Development of Innovative Technology to Provide Low-Cost Surface Atmospheric Observations in Data-sparse Regions Paul Kucera and Martin Steinson University Corporation for Atmospheric Research/COMET
3D-Printed Automated Weather Station (PAWS) Use 3D printers inexpensive technology Use low-cost, reliable microsensors Design a system that that can be assembled locally in country Print and replace components when systems fail Enable local agencies to take ownership in building and maintaining observation networks Weather Station Sensor Platform 3D-PAWS: Expanding the global weather observation data collection footprint"
3D-Printed Automated Weather Station (PAWS) Data acquisition and communication using Raspberry Pi Single Board Computer 3D Printer Precipitation Rate Wind Speed Wind Direction
3D-Printed Automated Weather Station (PAWS) Radiation Shield and State Variables: Pressure, Temperature & Humidity Power and Communications Commercial and solar power solutions Direct network, wireless link, and cell modem Future: satellite modem communication (Iridium, GOES, METEOSAT)
3D-Printed Automated Weather Station (3D-PAWS) Planned New Sensor Development Time Frame Lightning Detection Summer 2017 Soil Moisture/Temperature Fall/Winter 2017 Stream/water flow gauging Fall/Winter 2017 Heated Precipitation Gauge Summer 2018
3D-Printed Automated Weather Station (3D-PAWS) Evaluation 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 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 The sensors were stratified by day/night and season (e.g., warm season/cold season) to evaluate possible dependencies on measure error NCAR Testbed NOAA Testbed
Reference Sensors NCAR Testbed Temperature: Campbell Scientific 500 series sensor Pressure: Vaisala PTB101B Humidity: Campbell Scientific 500 series sensor Wind speed: RM Young 05108 anemometer Wind Direction: RM Young 05108 anemometer Precipitation: Geonor T-200 Weighing Gauge NOAA Testbed Temperature: Technical Services Laboratory 1088 hygrothermometer Pressure: Coastal Environmental Systems Precision Digital Barometer PDB-1 Humidity: Technical Services Laboratory 1088 hygrothermometer Wind speed: Vaisala, Inc. 425NWS Ice Free Wind Sensor Wind Direction: Vaisala, Inc. 425NWS Ice Free Wind Sensor Precipitation: OTT AWPAG weighing precipitation gauge
Evaluation Results - Temperature Temperature observations were stratified by month to examine the seasonal variability on observation error. For July, the 3D-PAWS temperature sensors were consistent with the reference sensor. The error for this period was ±0.4 C on average. The sensors were more inconsistent in January with larger errors (±0.7 C on average) along with larger variability being observed. The larger error variability was likely due to snow accumulating or frost on the radiation shield. July 2016 January 2017
Evaluation Results - Pressure The barometric station pressure was evaluated at both sites with similar performance being observed. The observed error for all measurements observed during the June 2016 to March 2017 period was ±0.49 hpa. Observations stratified by time of day show slightly larger error (±0.53 hpa) during day and better during the night (±0.37 hpa). There was little seasonal dependency observed in the results (not shown). All Daytime Nighttime
Evaluation Results Relative Humidity The plots below show the evaluation of 3D-PAWS relative humidity sensor for July 2016 (left: summer) and January 2017 (right: winter). During the warm season, the relative humidity sensor has less observed error than cold season. A dry bias is still observed at low and high humidities with the bias being more significant during the cold season. July 2016 January 2017
Evaluation Results Rainfall Rainfall from the 3D-PAWS tipping bucket rain gauge was compared to the NOAA Testbed weighing precipitation gauge 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 attributed to: Wind errors Precipitation rate errors Snow events? Precipitation Accumulation Oct 16 Nov 16 Dec 16 Jan 17 Feb 17 Mar 17
Summary of Results The 3D-PAWS sensor evaluation was conducted using a dataset collected at the NCAR Marshall Research Facility located in Boulder, Colorado and at the NOAA Testbed Center located in Sterling, Virginia Overall, the sensors compared well with calibrated reference sensors. The only exception is the performance of the relative humidity 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 Parameter Resolution Uncertainty Temperature ( C) 0.1 C ±0.4 C Pressure (hpa) 0.1 hpa ±0.4 hpa Relative Humidity (%) 1 % ±5.7 % Wind Speed (m/s) 0.1 m/s ±0.8 m/s Wind Direction (deg) 1 deg ±5 deg Rainfall (mm) 0.2 mm 10%
International Deployments 3D-PAWS Installations Zambia (6) Kenya (11) Curacao (1) Barbados (1) US (3) Germany (1) Austria (1)
The GLOBE Program
Kenya Education Outreach and Site Setup
Barbados CIMH Installation
Open Data Access Data are stored locally at each station - 2+ years of data can be stored on local flash drive Real-time Access: - Web-data services (e.g., CHORDS) - Local NMHS s - GLOBE data services Example: NSF EarthCube Initiative: CHORDS (Cloud- Hosted Real-time Data Services for Geosciences) data-portal Example - PAWS Project Data Portal: http://3d.chordsrt.com
Application Development Future Applications using 3D-PAWS Weather forecasting Early Warning Systems Flash flooding Severe weather Making engineering decisions Water resource management Agriculture