BARRA: A high-resolution atmospheric reanalysis over Australia for

Transcription

1 BARRA: A high-resolution atmospheric reanalysis over Australia for Chun-Hsu Su, Australian Bureau of Meteorology N. Eizenberg 1, G. Kuciuba 1, P. Steinle 1, D. Jakob 1, P. Fox-Hughes 1, R. Renshaw 2, P. Jermey 2, S. Moore 3 1 Bureau of Meteorology, Australia 2 UK Met Office 3 National Institute of Water and Atmospheric Research, New Zealand

2 BARRA: Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia Co-funded by State-level fire & emergency and environmental agencies:

3 50-year return period value of fire index Values of regional reanalysis to fire agencies Studying at municipal scale Estimating future likelihood Modelling the risk of bushfire to localities under different management plans

4 Current Products ACCESS is Bureau s Numerical Weather Prediction (NWP) system ACCESS-R(egional) ACCESS-C(ity) downscaler MSAS is Mesocale surface analysis system (Glowacki et al., 2012) AWAP is Aust. Water Avail. Project (Jones et al., 2007)???

5 BARRA: Closing an obvious gap in national climate data BARRA-R + BARRA-xx setups are based on ACCESS parallel suites???

6 BARRA-R(egional) and BARRA-xx (downscaler) FIG: Overview of reanalysis setup. BARRA-R is based on UERRA reanalysis suite. BARRA-SY BARRA-PH BARRA-AD BARRA-TA More downscaling sub-domains BARRA-xx can be added based on demand and funding * Unified Model (EndGame dynam. core) * JULES (Joint UK Land Environm. Simulator)

7 BARRA-R(egional) and BARRA-xx (downscaler) FIG: Observations used in BARRA-R. FIG: Suite cycling setup for BARRA-R and BARRA-xx models.

8 BARRA-R(egional) and BARRA-xx (downscaler) FIG: (Analysis obs) versus (Background obs) statistics over period. FIG: Suite cycling setup for BARRA-R and BARRA-xx models.

9 Evaluation: BARRA-R (12 km) cf. ERA-Interim ΔRMSE ~2K ΔRMSE ~1.5K ΔRMSE ~0.4m/s FIG: Evaluation of +6h fcst at 6Z and 18Z against surface observations at up to 1200 stations across BARRA-R domain. The RMSEs of screen temperature and dewpoint and 10m wind speed are calculated for each cycle, and 30-day moving averages of RMSE are shown.

10 Evaluation: BARRA-SY (1.5 km) cf. BARRA-R ΔRMSE ~0.2K ΔRMSE ~0.15m/s FIG: Same but for BARRA-SY (eastern New South Wales) domain. Up to 130 stations are used.

11 Status of production Completed reanalysis soon to be released Releases of 6 years of reanalysis every 6 months, by batches Data sets, up to 3 Pbytes Nearly 100 forecast fields produced Mostly hourly, but 10min data available for single-level fields netcdf4, CF-1.6, ACDD-1.3 Data available freely to research communities, via National Computational Infrastructure (NCI) NCI is Australian sponsor for ESGF

12 Key messages Increasing appreciation of importance of reanalysis by operational services and industries (environment, energy, defense, insurance, agriculture) within Australia Building a national asset BARRA-R: 12 km BARRA-* downscaler: 1.5km BARRA as the stepping stone towards a more major reanalysis km nationally Assimilate more obs: cloud, radar, rain Ensemble system, bounded by ERA5 Data rehabilitation Going further back in time Going forward in time (operational)

13 Contact: Webpage: Data access: National Computational Infrastructure (NCI)

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15 FFDI Value of regional reanalysis: Extreme event perspectives FFDI = 2 exp log DF RH T W (Forest fire danger index) Drought factor = func(rainfall, T) Wind speed Screen temperature Relative humidity

16 Value of regional reanalysis: Extreme event perspectives Importance of topographic effects (hills, valleys, coastlines) Capturing variations in land use (urban, veg), and subsurface characteristics Different physics when using higher resolution models Benefitting, 2014/02/28,18Z ERA-Interim (80 km) 10 m zonal wind Better T, Tmin, Tmax, dewpoint, wind, timing. Better representations of convective systems, storms Better winter rainfall at small catchments Meteorology at coastlines FIG: Change in scales

17 Value of regional reanalysis: Extreme event perspectives Importance of topographic effects (hills, valleys, coastlines) Capturing variations in land use (urban, veg), and subsurface characteristics Different physics when using higher resolution models Benefitting, 2014/02/28,18Z Reanalysis (12 km) Better T, Tmin, Tmax, dewpoint, wind, timing. Better representations of convective systems, storms Better winter rainfall at small catchments Meteorology at coastlines FIG: Change in scales

18 Value of regional reanalysis: Extreme event perspectives Importance of topographic effects (hills, valleys, coastlines) Capturing variations in land use (urban, veg), and subsurface characteristics Different physics when using higher resolution models Benefitting, 2014/02/28,18Z ERA-Interim (80 km) 10 m zonal wind Better T, Tmin, Tmax, dewpoint, wind, timing. Better representations of convective systems, storms Better winter rainfall at small catchments Meteorology at coastlines FIG: Change in scales

19 Value of regional reanalysis: Extreme event perspectives Importance of topographic effects (hills, valleys, coastlines) Capturing variations in land use (urban, veg), and subsurface characteristics Different physics when using higher resolution models Benefitting, 2014/02/28,18Z Reanalysis (12 km) Better T, Tmin, Tmax, dewpoint, wind, timing. Better representations of convective systems, storms Better winter rainfall at small catchments Meteorology at coastlines FIG: Change in scales

20 Value of regional reanalysis: Extreme event perspectives Importance of topographic effects (hills, valleys, coastlines) Capturing variations in land use (urban, veg), and subsurface characteristics Different physics when using higher resolution models Benefitting, 2014/02/28,18Z ERA-Interim (80 km) 10 m zonal wind Better T, Tmin, Tmax, dewpoint, wind, timing. Better representations of convective systems, storms Better winter rainfall at small catchments Meteorology at coastlines FIG: Change in scales

21 Value of regional reanalysis: Extreme event perspectives Importance of topographic effects (hills, valleys, coastlines) Capturing variations in land use (urban, veg), and subsurface characteristics Different physics when using higher resolution models Benefitting, 2014/02/28,18Z Reanalysis (12 km) Better T, Tmin, Tmax, dewpoint, wind, timing. Better representations of convective systems, storms Better winter rainfall at small catchments Meteorology at coastlines FIG: Change in scales

22 Value of regional reanalysis: Extreme event perspectives Importance of topographic effects (hills, valleys, coastlines) Capturing variations in land use (urban, veg), and subsurface characteristics Different physics when using higher resolution models Benefitting, 2014/02/28,18Z Reanalysis (1.5 km) Better T, Tmin, Tmax, dewpoint, wind, timing. Better representations of convective systems, storms Better winter rainfall at small catchments Meteorology at coastlines FIG: Change in scales

23 Value of regional reanalysis: Extreme event perspectives Importance of topographic effects (hills, valleys, coastlines) Capturing variations in land use (urban, veg), and subsurface characteristics Different physics when using higher resolution models Benefitting, 2014/02/28,18Z ERA-Interim (80 km) 10 m zonal wind Better T, Tmin, Tmax, dewpoint, wind, timing. Better representations of convective systems, storms Better winter rainfall at small catchments Meteorology at coastlines FIG: Change in scales

24 Value of regional reanalysis: Extreme event perspectives Importance of topographic effects (hills, valleys, coastlines) Capturing variations in land use (urban, veg), and subsurface characteristics Different physics when using higher resolution models Benefitting, 2014/02/28,18Z Reanalysis (12 km) Better T, Tmin, Tmax, dewpoint, wind, timing. Better representations of convective systems, storms Better winter rainfall at small catchments Meteorology at coastlines FIG: Change in scales

25 Value of regional reanalysis: Extreme event perspectives Importance of topographic effects (hills, valleys, coastlines) Capturing variations in land use (urban, veg), and subsurface characteristics Different physics when using higher resolution models Benefitting, 2014/02/28,18Z Reanalysis (1.5 km) Better T, Tmin, Tmax, dewpoint, wind, timing. Better representations of convective systems, storms Better winter rainfall at small catchments Meteorology at coastlines FIG: Change in scales