Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Developing a Convective-Scale EnKF Data Assimilation System for the Canadian MEOPAR Project

Developing a Convective-Scale EnKF Data Assimilation System for the Canadian MEOPAR Project AbstractThis study discusses the construction of a high-resolution ensemble Kalman filter system (the HREnKF) developed for the Marine Environmental Observation Prediction and Response (MEOPAR) network. The HREnKF runs at a horizontal resolution of 2.5 km and is intended to provide forecasts at lead times up to 12 h. This system was adapted from the global EnKF system in operation at Environment and Climate Change Canada. As a first development step, only the most necessary modifications have been implemented. The changes include an hourly cycling frequency, smaller localization radii, and the explicit representation of microphysical processes. To assess its performance and orient future developments, the HREnKF was continuously cycled for a period of 12 days. Verification against surface observations reveals that the skill of the forecasts initialized from the HREnKF is comparable to that of control forecasts also integrated at a resolution of 2.5 km. A key component of this study is the comparison of correlation estimated from ensembles at resolutions of 2.5, 15, and 50 km. At 2.5 km, correlation lengths are smaller than those found at 15 and 50 km. These short correlation lengths demand a high observational density, which is not available over the west coast domain where the HREnKF was tested. The spatial and temporal variability of the correlations is also assessed for the HREnKF system. It is found that correlation patterns are complex and do not generally decrease monotonically away from the reference point around which they are estimated. This result is important as it indicates that separation distance may not be the ideal parameter to use as a basis for localization strategies. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Monthly Weather Review American Meteorological Society

Developing a Convective-Scale EnKF Data Assimilation System for the Canadian MEOPAR Project

Download PDF
22 pages

Loading next page...
 
/lp/ams/developing-a-convective-scale-enkf-data-assimilation-system-for-the-2FvfbES0gX

References

References for this paper are not available at this time. We will be adding them shortly, thank you for your patience.

Publisher
American Meteorological Society
Copyright
Copyright © American Meteorological Society
ISSN
1520-0493
eISSN
1520-0493
DOI
10.1175/MWR-D-16-0135.1
Publisher site
See Article on Publisher Site

Abstract

AbstractThis study discusses the construction of a high-resolution ensemble Kalman filter system (the HREnKF) developed for the Marine Environmental Observation Prediction and Response (MEOPAR) network. The HREnKF runs at a horizontal resolution of 2.5 km and is intended to provide forecasts at lead times up to 12 h. This system was adapted from the global EnKF system in operation at Environment and Climate Change Canada. As a first development step, only the most necessary modifications have been implemented. The changes include an hourly cycling frequency, smaller localization radii, and the explicit representation of microphysical processes. To assess its performance and orient future developments, the HREnKF was continuously cycled for a period of 12 days. Verification against surface observations reveals that the skill of the forecasts initialized from the HREnKF is comparable to that of control forecasts also integrated at a resolution of 2.5 km. A key component of this study is the comparison of correlation estimated from ensembles at resolutions of 2.5, 15, and 50 km. At 2.5 km, correlation lengths are smaller than those found at 15 and 50 km. These short correlation lengths demand a high observational density, which is not available over the west coast domain where the HREnKF was tested. The spatial and temporal variability of the correlations is also assessed for the HREnKF system. It is found that correlation patterns are complex and do not generally decrease monotonically away from the reference point around which they are estimated. This result is important as it indicates that separation distance may not be the ideal parameter to use as a basis for localization strategies.

Journal

Monthly Weather Review – American Meteorological Society

Published: Apr 8, 2017

References

  • Evaluation of surface analyses and forecasts with a multiscale ensemble Kalman filter in regions of complex terrain
    Ancell, B. C.; Mass, C. F.; Hakim, G. J.
  • Comparison of surface wind and temperature analyses from an ensemble Kalman filter and the NWS real-time mesoscale analysis system
    Ancell, B. C.; Mass, C. F.; Cook, K.; Colman, B.
  • Operational implementation of the ISBA land surface scheme in the Canadian regional weather forecast model. Part I: Warm season results
    Bélair, S.; Crevier, L.-P.; Mailhot, J.; Bilodeau, B.; Delage, Y.
  • An hourly assimilation–forecast cycle: The RUC
    Benjamin, S. G.
  • Assimilation of 3D radar reflectivities with an ensemble Kalman filter on the convective scale
    Bick, T.
  • Data assimilation using incremental analysis updates
    Bloom, S. C.; Takacs, L. L.; da Silva, A. M.; Ledvina, D.
  • Background-error covariances for a convective-scale data-assimilation system: AROME–France 3D-Var
    Brousseau, P.; Berre, L.; Bouttier, F.; Desroziers, G.
  • Evaluation of a spatial/spectral covariance localization approach for atmospheric data assimilation
    Buehner, M.
  • Implementation of deterministic weather forecasting systems based on ensemble–variational data assimilation at Environment Canada. Part I: The global system
    Buehner, M.
  • Radar data assimilation in the Canadian high-resolution ensemble Kalman filter system: Performance and verification with real summer cases
    Chang, W.; Chung, K.-S.; Fillion, L.; Baek, S.-J.
  • Examination of situation-dependent background error covariances at the convective scale in the context of the ensemble Kalman filter
    Chung, K.-S.; Chang, W.; Fillion, L.; Tanguay, M.
  • Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics
    Evensen, G.
  • The impact of a digital filter finalization technique in a global data assimilation system
    Fillion, L.; Mitchell, H. L.; Ritchie, H.; Staniforth, A.
  • Divergence of the Kalman filter
    Fitzgerald, R.
  • Construction of correlation functions in two and three dimensions
    Gaspari, G.; Cohn, S. E.
  • On a simple, parameter-free partition between moistening and precipitation in the Kuo scheme
    Geleyn, J.-F.
  • Balance and ensemble Kalman filter localization techniques
    Greybush, S. J.; Kalnay, E.; Miyoshi, T.; Ide, K.; Hunt, B. R.
  • Influence of surface observations in mesoscale data assimilation using an ensemble Kalman filter
    Ha, S.-Y.; Snyder, C.
  • Ensemble Kalman filter assimilation of fixed screen-height observations in a parameterized PBL
    Hacker, J. P.; Snyder, C.
  • Initial conditions for convective-scale ensemble forecasting provided by ensemble data assimilation
    Harnisch, F.; Keil, C.
  • Decomposition of the continuous ranked probability score for ensemble prediction systems
    Hersbach, H.
  • Data assimilation using an ensemble Kalman filter technique
    Houtekamer, P. L.; Mitchell, H. L.
  • Ensemble Kalman filtering
    Houtekamer, P. L.; Mitchell, H. L.
  • Atmospheric data assimilation with an ensemble Kalman filter: Results with real observations
    Houtekamer, P. L.; Mitchell, H. L.; Pellerin, G.; Buehner, M.; Charron, M.; Spacek, L.; Hansen, B.
  • Model error representation in an operational ensemble Kalman filter
    Houtekamer, P. L.; Mitchell, H. L.; Deng, X.
  • Higher resolution in an operational ensemble Kalman filter
    Houtekamer, P. L.; Deng, X.; Mitchell, H. L.; Baek, S.-J.; Gagnon, N.
  • Diabatic digital-filtering initialization: Application to the HIRLAM model
    Huang, X.-Y.; Lynch, P.
  • The impacts of representing the correlation of errors in radar data assimilation. Part II: Model output as background estimates
    Jacques, D.; Zawadzki, I.
  • The impact of data assimilation length scales on analysis and prediction of convective storms
    Lange, H.; Craig, G. C.
  • Incremental analysis updates initialization technique applied to 10-km MM5 and MM5 3DVAR
    Lee, M.-S.; Kuo, Y.-H.; Barker, D. M.; Lim, E.
  • Dynamic initialization by repeated insertion of data
    Macpherson, B.
  • Does increasing horizontal resolution produce more skillful forecasts? The results of two years of real-time numerical weather prediction over the Pacific Northwest
    Mass, C. F.; Ovens, D.; Westrick, K.; Colle, B. A.
  • A multimoment bulk microphysics parameterization. Part I: Analysis of the role of the spectral shape parameter
    Milbrandt, J. A.; Yau, M. K.
  • Ensemble size, balance, and model-error representation in an ensemble Kalman filter
    Mitchell, H. L.; Houtekamer, P. L.; Pellerin, G.
  • A multi-scale localization approach to an ensemble Kalman filter
    Miyoshi, T.; Kondo, K.
  • A simple parameterization of land surface processes for meteorological models
    Noilhan, J.; Planton, S.
  • Model bias in a continuously cycled assimilation system and its influence on convection-permitting forecasts
    Romine, G. S.; Schwartz, C. S.; Snyder, C.; Anderson, J. L.; Weisman, M. L.
  • Representing forecast error in a convection-permitting ensemble system
    Romine, G. S.; Schwartz, C. S.; Berner, J.; Fossell, K. R.; Snyder, C.; Anderson, J. L.; Weisman, M. L.
  • Characterizing and optimizing precipitation forecasts from a convection-permitting ensemble initialized by a mesoscale ensemble Kalman filter
    Schwartz, C. S.; Romine, G. S.; Smith, K. R.; Weisman, M. L.
  • NCAR’s experimental real-time convection-allowing ensemble prediction system
    Schwartz, C. S.; Romine, G. S.; Sobash, R. A.; Fossell, K. R.; Weisman, M. L.
  • Multiscale EnKF assimilation of radar and conventional observations and ensemble forecasting for a tornadic mesoscale convective system
    Snook, N.; Xue, M.; Jung, Y.
  • Assimilating surface mesonet observations with the EnKF to improve ensemble forecasts of convection initiation on 29 May 2012
    Sobash, R. A.; Stensrud, D. J.
  • On the impact of additive noise in storm-scale EnKF experiments
    Sobash, R. A.; Wicker, L. J.
  • Storm-scale data assimilation and ensemble forecasts for the 27 April 2011 severe weather outbreak in Alabama
    Yussouf, N.; Dowell, D. C.; Wicker, L. J.; Knopfmeier, K. H.; Wheatley, D. M.
  • Cloud-resolving hurricane initialization and prediction through assimilation of Doppler radar observations with an ensemble Kalman filter
    Zhang, F.; Weng, Y.; Sippel, J. A.; Meng, Z.; Bishop, C. H.