Articles | Volume 25, issue 6
https://doi.org/10.5194/hess-25-3319-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-25-3319-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Technical note
| 
16 Jun 2021
Technical note |
| 16 Jun 2021
| 16 Jun 2021
Technical Note: Sequential ensemble data assimilation in convergent and divergent systems
Biosphere 2, University of Arizona, Tucson, AZ, USA
Institute of Environmental Physics (IUP), Heidelberg University, Heidelberg, Germany
Daniel Berg
Institute of Environmental Physics (IUP), Heidelberg University, Heidelberg, Germany
Heidelberg Graduate School, HGS MathComp, Heidelberg University, Heidelberg, Germany
Kurt Roth
Institute of Environmental Physics (IUP), Heidelberg University, Heidelberg, Germany
Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Heidelberg, Germany
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Cited articles
Aksoy, A., Zhang, F., and Nielsen-Gammon, J. W.: Ensemble-based simultaneous
state and parameter estimation in a two-dimensional sea-breeze model, Mon.
Weather Rev., 134, 2951–2970, https://doi.org/10.1175/MWR3224.1, 2006. a
Anderson, J. L. and Anderson, S. L.: A Monte Carlo implementation of the
nonlinear filtering problem to produce ensemble assimilations and forecasts,
Mon. Weather Rev., 127, 2741–2758, https://doi.org/10.1175/1520-0493(1999)127<2741:AMCIOT>2.0.CO;2, 1999. a
Bauser, H. H., Jaumann, S., Berg, D., and Roth, K.: EnKF with closed-eye
period – towards a consistent aggregation of information in soil hydrology,
Hydrol. Earth Syst. Sci., 20, 4999–5014, https://doi.org/10.5194/hess-20-4999-2016, 2016. a
Bauser, H. H., Berg, D., Klein, O., and Roth, K.: Inflation method for ensemble Kalman filter in soil hydrology, Hydrol. Earth Syst. Sci., 22,
4921–4934, https://doi.org/10.5194/hess-22-4921-2018, 2018. a, b
Berg, D.: Particle filters for nonlinear data assimilation, PhD thesis, Ruperto-Carola University Heidelberg, Heidelberg, Germany, 2018. a
Berg, D., Bauser, H. H., and Roth, K.: Covariance resampling for particle
filter – state and parameter estimation for soil hydrology, Hydrol. Earth Syst. Sci., 23, 1163–1178, https://doi.org/10.5194/hess-23-1163-2019, 2019. a, b
Bocquet, M., Elbern, H., Eskes, H., Hirtl, M., Žabkar, R., Carmichael, G. R., Flemming, J., Inness, A., Pagowski, M., Pérez Camaño, J. L.,
Saide, P. E., San Jose, R., Sofiev, M., Vira, J., Baklanov, A., Carnevale,
C., Grell, G., and Seigneur, C.: Data assimilation in atmospheric chemistry
models: current status and future prospects for coupled chemistry meteorology
models, Atmos. Chem. Phys., 15, 5325–5358, https://doi.org/10.5194/acp-15-5325-2015, 2015. a, b
Burgers, G., van Leeuwen, P. J., and Evensen, G.: Analysis scheme in the
ensemble Kalman filter, Mon. Weather Rev., 126, 1719–1724,
https://doi.org/10.1175/1520-0493(1998)126<1719:ASITEK>2.0.CO;2, 1998. a, b, c
Carmichael, G. R., Sandu, A., Chai, T., Daescu, D. N., Constantinescu, E. M.,
and Tang, Y.: Predicting air quality: Improvements through advanced methods
to integrate models and measurements, J. Comput. Phys., 227, 3540–3571, https://doi.org/10.1016/j.jcp.2007.02.024, 2008. a
Carrassi, A., Bocquet, M., Bertino, L., and Evensen, G.: Data assimilation in
the geosciences: An overview of methods, issues, and perspectives, Wiley
Interdisciplin. Rev.: Clim. Change, 9, e535, https://doi.org/10.1002/wcc.535, 2018. a
Carsel, R. F. and Parrish, R. S.: Developing joint probability distributions of soil water retention characteristics, Water Resour. Res., 24, 755–769,
https://doi.org/10.1029/WR024i005p00755, 1988. a
Constantinescu, E. M., Sandu, A., Chai, T., and Carmichael, G. R.:
Ensemble-based chemical data assimilation. I: General approach, Q. J. Roy. Meteorol. Soc., 133, 1229–1243, https://doi.org/10.1002/qj.76, 2007. a, b, c
Edwards, C. A., Moore, A. M., Hoteit, I., and Cornuelle, B. D.: Regional ocean data assimilation, Annu. Rev. Mar. Sci., 7, 21–42,
https://doi.org/10.1146/annurev-marine-010814-015821, 2015. a
Evensen, G.: Sequential data assimilation with a nonlinear quasi-geostrophic
model using Monte Carlo methods to forecast error statistics, J. Geophys. Res.-Oceans, 99, 10143–10162, https://doi.org/10.1029/94JC00572, 1994. a, b, c
Gaspari, G. and Cohn, S. E.: Construction of correlation functions in two and
three dimensions, Q. J. Roy. Meteorol. Soc., 125, 723–757, https://doi.org/10.1002/qj.49712555417, 1999. a
Gharamti, M. E.: Enhanced adaptive inflation algorithm for ensemble filters,
Mon. Weather Rev., 146, 623–640, https://doi.org/10.1175/MWR-D-17-0187.1, 2018. a
Han, X., Hendricks Franssen, H.-J., Montzka, C., and Vereecken, H.: Soil
moisture and soil properties estimation in the Community Land Model with
synthetic brightness temperature observations, Water Resour. Res., 50,
6081–6105, https://doi.org/10.1002/2013WR014586, 2014. a
Hendricks Franssen, H. J. and Kinzelbach, W.: Real‐time groundwater flow
modeling with the ensemble Kalman filter: Joint estimation of states and
parameters and the filter inbreeding problem, Water Resour. Res., 44,
W09408, https://doi.org/10.1029/2007WR006505, 2008. a
Houtekamer, P. L. 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
Ippisch, O., Vogel, H.-J., and Bastian, P.: Validity limits for the van Genuchten–Mualem model and implications for parameter estimation and
numerical simulation, Adv. Water Resour., 29, 1780–1789,
https://doi.org/10.1016/j.advwatres.2005.12.011, 2006. a, b
Jaumann, S. and Roth, K.: Effect of unrepresented model errors on estimated
soil hydraulic material properties, Hydrol. Earth Syst. Sci., 21, 4301–4322, https://doi.org/10.5194/hess-21-4301-2017, 2017. a
Kalman, R. E.: A new approach to linear filtering and prediction problems, J. Basic Eng., 82, 35–45, https://doi.org/10.1115/1.3662552, 1960. a
Li, C. and Ren, L.: Estimation of unsaturated soil hydraulic parameters using
the ensemble Kalman filter, Vadose Zone J., 10, 1205,
https://doi.org/10.2136/vzj2010.0159, 2011. a
Li, H., Kalnay, E., and Miyoshi, T.: Simultaneous estimation of covariance
inflation and observation errors within an ensemble Kalman filter, Q. J. Roy. Meteorol. Soc., 135, 523–533, https://doi.org/10.1002/qj.371, 2009. a
Liu, Y. and Gupta, H. V.: Uncertainty in hydrologic modeling: Toward an
integrated data assimilation framework, Water Resour. Res., 43, W07401, https://doi.org/10.1029/2006WR005756, 2007. a
Liu, Y., Weerts, A. H., Clark, M., Hendricks Franssen, H.-J., Kumar, S.,
Moradkhani, H., Seo, D.-J., Schwanenberg, D., Smith, P., van Dijk, A. I. J. M., van Velzen, N., He, M., Lee, H., Noh, S. J., Rakovec, O., and Restrepo, P.: Advancing data assimilation in operational hydrologic forecasting: progresses, challenges, and emerging opportunities, Hydrol. Earth Syst. Sci., 16, 3863–3887, https://doi.org/10.5194/hess-16-3863-2012, 2012. a, b
Lorenz, E. N.: Atmospheric predictability experiments with a large numerical
model, Tellus, 34, 505–513, https://doi.org/10.1111/j.2153-3490.1982.tb01839.x, 1982. a
Lorenz, E. N.: Designing chaotic models, J. Atmos. Sci., 62, 1574–1587, https://doi.org/10.1175/JAS3430.1, 2005. a
Lorenz, E. N. and Emanuel, K. A.: Optimal sites for supplementary weather
observations: Simulation with a small model, J. Atmos. Sci., 55, 399–414,
https://doi.org/10.1175/1520-0469(1998)055<0399:OSFSWO>2.0.CO;2, 1998. a, b, c
Luo, Y., Ogle, K., Tucker, C., Fei, S., Gao, C., LaDeau, S., Clark, J. S., and Schimel, D. S.: Ecological forecasting and data assimilation in a data-rich era, Ecol. Appl., 21, 1429–1442, https://doi.org/10.1890/09-1275.1, 2011. a
Mualem, Y.: A new model for predicting the hydraulic conductivity of unsaturated porous media, Water Resour. Res., 12, 513–522,
https://doi.org/10.1029/WR012i003p00513, 1976. a
Nakano, S., Ueno, G., and Higuchi, T.: Merging particle filter for sequential
data assimilation, Nonlin. Processes Geophys., 14, 395–408,
https://doi.org/10.5194/npg-14-395-2007, 2007.
a
Niu, S., Luo, Y., Dietze, M. C., Keenan, T. F., Shi, Z., Li, J., and Chapin III, F. S.: The role of data assimilation in predictive ecology, Ecosphere, 5, 65, https://doi.org/10.1890/ES13-00273.1, 2014. a
Poterjoy, J.: A localized particle filter for high-dimensional nonlinear
systems, Mon. Weather Rev., 144, 59–76, https://doi.org/10.1175/MWR-D-15-0163.1, 2016. a
Rasmussen, J., Madsen, H., Jensen, K. H., and Refsgaard, J. C.: Data assimilation in integrated hydrological modeling using ensemble Kalman filtering: evaluating the effect of ensemble size and localization on filter
performance, Hydrol. Earth Syst. Sci., 19, 2999–3013,
https://doi.org/10.5194/hess-19-2999-2015, 2015. a
Reichle, R. H.: Data assimilation methods in the Earth sciences, Adv. Water Resour., 31, 1411–1418, https://doi.org/10.1016/j.advwatres.2008.01.001, 2008. a
Ruiz, J. J., Pulido, M., and Miyoshi, T.: Estimating model parameters with
ensemble-based data assimilation: A review, J. Meteorol. Soc. Jpn. Ser. II, 91, 79–99, https://doi.org/10.2151/jmsj.2013-201, 2013. a
Shi, Y., Davis, K. J., Zhang, F., Duffy, C. J., and Yu, X.: Parameter
estimation of a physically based land surface hydrologic model using the
ensemble Kalman filter: A synthetic experiment, Water Resour. Res., 50, 706–724, https://doi.org/10.1002/2013WR014070, 2014. a
Stammer, D., Balmaseda, M., Heimbach, P., Köhl, A., and Weaver, A.: Ocean data assimilation in support of climate applications: Status and perspectives, Annu. Rev. Mar. Sci., 8, 491–518, https://doi.org/10.1146/annurev-marine-122414-034113, 2016. a
Van Genuchten, M. T.: A closed-form equation for predicting the hydraulic
conductivity of unsaturated soils, Soil Sci. Soc. Am. J., 44, 892–898, https://doi.org/10.2136/sssaj1980.03615995004400050002x, 1980. a
van Leeuwen, P. J.: Nonlinear data assimilation in geosciences: an extremely
efficient particle filter, Q. J. Roy. Meteorol. Soc., 136, 1991–1999, https://doi.org/10.1002/qj.699, 2010. a
van Leeuwen, P. J., Cheng, Y., and Reich, S.: Nonlinear data assimilation,
in: vol. 2, Springer, Switzerland, https://doi.org/10.1007/978-3-319-18347-3, 2015. a, b
Zhang, H., Hendricks Franssen, H.-J., Han, X., Vrugt, J. A., and Vereecken, H.: State and parameter estimation of two land surface models using the ensemble Kalman filter and the particle filter, Hydrol. Earth Syst. Sci., 21, 4927–4958, https://doi.org/10.5194/hess-21-4927-2017, 2017. a, b
Zhang, Y., Bocquet, M., Mallet, V., Seigneur, C., and Baklanov, A.: Real-time
air quality forecasting, part II: State of the science, current research needs, and future prospects, Atmos. Environ., 60, 656–676,
https://doi.org/10.1016/j.atmosenv.2012.02.041, 2012. a, b
Short summary
Data assimilation methods are used throughout the geosciences to combine information from uncertain models and uncertain measurement data. In this study, we distinguish between the characteristics of geophysical systems, i.e., divergent systems (initially nearby states will drift apart) and convergent systems (initially nearby states will coalesce), and demonstrate the implications for sequential ensemble data assimilation methods, which require a sufficient divergent component.
Data assimilation methods are used throughout the geosciences to combine information from...
