WG3 – Recent articles (December 2013)
· Alvarez-Garreton, C., Ryu, D., Western, A., Crow, W., Robertson, D., 2013. Impact of observation error structure on satellite soil moisture assimilation into a rainfall-runoff model.
· Davidson, N.E. et al., 2013. ACCESS-TC: Vortex Specification, 4D-VAR Initialization, Verification and Structure Diagnostics. Monthly Weather Review(2013).
· Dharssi, I., Candy, B., Bovis, K., Steinle, P., Macpherson, B., 2013. Analysis of the linearised observation operator in a land surface data assimilation scheme for numerical weather prediction, 20th International Congress on Modelling and Simulation (MODSIM2013), Adelaide, Australia. The Modelling and Simulation Society of Australia, pp. http://www.mssanz.org.au/.
· Dumedah, G., Walker, J.P., 2013. Evaluation of model parameter convergence when using data assimilation for soil moisture estimation. Journal of Hydrometeorology(2013).
· Dumedah, G., Walker, J.P., 2013. Joint model state-parameter retrieval through the evolutionary data assimilation approach, 20th MODSIM Congress, Adelaide, Australia, 2013.
· Haverd, V. et al., 2013. The Australian terrestrial carbon budget. Biogeosciences, 10(2): 851-869.
· Haverd, V. et al., 2013. Multiple Observation Types Jointly Constrain Australian Terrestrial Carbon and Water Cycles, EGU General Assembly Conference Abstracts, pp. 13448.
· Kala, J. et al., 2013. Influence of leaf area index prescriptions on simulations of heat, moisture, and carbon fluxes. Journal of Hydrometeorology(2013).
· Lerat, J., Dutta, D., Kim, S., Hughes, J., Vaze, J., 2013. Reducing propagation of uncertainty in river system modelling by optimal use of streamflow data, Proceedings of the 20th International Congress on Modelling and Simulation, Adelaide, Australia.
· Li, Y., Ryu, D., Western, A.W., Wang, Q., 2013. Assimilation of stream discharge for flood forecasting: The benefits of accounting for routing time lags. Water Resources Research.
· Pauwels, V.R., Lannoy, G.D., Hendricks Franssen, H.-J., Vereecken, H., 2013. Simultaneous estimation of model state variables and observation and forecast biases using a two-stage hybrid Kalman filter. Hydrology and Earth System Sciences Discussions, 10(4): 5169-5224.
· Pipunic, R.C., Walker, J.P., Western, A.W., Trudinger, C.M., 2013. Assimilation of multiple data types for improved heat flux prediction: A one-dimensional field study. Remote Sensing of Environment, 136(0): 315-329.
· Renzullo, L. et al., 2013. Improving soil water representation in the Australian Water Resource Assessment landscape model through the assimilation of remotely-sensed soil moisture products, 20th International Conference on Modelling and Simulation, Adelaide.
· Van Dijk, A.I.J.M., Renzullo, L.J., Wada, Y., Tregoning, P., 2013. A global water cycle reanalysis (2003-2012) reconciling satellite gravimetry and altimetry observations with a hydrological model ensemble. Hydrol. Earth Syst. Sci. Discuss., 10(12): 15475-15523
· Vleeshouwer, J. et al., 2013. Using scientific workflows to calibrate an Australian land surface model (AWRA-L), 20th MODSIM Congress, Adelaide, Australia, pp. 1-6.
