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Title: ARM: Raman LIDAR (RL): water vapor mixing ratio and relative humidity profiles, along with PWV

Raman LIDAR (RL): water vapor mixing ratio and relative humidity profiles, along with PWV
Authors:
;
Publication Date:
DOE Contract Number:
DE-AC05-00OR22725
Product Type:
Dataset
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)
Subject:
54 Environmental Sciences; Aerosol backscattered radiation; Aerosol extinction; Aerosol optical depth; Aerosol scattering; Atmospheric moisture; Atmospheric temperature; Backscatter depolarization ratio; Backscattered radiation; Cloud base height; Cloud top height; Liquid water content; Precipitable water
OSTI Identifier:
1027254
  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. Raman LIDAR (RL): 10-sec water vapor mixing ratio andrelative humidity profiles , along with PWV
  2. Planetary Boundary Layer (PBL) heights have been computed using potential temperature profiles derived from Raman lidar and AERI measurements. Raman lidar measurements of the rotational Raman scattering from nitrogen and oxygen are used to derive vertical profiles of potential temperature. AERI measurements of downwelling radiancemore » are used in a physical retrieval approach (Smith et al. 1999, Feltz et al. 1998) to derive profiles of temperature and water vapor. The Raman lidar and AERI potential temperature profiles are merged to create a single potential temperature profile for computing PBL heights. PBL heights were derived from these merged potential temperature profiles using a modified Heffter (1980) technique that was tailored to the SGP site (Della Monache et al., 2004). PBL heights were computed on an hourly basis for the period January 1, 2009 through December 31, 2011. These heights are provided as meters above ground level. « 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. These multi-wavelength lidar data were collected during the Combined HSRL and Raman lidar Measurement Study (CHARMS) IOP that occurred during July through September 2015 at SGP. During CHARMS the University of Wisconsin HSRL was located at SGP and acquired aerosol backscatter profiles at 532 nmmore » and 1064 nm and aerosol backscatter, extinction, and depolarization profiles at 532 nm. The HSRL aerosol profiles, when combined with the aerosol backscatter and extinction profiles (355 nm) collected by the SGP Raman lidar, provide a suite of three aerosol backscatter (355, 532, 1064 nm) and two aerosol extinction (355, 532 nm) profiles for use in advanced aerosol microphysical retrievals. The data files in this PI product contain this suite of aerosol backscatter (355, 532, 1064), extinction (355, 532 nm), and depolarization (532 nm) profiles. « less
  5. Cloud ice water concentration is one of the most important, yet poorly observed, cloud properties. Developing physical parameterizations used in general circulation models through single-column modeling is one of the key foci of the ARM program. In addition to the vertical profiles of temperature, watermore » vapor and condensed water at the model grids, large-scale horizontal advective tendencies of these variables are also required as forcing terms in the single-column models. Observed horizontal advection of condensed water has not been available because the radar/lidar/radiometer observations at the ARM site are single-point measurement, therefore, do not provide horizontal distribution of condensed water. The intention of this product is to provide large-scale distribution of cloud ice water by merging available surface and satellite measurements. The satellite cloud ice water algorithm uses ARM ground-based measurements as baseline, produces datasets for 3-D cloud ice water distributions in a 10 deg x 10 deg area near ARM site. The approach of the study is to expand a (surface) point measurement to an (satellite) areal measurement. That is, this study takes the advantage of the high quality cloud measurements at the point of ARM site. We use the cloud characteristics derived from the point measurement to guide/constrain satellite retrieval, then use the satellite algorithm to derive the cloud ice water distributions within an area, i.e., 10 deg x 10 deg centered at ARM site. « less