BARRA v1.0: the Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia

• Main Authors: C.-H. Su, N. Eizenberg, P. Steinle, D. Jakob, P. Fox-Hughes, C. J. White, S. Rennie, C. Franklin, I. Dharssi, H. Zhu
• Format: Article
• Language: English
• Published: Copernicus Publications 2019-05-01
• Series: Geoscientific Model Development
• Online Access: https://www.geosci-model-dev.net/12/2049/2019/gmd-12-2049-2019.pdf

<p>The Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia (BARRA) is the first atmospheric regional reanalysis over a large region covering Australia, New Zealand, and Southeast Asia. The production of the reanalysis with approximately 12&thinsp;km horizontal resolution – BARRA-R – is well underway with completion expected in 2019. This paper describes the numerical weather forecast model, the data assimilation methods, the forcing and observational data used to produce BARRA-R, and analyses results from the 2003–2016 reanalysis. BARRA-R provides a realistic depiction of the meteorology at and near the surface over land as diagnosed by temperature, wind speed, surface pressure, and precipitation. Comparing against the global reanalyses ERA-Interim and MERRA-2, BARRA-R scores lower root mean square errors when evaluated against (point-scale) 2&thinsp;m temperature, 10&thinsp;m wind speed, and surface pressure observations. It also shows reduced biases in daily 2&thinsp;m temperature maximum and minimum at 5&thinsp;km resolution and a higher frequency of very heavy precipitation days at 5 and 25&thinsp;km resolution when compared to gridded satellite and gauge analyses. Some issues with BARRA-R are also identified: biases in 10&thinsp;m wind, lower precipitation than observed over the tropical oceans, and higher precipitation over regions with higher elevations in south Asia and New Zealand. Some of these issues could be improved through dynamical downscaling of BARRA-R fields using convective-scale (<span class="inline-formula">&lt;2</span>&thinsp;km) models.</p>