Precipitable Water Vapor Retrieval and Analysis by Multiple Data Sources: Ground-Based GNSS, Radio Occultation, Radiosonde, Microwave Satellite, and NWP Reanalysis Data

Abstract

Precipitable water vapor (PWV) content detection is vital to heavy rain prediction; up to now, lots of different measuring methods and devices are developed to observe PWV. In general, these methods can be divided into two categories, ground-based or space-based. In this study, we analyze the advantages and disadvantages of these technologies, compare retrieved atmosphere parameters by different RO (radio occultation) observations, like FORMOSAT-3/COSMIC (Formosa Satellite-3 and Constellation Observing System for Meteorology, Ionosphere, and Climate) and FY3C (China Feng Yun 3C), and assess retrieved PWV precision with a radiosonde. Besides, we interpolate PWV from NWP (numerical weather prediction) reanalysis data for more comparison and analysis with RO. Specifically, ground-based GNSS is of high precision and continuous availability to monitor PWV distribution; in our paper, we show cases to validate and compare GNSS retrieving PWV with a radiosonde. Except GNSS PWV, we give two different radio occultation sounding results, COSMIC and FY3C, to validate the precision to monitor PWV from space in a global area. FY3C results containing Beidou (China Beidou Global Satellite Navigation System) radio occultation events need to be emphasized. So, in our study, we get the retrieved atmospheric profiles from GPS and Beidou radio occultation observations and derive atmosphere PWV by a variational retrieval method based on these data over a global area. Besides, other space-based methods, such as microwave satellite, are also useful in detecting PWV distribution situations in a global area from space; in this study, we present a case of retrieved PWV using microwave satellite observation. NWP reanalysis data ECMWF (European Centre for Medium-Range Weather Forecasts) ERA-Interim and the new-generation reanalysis data ERA5 provide global grid atmosphere parameters, like surface temperature, different-level pressures, and precipitable water. We show cases of retrieved PWV and validate the precision with radiosonde results and compare new reanalysis dataset ERA5 with ERA-Interim, finding that ERA5 can get higher precision-retrieved atmosphere parameters and PWV. In the end, from our comparison, we find that the retrieved PWV from RO (FY3C and COSMIC) and ECMWF reanalysis data (ERA-Interim and ERA5) have a high positive correlation and that almost all R2 values exceed 0.9, compare retrieved PWV with a radiosonde, and find that whether it is RO and ECMWF reanalysis data, ground-based GNSS, or microwave satellite, they all show small biases.

Application of GNSS Methodologies to Obtain Precipitable Water Vapor (PWV) and Its Comparison with Radiosonde Data

“…The differences with those estimations could be due to the uncertainties of the PWV radiosonde data and the spatial and temporal separations between GNSS and radiosonde data. Similar results can be found in Ohtani et al [9] (a little higher) or Zhang et al [11] that found a mean RMS of 2.41 mm in a multiple comparison of PWV.…”

“…The results shows that GPS-retrieved PWV and PWV derived from radiosonde agrees well, with a high correlation coefficient. The correlation coefficients obtained are consistent with the values found in Gui et al [10] and Zhang et al [11]. In general, GNSS-retrieved PWV are overestimated compared with PWV derived from Radiosonde.…”

Daily evaluation of 26 precipitation datasets using Stage-IV gauge-radar data for the CONUS

“…The third-generation, coarser-resolution reanalyses (ERA-Interim, JRA-55, and NCEP-CFSR) performed slightly worse overall (median KGE of 0.55, 0.52, and 0.52, respectively). ERA-Interim performed slightly better than other third-generation reanalyses, consistent with earlier studies focusing on P (Bromwich et al, 2011;Peña Arancibia et al, 2013;Palerme et al, 2017;Beck et al, 2017c) and other atmospheric variables (Bracegirdle and Marshall, 2012;Jin-Huan et al, 2014;Zhang et al, 2016). All (re)analyses, including the new ERA5-HRES, underestimated the variability (Figs.…”

Characteristics of Satellite Sampling Errors in Total Precipitable Water from SSMIS, HIRS, and COSMIC Observations

“…ERA5 can provide global TPW data at a high temporal resolution of 1 hr and a horizontal spatial resolution of 30 km (0.25° × 0.25° in grid). ERA5 is superior compared to widely used ERA‐Interim due to more data sources and new technology in data assimilation system, especially with its higher spatial and temporal resolution (Zhang et al, ). Since it is gap‐free across the whole globe, ERA5 can be adequately collocated to different satellite sampling patterns in space and time to get the satellite‐sampled data sets to construct the sampling errors in satellite data.…”