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Title: ARM Evaluation Product : Droplet Number Concentration Value-Added Product

Cloud droplet number concentration is an important factor in understanding aerosol-cloud interactions. As aerosol concentration increases, it is expected that droplet number concentration, Nd, will increase and droplet size decrease, for a given liquid water path (Twomey 1977), which will greatly affect cloud albedo as smaller droplets reflect more shortwave radiation. However, the magnitude and variability of these processes under different environmental conditions is still uncertain. McComiskey et al. (2009) have implemented a method, based on Boers and Mitchell (1994), for calculating Nd from ground-based remote sensing measurements of optical depth and liquid water path. They show that the magnitude of the aerosol-cloud interactions (ACI) varies with a range of factors, including the relative value of the cloud liquid water path (LWP), the aerosol size distribution, and the cloud updraft velocity. Estimates of Nd under a range of cloud types and conditions and at a variety of sites are needed to further quantify the impacts of aerosol cloud interactions.
Publication Date:
DOE Contract Number:
Product Type:
Research Org(s):
Atmospheric Radiation Measurement (ARM) Archive, Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (US)
Sponsoring Org:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)
54 Environmental Sciences; ARM; Cloud droplet number concentration
OSTI Identifier:
  1. ARM focuses on obtaining continuous measurements—supplemented by field campaigns—and providing data products that promote the advancement of climate models. ARM data include routine data products, value-added products (VAPs), field campaign data, complementary external data products from collaborating programs, and data contributed by ARM principal investigators for use by the scientific community. Data quality reports, graphical displays of data availability/quality, and data plots are also available from the ARM Data Center. Serving users worldwide, the ARM Data Center collects and archives approximately 20 terabytes of data per month. Datastreams are generally available for download within 48 hours.
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  1. The motivation for developing this product was to use the Dong et al. 1998 method to retrieve cloud microphysical properties, such as cloud droplet effective radius, cloud droplets number concentration, and optical thickness. These retrieved properties have been used to validate the satellite retrieval, andmore » evaluate the climate simulations and reanalyses. We had been using this method to retrieve cloud microphysical properties over ARM SGP and NSA sites. We also modified the method for the AMF at Shouxian, China and some IOPs, e.g. ARM IOP at SGP in March, 2000. The ARSCL data from ARM data archive over the SGP and NSA have been used to determine the cloud boundary and cloud phase. For these ARM permanent sites, the ARSCL data was developed based on MMCR measurements, however, there were no data available at the Azores field campaign. We followed the steps to generate this derived product and also include the MPLCMASK cloud retrievals to determine the most accurate cloud boundaries, including the thin cirrus clouds that WACR may under-detect. We use these as input to retrieve the cloud microphysical properties. Due to the different temporal resolutions of the derived cloud boundary heights product and the cloud properties product, we submit them as two separate netcdf files. « less
  2. These files were generated by Greg McFarquhar and Robert Jackson at the University of Illinois. Please contact or for more information or for assistance in interpreting the content of these files. We highly recommend that anyone wishing to use these files do somore » in a collaborative endeavor and we welcome queries and opportunities for collaboration. There are caveats associated with the use of the data which are difficult to thoroughly document and not all products for all time periods have been thoroughly examined. This is a value added data set of the best estimate of cloud microphysical parameters derived using data collected by the cloud microphysical probes installed on the Center for Interdisciplinary Remotely-Piloted Aircraft Studies (CIRPAS) Twin Otter during RACORO. These files contain best estimates of liquid size distributions N(D) in terms of droplet diameter D, liquid water content LWC, extinction of liquid drops beta, effective radius of cloud drops (re), total number concentration of droplets NT, and radar reflectivity factor Z at 1 second resolution. « less
  3. A cloud properties and radiative heating rates dataset is presented where cloud properties retrieved using lidar and radar observations are input into a radiative transfer model to compute radiative fluxes and heating rates at three ARM sites located in the Tropical Western Pacific (TWP) region.more » The cloud properties retrieval is a conditional retrieval that applies various retrieval techniques depending on the available data, that is if lidar, radar or both instruments detect cloud. This Combined Remote Sensor Retrieval Algorithm (CombRet) produces vertical profiles of liquid or ice water content (LWC or IWC), droplet effective radius (re), ice crystal generalized effective size (Dge), cloud phase, and cloud boundaries. The algorithm was compared with 3 other independent algorithms to help estimate the uncertainty in the cloud properties, fluxes, and heating rates (Comstock et al. 2013). The dataset is provided at 2 min temporal and 90 m vertical resolution. The current dataset is applied to time periods when the MMCR (Millimeter Cloud Radar) version of the ARSCL (Active Remotely-Sensed Cloud Locations) Value Added Product (VAP) is available. The MERGESONDE VAP is utilized where temperature and humidity profiles are required. Future additions to this dataset will utilize the new KAZR instrument and its associated VAPs. « less
  4. The hydrologic cycle of the Amazon Basin is one of the primary heat engines of the Southern Hemisphere. Any accurate climate model must succeed in a good description of the Basin, both in its natural state and in states perturbed by regional and global humanmore » activities. At the present time, however, tropical deep convection in a natural state is poorly understood and modeled, with insufficient observational data sets for model constraint. Furthermore, future climate scenarios resulting from human activities globally show the possible drying and the eventual possible conversion of rain forest to savanna in response to global climate change. Based on our current state of knowledge, the governing conditions of this catastrophic change are not defined. Human activities locally, including the economic development activities that are growing the population and the industry within the Basin, also have the potential to shift regional climate, most immediately by an increment in aerosol number and mass concentrations, and the shift is across the range of values to which cloud properties are most sensitive. The ARM Climate Research Facility in the Amazon Basin seeks to understand aerosol and cloud life cycles, particularly the susceptibility to cloud aerosol precipitation interactions, within the Amazon Basin. « less
  5. Best estimates of the size distributions of supercooled water droplets and ice crystals for mixed-phase clouds measured during M-PACE for spiral ascents/descents over Barrow and Oliktok Point, and for ramped ascents/descents between Barrow and Oliktok Point. Our best estimates of the bulk microphysical properties suchmore » as ice water content (IWC), liquid water content (LWC), effective radius of ice crystals defined following Fu (1996) (rei), effective radius of supercooled water droplets (rew), total ice crystal number concentration (Ni), total water droplet number concentration (Nw) and total condensed water content (CWC), are also provided. The quantities were derived from the FSSP, 1DC, 2DC, HVPS and the CVI. Note HVPS data are only available after 10 Oct 2004 and some procedures have been developed to account for the missing data. « less