Assimilation of GNSS tomography products into the Weather Research and Forecasting model using radio occultation data assimilation operator

Abstract

Abstract. From Global Navigation Satellite Systems (GNSS) signals, accurate and high-frequency atmospheric parameters can be determined in all-weather conditions. GNSS tomography is a technique that takes advantage of these parameters, especially of slant troposphere observations between GNSS receivers and satellites, traces these signals through a 3-D grid of voxels, and estimates by an inversion process the refractivity of the water vapour content within each voxel. In the last years, the GNSS tomography development focused on numerical methods to stabilize the solution, which has been achieved to a great extent. Currently, we are facing new challenges and possibilities in the application of GNSS tomography in numerical weather forecasting, the main research objective of this paper. In the first instance, refractivity fields were estimated using two different GNSS tomography models (TUW, WUELS), which cover the area of central Europe during the period of 29 May–14 June 2013, when heavy-precipitation events were observed. For both models, slant wet delays (SWDs) were calculated based on estimates of zenith total delay (ZTD) and horizontal gradients, provided for 88 GNSS sites by Geodetic Observatory Pecny (GOP). In total, three sets of SWD observations were tested (set0 without compensation for hydrostatic anisotropic effects, set1 with compensation of this effect, set2 cleaned by wet delays outside the inner voxel model), in order to assess the impact of different factors on the tomographic solution. The GNSS tomography outputs have been assimilated into the nested (12 and 36 km horizontal resolution) Weather Research and Forecasting (WRF) model, using its three-dimensional variational data assimilation (WRFDA 3D-Var) system, in particular, its radio occultation observation operator (GPSREF). As only total refractivity is assimilated in GPSREF, it was calculated as the sum of the hydrostatic part derived from the ALADIN-CZ model and the wet part from the GNSS tomography. We compared the results of the GNSS tomography data assimilation to the radiosonde (RS) observations. The validation shows the improvement in the weather forecasting of relative humidity (bias, standard deviation) and temperature (standard deviation) during heavy-precipitation events. Future improvements to the assimilation method are also discussed.

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

“…Concerning the categorical statistical measures, the ZTD assimilation results in the increase of the MAE by~1 mm (~12%) for the highest rainfall threshold from 0000-0600 UTC (Figure 9a). Statistically significant reductions by 5.4% (90% confidence level) and 8.5% (95% confidence level) provided by the ZTD experiment are found for the precipitation intervals [2,5) and [10,20) from 0000-0600 UTC and 0600-1200 UTC, respectively (Figure 9). MAE is also decreased by 8.4% (statistically significant at the 95% confidence interval) during the ZTD simulation when rainfall is greater than 20 mm in the 6 h forecast from 1200-1800 UTC (Figure 10a).…”

“…Overall, the modelling system's configuration, including the assimilation of ZTD observations, satisfactorily captures the spatial and temporal distribution of the observed rainfall and can therefore be used as the basis for examining further improvements in the future. meteorological applications, including nowcasting and numerical weather prediction (NWP) [8][9][10][11], as well as weather monitoring, including extreme events [12][13][14]. Special meteorological interest derives from the near-real time (NRT) ZTDs, which are estimated based on raw GNSS observations.…”

Beyond Mapping Functions and Gradients

“…The refractivity fields can then be converted, if needed, to water vapor levels according to Eq. (6) with suitable temperature profiles over the troposphere and/or feed high resolution NWM taking natively into account turbulent/convective processes [94]. Xia et al [95] tried to derive the refractivity field from slant delays by substituting Eq.…”

Tropospheric delay parameters derived from GNSS-tracking data of a fast-moving fleet of trains

“…Assimilation of tomography products into NWMs wasa lso shown to be beneficial by a number of studies, e.g. Hanna et al (2019)w ho used aR Oa ssimilation operatort o assimilate refractivityfi elds.…”