Investigation of Antarctic Precipitable Water Vapor Variability and Trend from 18 Year (2001 to 2018) Data of Four Reanalyses Based on Radiosonde and GNSS Observations

Precipitable water vapor (PWV) plays a vital role in climate research, especially for Antarctica in which meteorological observations are insufficient due to the adverse climate and topography therein. Reanalysis data sets provide a great opportunity for Antarctic water vapor research. This study investigates the climatological PWV means, variability and trends over Antarctica from four reanalyses, including the fifth generation of European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis (ERA5), the Second Modern-Era Retrospective analysis for Research and Applications (MERRA-2), Japanese 55-year Reanalysis (JRA-55) and National Centers for Environmental Prediction/Department of Energy (NCEP/DOE), in the period of 2001–2018 based on radiosonde and GNSS observations. PWV data from the ERA5, MERRA-2, JRA-55 and NCEP/DOE have been evaluated by radiosonde and GNSS observations, showing that ERA5 and MERRA-2 perform better than JRA-55 and NCEP/DOE with mean root mean square (RMS) errors below 1.2 mm. The climatological PWV mean distribution over Antarctica roughly shows a decreasing trend from west to east, with the highest content in summer and the lowest content in winter. The PWV variability is generally small over Antarctica, showing a seasonal dependence that is larger in the cold season and smaller in the warm season. PWV trends for all reanalyses at most Antarctic regions are insignificant and most reanalyses present overall drying trends from 2001 to 2018, except for ERA5 exhibiting a moistening trend. PWV trends also show seasonal and regional dependence. All reanalyses are generally consistent with radiosonde and GNSS observations in reproducing the PWV means (mean differences within 1.1 mm), variability (mean differences within 3%) and trends (mean differences within 6.4% decade−1) over Antarctica, except for NCEP/DOE showing spurious variability and trends in East Antarctica. Results can help us further understand these four reanalysis PWV products and promote climate research in Antarctica.

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

After 30 years since the beginning of the Global Positioning System (GPS), or, more generally, Global Navigation Satellite System (GNSS) meteorology, this technique has proven to be a reliable method for retrieving atmospheric water vapor; it is low-cost, weather independent, with high temporal resolution and is highly accurate and precise. GNSS ground-based networks are becoming denser, and the first stations installed have now quite long time-series that allow the study of the temporal features of water vapor and its relevant role inside the climate system. In this review, the different GNSS methodologies to retrieve atmospheric water vapor content re-examined, such as tomography, conversion of GNSS tropospheric delay to water vapor estimates, analyses of errors, and combinations of GNSS with other sources to enhance water vapor information. Moreover, the use of these data in different kinds of studies is discussed. For instance, the GNSS technique is commonly used as a reference tool for validating other water vapor products (e.g., radiosounding, radiometers onboard satellite platforms or ground-based instruments). Additionally, GNSS retrievals are largely used in order to determine the high spatio-temporal variability and long-term trends of atmospheric water vapor or in models with the goal of determining its notable influence on the climate system (e.g., assimilation in numerical prediction, as input to radiative transfer models, study of circulation patterns, etc.).

Evaluation of Precipitable Water Vapor from Five Reanalysis Products with Ground-Based GNSS Observations

At present, the global reliability and accuracy of Precipitable Water Vapor (PWV) from different reanalysis products have not been comprehensively evaluated. In this study, PWV values derived by 268 Global Navigation Satellite Systems (GNSS) stations around the world covering the period from 2016 to 2018 are used to evaluate the accuracies of PWV values from five reanalysis products. The temporal and spatial evolution is not taken into account in this analysis, although the temporal and spatial evolution of atmospheric flows is one of the most important information elements available in numerical weather prediction products. The evaluation results present that five reanalysis products with PWV accuracy from high to low are in the order of the fifth generation of European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis (ERA5), ERA-Interim, Japanese 55-year Reanalysis (JRA-55), National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR), and NCEP/DOE (Department of Energy) according to root mean square error (RMSE), bias and correlation coefficient. The ERA5 has the smallest RMSE value of 1.84 mm, while NCEP/NCAR and NCEP/DOE have bigger RMSE values of 3.34 mm and 3.51 mm, respectively. The findings demonstrate that ERA5 and two NCEP reanalysis products have the best and worst performance, respectively, among five reanalysis products. The differences in the accuracy of the five reanalysis products are mainly attributed to the differences in the spatial resolution of reanalysis products. There are some large absolute biases greater than 4 mm between GNSS PWV values and the PWV values of five reanalysis products in the southwest of South America and western China due to the limit of terrains and fewer observations. The accuracies of five reanalysis products are compared in different climatic zones. The results indicate that the absolute accuracies of five reanalysis products are highest in the polar regions and lowest in the tropics. Furthermore, the effects of different seasons on the accuracies of five reanalysis products are also analyzed, which indicates that RMSE values of five reanalysis products in summer and in winter are the largest and the smallest in the temperate regions. Evaluation results from five reanalysis products can help us to learn more about the advantages and disadvantages of the five released water vapor products and promote their applications.

Intercomparison of Integrated Water Vapor Measurements at High Latitudes from Co-Located and Near-Located Instruments

Data from global positioning system (GPS) ground-based receivers, ground-based microwave radiometers (MWRs), and radiosondes (RS) at two high-latitude sites were compared. At one site, the North Slope of Alaska (NSA), Barrow, Alaska (USA), the instruments were co-located, while at the other site, the second ARM Mobile Facility (AMF2), Hyytiälä, Finland, the GPS receiver was located about 20 km away from the MWRs and RS. Differences between the GPS-derived integrated water vapor (IWV) and the other three instruments were analyzed in terms of mean differences and standard deviation. A comparison of co-located and near-located independently calibrated instruments allowed us to isolate issues that may be specific to a single system and, to some extent, to isolate the effects of the distance between the GPS receiver and the remaining instruments. The results showed that at these two high-latitude sites, when the IWV was less than 15 kg/m2, the GPS agreed with other instruments within 0.5–0.7 kg/m2. When the variability of water vapor was higher, mostly in the summer months, the GPS agreed with other instruments within 0.8–1 kg/m2. The total random uncertainty between the GPS and the other systems was of the order of 0.6–1 kg/m2 and was the dominant effect when the IWV was higher than 15 kg/m2.

Biases and Abrupt Shifts of Monthly Precipitable Water from Terra MODIS

Monthly atmospheric precipitable water (PW) from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Terra satellite was assessed over land at 60°S–60°N. MODIS provides two PW products by using infrared (IR) and near-IR (NIR) algorithms, respectively. An assessment was performed for both MODIS PW data from 2000 to 2014, comparing them with the measurements at international stations of the global positioning systems and with a reanalysis to detect abrupt changes through monthly variations. It is noted that MODIS IR systematically underestimated PW in over 75% of stations, and that PW estimation declines with time. MODIS NIR significantly overestimated PW for tropical land and experienced two abrupt shifts. These data defects result in large spurious decreasing trends in MODIS IR and increasing trends in MODIS NIR. The two MODIS PW products are currently not suitable for a climatic-trend analysis, highlighting the need for data reprocessing and calibration.