Evaluation of two modified Kalman gain algorithms for radar data assimilation in the WRF model

Abstract

A B S T R A C TThis work attempts to validate two modified Kalman gain algorithms by assimilating a single radar simulation data set into the Weather Research and Forecasting model using an Ensemble Square Root Filter. Emphasis is placed on the comparison of assimilation performance between the two modified algorithms against the classical Kalman gain algorithm when the measurement operator is non-linear. Three ideal storm-scale experiments, which are configured identically except for the different Kalman gain algorithms, are designed in parallel for this purpose. The results show that the first modified algorithm can result in a better simulation of a storm, as measured by the root mean square error (RMSE). The second algorithm can also, to some extent, reduce the RMSE of the simulation of some state vectors, but with little improvement of the estimation of storm intensity. Overall, our preliminary experiments indicate that the two modified Kalman gain algorithms can benefit the assimilation of complex numerical models when the measurement operators are non-linear, confirming the earlier theoretical analysis and the results of simple models. Further work is needed to evaluate the impact of the modified Kalman gain algorithms on the assimilation performance of ensemble-based methods.

A local data assimilation method (Local DA v1.0) and its application in a simulated typhoon case

“…( 10) reduces the analysis error of reflectivity by approximately 2 dBZ in our early test (not shown). This result is consistent with that of Yang et al (2015).…”

“…Tang et al (2014) demonstrated that this alternative could lead to better results. Furthermore, Yang et al (2015) examined the application of this alternative in radar DA and showed that the alternative approach produced lower analysis errors for the model variables associated with radial velocity (three wind components) and reflectivity (mixing ratios of rain, snow, and graupel). Given that remote sensing observations such as those obtained by radars and satellites are important parts of a multiscale observation network, Local DA adopts the alternative approach proposed by Tang et al (2014).…”