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. From Global Navigation Satellite Systems (GNSS) signals,accurate and high-frequency atmospheric parameters can be determined inall-weather conditions. GNSS tomography is a technique that takes advantageof these parameters, especially of slant troposphere observations betweenGNSS receivers and satellites, traces these signals through a 3-D grid ofvoxels, and estimates by an inversion process the refractivity of the watervapour content within each voxel. In the last years, the GNSS tomographydevelopment focused on numerical methods to stabilize the solution, whichhas been achieved to a great extent. Currently, we are facing new challengesand possibilities in the application of GNSS tomography in numerical weatherforecasting, the main research objective of this paper. In the firstinstance, refractivity fields were estimated using two different GNSStomography models (TUW, WUELS), which cover the area of central Europeduring the period of 29 May–14 June 2013, when heavy-precipitation eventswere observed. For both models, slant wet delays (SWDs) were calculated basedon estimates of zenith total delay (ZTD) and horizontal gradients, providedfor 88 GNSS sites by Geodetic Observatory Pecny (GOP). In total, three setsof SWD observations were tested (set0 without compensation for hydrostaticanisotropic effects, set1 with compensation of this effect, set2 cleaned bywet delays outside the inner voxel model), in order to assess the impact ofdifferent factors on the tomographic solution. The GNSS tomography outputshave been assimilated into the nested (12 and 36 km horizontal resolution)Weather Research and Forecasting (WRF) model, using its three-dimensionalvariational data assimilation (WRFDA 3D-Var) system, in particular, its radiooccultation observation operator (GPSREF). As only total refractivity isassimilated in GPSREF, it was calculated as the sum of the hydrostatic partderived from the ALADIN-CZ model and the wet part from the GNSS tomography.We compared the results of the GNSS tomography data assimilation to theradiosonde (RS) observations. The validation shows the improvement in theweather forecasting of relative humidity (bias, standard deviation) andtemperature (standard deviation) during heavy-precipitation events. Futureimprovements to the assimilation method are also discussed.

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