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- Author or Editor: Sergey Y. Matrosov x
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The large horizontal extent, with its location in the cold upper troposphere, and ice composition make cirrus clouds important modulators of the Earth's radiation budget and climate. Cirrus cloud microphysical properties are difficult to measure and model because they are inhomogeneous in nature and their ice crystal size distribution and habit are not well characterized. Accurate retrievals of cloud properties are crucial for improving the representation of cloud-scale processes in largescale models and for accurately predicting the Earth's future climate. A number of passive and active remote sensing retrieval algorithms exist for estimating the microphysical properties of upper-tropospheric clouds. We believe significant progress has been made in the evolution of these retrieval algorithms in the last decade; however, there is room for improvement. Members of the Atmospheric Radiation Measurement (ARM) program Cloud Properties Working Group are involved in an intercomparison of optical depth Ï„ and ice water path in ice clouds retrieved using ground-based instruments. The goals of this intercomparison are to evaluate the accuracy of state-of-the-art algorithms, quantify the uncertainties, and make recommendations for their improvement.
Currently, there are significant discrepancies among the algorithms for ice clouds with very small optical depths (Ï„ < 0.3) and those with 1 < Ï„ < 5. The good news is that for thin clouds (0.3 < Ï„ < 1), the algorithms tend to converge. In this first stage of the intercomparison, we present results from a representative case study, compare the retrieved cloud properties with aircraft and satellite measurements, and perform a radiative closure experiment to begin gauging the accuracy of these retrieval algorithms.
The large horizontal extent, with its location in the cold upper troposphere, and ice composition make cirrus clouds important modulators of the Earth's radiation budget and climate. Cirrus cloud microphysical properties are difficult to measure and model because they are inhomogeneous in nature and their ice crystal size distribution and habit are not well characterized. Accurate retrievals of cloud properties are crucial for improving the representation of cloud-scale processes in largescale models and for accurately predicting the Earth's future climate. A number of passive and active remote sensing retrieval algorithms exist for estimating the microphysical properties of upper-tropospheric clouds. We believe significant progress has been made in the evolution of these retrieval algorithms in the last decade; however, there is room for improvement. Members of the Atmospheric Radiation Measurement (ARM) program Cloud Properties Working Group are involved in an intercomparison of optical depth Ï„ and ice water path in ice clouds retrieved using ground-based instruments. The goals of this intercomparison are to evaluate the accuracy of state-of-the-art algorithms, quantify the uncertainties, and make recommendations for their improvement.
Currently, there are significant discrepancies among the algorithms for ice clouds with very small optical depths (Ï„ < 0.3) and those with 1 < Ï„ < 5. The good news is that for thin clouds (0.3 < Ï„ < 1), the algorithms tend to converge. In this first stage of the intercomparison, we present results from a representative case study, compare the retrieved cloud properties with aircraft and satellite measurements, and perform a radiative closure experiment to begin gauging the accuracy of these retrieval algorithms.
Abstract
Thorough understanding of aerosols, clouds, boundary layer structure, and radiation is required to improve the representation of the Arctic atmosphere in weather forecasting and climate models. To develop such understanding, new perspectives are needed to provide details on the vertical structure and spatial variability of key atmospheric properties, along with information over difficult-to-reach surfaces such as newly forming sea ice. Over the last three years, the U.S. Department of Energy (DOE) has supported various flight campaigns using unmanned aircraft systems [UASs, also known as unmanned aerial vehicles (UAVs) and drones] and tethered balloon systems (TBSs) at Oliktok Point, Alaska. These activities have featured in situ measurements of the thermodynamic state, turbulence, radiation, aerosol properties, cloud microphysics, and turbulent fluxes to provide a detailed characterization of the lower atmosphere. Alongside a suite of active and passive ground-based sensors and radiosondes deployed by the DOE Atmospheric Radiation Measurement (ARM) program through the third ARM Mobile Facility (AMF-3), these flight activities demonstrate the ability of such platforms to provide critically needed information. In addition to providing new and unique datasets, lessons learned during initial campaigns have assisted in the development of an exciting new community resource.
Abstract
Thorough understanding of aerosols, clouds, boundary layer structure, and radiation is required to improve the representation of the Arctic atmosphere in weather forecasting and climate models. To develop such understanding, new perspectives are needed to provide details on the vertical structure and spatial variability of key atmospheric properties, along with information over difficult-to-reach surfaces such as newly forming sea ice. Over the last three years, the U.S. Department of Energy (DOE) has supported various flight campaigns using unmanned aircraft systems [UASs, also known as unmanned aerial vehicles (UAVs) and drones] and tethered balloon systems (TBSs) at Oliktok Point, Alaska. These activities have featured in situ measurements of the thermodynamic state, turbulence, radiation, aerosol properties, cloud microphysics, and turbulent fluxes to provide a detailed characterization of the lower atmosphere. Alongside a suite of active and passive ground-based sensors and radiosondes deployed by the DOE Atmospheric Radiation Measurement (ARM) program through the third ARM Mobile Facility (AMF-3), these flight activities demonstrate the ability of such platforms to provide critically needed information. In addition to providing new and unique datasets, lessons learned during initial campaigns have assisted in the development of an exciting new community resource.
