Atmospheric Radiation Measurement Climate Research Facility US Department of Energy

aerioe > AERIoe Thermodynamic Profile and Cloud RetrievalVAP Type(s) > Baseline • Evaluation • Guest

The Atmospheric Emitted Radiance Interferometer Optimal Estimation (AERIOE) value-added product (VAP) provides boundary-layer profiles of temperature and water vapor mixing ratio and liquid cloud retrievals.

AERIOE helps retrieve profiles in both clear and cloudy conditions so users can better understand the processes taking place in the boundary layer. The high-time-resolution retrievals of boundary-layer thermodynamic profiles provide new information about the evolution of the boundary layer that is difficult to measure. Improved liquid water path retrievals in optically thin clouds from AERIOE allow better quantification of cloud microphysical properties in clouds such as shallow cumulus.

The AERIOE retrieval algorithm (Turner and Löhnert 2014; Turner and Blumberg 2019) uses an optimal estimation framework to derive cloud and thermodynamic profiles using AERI radiances and additional inputs.

Purpose

The data were created to characterize the evolution of the planetary boundary layer and boundary layer clouds.

Locations

  • Fixed
  • AMF1
  • AMF2
  • AMF3

Active Locations

Facility Name Start Date
Central Facility, Lamont, OK 2016-01-01

Data Details

Developed By David Turner
Contact David Turner
Resource(s) Data Directory
ReadMe
Data format netCDF
Site SGP
Content time range 1 January 2016 - 13 June 2022
Attribute accuracy The retrieval uses an optimal estimation framework, so a full error covariance matrix of each solution is included in the output file. The 1-sigma uncertainty of each retrieved variable, which is derived from the error covariance matrix, is included for each scientific field and is named "sigma_X", where "X" is the name of the scientific field (e.g., 'temperature'). Note that there is also an overall "qc_flag" field that is set when a retrieval should not be trusted.
Positional accuracy No formal positional accuracy tests were conducted
Data Consistency and Completeness Data set is considered complete for the information presented, as described in the abstract. Users are advised to read the rest of the metadata record carefully for addtional details.
Access Restriction No access constraints are associated with this data.
Use Restriction No use constraints are associated with this data.
File naming convention Similar to ARM file naming convention
Directory Organization There will be subdirectories for different versions of the algorithm, with data and quick look subdirectories
Citations Turner, D.D., and U. Loehnert, 2014: Information content and uncertainties in thermodynamic profiles and liquid cloud properties retrieved from the ground-based Atmospheric Emitted Radiance Interferometer (AERI). J. Appl. Meteor. Clim., 53, 752-771, doi:10.1175/JAMC-D-13-0126.1

2022

Ye J, L Liu, Q Wang, S Hu, and S Li. 2022. "A Novel Machine Learning Algorithm for Planetary Boundary Layer Height Estimation Using AERI Measurement Data." IEEE Geoscience and Remote Sensing Letters, 19, 1002305, 10.1109/LGRS.2021.3073048.

2021

Turner D and U Löhnert. 2021. "Ground-based temperature and humidity profiling: combining active and passive remote sensors." Atmospheric Measurement Techniques, 14(4), 10.5194/amt-14-3033-2021.

Varble A, S Nesbitt, P Salio, J Hardin, N Bharadwaj, P Borque, P DeMott, Z Feng, T Hill, J Marquis, A Matthews, F Mei, R Öktem, V Castro, L Goldberger, A Hunzinger, K Barry, S Kreidenweis, G McFarquhar, L McMurdie, M Pekour, H Powers, D Romps, C Saulo, B Schmid, J Tomlinson, S van den Heever, A Zelenyuk, Z Zhang, and E Zipser. 2021. "Utilizing a Storm-Generating Hotspot to Study Convective Cloud Transitions: The CACTI Experiment." Bulletin of the American Meteorological Society, 102(8), 10.1175/BAMS-D-20-0030.1.
Research Highlight

2020

Degelia S, X Wang, D Stensrud, and D Turner. 2020. "Systematic Evaluation of the Impact of Assimilating a Network of Ground-Based Remote Sensing Profilers for Forecasts of Nocturnal Convection Initiation during PECAN." Monthly Weather Review, 148(12), 10.1175/MWR-D-20-0118.1.

2018

Angevine W, J Olson, J Kenyon, W Gustafson, S Endo, K Suselj, and D Turner. 2018. "Shallow cumulus in WRF parameterizations evaluated against LASSO large-eddy simulations." Monthly Weather Review, 146(12), 10.1175/MWR-D-18-0115.1.


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Contact

Damao Zhang
Translator
Pacific Northwest National Laboratory

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