This paper describes the data collected by the University of Nebraska-Lincoln (UNL) as part of the field deployments during the Lower Atmospheric Process Studies at Elevation – a Remotely-piloted Aircraft Team Experiment (LAPSE-RATE) flight campaign in July 2018.
The UNL deployed two multirotor unmanned aerial systems (UASs) at multiple sites in the San Luis Valley (Colorado, USA) for data collection to support three science missions: convection initiation, boundary layer transition, and cold air drainage flow.
We conducted 172 flights resulting in over 21 h of cumulative flight time.
Our novel design for the sensor housing onboard the UAS was employed in these flights to meet the aspiration and shielding requirements of the temperature and humidity sensors and to separate them from the mixed turbulent airflow from the propellers.
Data presented in this paper include timestamped temperature and humidity data collected from the sensors, along with the three-dimensional position and velocity of the UAS.
Data are quality-controlled and time-synchronized using a zero-order-hold interpolation without additional post-processing.
The full dataset is also made available for download at
A team of researchers from the University of Nebraska-Lincoln (UNL) participated in the Lower Atmospheric Process Studies at Elevation – a Remotely-piloted Aircraft Team Experiment (LAPSE-RATE) flight campaign between 14–19 July 2018 at San Luis Valley of Colorado, USA. LAPSE-RATE was organized as part of the International Society for Atmospheric Research using Remotely-piloted Aircraft (ISARRA) 2018 meeting. A total of 1287 flights were conducted by 13 institutions, including the UNL, which resulted in more than
Multirotor UASs are finding more routine uses for sampling and profiling the ABL, such as atmospheric profiling
The need for increased spatial resolution for atmospheric sampling is reflected in publications, such as improving numerical weather prediction (NWP) models
Our previous work
Images of
Two primary highlights of our novel sensor housing are its ability to obtain temperature and humidity sensor readings reliably during both ascent and descent profiles and its invariance to the aircraft orientation relative to the ambient wind. Two key design considerations in achieving these goals are the placement of the sensor and its consistent aspiration. Placement of the sensor on the UAS body can adversely affect the measurements
Our sensor housing design has evolved over multiple design iterations and has been field-tested in multiple Collaboration Leading Operational UAS Development for Meteorology and Atmospheric Physics (CLOUD-MAP) field campaigns
For the LAPSE-RATE campaign, the UNL deployed two identical UASs with one primary-sensor suite for measurements and a secondary-sensor suite for redundancy and testing.
These flights were conducted at five locations in San Luis Valley (Colorado, USA) through 14–19 July 2018.
The maximum altitude for each flight ranged from 100–500 m above ground level.
Figure
The rest of the paper describes the components of our system (Sect.
The two identical UASs deployed during the missions were developed on a DJI Matrice 600Pro platform equipped with DJI A3 Pro flight control systems.
The unfolded dimensions (including propellers, frame arms, GPS mounts, and landing gear) of the system are 1668 mm
Table
Some flights also used a nimbus-pth as the secondary sensor, which is a sensor package unit we designed and built for pressure, temperature, and humidity sensors. Several nimbus-pth's can be chained as nodes for data collection. In some data files, two of these nodes might be present. In such cases, one of them was aspirated inside our sensor housing, and the other one sat directly underneath the UAS in a traditional non-aspirated configuration. In the data files, the first two sensors were shielded and aspirated inside the housing, and the third sensor (when available) was in a traditional non-aspirated configuration.
The key manufacturer's specifications for the sensors used in different experiments: the unavailable fields are left blank. Data sheets for each sensor package are available at iMet XQ2
The sensor housing is designed to meet or exceed sensor placement requirements, such as consistent aspiration for the sensors and shielding from solar radiation and other indirect heat sources.
The housing draws air passively by exploiting the pressure differential between the region just above a propeller and the region just beyond the rotor wash.
The airflow through the housing is always maintained as long as the propellers are spinning and provides a consistent aspiration for the sensors
Sensors are placed inside the tube structure as shown in panels (c) and (d) of Fig.
The housing is also designed to be modular, printed entirely using a 3D printer, and has an easy screw-in assembly.
The impact of the housing on the UAS's stability and flight time is minimal.
Further details and the full schematic of the housing and the evaluation can be found in our previous work
Data were collected using a data acquisition (DAQ) system comprised of an Odroid-XU4
The communication with the DJI flight controller was implemented using the ROS interface of DJI Onboard SDK
The Odroid was connected with a ground computer using wireless 2.4 GHz XBee radios for the operation of DAQ, debugging, and periodic checks on the data when the UAS finished a flight. The data collected by the DAQ were retrieved to the ground computer for archiving at the end of each day using an ethernet connection.
Temperature and humidity sensors were connected over serial with the ROS to send periodic updates of the observations. The UAS's autopilot also interfaced with ROS to provide updates of position, velocity, altitude, attitude, etc., which were also recorded to spatially and temporally synchronize the observation.
As mentioned in Sect.
One XQ2 (sensor_1) was placed inside the left sensor housing, and one XQ1 (sensor_2) was on an identical right sensor housing. This placement location for the left housing is highlighted in panel (a) of Fig.
One XQ2 (sensor_1) was mounted inside the left sensor housing, one nimbus-pth (sensor_2) was mounted inside the right sensor housing, and an additional nimbus-pth (sensor_3) was placed under the body of the UAS without a housing. This form of sensor placement facilitates an evaluation between the sensor placed inside the housing versus under the body of the UAS without housing. It also allows comparison of the sensors mounted on the opposite ends of the UAS. Having secondary sensors also provides a fail-safe when the primary sensors fail, such as the case of XQ2 humidity sensors on 17 July 2018 data.
The UAS's total payload during the experiments was approximately
During the LAPSE-RATE field campaign, measurement objectives for each day were determined based on the weather forecast, site availability, and available team resources. Many designated locations of San Luis Valley of Colorado, USA, were planned beforehand as atmospheric sampling sites depending on atmospheric phenomena of interest. The planning of locations, atmospheric phenomenon to be observed for the day, and assignment of teams are described in
Latitude, longitude, and mean sea level (m.s.l.) altitude of operation locations in World Geodetic System 84 (WGS 84) decimal degrees.
Flight locations of UNL UASs overlaid on the terrain map. Inset
UAS locations and mission objectives for the day. Mission objectives are calibration flight (CLF), boundary layer transition (BLT), convection initiation (CI), cold air drainage flow (CDF).
Flight strategies for each day were dictated by atmospheric phenomena being measured. The teams participating in the LAPSE-RATE campaign coordinated flights across the San Luis Valley according to the atmospheric phenomena of interest for the day and the atmospheric variability expected at different sampling locations. Measurement objectives of LAPSE-RATE in which the UNL participated in data collection are calibration flight (CLF), boundary layer transition (BLT), convection initiation (CI), cold air drainage flow (CDF). Table
All the flights were conducted under the command of one remote pilot in command (PIC) with a “Federal Aviation Administration (FAA) part 107” license in accordance with the FAA's rule. All the flights included in the dataset were conducted using preprogrammed missions in the DJI Ground Station (GS) Pro app
All the flights were legally conducted under an FAA Certificate of Authorization (COA) for altitudes up to
On 14 July 2018, the mission objective was to compare both of the systems against a reference point, the Mobile UAS Research Collaboratory (MURC) tower
Coordinated data collection of the UNL UAS platforms and UNL Mobile Mesonets next to the MURC tower. UAS platforms were hovering at
One flight for each system was conducted, where the UAS ascended to the height of the MURC tower (
On 15 July 2018, the mission objective of the day was convection initiation (CI). Vertical profiling flights were conducted up to
At the Golf location, 10 flights were conducted between 08:59–15:14 MDT (local time). The weather was slightly cloudy in the morning and clear throughout the rest of the day. Very windy conditions existed for the last few flights.
At the Gamma location, nine flights were conducted between 09:02–03:15 MDT (local time). Two out of the nine data files could not be recovered due to an error in the onboard logging computer and a sensor issue. The weather was clear and windy in the morning and slightly cloudy for the last half of the flights.
On 16 July 2018, the scheduled mission objective was also CI, with flights at the same locations as the previous day. Flights were limited to
We performed 26 flights at Golf between 08:06–14:34 MDT (local time). The first two flights of the morning consisted of two consecutive profiles, but it was draining the battery a lot faster than our recharging capacity. We then switched to one profile every 15 min to reserve enough battery in each flight to maintain a consistent interval between profiles. The weather started slightly cloudy and then remained clear throughout the day.
At Gamma, 21 flights were conducted between 09:02–14:05 MDT (local time). All the profiles collected at this location are single profiles. The weather was clear throughout the day, with partly cloudy conditions persisting during the last few flights.
On 17 July 2018, the scheduled missions were for boundary layer transition (BLT). The experiments were geared towards validation of the sensor housing by the detection of the inversion layer. We conducted the experiments in the early morning in an inversion, before sunrise, to identify if the sensor housing introduces measurement error due to the upwash or downwash of the UAS. This effect is more easily detected in stable versus well-mixed conditions since air molecules from a stable air mass will maintain the temperature of the layer even when air is pushed up or down. Thus if we can verify that the sensor housing detects inversion at the same level as a standard measurement (such as radiosonde), we know the readings are not affected by upwash or downwash.
We conducted simultaneous flights for both UASs with six vertical profiles and three horizontal profiles at various UAS movement speeds between 07:00–08:48 MDT (local time). The NSSL launched coordinated radiosonde balloons at regular intervals at the same location to be used as the ground truth measurement for the UAS's data. UNL Mobile Mesonets collected measurements at
On 18 July 2018, the scheduled mission was for CI. Flights were conducted up to
At the Golf location, a total of 27 flights were performed between 07:08–14:20 MDT (local time). The first 17 flights were in support of the LAPSE-RATE campaign objective. At the conclusion of the day, 10 additional
At the Gamma location, 16 profile flights were conducted between 07:07–13:12 MDT (local time). At both locations, some flights were performed up to an altitude of
At both locations, the sky was clear for the first half of the flights and partly cloudy for the second half.
On 19 July 2018, the mission objective was cold drainage flow. UASs were placed at the Charlie and India locations for this mission. Flights were performed starting before sunrise at
At the India location, 23 flights were conducted between 05:34–11:08 MDT (local time). Maximum flight altitudes were up to
At the Charlie location, 21 flights were performed between 5:50–11:10 MDT (local time). Maximum flight altitudes were up to
At both locations, the sky was cloudy before sunrise but clear afterward.
Data are recorded from individual sensors and the UAS flight controller as they arrive at the DAQ, as described earlier. The recorded data are then processed in MATLAB to synchronize using the zero-order hold (ZOH) method to create a single output file. We used a discrete sample time of
We note that the humidity observations of the primary sensor on some flights for 17 July 2018 were saturated at 100 % in one of the UASs (M600P1), and the corresponding data are not usable. Secondary-sensor measurements should be used to replace these data. Also, humidity readings from nimbus-pth have sensitivity issues; although it displays a similar trend as the other sensors, it does not capture the whole range of observation and will need further calibration.
Files were formatted in NetCDF format, with common variable names and metadata added, to be consistent with all the entities collecting data for the LAPSE-RATE field campaign. A detailed explanation of the naming conventions and metadata that were requested can be obtained from “UNL” is the identifier for the data collecting institution, UNL; “MR6P1” and “MR6P2” are the platform identifiers for M600P1 and M600P2, respectively; “a0” indicates raw data converted to NetCDF; “20180714” is the UTC file date in yyyymmdd (year, month, day) format; “231633” is the UTC file start time in hhmmss (hours, minutes, seconds) format; “nc” is the NetCDF file extension.
All the files also contain metadata for each variable with an explanation of physical measurement units, time synchronization method, and sensors used for the measurement. File naming conventions and explanations are also described in the read-me file of the Zenodo data repository.
The following are special topics of interest that can be studied from the dataset. Our analysis that focused on these topics can be found in our previous work
Data from 14 July 2018 can be used with MURC data available at the Zenodo data repository
Ten flights from the M600P1 platform on 18 July 2020 starting at 20:21 UTC (local time 14:21 MDT) can be used to study the effect of ascent and descent speed on the sensor readings. Flights were conducted up to
The first six flights from each platform can be used from 17 July 2020 to study the sensor performance within an inversion layer. The speed of flight through the inversion layer ranged from 0.5–5
Data are available to study sensor performance during horizontal transect with different orientations relative to the wind. The last three flights from each platform on 17 July 2020 can be used for this purpose. Horizontal flight speed ranged from 2–10
Examples of two vertical profiles collected using UAS: M600P1. The top row corresponds to a
Figure
Figures
Temperature profiles from the primary sensor (XQ2) in all flights from 15–19 July 2018. The horizontal axis does
In Fig.
In Fig.
Relative humidity profile from the primary sensor (XQ2) in all flights from 15–19 July 2018. The horizontal axis does
The dataset is available at Zenodo with Creative Commons License (
As part of the LAPSE-RATE measurement campaign in July 2018 in San Luis Valley, Colorado, USA, the UNL participated in data collection in support of science missions focused on convection initiation, boundary layer transition, and cold air drainage flow. The UNL deployed two UASs in five locations for these missions. A total of 172 flights were conducted up to a maximum
AH and CD planned the contribution of the University of Nebraska-Lincoln contributions to LAPSE-RATE. AI designed the sensor housing and support structures. All authors contributed to data collection and analysis. AI, AS, and CD were part of the multirotor flight team. AI and AS contributed to data processing and presentation. AI constructed the manuscript. All authors contributed to manuscript edits. AH and CD acquired the funding for the paper.
The authors declare no competing interests. The funders had no role in the design of the study; in the collection, analyses, or interpretation of the data; in the writing of the manuscript; or in the decision to publish the results.
The views, findings, conclusions, and recommendations expressed in the paper are those of the authors and do not necessarily represent the views of the funding agencies.
This article is part of the special issue “Observational and model data from the 2018 Lower Atmospheric Process Studies at Elevation – a Remotely-piloted Aircraft Team Experiment (LAPSE-RATE) campaign”. It is a result of the International Society for Atmospheric Research using Remotely-piloted Aircraft (ISARRA 2018) conference, Boulder, USA, 9–12 July 2018.
This work was partially supported by NSF IIA-1539070, IIS-1638099, IIS-1925052, IIS-1925368, NASA ULI-80NSSC20M0162, and USDA-NIFA 2017-67021-25924. Limited general support for LAPSE-RATE was provided by the US National Science Foundation (AGS 1807199) and the US Department of Energy (DE-SC0018985) in the form of travel support for early-career participants. Support for the planning and execution of the campaign was provided by the NOAA Physical Sciences Division and NOAA UAS program.
We thank Jason Finnegan and Amy Guo for their help with data collection during the LAPSE-RATE flight campaign. We would like to thank Sean Waugh and the National Severe Storms Laboratory for the radiosonde data. We also thank Steve Borenstein and Cory Dixon for their help with the MURC operations.
This research has been supported by the National Science Foundation (grant nos. IIA-1539070, IIS-1638099, IIS-1925052, IIS-1925368, NASA ULI-80NSSC20M0162, and AGS 1807199); the US Department of Agriculture, National Institute of Food and Agriculture (grant no. 2017-67021-25924); and the US Department of Energy (grant no. DE-SC001898).
This paper was edited by Suzanne Smith and reviewed by two anonymous referees.