MPD OSSE data

This numerical simulation data set is prepared for manuscript submission to the the Monthly Weather Review journal of the American Meteorological Society (AMS).

The National Center for Atmospheric Research (NCAR) and Montana State University jointly developed water vapor MicroPulse Differential absorption lidars (MPDs) that are a significant advance in eye-safe, unattended, lidar-based water vapor remote sensing. We perform Observation System Simulation Experiments (OSSEs) to investigate MPD observation impact and its scientific significance of MPDs for convective weather analysis and predictions. In this study, the Data Assimilation Research Testbed (DART) and the Advanced Research version of the Weather Research and Forecasting model (WRF-ARW) are used to conduct OSSEs for a case study of a mesoscale convective system (MCS). We will submit OSSE results to the Monthly Weather Review journal of the American Meteorological Society (AMS). As this Journal is committed to the FAIR principles, all data used in the manuscript must be archived and cited/referenced properly. The dataset also have persistent identifiers such as DOIs.

DOI

https://doi.org/10.5065/231h-jw06

Temporal Range

2015-07-15 to 2015-07-15

Temporal Resolution

1.0 hour

Spatial Resolution

15.0 kilometer
3.0 kilometer

Related Links

N/A

GCMD Science Keywords

• Atmosphere > Atmospheric Water Vapor > Water Vapor Profiles > Vertically Resolved Backscatter Light
• Models > Weather Research/Forecast Models

GCMD Platform Types

• Other > Models > Wrf > Weather Research And Forecasting (Wrf) Model

GCMD Instruments

• Earth Remote Sensing Instruments > Active Remote Sensing > Profilers/Sounders > Lidar/Laser Sounders

File Media Types

• application/x-hdf

Support Contact

Junkyung Kay
UCAR/NCAR - Earth Observing Laboratory
junkyung@ucar.edu

Data Curator

GDEX Curator
UCAR/NCAR - GDEX
datahelp@ucar.edu

Credit

This material is based upon work supported by the National Center for Atmospheric Research, which is a major facility sponsored by the National Science Foundation under Cooperative Agreement No. 1852977.

We acknowledge the high-performance computing support from Cheyenne (doi:10.5065/D6RX99HX) provided by the Computational and Information Systems Laboratory (CISL) at the National Center for Atmospheric Research and sponsored by the NSF. NOAA Award No. NA19OAR4590320 is gratefully acknowledged.

Legal Constraints

Creative Commons Attribution 4.0 International License.

Access Constraints

None

Full Metadata

DIF XML
ISO19139 XML
OAI DC
JSON-LD

Version History

1.0