Abstract
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.
Keywords
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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
This technique has been used to perturb an initial best-guess unperturbed state of sea surface temperature (SST) to provide an ensemble of ocean-surface lower boundary conditions for atmospheric ensemble forecast (Hong et al. 2011).
References
AOSN: Autonomous ocean sampling network (2003) http://www.mbari.org/aosn/
Bishop CH, Toth Z (1999) Ensemble transformation and adaptive observations. J Atmos Sci 56:1748–1765
Bishop CH, Etherton BJ, Majumdar SJ (2001) Adaptive sampling with the ensemble transform Kalman filter part I: Theoretical aspects. Mon Wea Rev 129:420–435
Bishop CH, Etherton BJ, Majumdar SJ (2006) Verification region selection in adaptive sampling. Quart J Roy Met Soc 132:915–933
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 137:288–298
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 129:1269–1288
Cummings JA (2005) Operational multivariate ocean assimilation. Quart J Roy Meteorol Soc 131:3583–3604
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.dsr2.2008.08.009
Hoffman RN, Liu Z, Louis J-F, Grassotti C (1995) Distortion representation of forecast errors. Mon Wea Rev 123:2758–2770
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/s10236-011-0449-9
Hong X, Hodur RM, Martin PJ (2007) Numerical simulation of deep-water convection in the Gulf of Lion. Pure Appl Geophys 164:2101–2116
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
Hong X, Martin PJ, Wang S, Rowley C (2009b) High SST variability south of Martha’s Vineyard: observation and modeling study. J. Mar Syst 78:59–76
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 26:371–387
Kondo J (1975) Air-sea bulk transfer coefficients in diabatic conditions. Boundary-Layer Met 9:91–112
Leonard N, Robinson A (2003) Adaptive sampling and forecasting plan. http://www.princeton.edu/~dcsl/aosn/ http://www.princeton.edu/$\sim$ dcsl/aosn/. Accessed 25 May 2012
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 130:1356–1369
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 138:2803–2824
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 137:503–520
Martin PJ (2000) Description of the navy coastal ocean model version 1.0. Naval Research Laboratory, NRL/FR/7322—00-9962, pp 1–42
Martin PJ, Hodur RM (2003) Mean COAMPS air-sea fluxes over the mediterranean during 1999 report. Naval Research Laboratory, Stennis Space Center, Mississippi
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 134:2011–2027
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 128:3520–3537
Toth Z, Kalnay E (1993) Ensemble forecasting at NMC: the generation of perturbations. Bull Am Meteor Soc 74:2317–2330
Toth Z, Kalnay E (1997) Ensemble forecasting at NCEP and the breeding method. Mon Wea Rev 125:3297–3319
Acknowledgements
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Hong, X., Bishop, C. (2013). Ocean Ensemble Forecasting and Adaptive Sampling. In: Park, S., Xu, L. (eds) Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications (Vol. II). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35088-7_16
Download citation
DOI: https://doi.org/10.1007/978-3-642-35088-7_16
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35087-0
Online ISBN: 978-3-642-35088-7
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)