###### Abstract
To completely avoid the dependence on constraints and external data, in this study, we applied the unconstrained tomographic model proposed by [17], totally based on GNSS observations, to a one-year case study in Houston, U.S. We intend to validate the model's performance in real-world scenarios and ensure that it meets reliability standards across various atmospheric conditions. The expected reliability standards involve metrics such as accuracy in capturing atmospheric variations, consistency across different temporal and spatial scales, and stability when applied in diverse weather scenarios. In this approach, the rank of the design matrix is increased by applying observational angular interpolation and extrapolation. However, some changes were applied. The original inversion method uses the Moore-Penrose pseudoinverse algorithm [31], changed here to the Non-Negative Least Squares (NNLS) algorithm [32] to avoid unphysical negative values in the upper levels when the amount of water vapor is minimal. Next, we applied the approach implemented by [19] to increase the rank of the design matrix (contains information on the length of each ray per voxel), using the voxel geometric centers as a target to create a new angular domain (in both azimuth and elevation) instead of the regularly spaced angular domain initially proposed by [17]. Four different datasets were used to validate the tomography results. The European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) data provides hourly estimates of a large number of atmospheric variables with a horizontal grid of 31 km [33]. The Weather Research and Forecasting Model (WRF), initialized by ERA5 data, was used here to improve the horizontal and vertical resolution of ERA5 [34]. Vertical profiles were retrieved from the Global Navigation Satellite System Radio Occultation (GNSS-RO) [35], and for the first time, we used precipitable water vapor (PWV) maps estimated by the Interferometric Synthetic-Aperture Radar (InSAR) technique with a horizontal resolution close to 300 m to evaluate the 2D integrated water vapor estimated by tomography, enabling the validation in areas lacking GNSS stations [36; 37]. Using 1 year of data and the same tomography configuration, we evaluate the flexibility of the proposed algorithm to compute the 3D water vapor density variability for a full annual cycle in a humid subtropical climate with tropical influences. Additionally, we assess the algorithm's capability to capture the signature of Hurricane Harvey. This devastating category 4 hurricane made landfall in Texas and Louisiana in August 2017, causing catastrophic flooding and resulting in over 100 deaths. While satellites primarily observed this event, in situ observations can fail due to harsh conditions. GNSS tomography, however, presents a breakthrough in evaluating the dynamics of severe weather, such as major hurricanes, providing detailed insights into atmospheric moisture conditions and offering a valuable tool for monitoring and understanding extreme weather events. This study is organized as follows. Section 2 briefly describes the ERA5 and WRF model configuration, the GNSS-RO dataset, the corresponding InSAR dataset, and some details of the tomographic analysis methods. In Section 3, we present the results and discussion. A summary and concluding remarks are given in Section 4.