Adjoint retrieval of prognostic land surface model variables for an NWP model: Assimilation of ground surface temperature
Based on a 2-layer land surface model, a rather general variational data assimilation framework for estimatingmodel state variables is developed. The method minimizes the error of surface soil temperature predictionssubject to constraints imposed by the prediction model. Retrieval experiments for so...
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| Format: | Journal Article |
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Versita, co-published with Springer Verlag
2010
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| Online Access: | http://hdl.handle.net/20.500.11937/18335 |
| _version_ | 1848749715518128128 |
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| author | Ren, Diandong |
| author_facet | Ren, Diandong |
| author_sort | Ren, Diandong |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Based on a 2-layer land surface model, a rather general variational data assimilation framework for estimatingmodel state variables is developed. The method minimizes the error of surface soil temperature predictionssubject to constraints imposed by the prediction model. Retrieval experiments for soil prognostic variables areperformed and the results verified against model simulated data as well as real observations for the OklahomaAtmospheric Surface layer Instrumentation System (OASIS). The optimization scheme is robust with respect toa wide range of initial guess errors in surface soil temperature (as large as 30 K) and deep soil moisture (withinthe range between wilting point and saturation). When assimilating OASIS data, the scheme can reduce theinitial guess error by more than 90%, while for Observing Simulation System Experiments (OSSEs), the initialguess error is usually reduced by over four orders of magnitude.Using synthetic data, the robustness of the retrieval scheme as related to information content of the data andthe physical meaning of the adjoint variables and their use in sensitivity studies are investigated. Throughsensitivity analysis, it is confirmed that the vegetation coverage and growth condition determine whether ornot the optimally estimated initial soil moisture condition leads to an optimal estimation of the surface fluxes.This reconciles two recent studies.With the real data experiments, it is shown that observations during the daytime period are the most effectivefor the retrieval. Longer assimilation windows result in more accurate initial condition retrieval, underlining theimportance of information quantity, especially for schemes assimilating noisy observations. |
| first_indexed | 2025-11-14T07:25:21Z |
| format | Journal Article |
| id | curtin-20.500.11937-18335 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:25:21Z |
| publishDate | 2010 |
| publisher | Versita, co-published with Springer Verlag |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-183352017-09-13T13:43:51Z Adjoint retrieval of prognostic land surface model variables for an NWP model: Assimilation of ground surface temperature Ren, Diandong variational data assimilation adjoint - technique based 4D-Var land surface modelling numerical weather prediction (NWP) model Based on a 2-layer land surface model, a rather general variational data assimilation framework for estimatingmodel state variables is developed. The method minimizes the error of surface soil temperature predictionssubject to constraints imposed by the prediction model. Retrieval experiments for soil prognostic variables areperformed and the results verified against model simulated data as well as real observations for the OklahomaAtmospheric Surface layer Instrumentation System (OASIS). The optimization scheme is robust with respect toa wide range of initial guess errors in surface soil temperature (as large as 30 K) and deep soil moisture (withinthe range between wilting point and saturation). When assimilating OASIS data, the scheme can reduce theinitial guess error by more than 90%, while for Observing Simulation System Experiments (OSSEs), the initialguess error is usually reduced by over four orders of magnitude.Using synthetic data, the robustness of the retrieval scheme as related to information content of the data andthe physical meaning of the adjoint variables and their use in sensitivity studies are investigated. Throughsensitivity analysis, it is confirmed that the vegetation coverage and growth condition determine whether ornot the optimally estimated initial soil moisture condition leads to an optimal estimation of the surface fluxes.This reconciles two recent studies.With the real data experiments, it is shown that observations during the daytime period are the most effectivefor the retrieval. Longer assimilation windows result in more accurate initial condition retrieval, underlining theimportance of information quantity, especially for schemes assimilating noisy observations. 2010 Journal Article http://hdl.handle.net/20.500.11937/18335 10.2478/v10085-009-0043-2 Versita, co-published with Springer Verlag unknown |
| spellingShingle | variational data assimilation adjoint - technique based 4D-Var land surface modelling numerical weather prediction (NWP) model Ren, Diandong Adjoint retrieval of prognostic land surface model variables for an NWP model: Assimilation of ground surface temperature |
| title | Adjoint retrieval of prognostic land surface model variables for an NWP model: Assimilation of ground surface temperature |
| title_full | Adjoint retrieval of prognostic land surface model variables for an NWP model: Assimilation of ground surface temperature |
| title_fullStr | Adjoint retrieval of prognostic land surface model variables for an NWP model: Assimilation of ground surface temperature |
| title_full_unstemmed | Adjoint retrieval of prognostic land surface model variables for an NWP model: Assimilation of ground surface temperature |
| title_short | Adjoint retrieval of prognostic land surface model variables for an NWP model: Assimilation of ground surface temperature |
| title_sort | adjoint retrieval of prognostic land surface model variables for an nwp model: assimilation of ground surface temperature |
| topic | variational data assimilation adjoint - technique based 4D-Var land surface modelling numerical weather prediction (NWP) model |
| url | http://hdl.handle.net/20.500.11937/18335 |