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The latest ECMWF global reanalysis, ERA5, is able to provide hourly surface pressure and water vapor-weighted mean temperature ( T m ), which are two key factors in GPS precipitable water vapor (PWV) retrieval. Performance of surface pressure, surface air temperature, and T m derived from ERA5 and its predecessor ERA-Interim (ERAI) are evaluated by comparing with more than 2000 meteorological stations and 89 radiosonde stations in the year of 2016 over China. Average pressure error RMS is 0.7 hPa for ERA5, compared to 1.0 hPa for ERAI, and ERA5 pressure diurnal variations agree much better than ERAI with in situ measurements. Temperature and T m differences between ERA5 and ERAI are relatively smaller, with error RMS of 1.8 K and 1.6 K for ERA5-derived temperature and T m , respectively. PWV error contributed by reanalysis-derived parameters is also estimated. The ERA5-induced PWV error is generally less than 1 mm, with smaller errors (< 0.4 mm) in eastern China but larger errors (can exceed 0.6 mm) in northwestern China and in the southeast of the Tibetan Plateau. Diurnal variations of PWV retrieved using pressure and T m from meteorological measurements (MET) and reanalysis products are compared. Good agreements are found between ERA5-based PWV and MET-based PWV in diurnal variations, while artificial diurnal signals are introduced in ERAI-based PWV, especially in the Tibetan Plateau. This study indicates that ERA5 can support high-accuracy hourly GPS PWV retrieval over China without contaminating the diurnal cycles, which is of great importance for historical GPS PWV retrieval at stations without collocated meteorological sensors equipped.

Accurately retrieving precipitable water vapor (PWV) over wide‐area land surface remains challenging. Unlike passive infrared remote sensing, passive microwave (PMW) remote sensing provides almost all‐weather PWV retrievals. This study develops a PMW‐based land PWV retrieval algorithm using automated Machine learning (ML) (AutoML). Data from the Advanced Microwave Scanning Radiometer 2 serve as the main predictor variables and high‐quality Global Positioning System (GPS) PWV data as the target variable. Unprecedentedly large GPS training samples (over 50 million) from more than 12,000 stations worldwide are used to train the AutoML model. New predictors with clear physical mechanisms enable PWV retrieval over almost any land surface type, including snow cover and near open water. Validation shows good agreement between PWV retrievals and ground observations, with a root mean square error of 3.1 mm. This encouraging outcome highlights the potential of the algorithm for application with other PMW radiometers with similar wavelengths. Plain Language Summary Precipitable water vapor plays a critical role in the global hydrological cycle, but retrieving its value from remote‐sensed data is challenging, especially for scientific purposes that require high resolution and accuracy. This work proposes a new retrieval algorithm, which is attractive on three accounts. First is the use of information from the microwave radiometer onboard a solar‐synchronous‐orbit satellite, which has a high spatiotemporal resolution. The second attraction is the use of automated machine learning (AutoML), which could circumvent the complex model selection and tuning processes that are typically involved in machine‐learning tasks. Third, an unprecedented large ground‐based data set is gathered from Global Positioning System stations worldwide, which is to be used as target variables for AutoML training. The validation results reveal that the precipitable water vapor retrieval is remarkably successful over all land surface types, which is rarely seen before. The proposed algorithm can also be transferred and used with radiometers onboard other satellites. Key Points A machine‐learning‐based passive microwave land precipitable water vapor (PWV) retrieval method is developed using the latest enhanced Global Positioning System PWV data set Adding new features with clear physical meanings improves the PWV retrieval accuracy by about 30% The model performs well in areas that have been excluded in previous studies, such as open waters and permanently frozen areas

Water vapor plays an important role in Earth’s weather and climate processes and energy transfer. Plenty of techniques have developed to monitor precipitable water vapor (PWV), but joint use of different techniques has some problems, including systematic biases, different spatiotemporal coverages and resolutions among different datasets. To address the above problems and improve the data utilization, we propose to use a generalized regression neural network (GRNN) to fuse PWVs from Global Navigation Satellite System (GNSS), Moderate-Resolution Imaging Spectroradiometer (MODIS), and European Centre for Medium‐Range Weather Forecasts Reanalysis 5 (ERA5). The core idea of this method is to use the high-quality GNSS PWV to calibrate and optimize the relatively low-quality MODIS and ERA5 PWV through the constructed GRNNs. Using the proposed method, we generated more than 400 PWV maps that combine GNSS, MODIS, and ERA5 PWVs in North America in 2018. Results show that the overall bias, standard deviation (STD), and root-mean-square (RMS) error are 0.0 mm, 2.1 mm, and 2.2 mm for the improved MODIS PWV, and 0.0 mm, 1.6 mm, and 1.6 mm for the improved ERA5 PWV. Compared to the original MODIS and ERA5 PWV, the total improvements are 37.1% and 15.8% in terms of RMS. The RMS improvements are mainly contributed from the calibration of bias for the MODIS PWV and optimization for the ERA5 PWV. It also demonstrates that the original MODIS PWV tends to be greater than the GNSS PWV while the ERA5 PWV has very small biases. After calibration and optimization, the correlation coefficients between the modified PWV and the GNSS PWV are 0.96 for the MODIS PWV and 0.98 for the ERA5 PWV. The proposed method also diminishes the temporal and spatial variations in accuracy, generating homogeneous PWV products. Since the biases among the three datasets are well removed and data accuracies are improved to the same level, they are thus easily fused and jointly used.

In ground-based global positioning system (GPS) meteorology, atmospheric weighted mean temperature, T m , plays a very important role in the progress of retrieving precipitable water vapor (PWV) from the zenith wet delay of the GPS. Generally, most of the existing T m models only take either latitude or altitude into account in modeling. However, a great number of studies have shown that T m is highly correlated with both latitude and altitude. In this study, a new global grid empirical T m model, named as GGTm, was established by a sliding window algorithm using global gridded T m data over an 8-year period from 2007 to 2014 provided by TU Vienna, where both latitude and altitude variations are considered in modeling. And the performance of GGTm was assessed by comparing with the Bevis formula and the GPT2w model, where the high-precision global gridded T m data as provided by TU Vienna and the radiosonde data from 2015 are used as reference values. The results show the significant performance of the new GGTm model against other models when compared with gridded T m data and radiosonde data, especially in the areas with great undulating terrain. Additionally, GGTm has the global mean RMS PWV and RMS PWV / PWV values of 0.26 mm and 1.28%, respectively. The GGTm model, fed only by the day of the year and the station coordinates, could provide a reliable and accurate T m value, which shows the possible potential application in real-time GPS meteorology, especially for the application of low-latitude areas and western China.

Layer Precipitable Water (LPW) characterizes the vertical structure of atmospheric moisture and is essential for accurate weather forecasts. China's FY‐4B satellite delivers near‐real‐time LPW products, but is constrained by large uncertainties. To address these limitations, we first integrated spherical cap harmonic analysis with extreme gradient boosting to enhance the Total Precipitable Water (TPW), and then calibrated the LPW by proposing a novel proportional allocation model considering spatiotemporal variability. Validation against radiosonde indicates that the root‐mean‐square errors of the FY‐4B LPW were reduced from 3.0 to 1.9 mm in the low layer, 4.2 to 2.3 mm in the mid layer, and 2.5 to 1.8 mm in the high layer, with the bias reduced from a maximum of −1.5 mm to near zero. Further evaluation with ERA5 demonstrates enhanced spatial consistency of the modified product. This work fills the gap of high‐accuracy LPW data, and supports more accurate forecasting and early‐warning applications.

High‐quality precipitable water vapor (PWV) measurements have an essential role in climate change and weather prediction studies. The Atmospheric Infrared Sounder (AIRS) instrument provides an opportunity to measure PWV at infrared (IR) bands twice daily with nearly global coverage. However, AIRS IR PWV products are easily affected by the presence of clouds. We propose a Gradient Boosting Decision Tree (GBDT) based correction model (GBCorM) to enhance the accuracy of PWV products from AIRS IR observations in both clear‐sky and cloudy‐sky conditions. The GBCorM considers many dependence factors that are in association with the AIRS IR PWV's performance. The results show that the GBCorM greatly improves the all‐weather quality of AIRS IR PWV products, especially in dry atmospheric conditions. The GBCorM‐estimated PWV result in the presence of clouds shows an accuracy comparable with that of official AIRS IR PWV products in clear‐sky conditions, demonstrating the capability of the GBCorM model. Plain Language Summary Water vapor is a dominant natural greenhouse gas in the atmosphere, which plays a vital role in atmospheric circulation, energy exchange, and hydrological cycle in association with climate change. The Atmospheric Infrared Sounder (AIRS) instrument, onboard the Aqua satellite platform, can provide twice per day, near‐global precipitable water vapor (PWV) measurements using infrared (IR) bands. However, due to the effect of clouds, the accuracy of AIRS IR PWV products in cloudy sky conditions is often inferior to that in cloud‐free sky conditions. In this study, we proposed a Gradient Boosting Decision Tree (GBDT) based correction model (GBCorM) to improve the all‐weather quality of AIRS IR PWV products. We found that the GBCorM can significantly enhance the accuracy of AIRS IR PWV products in all weather conditions, especially for dry atmospheric conditions. The enhanced AIRS IR PWV data records will be more suitable for weather forecasting locally or globally as well as climate monitoring. In addition to the AIRS instrument, this GBCorM could be a promising approach to enhance the all‐weather quality of IR PWV products from other satellite‐born instruments. Key Points A Gradient Boosting Decision Tree based correction model for Atmospheric Infrared Sounder (AIRS) infrared (IR) precipitable water vapor (PWV) products is developed and validated The model can significantly enhance the all‐weather accuracy of AIRS IR PWV products, especially in dry atmospheric conditions The model reduces the root‐mean‐squared error of AIRS IR PWV by 21.43% with GNSS PWV, by 17.28% with radiosonde PWV, and by 18.13% with ERA5 PWV

For a rapid retrieval of zenith wet delay (ZWD) and multi-global navigation satellite system (GNSS) precise point positioning (PPP) enhancement, a lightweight ZWD retrieval model was constructed by combining ground-based GNSS observations and precipitable water vapor (PWV) data provided by the European Center for Medium-Range Weather Forecasts Reanalysis (ERA5). The proposed model can rapidly produce ZWD without relying on the meteorological profile parameters. The proposed ZWD retrieval model achieved an RMSE and STD of 1.74 cm, with a correlation coefficient of 0.98. The enhanced performance of PWV-generated ZWD in GNSS PPP was tested in this study. The results showed that the ZWD constraint in GNSS PPP mainly affects the convergence time of the standard PPP solution, with the most significant effect in the U-direction. The PPP convergence time can be shortened by a maximum of 43%, with an average reduction of 24% for the eight sites over the four seasons. In the PPP-ambiguity resolution solution, the time to first fix (TTFF) was shorter for all sites with ZWD enhancement than for those without ZWD enhancement. The TTFF of the eight sites was significantly shortened in all four seasons, with an average improvement of 31%. The ZWD retrieval method based on the ERA5 PWV proposed in this study can quickly generate ZWD with high accuracy and resolution over a large area and significantly enhance GNSS PPP. The methodology proposed in this study is valuable for utilizing multi-source PWV-generated ZWD services for GNSS PPP enhancement.

Precipitable Water Vapor (PWV) has strong relations with extreme rainfall and their increments in a future warming world are typically associated. It is, however, unclear how different climatic conditions and orographic effects modulate these changes in the equatorial region. We investigate PWV and heavy rainfall over Ethiopia using Regional Climate Models (RCMs) from the Coordinated Regional Climate Downscaling Experiment (CORDEX). An in-depth RCM evaluation is first provided by comparing the modeled annual cycle of PWV with those obtained from Global Positioning System observations and reanalysis, and, by investigating the changes in PWV before and after a heavy-rainfall event. Two characteristic timescales are found for the buildup and decline of PWV before and after such events: a short of about 2 days and a long timescale extending beyond ten days. Overall RCMs reproduce well the PWV annual cycle but substantial biases appear for some models in the very dry and in the tropical wet climate zones. CORDEX models simulate well the peak in PWV anomalies at the day of a heavy-rainfall event but strongly overestimate the timescales of buildup and decline. Future scenarios all point towards a PWV increase (up to 40%) for end-of-the-century RCP8.5 with limited spatial and seasonal variations. PWV changes align with near-surface temperature changes at a rate of 7.7% per degree warming. Changes in daily heavy rainfall, on the other hand, are lower especially in northwestern Ethiopia in the far future (RCP8.5), potentially caused by an overall drying.

The atmospheric precipitable water (APW) in the Bengal region plays an important role in indicating the abundance of water vapor in the monsoon regions of East Asia and India. Based on the ERA5 reanalysis data, the opposite effects of 10–30-day and 30–60-day water vapor income (WVI) on the increasing trend of APW in the Bengal region from 1979 to 2020 are found. There are obvious 10–30-day and 30–60-day oscillations of summer APW in the Bengal region. The WVI, which is represented by the sum of values at 8 phases of unfiltered WVI, exhibits a declining trend in 10–30-day, while the WVI of 30–60-day shows an increasing trend. On the timescales of 10–30-day and 30–60-day, the weakening phase of the WVI shows the largest downward and upward trends among the eight phases, accounting for 32.4% and 321% of each trend, respectively. During the weakening phase of the 10–30-day timescale, a configuration characterized by stronger negative Western North Pacific Monsoon (WNPM) and weaker positive Indian Monsoon (IM) favored the northward wind anomalies over the Indian subcontinent, leading to a negative trend of WVI in the Bengal region and thereby a negative contribution to the increasing of APW. In the weakening phase of the 30–60-day timescale, a configuration featuring weaker negative WNPM and stronger positive IM contributed to northward wind anomalies over the Bay of Bengal, resulting in an increasing trend of WVI and APW in the Bengal region. This study highlights the importance of the phase configuration of both the WNPM and IM in driving the opposing trends observed in 10–30-day and 30–60-day APW variations within the Bengal region.

As an attractive technique for measuring water vapor, the Global Navigation Satellite System (GNSS) faces additional challenges in dynamic applications such as in the open sea. We present a new method of retrieving precipitable water vapor (PWV) based on GNSS multi‐antenna precise point positioning (PPP), which uses GNSS data from multiple antennas and incorporates the constraints of known baseline vector and common tropospheric delay. The 4‐day shipborne dynamic experiment along the China coast demonstrates that the baseline vector constraint shortens the convergence time of positioning and atmospheric parameters, and also slightly improves their accuracies. The common tropospheric delay constraint helps to provide compromised, more robust, and sometimes more accurate PWV estimates. An evaluation with radiosonde‐derived PWVs shows that the combination of the two constraints achieves the best accuracy reaching 4.2 mm. This method helps to expand GNSS meteorology to the vast ocean and benefits satellite altimetry and weather forecasting. Plain Language Summary Water vapor plays an important role in atmospheric processes ranging from global climate change to mesoscale and micro‐scale weather systems. The Global Navigation Satellite System (GNSS) is an attractive technique to measure water vapor, which has been extensively investigated for ground‐based static stations. In dynamic applications such as in the open sea, it is faced with additional challenges which may potentially degrade the performance. This study presents a new method for retrieving precipitable water vapor (PWV) based on GNSS multi‐antenna precise point positioning (PPP). It uses GNSS data from multiple antennas closely spaced and mounted on the same platform, and incorporates additional constraints of known baseline vector and common tropospheric delay. We have conducted a 4‐day shipborne dynamic experiment along the China coast and demonstrated that this method can shorten the convergence time and provide more robust and sometimes more accurate PWVs compared to conventional PPP. An accuracy level of 4.2 mm is achieved with this method for the ocean‐based dynamic experiment. This study helps to expand GNSS meteorology to challenging environments such as in the open sea, which will benefit weather forecasting and nowcasting. Key Points A new method of retrieving precipitable water vapor (PWV) is presented based on Global Navigation Satellite System (GNSS) multi‐antenna precise point positioning (PPP) This method can shorten the convergence time and provide more robust and more accurate GNSS‐based PWV The improved performance is helpful to expand GNSS meteorology to the vast ocean and benefits satellite altimetry and weather forecasting