4DVAR assimilation of GNSS zenith path delays and precipitable water into a numerical weather prediction model WRF

Abstract

Abstract. 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.

Impact of GNSS Signal Delay Assimilation on Short Range Weather Forecasts Over the Indian Region

“…Initially, assimilation studies were accomplished using total precipitable water retrieved using zenith tropospheric delay (ZTD), surface pressure, and temperature (Abbasi et al, 2017;Falvey & Beavan, 2002;Guerova et al, 2006;Koizumi & Sato, 2004;Nakamura et al, 2004;Sharifi et al, 2015;Shoji et al, 2011;Smith et al, 2007;Yu et al, 2017). With the development of variational data assimilation methods, the direct assimilation of GPS ZTD has become possible (De Pondeca & Zou, 2001;Benjamin et al, 2010;Bennitt & Jupp, 2012;Boniface et al, 2009;Caldas-Alvarez et al, 2017;de Haan, 2013;Guerova et al, 2016;Faccani et al, 2005;Macpherson et al, 2008;Mahfouf et al, 2015;Poli et al, 2007;Rohm et al, 2019;Sanchez-Arriola et al, 2016;Vedel & Huang, 2004;. Most of the above-mentioned studies noticed the positive or neutral impact of the GNSS data (either with the assimilation of total precipitable water or ZTD directly) on the NWP systems.…”

Assessing the Impact of GNSS ZTD Data Assimilation into the WRF Modeling System during High-Impact Rainfall Events over Greece

“…In particular, the B matrix was calculated based on a series of WRF simulations of 24 h duration for the domain of interest (d02), which were initialized twice daily at 0000 UTC and 1200 UTC and covered a 10 day period, starting thus 10 days before each episode occurrence. Similar approaches for computing the B matrix have also been applied in previous studies [28,53]. Figure 2 illustrates the flow diagrams of the numerical experiments implemented in the current study.…”

“…The 3D-var/WRF system supports the assimilation of in-situ conventional measurements, remotely sensed observations, and satellite radiances. Ground-based GNSS ZTD data are included in the remote sensing observations category and they are handled by the GPSPW operator [28,39]. The success of the 3D-var assimilation depends heavily on the accuracy with which the observations and model background errors are specified.…”

“…Then, the corrections were estimated as the mean values of the ZTD differences and they were subtracted from the ZTD observations that were lined up for data assimilation. Even though this method provides statistical corrections, this is a standard bias correction approach for ZTD data that proved to be successful in reducing the observations-model divergences and capturing the systematic errors between the ZTD observations and model forecasts [16,[18][19][20][21][22][23][24][25]28]. The last stage of the ZTD data pre-processing included a selection algorithm based on the following conditions: (a) The formal ZTD error to be lower than the standard deviation of the difference between the observed and modeled ZTD, (b) the ZTD difference between observations and model output to be lower than five times the ZTD formal error, and (c) the difference between the receiver height and the model's orography to be below 100 m. Similar criteria have been applied in previous studies [19][20][21][22][23][24][25].…”

On the Potential of Improving WRF Model Forecasts by Assimilation of High-Resolution GPS-Derived Water-Vapor Maps Augmented with METEOSAT-11 Data

“…The second type of runs included GPS-IWV data assimilation only (defined here as AssimGPS runs). In contrast to other similar studies [49,50], where only single GPS derived IWV or ZWD/ZTD values were assimilated, in this study, we assimilate the quasi-continuous map of IWV consisted of 120,000 points, as described in Section 2.1. Lastly, the third type of runs assimilated into WRF the GPS-IWV derived maps augmented by the METEOSAT data, as described in Section 2.2 (defined here as AssimGPS-METEOSAT).…”