The magnitude of water‐vapour content and its temporal variability are factors that influence the thermodynamics of the atmosphere significantly and result in different meteorological phenomena or hazards. High‐quality observations of water‐vapour spatial and temporal distribution enable precise weather forecasts to be made. Global Navigation Satellite System (GNSS) troposphere tomography is a technique that enables derivation of a three‐dimensional (3D) distribution of the wet refractivity with low cost in all weather conditions, based on GNSS slant observations of tropospheric delay. The tomographic estimations of the wet refractivity distribution have the potential to improve numerical weather prediction (NWP) models. In this study, we established a near‐real‐time (NRT) tomographic solution in the area of Poland using the TOMO2 model in order to verify whether tomographic products can attain the required accuracy and be assimilated into operational NWP models. The assimilation of the TOMO2 output into a weather research and forecasting (WRF) model was performed, using the WRF Data Assimilation (WRFDA) system and a GPSREF observation operator dedicated to radio occultation (RO) total refractivity assimilation. Two selected analysis periods covered summer storms and autumn rainfalls. The validation of the WRF model analysis with GNSS integrated water vapour (IWV) data, synoptic observations, radiosonde profiles, and ERA‐Interim reanalysis indicated an improvement in the relative humidity in the top tropospheric layers (the bias decreased by 1.4–4.6% and the standard deviation by 0.8–2.8%). In the middle troposphere, a positive impact was noticed in the summer (the standard deviation of the relative humidity decreased by 0.15%) but not in the autumn. The forecast at lead times of 6–18 hr was visibly improved in the autumn (reduction of root‐mean‐square error (RMSE) by 0.5% in relative humidity and 0.25 °C in temperature, reduction in standard deviation of surface pressure by 0.5 hPa), while in the summer the results were neutral or negative (RMSE of relative humidity increased by 1.0%).
Global Navigation Satellite System (GNSS) tomography is a technique that aims to obtain a 3‐D field of humidity in the troposphere. It is based on observations of GNSS signal delays between satellites and ground‐based receivers. The technique has been developed in recent years, showing positive results in the monitoring of severe weather events. The previous studies on assimilation into the numerical weather prediction models are based on available observation operators which are not adjusted to the GNSS tomography data. In this study, we demonstrate an observation operator TOMOREF dedicated to the assimilation of the GNSS tomography‐derived 3‐D fields of wet refractivity in a Weather Research and Forecasting (WRF) Data Assimilation (DA) system. The new tool has been tested based on wet refractivity fields derived during a heavy precipitation event. The results were validated using radiosonde observations, synoptic data, ERA5 reanalysis, and radar data. In the presented experiment, a positive impact of the GNSS tomography data assimilation on the forecast of relative humidity (RH) has been noticed (an improvement of root‐mean‐square error up to 0.5%). Moreover, the validation of the precipitation forecasts reveals the positive impact of the GNSS data assimilation within 1 hr after assimilation (the mean bias values are reduced up to 0.1 mm). Additionally, it was observed that assimilation of GNSS tomography data has a greater influence on the WRF model than the Zenith Total Delay (ZTD) observations, which proves the potential of the GNSS tomography data for weather forecasting.
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.
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 novel technique that takes advantage of these parameters, especially of slant troposphere observations between GNSS receivers and satellites, traces these signals through a 3D 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 (SWD) were calculated based on estimates of Zenith Total Delay (ZTD) and horizontal gradients, provided for 72 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). 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 3DVar) system, in particular its radio occultation observations 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.
The Global Navigation Satellite System (GNSS) has the capacity for remote sensing of water vapor content in the atmosphere. Post-processing of GNSS data can provide Integrated Water Vapor (IWV) with accuracies comparable to measurements of traditional sensors, i.e. water vapor radiometers. While GNSS meteorology benefits from thousands of permanent GNSS stations operating worldwide the spatial resolution of GNSS-derived IWV is limited to tens of kilometers. Further densification of GNSS networks is achievable with low-cost GNSS receivers. We investigated the feasibility of low-cost multi-GNSS receivers for monitoring IWV. The post-processing and the realtime solution are validated against 1) the results from a geodeticgrade GNSS receiver, 2) co-located water vapor radiometer, and 3) numerical weather model (NWM). Despite the high variability of the IWV during the validation period, the standard deviation of IWV differences with respect to the water vapor radiometer was 1.0 kg/m 2 and 1.5 kg/m 2 in post-processing and real-time, respectively. The city-scale variability of water vapor content in the atmosphere was monitored by a network of 16 low-cost GNSS receivers deployed in the city of Wroclaw, Poland. During rapidly changing weather conditions the disagreement between the low-cost GNSS-derived IWV field and the NWM reached up to 5.4 kg/m 2 and inter-station IWV differences exceeded 5 kg/m 2 . It has been demonstrated that low-cost GNSS receivers are reliable tools for precise determination of IWV, also in realtime. This study is the first to measure the water vapor content with a spatial resolution of single kilometers and to present a significantly diversified city-scale IWV field.
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