Breakout Summary Report
ARM/ASR User and PI Meeting
Lidar Applications
22 June 2021
11:00 AM - 1:00 PM
30
Rob Newsom, Raghavendra Krishnamurthy, Virendra Ghate
22 June 2021
11:00 AM - 1:00 PM
30
Rob Newsom, Raghavendra Krishnamurthy, Virendra Ghate
Breakout Description
The goal of this session was to provide an opportunity for scientists to describe how ARM lidar data are being used in their research, and to provide a forum to discuss scientific and data-related challenges associated with the use of that data. Brief instrument and VAP updates were provided along with plans for relocating existing instrumentation, new instrument procurements, and VAD development. The primary focus of this session was on science applications of the various ARM lidar systems (e.g., ceilometer, MPL, Doppler, Raman, and HSRL). Short flash talks were solicited on a wide range of topics, including (but not necessarily limited to) the following:a. Cloud and aerosol properties as retrieved from the lidar data
b. Lidar observations of the boundary layer
c. Applications of edge computing, and machine learning
d. Lidar observations of the polar environment
e. Temperature and humidity profiling
f. Model verification and data assimilation
g. Comparisons with airborne or space-based sensors.
Main Discussion
Rob Newsom and Ray Bambha started off the session with updates on the Doppler lidars, Raman lidars, MPLs, Ceilometers, and HSRLs. This was followed by a presentation from Robert Jackson describing ongoing work to bring Edge computing technology into ARM with a specific focus on its application to processing Doppler lidar spectra.In this year’s breakout there were several talks dealing with retrieval of Planetary Boundary-Layer (PBL) depths, as well as the use of machine learning techniques for information retrieval. Damao Zhang described current efforts to develop a new PBL Depth VAP. This was followed by two talks that were also focused on PBL depth retrieval, each using different approaches. Yufei Chu described a technique for estimating the mixed layer depth from Doppler and Raman lidar data that uses a dynamic threshold based on the distribution of energy in the vertical velocity spectra. Raghavendra Krishnamurthy described a machine learning (ML) method that uses both lidar vertical velocity variance observations and surface measurements (e.g., temperature, humidity, stability) to estimate the PBL depth. Erol Cromwell also described a ML approach for retrieval of cloud masks from MPL data. The ML approach was shown to improve estimates of the PBL depth, and to produce cleaner cloud masks by reducing the oversampling of cloud boundaries.
Virendra Ghate presented work on turbulence in the marine BL at ENA, and described a technique for discriminating hydrometers from aerosols in the DL data using KAZR data to identify drizzle. He shows that turbulence is higher in winter than in summer, and higher at night than during the day.
Lastly, Hailing Xie gave a presentation on the analysis of arctic and antarctic aerosol profiles derived from HSRL and MPL data. Hailing showed that aerosol backscatter is stronger in the Arctic than in the Antarctic. She also demonstrated the importance of applying an after-pulse correction to the MPL data.