Prof Dale Barker
Prof Dale BARKER
As the Director of CCRS, Prof Dale Barker provides strategic direction, oversight of the centre’s weather/climate science activities, and engages with senior stakeholders in the national/international weather/climate community.
Prof Barker has extensive experience in data assimilation research, use of observations in Numerical Weather Prediction (NWP), and regional climate reanalysis. He led the WRF data assimilation programme at the US National Center for Atmospheric Research (NCAR) in Boulder, Colorado (1999-2009). Between 2010 and 2018 he led the scientific development of the first EU-funded European regional reanalysis. In his previous role before joining CCRS, Prof Barker was the Associate Director for Weather Science at the Met Office, leading 200 staff working in meteorological R&D and the research-to-operational transition of global/local NWP, ocean/wave forecasting, air quality, and atmospheric dispersion systems.
Prof Barker is a Fellow of the Royal Astronomical Society (FRAS) and Royal Meteorological Society (FRMetS). He is a visiting professor at Reading University UK, an NCAR affiliate scientist, previous member of WMO/WWRP’s mesoscale weather forecasting WG, and chairs the scientific advisory committee for the new KIAPS Korean NWP system.
- PhD in Astrophysics, University of Sussex, UK
- BSc in Astronomy and Astrophysics, Newcastle University, UK
- Director, Centre for Climate Research Singapore, MSS
- Associate Director for Weather Science, Met Office, Exeter, UK
- Strategic Head of Data Assimlation, Met Office, Exeter, UK
- Programme Manager, NCAR, Boulder, Colorado, USA
- Project Scientist, NCAR, Boulder, Colorado, USA
- Higher/Senior Scientific Officer, Met Office, Bracknell, UK
- Numerical Weather Prediction
- Data Assimilation
- Climate Science
- Weather/Climate Impacts
Dipankar, A., S. Webster, K. Furtado, J. Wilkinson, C. Sanchez, A. Lock, R. North, X. Sun, S. Vosper, X. – Y. Huang, and D. M. Barker, 2020:
SINGV: a convective-scale weather-forecast model for Singapore.
Q J R Meteorol Soc., In Revision.
Sun, X., and Coauthors, 2020:
A Subjective and Objective Evaluation of Model Forecasts of Sumatra Squall Events.
Wea. Forecasting, 35, 489–506, doi: 10.1175/WAF-D-19-0187.1.
Heng, B. C. P, Tubbs, R, Huang, X‐Y, et al. :
SINGV‐DA: A data assimilation system for convective‐scale numerical weather prediction over Singapore.
Q J R Meteorol Soc. 2020; 146: 1923– 1938. doi: 10.1002/qj.3774
Huang XY, Barker D, Webster S, Dipankar A, Lock A, Mittermaier M, Sun X, North R, Darvell R, Boyd D, Lo J.:
SINGV–the Convective-Scale Numerical Weather Prediction System for Singapore.
ASEAN Journal on Science and Technology for Development. 2019 Dec 27;36(3):81-90. doi: 10.29037/ajstd.581.
Skamarock, W. C., J. B. Klemp, J. Dudhia, D. O. Gill, Z. Liu, J. Berner, W. Wang, J. G. Powers, M. G. Duda, D. M. Barker, and X. H. Huang, 2019:
A Description Of The Advanced Research WRF Version 4.
NCAR Tech Note, NCAR/TN-556+STR.
Bowler, N.E., Clayton, A.M., Jardak, M., Lee, E., Lorenc, A.C., Piccolo, C., Pring, S.R., Wlasak, M.A., Barker, D.M., Inverarity, G.W. and Swinbank, R.,2017:
Inflation and localization tests in the development of an ensemble of 4D‐ensemble variational assimilations.
Q.J.R. Meteorol. Soc., 143: 1280-1302. doi:10.1002/qj.3004
Bowler, N.E., Clayton, A.M., Jardak, M., Jermey, P.M., Lorenc, A.C., Wlasak, M.A., Barker, D.M., Inverarity, G.W. and Swinbank, R., 2017:
The effect of improved ensemble covariances on hybrid variational data assimilation.
Q.J.R. Meteorol. Soc., 143: 785-797. doi:10.1002/qj.2964.
Descombes, G., Auligné, T., Vandenberghe, F., Barker, D. M., and Barré, J., 2015:
Generalized background error covariance matrix model (GEN_BE v2.0)
Geosci. Model Dev., 8, 669–696, doi: 10.5194/gmd-8-669-2015
Sun, J., and Coauthors, 2014:
Use of NWP for Nowcasting Convective Precipitation: Recent Progress and Challenges.
Bull. Amer. Meteor. Soc., 95, 409–426, doi: 10.1175/BAMS-D-11-00263.1.
Clayton, A.M., Lorenc, A.C. and Barker, D.M., 2013:
Operational implementation of a hybrid ensemble/4D‐Var global data assimilation system at the Met Office.
Q.J.R. Meteorol. Soc., 139: 1445-1461. doi:10.1002/qj.2054