TY - JOUR
T1 - Efficient Dynamical Downscaling of General Circulation Models Using Continuous Data Assimilation
AU - Desamsetti, Srinivas
AU - Dasari, Hari Prasad
AU - Langodan, Sabique
AU - Titi, Edriss S.
AU - Knio, Omar
AU - Hoteit, Ibrahim
N1 - KAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): grant no. REP/1/3268-01-01
Acknowledgements: The research was supported by the office of Sponsor Research (OSR) at King Abdullah University of Science and Technology (KAUST) under the Virtual Red Sea Initiative (grant no. REP/1/3268-01-01) and the Saudi ARAMCO-KAUST Marine Environmental Observatory (SAKMEO). This research made use of the Supercomputing Laboratory resources at KAUST. The work of Edriss S. Titi was supported in part by ONR grant N00014-15-1-2333, the Einstein Stiftung/Foundation - Berlin, through the Einstein Visiting Fellow Program, and by the John Simon Guggenheim Memorial Foundation.
PY - 2019/7/9
Y1 - 2019/7/9
N2 - Continuous data assimilation (CDA) is successfully implemented for the first timefor efficient dynamical downscaling of a global atmospheric reanalysis. A com-parison of the performance of CDA with the standard grid and spectral nudgingtechniques for representing long- and short-scale features in the downscaled fieldsusing the Weather Research and Forecast (WRF) model is further presented andanalysed. The WRF model is configured at 0.25◦×0.25◦horizontal resolution andis driven by 2.5◦×2.5◦initial and boundary conditions from NCEP/NCAR reanal-ysis fields. Downscaling experiments are performed over a one-month period inJanuary 2016. The similarity metric is used to evaluate the performance of thedownscaling methods for large (2,000 km) and small (300 km) scales. Similarityresults are compared for the outputs of the WRF model with different downscalingtechniques, NCEP/NCAR reanalysis, and NCEP Final Analysis (FNL, available at0.25◦×0.25◦horizontal resolution). Both spectral nudging and CDA describe betterthe small-scale features compared to grid nudging. The choice of the wave number iscritical in spectral nudging; increasing the number of retained frequencies generallyproduced better small-scale features, but only up to a certain threshold after which itssolution gradually became closer to grid nudging. CDA maintains the balance of thelarge- and small-scale features similar to that of the best simulation achieved by thebest spectral nudging configuration, without the need of a spectral decomposition.The different downscaled atmospheric variables, including rainfall distribution, withCDA is most consistent with the observations. The Brier skill score values furtherindicate that the added value of CDA is distributed over the entire model domain.The overall results clearly suggest that CDA provides an efficient new approach fordynamical downscaling by maintaining better balance between the global model andthe downscaled fields
AB - Continuous data assimilation (CDA) is successfully implemented for the first timefor efficient dynamical downscaling of a global atmospheric reanalysis. A com-parison of the performance of CDA with the standard grid and spectral nudgingtechniques for representing long- and short-scale features in the downscaled fieldsusing the Weather Research and Forecast (WRF) model is further presented andanalysed. The WRF model is configured at 0.25◦×0.25◦horizontal resolution andis driven by 2.5◦×2.5◦initial and boundary conditions from NCEP/NCAR reanal-ysis fields. Downscaling experiments are performed over a one-month period inJanuary 2016. The similarity metric is used to evaluate the performance of thedownscaling methods for large (2,000 km) and small (300 km) scales. Similarityresults are compared for the outputs of the WRF model with different downscalingtechniques, NCEP/NCAR reanalysis, and NCEP Final Analysis (FNL, available at0.25◦×0.25◦horizontal resolution). Both spectral nudging and CDA describe betterthe small-scale features compared to grid nudging. The choice of the wave number iscritical in spectral nudging; increasing the number of retained frequencies generallyproduced better small-scale features, but only up to a certain threshold after which itssolution gradually became closer to grid nudging. CDA maintains the balance of thelarge- and small-scale features similar to that of the best simulation achieved by thebest spectral nudging configuration, without the need of a spectral decomposition.The different downscaled atmospheric variables, including rainfall distribution, withCDA is most consistent with the observations. The Brier skill score values furtherindicate that the added value of CDA is distributed over the entire model domain.The overall results clearly suggest that CDA provides an efficient new approach fordynamical downscaling by maintaining better balance between the global model andthe downscaled fields
UR - http://hdl.handle.net/10754/656325
UR - https://onlinelibrary.wiley.com/doi/abs/10.1002/qj.3612
UR - http://www.scopus.com/inward/record.url?scp=85070110656&partnerID=8YFLogxK
U2 - 10.1002/qj.3612
DO - 10.1002/qj.3612
M3 - Article
SN - 0035-9009
VL - 145
SP - 3175
EP - 3194
JO - Quarterly Journal of the Royal Meteorological Society
JF - Quarterly Journal of the Royal Meteorological Society
IS - 724
ER -