Data assimilation of incoherent scatter radar observation into a one-dimensional midlatitude ionospheric model by applying ensemble Kalman filter
نویسندگان
چکیده
[1] In this paper, electron densities during 25–28 September 2000 observed by the Millstone Hill incoherent scatter radar (ISR) are assimilated into a one-dimensional midlatitude ionospheric theoretical model by using an ensemble Kalman filter (EnKF) technique. It is found that (1) the derived vertical correlation coefficients of electron density show obvious altitude dependence. These variations are consistent with those from ISR observations. (2) The EnKF technique has a better performance than the 3DVAR technique especially in the data-gap regions, which indicates that the EnKF technique can extend the influences of observations from data-rich regions to data-gap regions more effectively. (3) Both the altitude and local time variations of the root mean square error (RMSE) of electron densities for the ensemble spread and ensemble mean from observation behave similarly. It is shown that the spread of the ensemble members can represent the deviations of ensemble mean from observations. (4) To achieve a better prediction performance, the external driving forces should also be adjusted simultaneously to the real weather conditions. For example, the performance of prediction can be improved by adjusting neutral meridional wind using equivalent wind method. (5) In the EnKF, there are often erroneous correlations over large distance because of the sampling error. This problem may be avoided by using a relative larger ensemble size.
منابع مشابه
Assimilation of Simulated Polarimetric Radar Data Using Ensemble Kalman Filter: Observation Operators, Error Modeling and Data Impact
متن کامل
Error modeling of simulated reflectivity observations for ensemble Kalman filter assimilation of convective storms
[1] The impact of two different ways of modeling errors in simulated radar reflectivity data for observing system simulation experiments (OSSEs) with an ensemble Kalman filter is investigated. An error model different from the one used in earlier studies is introduced, and it specifies relative Gaussian-distributed errors in the linear domain of the equivalent radar reflectivity factor. This mo...
متن کاملEnhanced Predictions of Tides and Surges through Data Assimilation (TECHNICAL NOTE)
The regional waters in Singapore Strait are characterized by complex hydrodynamic phenomena as a result of the combined effect of three large water bodies viz. the South China Sea, the Andaman Sea, and the Java Sea. This leads to anomalies in water levels and generates residual currents. Numerical hydrodynamic models are generally used for predicting water levels in the ocean and seas. But thei...
متن کامل4d Ensemble Kalman Filtering for Assimilation of Asynchronous Observations
A 4-dimensional ensemble Kalman filter method (4DEnKF), which adapts ensemble Kalman filtering to the assimilation of observations that are asynchronous with the analysis cycle, is discussed. In the ideal case of linear dynamics between consecutive analyses, the algorithm is equivalent to Kalman filtering assimilation at each observation time. Tests of 4DEnKF on the Lorenz 40 variable model are...
متن کاملSoil Moisture Data Assimilation in a Hydrological Model: A Case Study in Belgium Using Large-Scale Satellite Data
In the present study, we focus on the assimilation of satellite observations for Surface Soil Moisture (SSM) in a hydrological model. The satellite data are produced in the framework of the EUMETSAT project H-SAF and are based on measurements with the Advanced radar Scatterometer (ASCAT), embarked on the Meteorological Operational satellites (MetOp). The product generated with these measurement...
متن کامل