Abstract. Traditionally, balloon-based radiosonde soundings are used to study the spatial distribution of atmospheric water vapour. However, this approach cannot be frequently employed due to its high cost. In contrast, GPS tomography technique can obtain water vapour in a high temporal resolution. In the tomography technique, an iterative or non-iterative reconstruction algorithm is usually utilised to overcome rank deficiency of observation equations for water vapour inversion. However, the single iterative or noniterative reconstruction algorithm has their limitations. For instance, the iterative reconstruction algorithm requires accurate initial values of water vapour while the non-iterative reconstruction algorithm needs proper constraint conditions. To overcome these drawbacks, we present a combined iterative and non-iterative reconstruction approach for the threedimensional (3-D) water vapour inversion using GPS observations and COSMIC profiles. In this approach, the noniterative reconstruction algorithm is first used to estimate water vapour density based on a priori water vapour information derived from COSMIC radio occultation data. The estimates are then employed as initial values in the iterative reconstruction algorithm. The largest advantage of this approach is that precise initial values of water vapour density that are essential in the iterative reconstruction algorithm can be obtained. This combined reconstruction algorithm (CRA) is evaluated using 10-day GPS observations in Hong Kong and COSMIC profiles. The test results indicate that the water vapor accuracy from CRA is 16 and 14 % higher than that of iterative and non-iterative reconstruction approaches, respectively. In addition, the tomography results obtained from the CRA are further validated using radiosonde data. Results indicate that water vapour densities derived from the CRA agree with radiosonde results very well at altitudes above 2.5 km. The average RMS value of their differences above 2.5 km is 0.44 g m −3 .

GPS has become a very effective tool to remotely sense precipitable water vapor (PWV) information, which is important for weather forecasting and nowcasting. The number of geodetic GNSS stations set up in China has substantially increased over the last few decades. However, GPS PWV derivation requires surface pressure to calculate the precise zenith hydrostatic delay and weighted mean temperature to map the zenith wet delay to precipitable water vapor. GPS stations without collocated meteorological sensors can retrieve water vapor using standard atmosphere parameters, which lead to a decrease in accuracy. In this paper, a method of interpolating NWP reanalysis data to site locations for generating corresponding meteorological elements is explored over China. The NCEP FNL dataset provided by the NCEP (National Centers for Environmental Prediction) and over 600 observed stations from different sources was selected to assess the quality of the results. A one-year experiment was performed in our study. The types of stations selected include meteorological sites, GPS stations, radio sounding stations, and a sun photometer station. Compared with real surface measurements, the accuracy of the interpolated surface pressure and air temperature both meet the requirements of GPS PWV derivation in most areas; however, the interpolated surface air temperature exhibits lower precision than the interpolated surface pressure. At more than 96% of selected stations, PWV differences caused by the differences between the interpolation results and real measurements were less than 1.0 mm. Our study also indicates that relief amplitude exerts great influence on the accuracy of the interpolation approach. Unsatisfactory interpolation results always occurred in areas of strong relief. GPS PWV data generated from interpolated meteorological parameters are consistent with other PWV products (radio soundings, the NWP reanalysis dataset, and sun photometer PWV data). The differences between them were approximately 1~3 mm at most at our selected stations, and GPS data processing is the main factor influencing the agreement of the GPS PWV results with those of other methods.

No abstract

Abstract.Water vapor tomography is a promising technique for reconstructing the 4-D moisture field, which is important to the weather forecasting and nowcasting as well as to the numerical weather prediction. A near real-time 4-D water vapor tomographic system is developed in this study. GPS slant water vapor (SWV) observations are derived by a sliding time window strategy using double-difference model and predicted orbits. Besides GPS SWV, surface water vapor measurements are also assimilated as real time observations into the tomographic system in order to improve the distribution of observations in the lowest layers of tomographic grid. A 1-year term experiment in Hong Kong was carried out. The feasibility of the GPS data processing strategy is demonstrated by the good agreement between the time series of GPS-derived Precipitable Water Vapor (PWV) and radio-sounding-derived PWV with a bias of 0.04 mm and a root-mean-square error (RMSE) of 1.75 mm. Using surface humidity observations in the tomographic system, the bias and RMSE between tomography and radiosonde data are decreased by half in the ground level, but such improved effects weaken gradually with the rise of altitude until becoming adverse above 4000 m. The overall bias is decreased from 0.17 to 0.13 g m −3 and RMSE is reduced from 1.43 to 1.28 g m −3 . By taking the correlation coefficient and RMSE between tomography and radiosonde individual profile as the statistical measures, quality of individual profile is also improved as the success rate of tomographic solution is increased from 44.44 to 63.82 % while the failure rate is reduced from 55.56 to 36.18 %.