Chitra Sivaraman

Simultaneous analog and photon counting detection for Raman lidar

The Atmospheric Radiation Measurement program Raman lidar was upgraded in 2004 with a new data system that provides simultaneous measurements of both the photomultiplier analog output voltage and photon counts. We describe recent improvements to the algorithm used to merge these two signals into a single signal with improved dynamic range. The effect of modifications to the algorithm are evaluated by comparing profiles of water vapor mixing ratio from the lidar with radiosonde measurements over a six month period. The modifications that were implemented resulted in a reduction of the mean bias in the daytime water vapor mixing ratio from a 3% dry bias to well within 1%. This improvement was obtained by ignoring the temporal variation of the glue coefficients and using only the nighttime average glue coefficients throughout the entire diurnal cycle.

Cloud properties derived from two lidars over the ARM SGP site

Active remote sensors such as lidars or radars can be used with other data to quantify the cloud properties at regional scale and at global scale. Relative to radar, lidar remote sensing is sensitive to very thin and high clouds but has a significant limitation due to signal attenuation in the ability to precisely quantify the properties of clouds with a cloud optical thickness larger than 3. The cloud properties for all levels of clouds are derived and distributions of cloud base height (CBH), top height (CTH), physical cloud thickness (CT), and optical thickness (COT) from local statistics are compared. The goal of this study is (1) to establish a climatology of macrophysical and optical properties for all levels of clouds observed over the ARM SGP site and (2) to estimate the discrepancies between the two remote sensing systems (pulse energy, sampling, resolution, etc.). Our first results tend to show that the MPL, which are the primary ARM lidars, have a distinctly limited range within which all of these cloud properties are detectable, especially cloud top and cloud thickness, but this can include cloud base particularly during summer daytime period. According to the comparisons between RL and MPL, almost 50% of situations show a signal to noise ratio too low (smaller than 3) for the MPL in order to detect clouds higher than 7km during daytime period in summer. Consequently, the MPL-derived annual cycle of cirrus cloud base (top) altitude is biased low, especially for daylight periods, compared with those derived from the RL data, which detects cloud base ranging from 7.5 km in winter to 9.5 km in summer (and tops ranging from 8.6 to 10.5 km). The optically thickest cirrus clouds (COT > 0.3) reach 50% of the total population for the Raman lidar and only 20% for the Micropulse lidar due to the difference of pulse energy and the effect of solar irradiance contamination. A complementary study using the cloud fraction derived from the Micropulse lidar for clouds below 5 km and from the Raman lidar for cloud above 5 km allows for better estimation of the total cloud fraction between the ground and the top of the atmosphere. This study presents the diurnal cycle of cloud fraction for each season in comparisons with Long et al.s (2006) cloud fraction calculation derived from radiative flux analysis. Copyright

Semantic catalog of things, services, and data to support a wind data management facility

Transparency and data integrity are crucial to any scientific study wanting to garner impact and credibility in the scientific community. The purpose of this paper is to discuss how this can be achieved using what we define as the Semantic Catalog. The catalog exploits community vocabularies as well as linked open data best practices to seamlessly describe and link things, data, and off-the-shelf (OTS) services to support scientific offshore wind energy research for the U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy (EERE) Wind and Water Power Program. This is largely made possible by leveraging collaborative advances in the Internet of Things (IoT), Semantic Web, Linked Services, Linked Open Data (LOD), and Resource Description Framework (RDF) vocabulary communities, which provides the foundation for our design. By adapting these linked community best practices, we designed a wind characterization Data Management Facility (DMF) capable of continuous data collection, processing, and preservation of in situ and remote sensing instrument measurements. The design incorporates the aforementioned Semantic Catalog which provides a transparent and ubiquitous interface for its user community to the things, data, and services for which the DMF is composed.

Deep web scientific sensor measurements usage: A standards-based approach

There is a growing need for scientists producing sensor measurements from scientific studies to make these available to global consumer scientific communities. The authors believe that this can largely be achieved by relying on well-established international standards bodies such as the World Wide Web Consortium (w3.org), building momentum in scientific communities to establish best practices for data publication, and creating collaborative capabilities for consumers to explore, reuse, and contribute their knowledge about the data.

ARM: 2-minute Raman Lidar: aerosol depolarization profiles and single layer cloud optical depths

2-minute Raman Lidar: aerosol depolarization profiles and single layer cloud optical depths

ARM: 10-second Raman Lidar: aerosol scattering ratio and backscattering coefficient profiles, from first Ferrare algorithm

10-minute TEMPORARY Raman Lidar: aerosol scattering ratio and backscattering coefficient profiles, from first Ferrare algorithm

ARM: Raman LIDAR (RL): Best-estimate state of the atmos. profiles from RL and AERI+GOES retrievals

Raman LIDAR (RL): Best-estimate state of the atmos. profiles from RL and AERI+GOES retrievals

ARM: Raman LIDAR (RL): 10-sec water vapor mixing ratio andrelative humidity profiles , along with PWV

Raman LIDAR (RL): 10-sec water vapor mixing ratio andrelative humidity profiles , along with PWV

ARM: 10-minute Raman Lidar: aerosol extinction profiles and aerosol optical thickness, from first Ferrare algorithm

10-minute Raman Lidar: aerosol extinction profiles and aerosol optical thickness, from first Ferrare algorithm

ARM: 10-minute Raman Lidar: aerosol depolarization profiles and single layer cloud optical depths from first Turner algorithm

10-minute Raman Lidar: aerosol depolarization profiles and single layer cloud optical depths from first Turner algorithm