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Randy A. Peppler, Kenneth E. Kehoe, Justin W. Monroe, Adam K. Theisen, and Sean T. Moore

). Results showed excellent agreement between satellite and Raman lidar observations of upper tropospheric humidity with systematic differences of about 10%; radiosondes, conversely, were found to be systematically drier by 40% relative to both satellite and lidar measurements ( Soden et al. 2004 ). Existing strategies for correcting radiosonde dry biases were found to be inadequate in the upper troposphere, and an alternative method was suggested that considerably improved radiosonde measurement

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Graham Feingold and Allison McComiskey

-phase Arctic stratus cloud appears to be a particularly robust state, often persisting for many days at a time, in spite of the inherently unstable mixture of ice and water. Lidar and radar imagery at NSA show ice precipitating from the base of these clouds and yet the clouds are not consumed. Why is this so? Work based on many years of observations and modeling studies ( Morrison et al. 2012 ; Fridlind et al. 2012a ) suggests that the following factors, singly or in combination, might all play a role in

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E. J. Mlawer and D. D. Turner

observations was the organization of the Spectral Radiation Experiment (SPECTRE; Ellingson and Wiscombe 1996 ; Ellingson et al. 2016 , chapter 1). This one-month field experiment deployed several infrared interferometers to Coffeyville, Kansas, to measure the downwelling infrared spectral radiance along with a range of sensors, both in situ (e.g., radiosonde, flask measurements of trace gases like carbon dioxide and methane, etc.) and remote (e.g., Raman lidar, Radio Acoustic Sounding System, cloud radar

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Ismail Gultepe, Andrew J. Heymsfield, Martin Gallagher, Luisa Ickes, and Darrel Baumgardner

, particularly at sizes less than 50 μ m; 4) growth by diffusion and aggregation of ice; 5) ice crystal optical properties; and 6) Earth’s surface characteristics, such as moisture and albedo. Much of the missing information is not only due to the paucity of observations that have been made in ice fog but also because of the limitations and uncertainties of the sensors that gather the data. In the remainder of this chapter the reader will be introduced to how ice fog forms and evolves, accompanied by what

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David A. Randall, Anthony D. Del Genio, Leo J. Donner, William D. Collins, and Stephen A. Klein

“from scratch,” as outlined above, existing AGCMs are updated routinely to incorporate new understanding and to address inadequacies of their formulations ( Jakob 2003 ). Key steps are to identify model deficiencies through comparison with observations, attribute these deficiencies to particular defects of the model’s formulation, and test new modeling concepts at the component level, in the same way that the engines, airframe, and other components of a new type of aircraft are tested individually

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Thomas P. Ackerman, Ted S. Cress, Wanda R. Ferrell, James H. Mather, and David D. Turner

measurements or, in some cases, instruments for some required observations did not exist (or existed only in some relatively primitive state). The ARM-related histories of specific instruments (e.g., microwave radiometers, infrared interferometers, Raman lidar, and cloud radars) are discussed elsewhere in this monograph; here we comment only on the broad ARM approach. Early on, the ARM management recognized the need to have instrument experts available to the program and devised the idea of instrument

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Steven K. Krueger, Hugh Morrison, and Ann M. Fridlind

1. The GEWEX Cloud System Study The Global Energy and Water Cycle Experiment (GEWEX) Cloud System Study (GCSS) was created in 1992. As described by Browning et al. (1993 , p. 387), “The focus of GCSS is on cloud systems spanning the mesoscale rather than on individual clouds. Observations from field programs will be used to develop and validate the cloud-resolving models, which in turn will be used as test-beds to develop the parameterizations for the large-scale models.” The most important

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C. N. Long, J. H. Mather, and T. P. Ackerman

decline and deteriorating infrastructure has made working there too difficult. The active sensors (cloud radar, lidar, etc.) at the ARM site at Nauru were removed in February of 2009, and, sadly, all ARM observations on Nauru have now ended. The remaining site infrastructure was handed off to the Nauruan government as the foundation for establishing a meteorological station in August of 2013. c. Darwin While the original sampling strategy called for distributing additional sites to more fully explore

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Ted S. Cress and Douglas L. Sisterson

) of “real observations” to evaluate radiative modeling results. At the same time, it was realized that observations were needed to evaluate the accuracy of cloud properties that were being predicted by models and that there was much work needed to improve GCMs in this regard. As discussed by Stokes (2016 , chapter 2), these influences led the DOE to direct the preparation of a proposal for the incipient USGCRP. Stokes (2016 , chapter 2) discusses the preparation of the initial DOE proposal

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Lee-Lueng Fu, Tong Lee, W. Timothy Liu, and Ronald Kwok

from ICESat and CryoSat-2 . In the retrieval of sea ice thickness from freeboard, estimates of snow loading (snow depth and density)—a source of uncertainty—are required ( Kwok 2011 ). Currently, routine observations of snow depth and density over the Arctic Ocean are not available. Retrievals from satellite altimetry provide only sea ice freeboard (e.g., radar altimeters on CryoSat-2 , AltiKa, Sentinel-3 ) or the combined snow and ice freeboard (lidars on ICESat , ICESat-2 ), and snow

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