Comparisons between the WRF data assimilation and the GNSS tomography technique in retrieving 3-D wet refractivity fields in Hong Kong

Abstract

Abstract. Water vapor plays an important role in various scales of weather processes. However, there are limited means to accurately describe its three-dimensional (3-D) dynamical changes. The data assimilation technique and the Global Navigation Satellite System (GNSS) tomography technique are two of the limited means. Here, we conduct an interesting comparison between the GNSS tomography technique and the Weather Research and Forecasting Data Assimilation (WRFDA) model (a representative of the data assimilation models) in retrieving wet refractivity (WR) in the Hong Kong area during a wet period and a dry period. The GNSS tomography technique is used to retrieve WR from the GNSS slant wet delays. The WRFDA is used to assimilate the zenith tropospheric delay to improve the background data. The radiosonde data are used to validate the WR derived from the GNSS tomography, the WRFDA output, and the background data. The root mean square (rms) of the WR derived from the tomography results, the WRFDA output, and the background data are 6.50, 4.31, and 4.15 mm km−1 in the wet period. The rms becomes 7.02, 7.26, and 6.35 mm km−1 in the dry period. The lower accuracy in the dry period is mainly due to the sharp variation of WR in the vertical direction. The results also show that assimilating GNSS ZTD into the WRFDA only slightly improves the accuracy of the WR and that the WRFDA WR is better than the tomographic WR in most cases. However, in a special experimental period when the water vapor is highly concentrated in the lower troposphere, the tomographic WR outperforms the WRFDA WR in the lower troposphere. When we assimilate the tomographic WR in the lower troposphere into the WRFDA, the retrieved WR is improved.

A Two-Step Projected Iterative Algorithm for Tropospheric Water Vapor Tomography

“…Since the radiosonde data in Hong Kong, used as a reference of GNSS tropospheric tomography, are only available at 0:00 and 12:00 UTC daily, tomographic results only at these two epochs can be validated. Moreover, the radiosonde data also need to be interpolated for those tomographic nodes for the validation [13]. The WR obtained from ERA5 data was also used as the other reference of the tomographic results of this study.…”

“…This may be because the vertical distribution of water vapor during a wet period is more even and the spatio-temporal variation is more stable than that of a dry period. The tomographic results will be adversely affected if there are fluctuations in water vapor distribution with time and space [13], [56]. Furthermore, the water vapor distribution during the dry period does not show an exponential decrease with height at some epochs and even possibly has an inverse increase trend near the surface, as shown in Fig.…”

Choice of the parameters in a primal-dual algorithm for Bregman iterated variational regularization

“…Thus in this section, we will rather present the numerical results of Algorithm 2 when it is applied to the GPS-Tomography problem. Specifically in this problem, recent novel reserch works [81,83] indicate the importance of the number and the sources of the measured data. In this work, we are only able to emphasize that how number of the measurement data, e.g.…”

Comparisons of Performance Using Data Assimilation and Data Fusion Approaches in Acquiring Precipitable Water Vapor: A Case Study of a Western United States of America Area

“…where p is the pressure in Pa, q the specific humidity in g/g, T the temperature in K, k 1 = 2.21 × 10 -7 K/Pa, k 2 = 3.73 × 10 −3 K 2 /Pa, h the layer height in m. It is noted that the assimilation results are unrelated to the physical parameter setting of the WRF [23]. However, it would still be useful to examine whether the grid resolution used in the assimilation experiment has any significant effect on the results.…”