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

BER Research Highlights

Using ARM Cloud Observations to Confront Model Cloud Transitions
Published: March 01, 2018
Posted: July 02, 2018

ARM radar observations and high-resolution simulations provide insight into transitions from shallow clouds to storm clouds and how to better capture these transitions in numerical models.

The Science
Due to their complexity and range of scales, all the equations controlling cloud processes cannot be explicitly represented in global Earth System Models.  Therefore, clouds are represented in these models using simplified equations that are often based on benchmark simulations from high-resolution process models. But how reliable are the cloud properties and processes produced by these benchmark models? A new paper explores model transitions of cloud fields from shallow cumulus to deep, precipitating cloud systems in a highly variable meteorological environment observed during the U.S. Department of Energy’s Atmospheric Radiation Measurement (ARM) Midlatitude Continental Convective Clouds Experiment (MC3E) field campaign. The effort uses innovative ARM radar observations from the MC3E field campaign to evaluate a series of high-resolution simulations, which results in an improved understanding of cloud transitions and how to diagnose these transitions in models.

The Impact
This research provides insights into the difficulties of constraining models from observations, since matching ARM profiling and scanning radar precipitation characteristics alone does not guarantee good simulations. The subtle changes governing cloud and precipitation transitions are not apparent in traditional meteorological observations, and the greatest insight into cloud transitions is found using conditionally sampled cloud properties from the simulations. This finding strongly argues for hybrid observational/modeling approaches. These combined approaches enable a more complete physical understanding of cloud systems by combining observational sampling of time-varying three-dimensional meteorological quantities and cloud properties from the ARM instrument suite, along with detailed representation of cloud microphysical and dynamical processes from numerical models.

Both Earth System Models and high-resolution process models continue to struggle representing boundary-layer clouds and the transitions to deeper cloud types. Furthermore, it is difficult to compare these models with observations in cases of substantial spatial and temporal variability. This difficulty results from a combination of imperfect models run with uncertain estimates of environmental forcing and comparison against incomplete and uncertain observations of cloud properties. A suite of 16 simulations based on the 25 May 2011 event from the Midlatitude Continental Convective Clouds Experiment (MC3E) is employed to better understand how variability or uncertainty in forcing controls precipitation onset and the transition from shallow cumulus to congestus.

Three of the 16 simulations best matching the observed total precipitation and onset time are chosen for deeper analysis. All three simulations exhibit a destabilization over time, which leads to a transition to deeper clouds. However, the evolution of traditional parcel-theory stability metrics are not by themselves able to explain differences among the simulations. Conditionally sampled cloud properties (in particular, mean cloud buoyancy), however, do elicit differences across the simulations, and provides insight to reject one of the simulations on physical grounds. The inability of environmental profiles alone to discern subtle differences among the simulations and the usefulness of conditionally sampled model quantities argue for hybrid observational/modeling approaches.

Contacts (BER PM)
Sally McFarlane
ARM Program Manager

Shaima Nasiri
ASR Program Manager

Ashley Williamson
ASR Program Manager

(PI Contact)
David B. Mechem
University of Kansas

This research was supported by the Department of Energy Office of Science grants DE-SC0006736, DE-SC0016522, and DE-SC0012704.  Observational data were obtained from the Atmospheric Radiation Measurement (ARM) User Facility. 

Mechem, D.B. and S.E. Giangrande. "The Challenge of Identifying Controls on Cloud Properties and Precipitation Onset for Cumulus Congestus Sampled During MC3E." Journal of Geophysical Research: Atmospheres 123: 3126-3144 (2018). [DOI: 10.1002/2017JD027457


Related Links
PI-submitted ARM/ASR research highlight

Topic Areas:

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

Division: SC-23.1 Climate 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

May 10, 2019
Quantifying Decision Uncertainty in Water Management via a Coupled Agent-Based Model
Considering risk perception can improve the representation of human decision-making processes in age [more...]

May 09, 2019
Projecting Global Urban Area Growth Through 2100 Based on Historical Time Series Data and Future Scenarios
Study provides country-specific urban area growth models and the first dataset on country-level urba [more...]

May 05, 2019
Calibrating Building Energy Demand Models to Refine Long-Term Energy Planning
A new, flexible calibration approach improved model accuracy in capturing year-to-year changes in bu [more...]

May 03, 2019
Calibration and Uncertainty Analysis of Demeter for Better Downscaling of Global Land Use and Land Cover Projections
Researchers improved the Demeter model’s performance by calibrating key parameters and establi [more...]

Apr 22, 2019
Representation of U.S. Warm Temperature Extremes in Global Climate Model Ensembles
Representation of warm temperature events varies considerably among global climate models, which has [more...]

List all highlights (possible long download time)