Progress in the Development of Practical Remote Detection of Icing Conditions

The NASA Icing Remote Sensing System (NIRSS) has been under definition and development at NASA Glenn Research Center since 1997. The goal of this development activity is to produce and demonstrate the required sensing and data processing technologies required to accurately remotely detect and measure icing conditions aloft. As part of that effort NASA has teamed with NCAR to develop software to fuse data from multiple instruments into a single detected icing condition product. The multiple instrument approach utilizes a X-band vertical staring radar, a multifrequency microwave, and a lidar ceilometer. The radar data determine cloud boundaries, the radiometer determines the sub-freezing temperature heights and total liquid water content, and the ceilometer refines the lower cloud boundary. Data is post-processed with a LabVIEW program with a resultant supercooled liquid water profile and aircraft hazard depiction. Ground-based, remotely-sensed measurements and in-situ measurements from research aircraft were gathered during the international 2003-2004 Alliance Icing Research Study (AIRS II). Comparisons between the remote sensing system s fused icing product and the aircraft measurements are reviewed here. While there are areas where improvement can be made, the cases examined suggest that the fused sensor remote sensing technique appears to be a valid approach

Experiences Developing a Semantic Representation of Product Quality, Bias, and Uncertainty for a Satellite Data Product

No abstract availabl

PROV-XML: The PROV XML Schema

Provenance is information about entities, activities, and people involved in producing a piece of data or thing, which can be used to form assessments about its quality, reliability or trustworthiness. PROV-DM is the conceptual data model that forms a basis for the W3C provenance (PROV) family of specifications. It defines a concepts for expressing provenance information enabling interchange. This document introduces an XML schema for the PROV data model (PROV-DM), allowing instances of the PROV data model to be serialized in XML

Semantic Specification of Data Types for a World of Open Data

Data interoperability is an ongoing challenge for global open data initiatives. The machine-readable specification of data types for datasets will help address interoperability issues. Data types have typically been at the syntactical level such as integer, float and string, etc. in programming languages. The work presented in this paper is a model design for the semantic specification of data types, such as a topographic map. The work was conducted in the context of the Semantic Web. The model differentiates the semantic data type from the basic data type. The former are instances (e.g., topographic map) of a specific data type class that is defined in the developed model. The latter are classes (e.g., Image) of resource types in existing ontologies. A data resource is an instance of a basic data type and is tagged with one or more specific data types. The implementation of the model is given within an existing production data portal that enables one to register specific data types and use them to annotate data resources. Data users can obtain explicating assumptions or information inherent in a dataset through the specific data types of that dataset. The machine-readable information of specific data types also paves the way for further studies, such as dataset recommendation