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Data and code underlying the research of: Improving the forecast performance of hydrological models using the cubature Kalman filter and unscented Kalman filter
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| Created: | Aug 27, 2022 at 2:22 p.m. | |
| Last updated: | Sep 06, 2022 at 6 a.m.
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| Sharing Status: | Public |
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Abstract
research dataset underlying peer reviewed manuscript containing:
(1) State update results : 1000 Monte Carlo runs for the CKF, UKF, and open-loop run covered a year from 1999 to 2000 with an hourly time step;
(2) Forecst results: drological streamflow forecasts (using perfect forcing) for six scenarios covering a period of 1 years (1999 to 2000, forecast for 120 hours with hourly time step) to determine the benefits of streamflow assimilation of the CKF and UKF using the fixed time step version of WALRUS hydrological model for a Dutch lowland area (Regge catchment).
research code underlying peer reviewed manuscript containing:
(1) Codes and scripts in "test_ukf_robustness_alpha" are used to reproduce the "State update results". The output files "ckf_upd_1999010101_2000010101_all.mat" and "ukf_upd_1999010101_2000010101_all" should be copied to the folder "forecast" since the updated states are used as initial conditions for forecasting.
(2) Codes and scripts in "forecast" are used to reproduce the "Forecst results". The sql server database files "paper_ukf_ckf.mdf" and "paper_ukf_ckf_log.ldf" must be first attached. It is recommeded that database is connected via "obdc" using the "sql server verification" with the following information: database = "paper_ukf_ckf", username = "sa"; password = "247156". Except for the table "tmp_tm" (must be kept), all the tables can be deleted before running the codes and scripts.
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This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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