Current UCN Website Team Members:

Ruben Delgado (Principal Investigator)

Vanessa Caicedo (Principal Investigator)

Kent Taguba (Lead Software Engineer), [LinkedInwebsite]

Kyle Rivera (Software Engineering Intern) [LinkedIn, website]

Alyson Mulato (Software Engineering Intern)

Nahim Kamruzzaman (Software Engineering Intern)

Past UCN Team Members:

Wambugu Kironji

Ben Ireland

Pranav Maniktala 

Mohammed Niyas 

Rohn Perona 

Daniel Taylor

Dan Voung

Jenna Westfall 

About Us

The Unified Ceilometer Network (UCN) is a collaboration between the Hampton University (HU), University of Maryland, Baltimore County (UMBC), the U.S. Environmental Protection Agency (EPA), National Aeronautics and Space Administration (NASA) and National Atmospheric and Oceanic Administration (NOAA) on a ground-based ceilometer network to support activities that will provide a comprehensive three-dimensional assessment of the chemical and dynamical processes in the lower atmosphere that can aid future policy decisions and strategies to key questions on the influence of gases and aerosols in air quality, atmospheric composition and climate.


In 2009, the National Research Council released the report titled “Observing Weather and Climate From the Ground Up: A Nationwide Network of Networks“.[1] This report recommended a “network of networks” which builds upon already existing radiosonde launch sites, wind profilers, and lidars into a national network to address the current inadequacies in determining the Mixing Layer Height (MLH). They stated that after sixty (60) years of remote sensing research, it is astounding that the MLH, an important meteorological variable, is not measured regularly throughout its diurnal cycle.

Due to the harmful effects on health caused by particulate matter and ozone, accurate forecasting of air quality conditions is needed for the public well-being. The MLH is one of the key diagnostics for identifying uncertainties in forecasting models as it contains most of the aerosols and its height determines the volume of the air available for pollutant dilution. Models with inaccurate MLHs will generally not predict surface pollutant concentrations correctly, likely an indication of inadequately simulated meteorological setup. Because of this, tools and methodologies that can accurately determine the height of the mixing layer are needed.

Ceilometer networks have been typically established by national weather services around the globe primarily designed for detection of clouds. The instrument capability of providing observations of the vertical structure of the boundary layer makes it a resourceful tool to study atmospheric phenomena. The U.S. Environmental Protection Agency (EPA) requirement to state and local air quality agencies to measure hourly mixing layer height (MLH) at the national PAMS, will be the first concerted effort to use the ceilometer aerosol profiles for the determination of MLH in the Planetary Boundary Layer (PBL). The primary purpose for the hourly MLH under PAMS was driven by the state’s State Implementation Plan (SIP) modeling data needs.

Real-time Data

[1] National Research Council. 2009. Observing Weather and Climate from the Ground Up: A Nationwide Network of Networks. Washington, DC: The National Academies Press.

Adding your Ceilometer to UCN

Minimum system requirements: 

  • Windows 10 OS
  • Ability to send/receive data over at port 80/443
  • Working internet connection
  • Administrator rights or the ability to obtain one for the computer receiving from the instrument


The UCN Division of Atmospheric Technologies and Analytics (DATA) is a research area integrating various applications of computer science in the field of atmospheric science and meteorology. Currently efforts focus in cyber security, database management, web servers, data science and software engineering.

DATA developed and maintains this website and data archive for all data in this network to  facilitate the processing and visualization of lidar/ceilometer data. Questions, comments and feedback on this website can be made by emailing us at ucn.

If you are interested in join this network please fill out the solicited information in this Google Form.


A.K. Huff,  S. Kondragunta, H. Zhang, I. Laszlo, M. Zhou, V. Caicedo, R. Delgado, R. Levy, (2021) Tracking Smoke from a Prescribed Fire and Its Impacts on Local Air Quality Using Temporally Resolved GOES-16 ABI Aerosol Optical Depth (AOD), Journal of Atmospheric and Oceanic Technology, 38 (5), 963–976,

V. Caicedo, R. Delgado, R. Sakai, T. Knepp, D. Williams,  K. Cavender, B. Lefer, J. Szykman (2020) An automated common algorithm for planetary boundary layer retrievals using aerosol lidars in support of the U.S. EPA Photochemical Assessment Monitoring Stations ProgramJ. Atmos. Oceanic Technol.J. Atmos. Oceanic Technol., 37 (10), 1847–1864, 10.1175/JTECH-D-20-0050.1

D. Li, Y. Wu, B. Gross, F. Moshary (2021), Capabilities of an Automatic Lidar Ceilometer to Retrieve Aerosol Characteristics within the Planetary Boundary Layer, Remote Sensing, 13 (18), 3626, 10.3393/rs1318626