BER launches Environmental System Science Program. Visit our new website under construction!

U.S. Department of Energy Office of Biological and Environmental Research

BER Research Highlights


Liquid, Ice, or Both?
Published: June 10, 2016
Posted: August 12, 2016

A new algorithm uses ARM remote-sensing measurements and multivariate statistics to determine whether clouds consist of liquid droplets, ice crystals, or both.

The Science
The phase of a cloud (whether it consists of liquid droplets, ice crystals, or both) is an important factor in both the lifetime and radiative impact of a cloud. However, cloud phase is a property that is difficult to simulate correctly in climate models as it depends on interactions among thermodynamic, dynamical, and microphysical processes. A necessary step toward improving climate models is making observations of cloud phase with sufficient accuracy to constrain model representations of the processes that govern cloud phase.

The Impact
A new methodology estimates the probability of a given cloud phase from observations taken by vertically pointing active remote sensors at the Department of Energy’s (DOE) Atmospheric Radiation Measurement (ARM) Climate Research Facility. An advantage over previous methods is that the new method includes additional higher-order radar moments and provides uncertainty information on the cloud-phase classification.

Summary
This study outlines a methodology using a basic Bayesian classifier to estimate the probabilities of cloud-phase class from ARM vertically pointing active remote sensors. The advantage of this method over previous methods is that it provides uncertainty information on the phase classification. The study also tested the value of including higher moments of the cloud radar Doppler spectrum than are traditionally used operationally. Using training data of known phase from the ARM Mixed-Phase Arctic Cloud Experiment (M-PACE) field campaign, the study demonstrates a proof of concept for how the method can be used to train an algorithm that identifies ice, liquid, mixed phase, and snow. Over 95% of data are identified correctly for pure ice and liquid cases used in this study. Mixed-phase and snow cases are more problematic to identify correctly. When lidar data are not available, including additional information from the Doppler spectrum provides substantial improvement to the algorithm. This study is a first step toward an operational algorithm and can be expanded to include additional categories such as drizzle with additional training data.

Contacts (BER PM)
Sally McFarlane
ARM Program Manager
Sally.McFarlane@science.doe.gov

 (PI Contact)
Laura Riihimaki
Pacific Northwest National Laboratory
laura.riihimaki@pnnl.gov

Funding
Research was conducted under the Pacific Northwest National Laboratory’s Laboratory Directed Research and Development Program. Data were obtained from the ARM Climate Research Facility, a DOE Office of Science user facility sponsored by DOE’s Office of Biological and Environmental Research.

Publication
Riihimaki, L. D., J. M. Comstock, K. K. Anderson, A. Holmens, and E. Luke. 2016. “A Path Towards Uncertainty Assignment in an Operational Cloud-Phase Algorithm from ARM Vertically Pointing Active Sensors,” Advances in Statistical Climatology, Meteorology and Oceanography 2, 49-62. DOI: 10.5194/ascmo-2-49-2016. (Reference link)

Topic Areas:

  • Research Area: Earth and Environmental Systems Modeling
  • Research Area: Atmospheric System Research
  • Facility: DOE ARM User Facility

Division: SC-33.1 Earth and Environmental Sciences Division, BER

 

BER supports basic research and scientific user facilities to advance DOE missions in energy and environment. More about BER

Recent Highlights

Jan 11, 2022
No Honor Among Copper Thieves
Findings provide a novel means to manipulate methanotrophs for a variety of environmental and in [more...]

Dec 06, 2021
New Genome Editing Tools Can Edit Within Microbial Communities
Two new technologies allow scientists to edit specific species and genes within complex laborato [more...]

Oct 27, 2021
Fungal Recyclers: Fungi Reuse Fire-Altered Organic Matter
Degrading pyrogenic (fire-affected) organic matter is an important ecosystem function of fungi i [more...]

Oct 19, 2021
Microbes Offer a Glimpse into the Future of Climate Change
Scientists identify key features in microbes that predict how warming affects carbon dioxide emi [more...]

Aug 25, 2021
Assessing the Production Cost and Carbon Footprint of a Promising Aviation Biofuel
Biomass-derived DMCO has the potential to serve as a low-carbon, high-performance jet fuel blend [more...]

List all highlights (possible long download time)