Search

Atmospheric water vapor plays an essential role in climate change and weather forecasting. However, monitoring water vapor with high spatial and temporal resolutions remains a challenge, especially over ocean regions where observations are insufficient. Shipborne global navigation satellite systems (GNSSs) contribute to enriching water vapor measurements over oceans and also can help validate satellite observations. Due to the lack of long-time serial observations, the performance of shipborne GNSS-derived precipitable water vapor (PWV) is inadequately evaluated on the global ocean scale. In this study, an overall assessment of shipborne GNSS PWV over global oceans is performed based on six voyages from 2014 to 2018. In coastal areas, the PWV differences of shipborne GNSS with respect to (w.r.t.) ground-based GNSS and ground-launched radiosonde data are 2.64 and 2.85 mm in the root mean square (rms), respectively. In open oceans, compared to ship-launched radiosonde profiles and satellite measurements, shipborne GNSS PWV shows the rms of differences of 2.54 and 2.53 mm, respectively. In addition, the rms of PWV differences between the whole track of shipborne GNSS PWV and National Centers for Environmental Prediction (NCEP) Climate Forecast System Version 2 (CFSv2) products is 2.96 mm. The intertechnique validations demonstrate that the accuracy of shipborne GNSS PWV is superior to 3 mm, which meets the requirements of climate research and numerical weather prediction (NWP). [ABSTRACT FROM AUTHOR]

The atmospheric water vapour is an important indicator of the climate state and evolution, and also the key parameter affecting the hydrological cycle, atmospheric convection, and the weather. This study validated the accuracy of surface pressure and temperature data interpolated from European Centre for Medium-Range Weather Forecasts (ECMWF) Interim ReAnalysis (ERA-Interim) and ECMWF 5th ReAnalysis (ERA5) datasets, and evaluated the performance of the interpolation meteorological data for precipitable water vapour (PWV) retrieval using the measured meteorological data and Global Positioning System (GPS) data of the year 2014 obtained from 25 stations of the Chinese Coastal GPS Observation Network. We then analysed the temporal and spatial distribution of water vapour in the coastal regions of China using a 3-year GPS PWV time series calculated with the interpolated reanalysis meteorological data at 25 stations from 2014 to 2016. Evaluation of the reanalysis meteorological data results showed that the accuracies of the interpolated pressure and temperature data from ERA5 were slightly superior than those from ERA-Interim. The root-mean-square errors (RMSEs) of the surface temperature and pressure interpolated from two reanalysis datasets were less than 2.4 K and 1.6 hPa, respectively. Data based on GPS PWV products that used interpolated parameters were very close to those of meteorological observations, with biases within ±0.4 mm and RMSEs below 0.5 mm in most areas; these data also strongly agreed with radiosonde observations. However, the interpolated meteorological data from reanalysis datasets could not reflect the true change during typhoon events, which could not be used in GPS PWV retrieval. The analysis of the temporal and spatial distribution showed that the distribution of water vapour was mainly affected by latitude, land-sea distribution, and water vapour advection. The variations in water vapour were mainly seasonal with the highest PWV occurring in the summer and the lowest always occurring in the winter. Significant diurnal variations of water vapour varied with season and latitude, with an amplitude of about 0.9–3.5 mm; the peak value occurred in the afternoon or early morning, and was affected by surface evaporation, large-scale land–sea breeze circulation, and valley-wind circulation. [ABSTRACT FROM AUTHOR]

This paper investigated the quality of site-specific surface temperature and surface pressure data in the coastal regions of China, which were interpolated from the European Centre for Medium-Range Weather Forecast Interim reanalysis (ERA-Interim), Japanese 55-year Reanalysis Project (JRA-55), and the National Centers for Environmental Prediction Final (NCEP FNL) reanalysis surface meteorological datasets as well as from the new Global Pressure and Temperature (GPT2) model data. The measured temperature and pressure along with the collocated GPS data from 2014 were collected from 25 observation stations evenly located in the region. Compared with the actual meteorological observations, the performances of the interpolated data from three reanalysis datasets differ marginally, with the root mean square errors (RMSEs) of the interpolated surface temperature and pressure less than 2.4 K and 1.6 hPa, respectively; however, the RMSEs of the surface temperature and pressure interpolated from the GPT2 model were 3.0 K and 4.2 hPa, respectively. Data based on GPS PWV products that used the meteorological parameters interpolated from three reanalysis data were very close to those of meteorological observations, with biases within ± 0.4 mm and RMSEs below 0.5 mm in most areas, and the RMSE of PWV using the GPT2 model interpolation data was superior by 2 mm. The measurement of GPS PWV using the interpolated reanalysis meteorological data also compared well with radiosonde observations, with RMSE between them tending to increase with a decrease of the GPS station's latitude. However, the GPS PWV based on the interpolated data could not reflect the true change in water vapor during typhoon events. [ABSTRACT FROM AUTHOR]