup delivers 6.12V peak voltage and 940mA current, sufficient for low-power cellular data logging. The V50 battery ensures overnight operation, while the panel’s 50cm waterproof cable simplifies outdoor mounting.

For the Raspberry Pi 3B+ (which demands higher power), a 20W 12V solar panel charges a 12V battery via a charge controller (e.g., PWM or MPPT) to prevent overcharging. A buck converter steps down the 12V battery output to 5V/2A for the Pi, ensuring stable operation with peripherals like Grove sensors or cellular modems. This setup accounts for the Pi’s ~5.25W consumption and includes a low-voltage cutoff to protect the battery.

Video Tutorial

This video demonstrates how to build the solar panel mount for the Particle Data Logger. There is only one video in this playlist.

Instruction Slides

Solar Panel mount for Particle Data Logger

Solar Panel Mount for Raspberry Pi Data Logger

The following spreadsheet contains a few lists for materials and tools that are not 3D-printed

1. 3DPAWS parts list

2. Tools required to build a 3D-PAWS (Recommended Tools)

3. Breakdown of parts for each sensor on the 3DPAWS

4. Parts list for the Stream Gauge/Storm Surge Sensor

5. Breakdown of parts for each sensor on the Snow Gauge

The air quality sensor in the 3D-PAWS system is based on the Adafruit PMSA003I Air Quality Breakout, a compact and highly integrated particulate matter sensor. Using laser scattering technology, the PMSA003I detects and counts airborne particles across multiple size ranges (PM1.0, PM2.5, PM10) and provides real-time measurements of particulate concentrations via an easy-to-use I2C interface.

Instruction Slides

Accurate weather data depends on selecting a location that minimizes environmental interference. The station should be placed in an open, level area away from obstructions like buildings, trees, or heat sources (e.g., pavement, vents, bodies of water) that could distort temperature, wind, or rainfall readings. Grass or natural terrain is ideal for temperature sensors, while wind instruments require clear exposure to prevailing winds. Prioritize accessibility for maintenance and repairs while balancing security needs—select a visible yet secure spot to deter vandalism, such as within fenced property or monitored areas. Mount the station on a sturdy, tamper-resistant pole (e.g., steel set in concrete) and consider using lockable enclosures for electronics. Careful siting minimizes microclimate effects and ensures data aligns with official meteorological standards, improving reliability for analysis or comparisons.

Instruction Slides

Overview

The 3D-PAWS system provides a robust, cloud-based data access and visualization platform designed to streamline the journey from sensor data collection to actionable insights. The platform is organized into three main sub-pages: CHORDS, Grafana, and Particle / CHORDS Integrations, each supporting different aspects of data management and visualization.

Data Flow and Connectivity

Sensor data from the 3D-PAWS data logger is transmitted to the internet via a local access point connection (Cellular, Wifi, or LoRaWAN). Depending on the hardware configuration, the logger connects either through the Particle Cloud (for Particle-based devices) or directly to the ICDP server (for Raspberry Pi or Adalogger Feather setups). Once online, the data is securely forwarded to the CHORDS data portal using simple HTTP requests or cloud integrations. For Particle devices, this process leverages Particle’s integration features to seamlessly push published events to CHORDS, while other devices utilize direct server communication.

CHORDS Data Portal

The CHORDS portal serves as the central hub for data ingestion, management, and access. Users can observe real-time data, monitor instrument performance, and download datasets for further analysis. The portal supports easy integration with external applications and provides automatic registration functions for visualization and mapping services.

Grafana Visualization

For in-depth data analysis and visualization, the system employs Grafana, an open-source platform renowned for its interactive dashboards and advanced querying capabilities. Grafana connects to the CHORDS portal, enabling users to create custom dashboards, visualize trends, and share insights across teams. This integration empowers users to interpret complex environmental datasets through charts, graphs, and maps, facilitating rapid identification of patterns and anomalies.

Particle / CHORDS Integrations

The Particle/CHORDS integration page details the configuration steps required to link Particle devices with the CHORDS portal. Using Particle’s webhook and integration features, users can automate the forwarding of sensor data to CHORDS, ensuring seamless data flow from remote devices to the cloud and into the visualization ecosystem.

Together, these tools provide a comprehensive, user-friendly interface for accessing, analyzing, and visualizing environmental data collected by the 3D-PAWS system.

The snow gauge in the 3D-PAWS system combines a MaxBotix HRXL-MaxSonar-WRLS ultrasonic sensor with an external MB79XX HR-MaxTemp temperature correction sensor to deliver precise snow depth measurements. The ultrasonic sensor provides accurate, real-time distance readings to the snow surface, while the external temperature sensor compensates for variations in the speed of sound caused by changing air temperatures. This dual-sensor approach significantly improves measurement accuracy, especially during periods of rapid temperature fluctuation, and is recommended for weather station and snowpack monitoring applications.

Instruction Slides

Below is a link to our Youtube Chanel for examples when building a 3D-PAWS.

Below is a video on periodic maintenance recommendations for the Bambu Printers.

Particle IoT Cloud Webhooks are a powerful feature that enables seamless integration between Particle devices and external services, such as CHORDS (Cloud-Hosted Realtime Data Services for the Geosciences). Here's an overview of what they are and how they facilitate data transfer:

What Are Particle Webhooks?

Webhooks are a mechanism for sending data from Particle devices to external web services. They act as a bridge between the physical world (data collected by Particle devices) and the digital world (cloud-based services). When a Particle device publishes an event, the webhook listens for that event and triggers an HTTP request to a specified URL. This request can include data from the event, formatted according to your needs, and can also receive responses back from the external service.

This is the original version of the 3D-PAWS manual before the introduction of qwiic cable technology.

This version of the manual describes build the 3D-PAWS using only Qwiic Cables. The current version of the manual shows options to use Qwiic or M5Stack Grove cables.

The course is composed of the following 10 lessons:

• Foundations of Meteorological Instrumentation and Measurements

• Meteorological Instrument Performance Characteristics

• Instrumentation and Measurement of Atmospheric Temperature

• Instrumentation and Measurement of Atmospheric Pressure

• Instrumentation and Measurement of Atmospheric Humidity

• Instrumentation and Measurement of Precipitation

• Instrumentation and Measurement of Winds

• Instrumentation and Measurement of Atmospheric Trace Gases

• Instrumentation and Measurement of Radiation

• Instrumentation and Measurement of Clouds and Aerosols