4DVAR assimilation of GNSS zenith path delays and precipitable water into a numerical weather prediction model WRF
The GNSS data assimilation is currently widely discussed in the literature with respect to the various applications for meteorology and numerical weather models. Data assimilation combines atmospheric measurements with knowledge of atmospheric behavior as codified in computer models. With this approach, the “best” estimate of current conditions consistent with both information sources is produced. Some approaches also allow assimilating the non-prognostic variables, including remote sensing data from radar or GNSS (global navigation satellite system). These techniques are named variational data assimilation schemes and are based on a minimization of the cost function, which contains the differences between the model state (background) and the observations. The variational assimilation is the first choice for data assimilation in the weather forecast centers, however, current research is consequently looking into use of an iterative, filtering approach such as an extended Kalman filter (EKF). This paper shows the results of assimilation of the GNSS data into numerical weather prediction (NWP) model WRF (Weather Research and Forecasting). The WRF model offers two different variational approaches: 3DVAR and 4DVAR, both available through the WRF data assimilation (WRFDA) package. The WRFDA assimilation procedure was modified to correct for bias and observation errors. We assimilated the zenith total delay (ZTD), precipitable water (PW), radiosonde (RS) and surface synoptic observations (SYNOP) using a 4DVAR assimilation scheme. Three experiments have been performed: (1) assimilation of PW and ZTD for May and June 2013, (2) assimilation of PW alone; PW, with RS and SYNOP; ZTD alone; and finally ZTD, with RS and SYNOP for 5–23 May 2013, and (3) assimilation of PW or ZTD during severe weather events in June 2013. Once the initial conditions were established, the forecast was run for 24 h. The major conclusion of this study is that for all analyzed cases, there are two parameters significantly changed once GNSS data are assimilated in the WRF model using GPSPW operator and these are moisture fields and rain. The GNSS observations improves forecast in the first 24 h, with the strongest impact starting from a 9 h lead time. The relative humidity forecast in a vertical profile after assimilation of ZTD shows an over 20 % decrease of mean error starting from 2.5 km upward. Assimilation of PW alone does not bring such a spectacular improvement. However, combination of PW, SYNOP and radiosonde improves distribution of humidity in the vertical profile by maximum of 12 %. In the three analyzed severe weather cases PW always improved the rain forecast and ZTD always reduced the humidity field bias. Binary rain analysis shows that GNSS parameters have significant impact on the rain forecast in the class above 1 mm h−1.