Articles | Volume 29, issue 4
https://doi.org/10.5194/npg-29-329-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/npg-29-329-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
06 Oct 2022
Research article |
| 06 Oct 2022
| 06 Oct 2022
Using a hybrid optimal interpolation–ensemble Kalman filter for the Canadian Precipitation Analysis
Dikraa Khedhaouiria
CORRESPONDING AUTHOR
Meteorological Research Division, Environment and Climate Change Canada, Dorval, QC, Canada
Stéphane Bélair
Meteorological Research Division, Environment and Climate Change Canada, Dorval, QC, Canada
Vincent Fortin
Meteorological Research Division, Environment and Climate Change Canada, Dorval, QC, Canada
Guy Roy
Meteorological Service of Canada, Environment and Climate Change Canada, Dorval, QC, Canada
Franck Lespinas
Meteorological Service of Canada, Environment and Climate Change Canada, Dorval, QC, Canada
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Cited articles
Bachmann, K., Keil, C., and Weissmann, M.: Impact of radar data assimilation
and orography on predictability of deep convection, Q. J. Roy. Meteor. Soc., 145, 117–130,
https://doi.org/10.1002/qj.3412, 2019. a
Bonavita, M., Hamrud, M., and Isaksen, L.: EnKF and Hybrid Gain Ensemble Data
Assimilation. Part II: EnKF and Hybrid Gain Results, Mon. Weather Rev.,
143, 4865–4882, https://doi.org/10.1175/MWR-D-15-0071.1, 2015. a
Brown, J. D., Seo, D.-J., and Du, J.: Verification of Precipitation Forecasts
from NCEP's Short-Range Ensemble Forecast (SREF) System with Reference to
Ensemble Streamflow Prediction Using Lumped Hydrologic Models, J.
Hydrometeorol., 13, 808–836, https://doi.org/10.1175/JHM-D-11-036.1, 2012. a
Caron, J.-F., Milewski, T., Buehner, M., Fillion, L., Reszka, M., Macpherson,
S., and St-James, J.: Implementation of Deterministic Weather Forecasting
Systems Based on Ensemble–Variational Data Assimilation at Environment
Canada. Part II: The Regional System, Mon. Weather Rev., 143,
2560–2580, 2015. a, b
Counillon, F., Sakov, P., and Bertino, L.: Application of a hybrid EnKF-OI to ocean forecasting, Ocean Sci., 5, 389–401, https://doi.org/10.5194/os-5-389-2009, 2009. a, b, c, d
Cressie, N.: Statistics for Spatial Data, Revised Edition, Wiley Classics Library, John Wiley & Sons, ISBN: 978-1-119-11461-1, 2015. a
Ebert, E.: Ability of a poor man's ensemble to predict the probability and
distribution of precipitation, Mon. Weather Rev., 129, 2461–2480, 2001. a
ECCC: Regional Ensemble Prediction System (REPS) Version 3.0.0 Summary of
changes with respect to version 2.4.0 and validation, Dorval,
Qc, Canada, Tech. rep.,
https://collaboration.cmc.ec.gc.ca/cmc/cmoi/product_guide/docs/lib/technote_reps-300_20190703_e.pdf (last access: 28 September 2022),
2019. a
ECCC: Index of /ensemble/reps/10km/grib2, Government of Canada [data set], https://dd.meteo.gc.ca/ensemble/reps/10km/grib2, last access: 28 September 2022a. a
ECCC: Index of /analysis/precip/rdpa/grib2/polar_stereographic, Government of Canada [data set], https://dd.meteo.gc.ca/analysis/precip/rdpa/grib2/polar_stereographic, last access: 28 September 2022b. a
Evans, A. M.: Investigation of enhancements to two fundamental components of
the statistical interpolation method used by the Canadian Precipitation
Analysis (CaPA), MS thesis, University of Manitoba,Winnipeg, Manitoba, https://mspace.lib.umanitoba.ca/xmlui/handle/1993/22276
(last access: 28 September 2022),
2013. a
Evensen, G.: The ensemble Kalman filter: Theoretical formulation and practical
implementation, Ocean Dynam., 53, 343–367,
https://doi.org/10.1007/s10236-003-0036-9, 2003. a
Fortin, V., Roy, G., Stadnyk, T., Koenig, K., Gasset, N., and Mahidjiba, A.:
Ten Years of Science Based on the Canadian Precipitation Analysis: A CaPA
System Overview and Literature Review, Atmos. Ocean, 56, 178–196,
https://doi.org/10.1080/07055900.2018.1474728, 2018. a, b
Houtekamer, P. and Zhang, F.: Review of the Ensemble Kalman Filter for
atmospheric data assimilation, Mon. Weather Rev., 144, 4489–4532,
https://doi.org/10.1175/MWR-D-15-0440.1, 2016. a, b
Houtekamer, P. L. and Mitchell, H. L.: Data assimilation using an Ensemble
Kalman Filter technique, Mon. Weather Rev., 126, 796–811,
https://doi.org/10.1175/1520-0493(1998)126<0796:DAUAEK>2.0.CO;2, 1998. a, b
Houtekamer, P. L., Deng, X., Mitchell, H. L., Baek, S.-J., and Gagnon, N.:
Higher Resolution in an Operational Ensemble Kalman Filter, Mon. Weather Rev., 142, 1143–1162, https://doi.org/10.1175/MWR-D-13-00138.1, 2014. a, b
Houtekamer, P. L., Buehner, M., and De La Chevrotière, M.: Using the hybrid
gain algorithm to sample data assimilation uncertainty, Q. J. Roy. Meteorol. Soc., 145, 35–56,
https://doi.org/10.1002/qj.3426, 2019. a, b
Jacques, D., Michelson, D., Caron, J. F., , and Fillion, L.: Latent heat
nudging in the Canadian Regional Deterministic Prediction System, Mon. Weather Rev., 146, 3995–4014, https://doi.org/10.1175/MWR-D-18-0118.1, 2018. a
Kalnay, E., Li, H., Miyoshi, T., Yang, S.-C., and Ballabrera-Poy, J.: 4D-Var
or ensemble Kalman filter?, Tellus A, 59, 758–773,
https://doi.org/10.1111/j.1600-0870.2007.00261.x, 2007. a
Kleist, D. T. and Ide, K.: An OSSE-Based Evaluation of Hybrid
Variational–Ensemble Data Assimilation for the NCEP GFS. Part I: System
Description and 3D-Hybrid Results, Mon. Weather Rev., 143, 433–451,
https://doi.org/10.1175/MWR-D-13-00351.1, 2015. a
Kollias, P., Bharadwaj, N., Clothiaux, E. E., Lamer, K., Oue, M., Hardin, J., Isom, B., Lindenmaier, I., Matthews, A., Luke, E. P., Giangrande, S. E., Johnson, K., Collis, S., Comstock, J., and Mather, J. H.: The ARM Radar
Network: At the Leading-edge of Cloud and Precipitation Observations,
B. Am. Meteorol. Soc., 101, E588–E607,
https://doi.org/10.1175/BAMS-D-18-0288.1, 2020. a
Lin, Y. and Mitchell, K.: The NCEP Stage II/IV hourly precipitation analyses:
development and applications, in: Preprints of the 19th Conference on
Hydrology, American Meteorological Society, San Diego, CA, 9–13 January 2005, 1–3, https://www.google.com/url2ahUKEwjXvYQ
(last access: 28 September 2022), 2005. a, b
Lorenz, E. N.: Deterministic nonperiodic flow, J. Atmos. Sci., 20, 130–141, 1963. a
Mahfouf, J., Brasnett, B., and Gagnon, S.: A Canadian Precipitation
Analysis (CaPA) project: Description and preliminary results,
Atmos. Ocean, 45, 1–17, https://doi.org/10.3137/ao.v450101, 2007. a
Nelson, B. R., Prat, O. P., Seo, D. J., and Habib, E.: Assessment and
implications of NCEP stage IV quantitative precipitation estimates for
product intercomparisons, Weather Forecast., 31, 371–394,
https://doi.org/10.1175/WAF-D-14-00112.1, 2016. a
Penny, S. G., Behringer, D. W., Carton, J. A., and Kalnay, E.: A Hybrid Global
Ocean Data Assimilation System at NCEP, Mon. Weather Rev., 143,
4660–4677, https://doi.org/10.1175/MWR-D-14-00376.1, 2015.
a
Rasmussen, R., Baker, B., Kochendorfer, J., Meyers, T., Landolt, S., Fischer, A. P., Black, J., Thériault, J. M., Kucera, P., Gochis, D., Smith, C., Nitu, R., Hall, M., Ikeda, K., and Gutmann, E.: How
well are we measuring snow: The NOAA/FAA/NCAR winter precipitation test bed,
B. Am. Meteorol. Soc., 93, 811–829, 2012. a
Roberts, N. M. and Lean, H. W.: Scale-Selective Verification of Rainfall
Accumulations from High-Resolution Forecasts of Convective Events, Mon. Weather Rev., 136, 78–97, https://doi.org/10.1175/2007MWR2123.1, 2008. a
Schwartz, C. S., Kain, J. S., Weiss, S. J., Xue, M., Bright, D. R., Kong, F.,
Thomas, K. W., Levit, J. J., and Coniglio, M. C.: Next-Day
Convection-Allowing WRF Model Guidance: A Second Look at 2-km versus 4-km
Grid Spacing, Mon. Weather Rev., 137, 3351–3372,
https://doi.org/10.1175/2009MWR2924.1, 2009. a
Wang, X., Hamill, T. M., Whitaker, J. S., and Bishop, C. H.: A Comparison of
Hybrid Ensemble Transform Kalman Filter – Optimum Interpolation and Ensemble
Square Root Filter Analysis Schemes, Mon. Weather Rev., 135,
1055–1076, https://doi.org/10.1175/MWR3307.1, 2007. a, b
Wang, X., Barker, D. M., Snyder, C., and Hamill, T. M.: A Hybrid ETKF–3DVAR
Data Assimilation Scheme for the WRF Model. Part II: Real Observation
Experiments, Mon. Weather Rev., 136, 5132–5147,
https://doi.org/10.1175/2008MWR2445.1, 2008b. a, b
Short summary
This study introduces a well-known use of hybrid methods in data assimilation (DA) algorithms that has not yet been explored for precipitation analyses. Our approach combined an ensemble-based DA approach with an existing deterministically based DA. Both DA scheme families have desirable aspects that can be leveraged if combined. The DA hybrid method showed better precipitation analyses in regions with a low rate of assimilated surface observations, which is typically the case in winter.
This study introduces a well-known use of hybrid methods in data assimilation (DA) algorithms...
