David D. Turner

The Mixed-Phase Arctic Cloud Experiment

The Mixed-Phase Arctic Cloud Experiment (M-PACE) was conducted from 27 September through 22 October 2004 over the Department of Energys Atmospheric Radiation Measurement (ARM) Climate Research Facility (ACRF) on the North Slope of Alaska. The primary objectives were to collect a dataset suitable to study interactions between microphysics, dynamics, and radiative transfer in mixed-phase Arctic clouds, and to develop/evaluate cloud property retrievals from surface-and satellite-based remote sensing instruments. Observations taken during the 1977/98 Surface Heat and Energy Budget of the Arctic (SHEBA) experiment revealed that Arctic clouds frequently consist of one (or more) liquid layers precipitating ice. M-PACE sought to investigate the physical processes of these clouds by utilizing two aircraft (an in situ aircraft to characterize the microphysical properties of the clouds and a remote sensing aircraft to constraint the upwelling radiation) over the ACRF site on the North Slope of Alaska. The measureme...

Retrieving Liquid Wat0er Path and Precipitable Water Vapor From the Atmospheric Radiation Measurement (ARM) Microwave Radiometers

Ground-based two-channel microwave radiometers (MWRs) have been used for over 15 years by the Atmospheric Radiation Measurement (ARM) program to provide observations of downwelling emitted radiance from which precipitable water vapor (PWV) and liquid water path (LWP) - two geophysical parameters critical for many areas of atmospheric research - are retrieved. An algorithm that incorporates output from two advanced retrieval techniques, namely, a physical-iterative approach and a computationally efficient statistical method, has been developed to retrieve these parameters. The forward model used in both methods is the monochromatic radiative transfer model MonoRTM. An important component of this MWR RETrieval (MWRRET) algorithm is the determination of small (< 1 K) offsets that are subtracted from the observed brightness temperatures before the retrievals are performed. Accounting for these offsets removes systematic biases from the observations and/or the model spectroscopy necessary for the retrieval, significantly reducing the systematic biases in the retrieved LWP. The MWRRET algorithm significantly provides more accurate retrievals than the original ARM statistical retrieval, which uses monthly retrieval coefficients. By combining the two retrieval methods with the application of brightness temperature offsets to reduce the spurious LWP bias in clear skies, the MWRRET algorithm significantly provides better retrievals of PWV and LWP from the ARM two-channel MWRs compared to the original ARM product.

Dry Bias and Variability in Vaisala RS80-H Radiosondes: The ARM Experience

Thousands of comparisons between total precipitable water vapor (PWV) obtained from radiosonde (Vaisala RS80-H) profiles and PWV retrieved from a collocated microwave radiometer (MWR) were made at the Atmospheric Radiation Measurement (ARM) Program’s Southern Great Plains Cloud and Radiation Testbed (SGP CART) site in northern Oklahoma from 1994 to 2000. These comparisons show that the RS80-H radiosonde has an approximate 5% dry bias compared to the MWR. This observation is consistent with interpretations of Vaisala RS80 radiosonde data obtained during the Tropical Ocean Global Atmosphere Coupled Ocean‐Atmosphere Response Experiment (TOGA COARE). In addition to the dry bias, analysis of the PWV comparisons as well as of data obtained from dual-sonde soundings done at the SGP show that the calibration of the radiosonde humidity measurements varies considerably both when the radiosondes come from different calibration batches and when the radiosondes come from the same calibration batch. This variability can result in peak-to-peak differences between radiosondes of greater than 25% in PWV. Because accurate representation of the vertical profile of water vapor is critical for ARM’s science objectives, an empirical method for correcting the radiosonde humidity profiles is developed based on a constant scaling factor. By using an independent set of observations and radiative transfer models to test the correction, it is shown that the constant humidity scaling method appears both to improve the accuracy and reduce the uncertainty of the radiosonde data. The ARM data are also used to examine a different, physically based, correction scheme that was developed recently by scientists from Vaisala and the National Center for Atmospheric Research (NCAR). This scheme, which addresses the dry bias problem as well as other calibration-related problems with the RS80-H sensor, results in excellent agreement between the PWV retrieved from the MWR and integrated from the corrected radiosonde. However, because the physically based correction scheme does not address the apparently random calibration variations observed, it does not reduce the variability either between radiosonde calibration batches or within individual calibration batches.

Turn-key Raman lidar for profiling atmospheric water vapor, clouds, and aerosols

We describe an operational, self-contained, fully autonomous Raman lidar system that has been developed for unattended, around-the-clock atmospheric profiling of water vapor, aerosols, and clouds. During a 1996 three-week intensive observational period, the system operated during all periods of good weather (339 out of 504 h), including one continuous five-day period. The system is based on a dual-field-of-view design that provides excellent daytime capability without sacrificing nighttime performance. It is fully computer automated and runs unattended following a simple, brief (~5-min) start-up period. We discuss the theory and design of the system and present detailed analyses of the derivation of water-vapor profiles from the lidar measurements.

Thin Liquid Water Clouds: Their Importance and Our Challenge

Abstract Many of the clouds important to the Earths energy balance, from the Tropics to the Arctic, contain small amounts of liquid water. Longwave and shortwave radiative fluxes are very sensitive to small perturbations of the cloud liquid water path (LWP), when the LWP is small (i.e., < 100 g m−2; clouds with LWP less than this threshold will be referred to as “thin”). Thus, the radiative properties of these thin liquid water clouds must be well understood to capture them correctly in climate models. We review the importance of these thin clouds to the Earths energy balance, and explain the difficulties in observing them. In particular, because these clouds are thin, potentially mixed phase, and often broken (i.e., have large 3D variability), it is challenging to retrieve their microphysical properties accurately. We describe a retrieval algorithm intercomparison that was conducted to evaluate the issues involved. The intercomparison used data collected at the Atmospheric Radiation Measurement (ARM) S...

The Convective and Orographically Induced Precipitation Study:A Research and Development Project of the World Weather Research Program for Improving Quantitative Precipitation Forecasting in Low-mountain Regions

Abstract The international field campaign called the Convective and Orographically-induced Precipitation Study (COPS) took place from June to August 2007 in southwestern Germany/eastern France. The overarching goal of COPS is to advance the quality of forecasts of orographically-induced convective precipitation by four-dimensional observations and modeling of its life cycle. COPS was endorsed as one of the Research and Development Projects of the World Weather Research Program (WWRP), and combines the efforts of institutions and scientists from eight countries. A strong collaboration between instrument principal investigators and experts on mesoscale modeling has been established within COPS. In order to study the relative importance of large-scale and small-scale forcing leading to convection initiation in low mountains, COPS is coordinated with a one-year General Observations Period in central Europe, the WWRP Forecast Demonstration Project MAP D-PHASE, and the first summertime European THORPEX Regional...

July 2012 Greenland melt extent enhanced by low-level liquid clouds

Melting of the world’s major ice sheets can affect human and environmental conditions by contributing to sea-level rise. In July 2012, an historically rare period of extended surface melting was observed across almost the entire Greenland ice sheet, raising questions about the frequency and spatial extent of such events. Here we show that low-level clouds consisting of liquid water droplets (‘liquid clouds’), via their radiative effects, played a key part in this melt event by increasing near-surface temperatures. We used a suite of surface-based observations, remote sensing data, and a surface energy-balance model. At the critical surface melt time, the clouds were optically thick enough and low enough to enhance the downwelling infrared flux at the surface. At the same time they were optically thin enough to allow sufficient solar radiation to penetrate through them and raise surface temperatures above the melting point. Outside this narrow range in cloud optical thickness, the radiative contribution to the surface energy budget would have been diminished, and the spatial extent of this melting event would have been smaller. We further show that these thin, low-level liquid clouds occur frequently, both over Greenland and across the Arctic, being present around 30–50 per cent of the time. Our results may help to explain the difficulties that global climate models have in simulating the Arctic surface energy budget, particularly as models tend to under-predict the formation of optically thin liquid clouds at supercooled temperatures—a process potentially necessary to account fully for temperature feedbacks in a warming Arctic climate.

Arctic Mixed-Phase Cloud Properties from AERI Lidar Observations: Algorithm and Results from SHEBA

Abstract A new approach to retrieve microphysical properties from mixed-phase Arctic clouds is presented. This mixed-phase cloud property retrieval algorithm (MIXCRA) retrieves cloud optical depth, ice fraction, and the effective radius of the water and ice particles from ground-based, high-resolution infrared radiance and lidar cloud boundary observations. The theoretical basis for this technique is that the absorption coefficient of ice is greater than that of liquid water from 10 to 13 μm, whereas liquid water is more absorbing than ice from 16 to 25 μm. MIXCRA retrievals are only valid for optically thin (τvisible < 6) single-layer clouds when the precipitable water vapor is less than 1 cm. MIXCRA was applied to the Atmospheric Emitted Radiance Interferometer (AERI) data that were collected during the Surface Heat Budget of the Arctic Ocean (SHEBA) experiment from November 1997 to May 1998, where 63% of all of the cloudy scenes above the SHEBA site met this specification. The retrieval determined that...

Automated retrievals of water vapor and aerosol profiles from an operational Raman lidar

Abstract Automated routines have been developed to derive water vapor mixing ratio, relative humidity, aerosol extinction and backscatter coefficient, and linear depolarization profiles, as well as total precipitable water vapor and aerosol optical thickness, from the operational Raman lidar at the Atmospheric Radiation Measurement (ARM) programs site in north-central Oklahoma. These routines have been devised to maintain the calibration of these data products, which have proven sensitive to the automatic alignment adjustments that are made periodically by the instrument. Since this Raman lidar does not scan, aerosol extinction cannot be directly computed below approximately 800 m due to the incomplete overlap of the outgoing laser beam with the detectors field of view. Therefore, the extinction-to-backscatter ratio at 1 km is used with the aerosol backscatter coefficient profile to compute aerosol extinction from 60 m to the level of complete overlap. Comparisons of aerosol optical depth derived using ...

Toward Understanding of Differences in Current Cloud Retrievals of ARM Ground-Based Measurements

Accurate observations of cloud microphysical properties are needed for evaluating and improving the representation of cloud processes in climate models and better estimate of the Earth radiative budget. However, large differences are found in current cloud products retrieved from ground-based remote sensing measurements using various retrieval algorithms. Understanding the differences is an important step to address uncertainties in the cloud retrievals. In this study, an in-depth analysis of nine existing ground-based cloud retrievals using ARM remote sensing measurements is carried out. We place emphasis on boundary layer overcast clouds and high level ice clouds, which are the focus of many current retrieval development efforts due to their radiative importance and relatively simple structure. Large systematic discrepancies in cloud microphysical properties are found in these two types of clouds among the nine cloud retrieval products, particularly for the cloud liquid and ice particle effective radius. Note that the differences among some retrieval products are even larger than the prescribed uncertainties reported by the retrieval algorithm developers. It is shown that most of these large differences have their roots in the retrieval theoretical bases, assumptions, as well as input and constraint parameters. This study suggests the need to further validate current retrieval theories and assumptions and even the development of new retrieval algorithms with more observations under different cloud regimes.