Scientific hypothesis: The statistics of the radiative forcing within a mesoscale model grid box are quite sensitive to the three-dimensional (3-D) structure of hydrometer properties within the grid box. In an operational sense, ARM is limited to observing the vertical profile of clouds as they advect over the Central Facility (CF) and must, therefore, approximate the 3D structure of cloud within a gridbox by inference. Our working hypothesis is that simple approaches to this problem are sufficient in certain meteorological circumstances and not in others.
Approach to test hypothesis: In order to validate/justify/improve our methodology for generating such inferences, we will deploy a fleet of vertically pointing and scanning lidars and millimeter radars within a mesoscale region around the CF where the 3-D structure of cloud fields will be intensively measured during a multi-week period.
Preliminary Deployment plan: We would like to deploy a nested array of remote sensors with the scanning radars and lidars stationed at the CF and the vertically pointing systems distributed in a triangle away from the CF. This would depend on the cloud field, but in general two radars at the CF would scan in orthogonal planes (along and across the wind) as would the lidars. A triangle would have vertices at Billings, Tonkawa and Lamont (roughly 25 km on a side). A larger triangle might be desirable in addition to or instead of the smaller with vertices at Ponca City (I volunteer for that one), Medford and Garber or Enid (roughly 70 km on a side). We should discuss the relative merits of the array size and orientation. I personally see limited use in going out much farther than 75 km with the limited number of radars and lidars we have at our disposal. We could probably live with a single radar and lidar scanning at the CF but that would compromise the data set integrity in my view. At least three radars are needed for the vertices of a triangle.
The Atmospheric Radiation Measurement (ARM) Program conducted a Cloud Intensive Operational Period (IOP) in March 2000 that was the first-ever effort to document the 3-dimensional cloud field from observational data. Prior numerical studies of solar radiation propagation through the atmosphere in the presence of clouds have been limited by the necessity to use theoretical representations of clouds. Three-dimensional representations of actual clouds and their microphysical properties, such as the distribution of ice and water, had previously not been possible because instrumentation appropriate to the task was not available.
Recent development of improved lidar and radar capabilities by ARM were hypothesized to permit measurements over time that would permit an accurate "representation" of the "real" 3-dimensional cloud field. The IOP observational capability included the standard set of instruments at ARM's Southern Great Plains site as well as a special set of temporary lidars and radars. Aircraft data taken during the IOP supplemented the ground-based remotely sensed data.
Validation of TERRA Satellite: The IOP also served the interests of National Aeronautics and Space Administration (NASA) in its efforts to validate data from the recently launched TERRA satellite. TERRA is the first of the long awaited Earth Observing System (EOS) primary satellites. NASA scientists were members of the IOP planning team and are using the IOP data, combined with data from overflights by NASA's ER-2 aircraft, in the Terra validation effort.
ARESE II Overlap: The Cloud IOP also overlapped with the ARM/Unmanned Aerospace Vehicle (UAV) Enhanced Shortwave Experiment (ARESE) II, also being conducted over the Southern Great Plains site. Throughout the IOP period, efforts were coordinated with the ARM/UAV Twin Otter aircraft. In addition to its own ARESE II missions, the Twin Otter flew four missions in direct support of the objectives of the cloud IOP, contributing valuable radiometric and in situ data to the IOP. The IOP began on March 1 and ended on March 26. Near ideal cloud conditions permitted substantive data sets to be acquired on 10 days during the IOP.
In all, 12 IOP flights were made by the University of North Dakota's Cessna Citation aircraft. While a great deal of analytical work remains to be done, all preliminary evidence suggests that the IOP produced a robust data set that addresses all primary and most secondary scientific objectives of the IOP. Pure ice clouds (cirrus), cirrus clouds transitioning to high-level water clouds (altostratus), and high-level and low-level pure water clouds were all captured and documented. In all cases, ground-based radars and lidars, both permanent and temporary, operated nearly flawlessly, providing an excellent and unprecedented data set.
Przybylo V, K Sulia, C Schmitt, and Z Lebo. 2022. "Classification of Cloud Particle Imagery from Aircraft Platforms Using Convolutional Neural Networks." Journal of Atmospheric and Oceanic Technology, 39(4), 10.1175/JTECH-D-21-0094.1.
Tang S, S Xie, and M Zhang. 2020. Description of the Three-Dimensional Large-Scale Forcing Data from the 3D Constrained Variational Analysis (VARANAL3D). Ed. by Robert Stafford, ARM user facility. DOE/SC-ARM-TR-253. 10.2172/1648153.
Zheng X, B Xi, X Dong, T Logan, Y Wang, and P Wu. 2020. "Investigation of aerosol-cloud interactions under different absorptive aerosol regimes using Atmospheric Radiation Measurement (ARM) southern Great Plains (SGP) ground-based measurements." Atmospheric Chemistry and Physics, 20(6), 10.5194/acp-20-3483-2020.
Schmitt C, K Sulia, Z Lebo, A Heymsfield, V Przybyo, and P Connolly. 2019. "The fall speed variability of similarly sized ice particle aggregates." Journal of Applied Meteorology and Climatology, 58(8), doi:10.1175/JAMC-D-18-0291.1.
McHardy T, X Dong, B Xi, M Thieman, P Minnis, and R Palikonda. 2018. "Comparison of Daytime Low-Level Cloud Properties Derived from GOES and ARM SGP Measurements." Journal of Geophysical Research: Atmospheres, 123(15), 10.1029/2018JD028911.
Wang Y, J Vogel, Y Lin, B Pan, J Hu, Y Liu, X Dong, J Jiang, Y Yung, and R Zhang. 2018. "Aerosol microphysical and radiative effects on continental cloud ensembles." Advances in Atmospheric Sciences, 35(2), 10.1007/s00376-017-7091-5.
Wu C, X Liu, M Diao, K Zhang, A Gettelman, Z Lu, J Penner, and Z Lin. 2017. "Direct comparisons of ice cloud macro- and microphysical properties simulated by the Community Atmosphere Model version 5 with HIPPO aircraft observations." Atmospheric Chemistry and Physics, 17(7), 10.5194/acp-17-4731-2017.
Heymsfield A, M Kramer, NB Wood, A Gettelman, and PR Field. 2017. "Dependence of the Ice Water Content and Snowfall Rate on Temperature, Globally: Comparison of In-Situ Observations, Satellite Active Remote Sensing Retrievals and Global Climate Model Simulations." Journal of Applied Meteorology and Climatology, 56(1), 10.1175/jamc-d-16-0230.1.
Eidhammer T, H Morrison, D Mitchell, A Gettelman, and E Erfani. 2017. "Improvements in global climate model microphysics using a consistent representation of ice particle properties." Journal of Climate, 30(2), 10.1175/jcli-d-16-0050.1.
Tang S, M Zhang, and S Xie. 2016. "An ensemble constrained variation alanalysis of atmospheric forcing data and its application to evaluate clouds in CAM5." Journal of Geophysical Research: Atmospheres, 121(1), 10.1002/2015jd024167.
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Campaign Data Sets
|IOP Participant||Data Source Name||Final Data|
|Bruce Albrecht||Radar||Order Data|
|James Barnard||MFRSR||Order Data|
|Mark Beaubien||tsi880||Order Data|
|Brian Cairns||RSP data||Order Data|
|Connor Flynn||Micropulse Lidar||Order Data|
|Connor Flynn||ceilometer||Order Data|
|Andrew Heymsfield||PMS||Order Data|
|William Jeffries||IRMFR||Order Data|
|Paul Lawson||Lear Jet||Order Data|
|James Liljegren||Microwave Radiometer||Order Data|
|James Liljegren||Microwave Radiometer Profiler||Order Data|
|Guosheng Liu||Liu_cloud_ice_water||Order Data|
|Chuck Long (deceased)||RSR||Order Data|
|Chuck Long (deceased)||Total Sky Imager||Order Data|
|Roger Marchand||Parabola||Order Data|
|Frank Murcray||ASTI||Order Data|
|Michael Poellot||Citation A/C||Order Data|
|Scott Richardson||Chilled Mirror||Order Data|
|Tim Tooman||WSI||Order Data|
|Cynthia Twohy||Counterflow Virtual Impactor (CVI)||Order Data|