Data assimilation is the process by which measurements and model predictions are combined to obtain an accurate representation of the state of the modeled system. We implemented a data assimilation scheme called LETKF...Data assimilation is the process by which measurements and model predictions are combined to obtain an accurate representation of the state of the modeled system. We implemented a data assimilation scheme called LETKF （local ensemble transform Kalman filter） with FSUGSM （Florida State University Global Spectral Model） and made an experiment to evaluate the initial condition generated to numerical weather prediction to FSUGSM model. The LETKF analysis carries out independently at each grid point with the use of ＂local＂ observations. An ensemble of estimates in state space represents uncertainty. The FSUGSM is a multilevel （27 vertical levels） spectral primitive equation model, where the variables are expanded horizontally in a truncated series of spherical harmonic functions （at resolution T63） and a transform technique is applied to calculate the physical processes in real space The assimilation cycle runs on the period 01/01/2001 to 31/01/2001 at （00, 06, 12 and 18 GMT） for each day. We examined the atmospheric fields during the period and the OMF （observation-minus-forecast） and the OMA （observation-minus-analysis） statistics to verify the analysis quality comparing with forecasts and observations. The analyses present stability and show suitable to initiate the weather predictions.展开更多
Gradient based UCODE_2005 and data assimilation based on the Ensemble Kalman Filter(EnKF) are two different inverse methods. A synthetic two-dimensional flow case with four no-flow boundaries is used to compare the UC...Gradient based UCODE_2005 and data assimilation based on the Ensemble Kalman Filter(EnKF) are two different inverse methods. A synthetic two-dimensional flow case with four no-flow boundaries is used to compare the UCODE_2005 with the Ensemble Kalman Filter(EnKF) for their efficiency to inversely calculate and calibrate a hydraulic conductivity field based on hydraulic head data. A zonal, random heterogeneous conductivity field is calibrated by assimilating the time series of heads observed in monitoring wells. The study results indicate that the two inverse methods, UCODE_2005 and EnKF, could be used to calibrate the hydraulic conductivity field to a certain degree. More available observations and information about the conductivity field, more accurate inverse results will be obtained for the UCODE_2005. On the other hand, for a realistic zonal heterogeneous hydraulic conductivity field, EnKF can only efficiently determine the hydraulic conductivity field at the first several assimilated time steps. The results obtained by the UCODE_2005 look better than those by the EnKF. This is possibly due to the fact that the UCODE_2005 uses observed head data at every time step, while EnKF can only use observed heads at first several steps due to the filter divergence problem.展开更多
文摘Data assimilation is the process by which measurements and model predictions are combined to obtain an accurate representation of the state of the modeled system. We implemented a data assimilation scheme called LETKF （local ensemble transform Kalman filter） with FSUGSM （Florida State University Global Spectral Model） and made an experiment to evaluate the initial condition generated to numerical weather prediction to FSUGSM model. The LETKF analysis carries out independently at each grid point with the use of ＂local＂ observations. An ensemble of estimates in state space represents uncertainty. The FSUGSM is a multilevel （27 vertical levels） spectral primitive equation model, where the variables are expanded horizontally in a truncated series of spherical harmonic functions （at resolution T63） and a transform technique is applied to calculate the physical processes in real space The assimilation cycle runs on the period 01/01/2001 to 31/01/2001 at （00, 06, 12 and 18 GMT） for each day. We examined the atmospheric fields during the period and the OMF （observation-minus-forecast） and the OMA （observation-minus-analysis） statistics to verify the analysis quality comparing with forecasts and observations. The analyses present stability and show suitable to initiate the weather predictions.
基金supported by the Basic Research Funds for the Central Universities (Grant No. 2652015116)the National Natural Science Foundation of China (Grant Nos. 51209187, 41530316 & 91125024)+1 种基金the National Key Research and Development Program of China (Grant No. 2016YFC0402805)the Beijing Higher Education Young Elite Teacher Project (Grant No. YETP0653)
文摘Gradient based UCODE_2005 and data assimilation based on the Ensemble Kalman Filter(EnKF) are two different inverse methods. A synthetic two-dimensional flow case with four no-flow boundaries is used to compare the UCODE_2005 with the Ensemble Kalman Filter(EnKF) for their efficiency to inversely calculate and calibrate a hydraulic conductivity field based on hydraulic head data. A zonal, random heterogeneous conductivity field is calibrated by assimilating the time series of heads observed in monitoring wells. The study results indicate that the two inverse methods, UCODE_2005 and EnKF, could be used to calibrate the hydraulic conductivity field to a certain degree. More available observations and information about the conductivity field, more accurate inverse results will be obtained for the UCODE_2005. On the other hand, for a realistic zonal heterogeneous hydraulic conductivity field, EnKF can only efficiently determine the hydraulic conductivity field at the first several assimilated time steps. The results obtained by the UCODE_2005 look better than those by the EnKF. This is possibly due to the fact that the UCODE_2005 uses observed head data at every time step, while EnKF can only use observed heads at first several steps due to the filter divergence problem.
