NCAR Global Climate Four-Dimensional Data Assimilation (CFDDA) Hourly 40 km Reanalysis: a high-resolution dynamically downscaled climatography

Thursday, 18 December 2014
Grace S Peng1, Chung-Yi Hou2, Darren L Rife3 and Robert Dattore1, (1)National Center for Atmospheric Research, CISL/DSS, Boulder, CO, United States, (2)University of Illinois at Urbana Champaign, Urbana, IL, United States, (3)DNV GL, Denver, United States
Wind energy cost models incur inaccuracies from uncertainty in ambient wind measurements and estimates. This inhibits the best possible investment in wind energy infrastructure and management systems.

High-resolution temporal and spatial wind data needed for wind availability analysis—usually created with regional-scale models—have traditionally been proprietary and costly to obtain. Freely available global model data suffers from either lower spatial or temporal resolution, or both. Low spatial resolution fails to realistically represent wind speeds in complex terrain. Low temporal resolution fails to capture the full diurnal cycle of wind behavior.

The NCAR Global Climate Four-Dimensional Data Assimilation (CFDDA) Hourly 40 km Reanalysis was developed in 2009-2010 by the Research Applications Laboratory (RAL) to provide the most accurate boundary layer wind estimates available at that time. CFDDA used 28 sigma levels, with 19 between the surface and 700 hPa, a four-fold improvement over the contemporary NWP models. The dataset spans 21 years, 1985-2005, providing hourly atmospheric parameters, including winds, on 28 vertical levels on a global 40 km grid.

This presentation will introduce the modeling and assimilation strategy, highlight the available data content including the parameter set, and review the data access options available from the RDA.

CFDDA project partners, Defense Threat Reduction Agency (DTRA), NCAR RAL and NCAR Mesoscale & Microscale Meteorology (MMM) divisions are offering this dataset to the public for free with minor restrictions. NCAR Research Data Archive (RDA), hosted by the Computational and Information Systems Laboratory, provides data support. It is available at http://rda.ucar.edu/datasets/ds604.0/