Ocean Ensemble Forecasting and Adaptive Sampling

  • Xiaodong HongEmail author
  • Craig Bishop


An ocean adaptive sampling algorithm, derived from the Ensemble Transform Kalman Filter (ETKF) technique, is illustrated in this Chapter using the glider observations collected during the Autonomous Ocean Sampling Network (AOSN) II field campaign. This algorithm can rapidly obtain the prediction error covariance matrix associated with a particular deployment of the observation and quickly assess the ability of a large number of future feasible sequences of observations to reduce the forecast error variance. The uncertainty in atmospheric forcing is represented by using a time-shift technique to generate a forcing ensemble from a single deterministic atmospheric forecast. The uncertainty in the ocean initial condition is provided by using the Ensemble Transform (ET) technique, which ensures that the ocean ensemble is consistent with estimates of the analysis error variance. The ocean ensemble forecast is set up for a 72 h forecast with a 24 h update cycle for the ocean data assimilation. Results from the atmospheric forcing perturbation and ET ocean ensemble mean are examined and discussed. Measurements of the ability of the ETKF to predict 24–48 h ocean forecast error variance reductions over the Monterey Bay due to the additional glider observations are displayed and discussed using the signal variance, signal variance summary map, and signal variance summary bar charts, respectively.


Forecast Error Ensemble Forecast Adaptive Sampling Verification Time Forecast Error Variance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The support of the sponsors, the Office of Naval Research, Ocean Modeling and Prediction Program, through program element N0001405WX20669 is gratefully acknowledged. Computations were performed on zornig, which is a SGI ORIGIN 3800 with IRIX 6.5 OS and 512 R12000 400 MHz PEs and is located at the U.S. Army Research Laboratory (ARL) DoD Supercomputing Resource Center (DSRC), Aberdeen Proving ground, MD.


  1. AOSN: Autonomous ocean sampling network (2003)
  2. Bishop CH, Toth Z (1999) Ensemble transformation and adaptive observations. J Atmos Sci 56:1748–1765CrossRefGoogle Scholar
  3. Bishop CH, Etherton BJ, Majumdar SJ (2001) Adaptive sampling with the ensemble transform Kalman filter part I: Theoretical aspects. Mon Wea Rev 129:420–435CrossRefGoogle Scholar
  4. Bishop CH, Etherton BJ, Majumdar SJ (2006) Verification region selection in adaptive sampling. Quart J Roy Met Soc 132:915–933CrossRefGoogle Scholar
  5. Bishop CH, Holt T, Nachamkin J, Chen S, McLay JG, Doyle JD, Thompson WT (2009) Regional ensemble forecasts using the ensemble transform technique. Mon Wea Rev 137:288–298CrossRefGoogle Scholar
  6. Buizza R, Richardson DS, Palmer TN (2003) Benefits of increased resolution in the ECMWF ensemble system and comparison with poor-man’s ensembles. Quart J Roy Meteor Soc 129:1269–1288CrossRefGoogle Scholar
  7. Cummings JA (2005) Operational multivariate ocean assimilation. Quart J Roy Meteorol Soc 131:3583–3604CrossRefGoogle Scholar
  8. Doyle JD, Jiang Q, Chao Y, Farrara J (2008) High resolution atmospheric modeling over the Monterey Bay during AOSN II. Deep Sea Res. doi:10.1016/j.dsr2.2008.08.009Google Scholar
  9. Hoffman RN, Liu Z, Louis J-F, Grassotti C (1995) Distortion representation of forecast errors. Mon Wea Rev 123:2758–2770CrossRefGoogle Scholar
  10. Holt RT, Cummings JA, Bishop CH, Doyle JD, Hong X, Chen S, Jin Y (2011) Development and testing of a coupled ocean-atmosphere mesoscale ensemble prediction system. Ocean Dyn 61:1937–1954. doi:10.1007/s10236-011-0449-9CrossRefGoogle Scholar
  11. Hong X, Hodur RM, Martin PJ (2007) Numerical simulation of deep-water convection in the Gulf of Lion. Pure Appl Geophys 164:2101–2116CrossRefGoogle Scholar
  12. Hong X, Cummings JA, Martin PJ, Doyle JD (2009a) Ocean data assimilation: a coastal application. In: Parks S, Xu L (eds) Data assimilation for atmospheric, oceanic and hydrologic applications. Springer, Berlin/Heidelberg, pp 269–292. doi: 10.1007/978-3-540-71056-1_14 CrossRefGoogle Scholar
  13. Hong X, Martin PJ, Wang S, Rowley C (2009b) High SST variability south of Martha’s Vineyard: observation and modeling study. J. Mar Syst 78:59–76CrossRefGoogle Scholar
  14. Hong X, Bishop CH, Holt T, O’Neill L (2011) Impacts of sea surface temperature uncertainty on the western north Pacific subtropical high (WNPSH) and rainfall. Weather Forecast 26:371–387CrossRefGoogle Scholar
  15. Kondo J (1975) Air-sea bulk transfer coefficients in diabatic conditions. Boundary-Layer Met 9:91–112CrossRefGoogle Scholar
  16. Leonard N, Robinson A (2003) Adaptive sampling and forecasting plan.$\sim$ dcsl/aosn/. Accessed 25 May 2012
  17. Majumdar SJ, Bishop CH, Etherton BJ, Toth Z (2002) Adaptive sampling with the ensemble transform Kalman filter part II: Field program implementation. Mon Wea Rev 130:1356–1369CrossRefGoogle Scholar
  18. Majumdar SJ, Sellwood KJ, Hodyss D, Toth Z, Song Y (2010) Characteristics of target areas selected by the ensemble transform Kalman filter for medium-range forecasts of high-impact winter weather. Mon Wea Rev 138:2803–2824CrossRefGoogle Scholar
  19. Majumdar SJ, Chen S-G, Wu C-C (2011) Characteristics of ensemble transform Kalman filter adaptive sampling guidance for tropical cyclones. Quart J Roy Meteorol Soc 137:503–520CrossRefGoogle Scholar
  20. Martin PJ (2000) Description of the navy coastal ocean model version 1.0. Naval Research Laboratory, NRL/FR/7322—00-9962, pp 1–42Google Scholar
  21. Martin PJ, Hodur RM (2003) Mean COAMPS air-sea fluxes over the mediterranean during 1999 report. Naval Research Laboratory, Stennis Space Center, MississippiGoogle Scholar
  22. Sellwood KJ, Majumdar SJ, Mapes BE, Szunyogh I (2008) Predicting the influence of observations on mediumrange forecasts of atmospheric flow. Quart J Roy Meteor Soc 134:2011–2027CrossRefGoogle Scholar
  23. Szunyogh I, Toth Z, Morss RE, Majumdar S, Etherton BJ, Bishop CH (2000) The effect of targeted dropsonde observations during the 1999 winter storm reconnaissance program. Mon Wea Rev 128:3520–3537CrossRefGoogle Scholar
  24. Toth Z, Kalnay E (1993) Ensemble forecasting at NMC: the generation of perturbations. Bull Am Meteor Soc 74:2317–2330CrossRefGoogle Scholar
  25. Toth Z, Kalnay E (1997) Ensemble forecasting at NCEP and the breeding method. Mon Wea Rev 125:3297–3319CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Marine Meteorology DivisionNaval Research LaboratoryMontereyUSA

Personalised recommendations