Review on the Role of GNSS Meteorology in Monitoring Water Vapor for Atmospheric Physics

Assimilation of ground-based GNSS data using a local ensemble Kalman filter

Tropical cyclones become increasingly nonlinear and dynamically unstable in high-resolution models. The initial conditions are typically sub-optimal, leaving scope to improve the accuracy of forecasts with improved data assimilation. Simultaneously, the lack of real ground-based GNSS observations over the ocean poses significant challenges when evaluating the assimilation results in oceanic regions. In this study, an Observation System Simulation Experiment is carried out based on a tropical cyclone case. Assimilation experiments using the WRF-PDAF framework are conducted. Conventional and GNSS observation operators are implemented. A diverse array of synthetic observations, encompassing temperature (T), wind components (U and V), precipitable water (PW), and zenith total delay (ZTD), are assimilated utilizing the Local Error-Subspace Transform Kalman filter (LESTKF). The findings highlight the improvement in forecast accuracy achieved through the assimilation process over the ocean. Multiple observation types further improve the forecast accuracy. The study underscores the crucial role of GNSS data assimilation techniques. The assimilation of GNSS data presents potential for advancing weather forecasting capabilities. Thus, the construction of ground-based GNSS observation stations over the ocean is promising.

ZPD Retrieval Performances of the First Operational Ship-Based Network of GNSS Receivers over the North-West Mediterranean Sea

This work presents the design and implementation of an operational infrastructure for the monitoring of atmospheric parameters at sea through GNSS meteorology sensors installed on liners operating in the north-west Mediterranean Sea. A measurement system, capable of operationally and continuously providing the values of surface parameters, is implemented together with software procedures based on a float-PPP approach for estimating zenith path delay (ZPD) values. The values continuously registered over a three year period (2020-2022) from this infrastructure are compared with the data from a numerical meteorological reanalysis model (MERRA-2). The results clearly prove the ability of the system to estimate the ZPD from ship-based GNSS-meteo equipment, with the accuracy evaluated in terms of correlation and root mean square error reaching values between 0.94 and 0.65 and between 18.4 and 42.9 mm, these extreme values being from the best and worst performing installations, respectively. This offers a new perspective on the operational exploitation of GNSS signals over sea areas in climate and operational meteorological applications.

Evaluation of Water Vapor Product from TROPOMI and GOME-2 Satellites against Ground-Based GNSS Data over Europe

A novel integrated water vapor (IWV) product from TROPOspheric Monitoring Instrument (TROPOMI) is validated together with a Global Ozone Monitoring Instrument-2 (GOME-2) standard product. As reference, ground-based Global Navigation Satellite Systems (GNSS) IWV data in 235 European stations from May 2018 to May 2019 are used. Under cloud free situations, a general comparison is carried out. It suggests that TROPOMI IWV exhibits less bias than GOME-2 and better results in the dispersion and regression parameters. Moreover, TROPOMI presents more homogeneous results along the different stations. However, TROPOMI is found to be overestimating the IWV uncertainties and being, therefore, too conservative in the confidence interval considered. The dependence of satellite product performance on several variables is also discussed. TROPOMI IWV shows wet bias of 5.7% or less for IWV < 10 mm (TROPOMI) and dry bias of up to −3% (TROPOMI). In contrast, GOME-2 shows wet bias of 30% or less for IWV < 25 mm (GOME-2) and dry bias of −12.3% for IWV > 25 mm. In addition, relative standard deviation (rSD) increases as IWV increases. In addition, the dependence on solar zenith angle (SZA) was also analyzed, as solar radiation bands are used in the retrieval algorithm of both instruments. Relative mean bias error (rMBE) shows positive values for GOME-2, slightly increasing with SZA, while TROPOMI shows more stable values. However, under high SZA, GOME-2 IWV exhibits a steep increase in rMBE (overestimation), while TROPOMI IWV exhibits a moderate decrease (underestimation). rSD is slightly increasing with SZA. The influence of cloudiness on satellite IWV observations is such that TROPOMI tends to overestimate IWV more as cloudiness increases, especially for high IWV. In the case of GOME-2, the rSD slightly increases with cloudiness, but TROPOMI rSD has a marked increase with increasing cloudiness. TROPOMI IWV is an important source of information about moisture, but its algorithm could still benefit from further improvement to respond better to cloudy situations.

Global Spatiotemporal Variability of Integrated Water Vapor Derived from GPS, GOME/SCIAMACHY and ERA-Interim: Annual Cycle, Frequency Distribution and Linear Trends

Atmospheric water vapor plays a prominent role in climate change and atmospheric, meteorological, and hydrological processes. Because of its high spatiotemporal variability, precise quantification of water vapor is challenging. This study investigates Integrated Water Vapor (IWV) variability for the period 1995–2010 at 118 globally distributed Global Positioning System (GPS) sites, using additional UV/VIS satellite retrievals by GOME, SCIAMACHY, and GOME-2 (denoted as GOMESCIA below), plus ERA-Interim reanalysis output. Apart from spatial representativeness differences, particularly at coastal and island sites, all three IWV datasets correlate well with the lowest mean correlation coefficient of 0.878 (averaged over all the sites) between GPS and GOMESCIA. We confirm the dominance of standard lognormal distribution of the IWV time series, which can be explained by the combination of a lower mode (dry season characterized by a standard lognormal distribution with a low median value) and an upper mode (wet season characterized by a reverse lognormal distribution with high median value) in European, Western American, and subtropical sites. Despite the relatively short length of the time series, we found a good consistency in the sign of the continental IWV trends, not only between the different datasets, but also compared to temperature and precipitation trends.

Understanding the Present-Day Spatiotemporal Variability of Precipitable Water Vapor over Ethiopia: A Comparative Study between ERA5 and GPS

Atmospheric water vapor plays a crucial role in atmospheric, climate change, meteorological, and hydrological processes. In a country like Ethiopia, with its complex topography and synoptic-scale spatiotemporal circulation patterns, the analysis of the spatiotemporal variability of precipitable water vapor (PWV) is very challenging, and is hampered by the lack of long observational datasets. In this study, we process the PWV over eight Ethiopian global positioning system (GPS) sites and one close to the Ethiopian eastern border, for the available common period 2013–2020, and compare with the PWV retrieved from the state-of-the-art ERA5 reanalysis. Both PWV datasets agree very well at our sample, with correlation coefficients between 0.96 and 0.99, GPS-PWV show a moderate wet bias compared to ERA5-PWV for the majority of the sites, and an overall root mean square error of 3.4 mm. Seasonal and diurnal cycles are also well captured by these datasets. The seasonal variations of PWV and precipitation at the sites agree very well. Maximum diurnal PWV amplitudes are observed for stations near water bodies or dense vegetation, such as Arbaminch (ARMI) and Bahir Dar (BDMT). At those stations, the PWV behavior at heavy rainfall events has been investigated and an average 25% increase (resp. decrease) from 12 h before (resp. 12 h after) the start of the rainfall event, when the PWV peaks, has been observed.