the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 31 May 2024
ARMTRAJ: A Set of Multi-Purpose Trajectory Datasets Augmenting the Atmospheric Radiation Measurement (ARM) User Facility Measurements
Abstract. Ground-based instruments offer unique capabilities such as detailed atmospheric thermodynamic, cloud, and aerosol profiling at a high temporal sampling rate. The U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility provides comprehensive datasets from key locations around the globe, facilitating long-term characterization and process-level understanding of clouds, aerosol, and aerosol-cloud interactions. However, as with other ground-based datasets, the fixed (Eulerian) nature of these measurements often introduces a knowledge gap in relating those observations with airmass hysteresis. Here, we describe ARMTRAJ, a set of multi-purpose trajectory datasets that helps close this gap in ARM deployments. Each dataset targets a different aspect of atmospheric research, including the analysis of surface, planetary boundary layer, distinct liquid-bearing cloud layers, and (primary) cloud decks. Trajectories are calculated using the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model informed by the European Centre for Medium-Range Weather Forecasts ERA5 reanalysis dataset at its highest spatial resolution (0.25 degrees) and are initialized using ARM datasets. The trajectory datasets include information about airmass coordinates and state variables extracted from ERA5 before and after the ARM site overpass. Ensemble runs generated for each model initialization enhance trajectory consistency, while ensemble variability serves as a valuable uncertainty metric for those reported airmass coordinates and state variables. Following the description of dataset processing and structure, we demonstrate applications of ARMTRAJ to a case study and a few bulk analyses of observations collected during ARM’s Eastern Pacific Cloud Aerosol Precipitation Experiment (EPCAPE) field deployment. ARMTRAJ is expected to become a near real-time product accompanying new ARM deployments and an augmenting product to ongoing and previous deployments, promoting reaching science goals of research relying on ARM observations.
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RC1: 'Comment on essd-2024-127', Anonymous Referee #1, 21 Jul 2024
This manuscript presents new trajectory datasets for the surface, planetary boundary layer, and clouds, complementing existing ARM measurements. While the integration of HYSPLIT with ERA5 data is not novel, as it has been employed in previous studies, this paper uniquely synthesizes ARM radiosonde, radar and lidar data to enhance our understanding and potentially benefit future aerosol and cloud research. The study currently focuses on the EPCAPE site during 2023-2024. It is valuable to expand the datasets to more ARM sites and extend the temporal coverage. I believe these datasets will be of great interest to the aerosol, cloud or aerosol-cloud interaction community, as well as useful for field campaigns at ARM sites. The paper is overall well-structured and clearly written.
The primary concern with the manuscript is the inadequate assessment of uncertainties and the lack of a comprehensive quality check of the datasets. Although ensemble runs are run to indicate uncertainty, this primarily addresses uncertainties associated with the coarse resolution of meteorological fields. I recommend that the authors elaborate on uncertainties in the ERA5 data, uncertainties in the ARM measurements used to initiate the HYSPLIT, and propagation of uncertainties through the modeling processes. Additionally, it is not clear for example, whether the trajectory products on land cover and thermodynamic variables exhibit similar uncertainty levels, and how these uncertainties vary with the length of the backward or forward trajectory period (e.g., 2 days vs. 10 days). Addressing these aspects is crucial for users applying these datasets and interpreting results.
Some specific comments:
- Introduction: The manuscript adequately explains the utility of ARM observations and air mass trajectory analysis. However, given the common usage of the HYSPLIT model in prior studies, it would be beneficial for the authors to clarify what unique insights these new datasets provide, and what specific scientific questions they enable users to address beyond the capabilities of traditional HYSPLIT applications.
- 2.1 Surface Trajectory Dataset: There is a potential issue with trajectories initiating at low altitudes as they may hit the ground and lose accuracy. Have the authors observed this issue in their datasets? A discussion on the impact of terrain on data quality would enhance this section.
- Table 1: Identification of potential applications for backward and forward ARMTRAJ-CLD and ARMTRAJ-ARSCL trajectories would provide clarity and usefulness to the readers.
- #198: The mention of “the resulting 8 sets of cloud deck base, top, and free-tropospheric heights” is unclear. For better context, consider placing the sentence from paragraph #190 before this statement.
- Figure 4, please include standard deviation of these numbers.
Citation: https://doi.org/10.5194/essd-2024-127-RC1 -
RC2: 'Comment on essd-2024-127', Anonymous Referee #2, 24 Jul 2024
The comment was uploaded in the form of a supplement: https://essd.copernicus.org/preprints/essd-2024-127/essd-2024-127-RC2-supplement.pdf
- AC1: 'Response to reviewers', Israel Silber, 28 Aug 2024
Status: closed
-
RC1: 'Comment on essd-2024-127', Anonymous Referee #1, 21 Jul 2024
This manuscript presents new trajectory datasets for the surface, planetary boundary layer, and clouds, complementing existing ARM measurements. While the integration of HYSPLIT with ERA5 data is not novel, as it has been employed in previous studies, this paper uniquely synthesizes ARM radiosonde, radar and lidar data to enhance our understanding and potentially benefit future aerosol and cloud research. The study currently focuses on the EPCAPE site during 2023-2024. It is valuable to expand the datasets to more ARM sites and extend the temporal coverage. I believe these datasets will be of great interest to the aerosol, cloud or aerosol-cloud interaction community, as well as useful for field campaigns at ARM sites. The paper is overall well-structured and clearly written.
The primary concern with the manuscript is the inadequate assessment of uncertainties and the lack of a comprehensive quality check of the datasets. Although ensemble runs are run to indicate uncertainty, this primarily addresses uncertainties associated with the coarse resolution of meteorological fields. I recommend that the authors elaborate on uncertainties in the ERA5 data, uncertainties in the ARM measurements used to initiate the HYSPLIT, and propagation of uncertainties through the modeling processes. Additionally, it is not clear for example, whether the trajectory products on land cover and thermodynamic variables exhibit similar uncertainty levels, and how these uncertainties vary with the length of the backward or forward trajectory period (e.g., 2 days vs. 10 days). Addressing these aspects is crucial for users applying these datasets and interpreting results.
Some specific comments:
- Introduction: The manuscript adequately explains the utility of ARM observations and air mass trajectory analysis. However, given the common usage of the HYSPLIT model in prior studies, it would be beneficial for the authors to clarify what unique insights these new datasets provide, and what specific scientific questions they enable users to address beyond the capabilities of traditional HYSPLIT applications.
- 2.1 Surface Trajectory Dataset: There is a potential issue with trajectories initiating at low altitudes as they may hit the ground and lose accuracy. Have the authors observed this issue in their datasets? A discussion on the impact of terrain on data quality would enhance this section.
- Table 1: Identification of potential applications for backward and forward ARMTRAJ-CLD and ARMTRAJ-ARSCL trajectories would provide clarity and usefulness to the readers.
- #198: The mention of “the resulting 8 sets of cloud deck base, top, and free-tropospheric heights” is unclear. For better context, consider placing the sentence from paragraph #190 before this statement.
- Figure 4, please include standard deviation of these numbers.
Citation: https://doi.org/10.5194/essd-2024-127-RC1 -
RC2: 'Comment on essd-2024-127', Anonymous Referee #2, 24 Jul 2024
The comment was uploaded in the form of a supplement: https://essd.copernicus.org/preprints/essd-2024-127/essd-2024-127-RC2-supplement.pdf
- AC1: 'Response to reviewers', Israel Silber, 28 Aug 2024
Data sets
Back and forward trajectories for primary cloud decks based on ARSCL (ARMTRAJARSCL) Israel Silber https://doi.org/10.5439/2309849
Back trajectories for ARM surface deployments supporting aerosol studies (ARMTRAJSFC) Israel Silber https://doi.org/10.5439/2309850
Back trajectories for PBL and related aerosol and cloud studies (ARMTRAJPBL) Israel Silber https://doi.org/10.5439/2309848
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