Precipitable Water Vapor Research Articles

Rising trends of global precipitable water vapor and its correlation with flood frequency

Using 4 global reanalysis data sets, significant upward trends of precipitable water vapor (PWV) were found in the 3 time periods of 1958–2020, 1979–2020, and 2000–2020. During 1958–2020, the global PWV trends obtained using the ERA5 and JRA55 data sets are 0.19 ± 0.01 mm per decade (1.15 ± 0.31%) and 0.23 ± 0.01 mm per decade (1.45 ± 0.32%), respectively. The PWV trends obtained using the ERA5, JRA55, NCEP-NCAR, and NCEP-DOE data sets are 0.22 ± 0.01 mm per decade (1.18 ± 0.54%), 0.21 ± 0.00 mm per decade (1.76 ± 0.56%), 0.27 ± 0.01 mm per decade (2.20 ± 0.70%) and 0.28 ± 0.01 mm per decade (2.19 ± 0.70%) for the period 1979–2020. During 2000–2020, the PWV trends obtained using ERA5, JRA55, NCEP-DOE, and NCEP-NCAR data sets are 0.40 ± 0.25 mm per decade (2.66 ± 1.51%), 0.37 ± 0.24 mm per decade (2.19 ± 1.54%), 0.40 ± 0.26 mm per decade (1.96 ± 1.53%) and 0.36 ± 0.25 mm per decade (2.47 ± 1.72%), respectively. Rising PWV has a positive impact on changes in precipitation, increasing the probability of extreme precipitation and then changing the frequency of flood disasters. Therefore, exploring the relationship between PWV (derived from ERA5 and JRA55) change and flood disaster frequency from 1958 to 2020 revealed a significant positive correlation between them, with correlation coefficients of 0.68 and 0.79, respectively, which explains the effect of climate change on the increase in flood disaster frequency to a certain extent. The study can provide a reference for assessing the evolution of flood disasters and predicting their frequency trends.

Regional modeling and forecasting of precipitable water vapor using least square support vector regression

We propose a new model for spatio-temporal modeling and forecasting of precipitable water vapor (PWV) using least square support vector regression (LS-SVR) method. The LS-SVR uses simple linear equations. As a result, the complexity of the computational algorithm is reduced. In addition, the convergence speed and accuracy of the results increase. The evaluation of the new method has been done with the observations of the GPS networks at north-west and central Alborz in Iran. In the north-west GPS network, observations of 23 GPS stations in the period of 27 October to 10 November 2011 are used. However, in central Alborz network, the observations of 11 GPS stations in the period of 10 to 24 June 2016 have been used. The north-west GPS network is in the mountainous region and its observations are for the winter season. But, the second network is near the coastal area and summer season measurements are used. The latitude, longitude and height of GPS stations, DOY, time, relative humidity, temperature and pressure are considered as an input of the LS-SVR model. The output of the new model is the PWV (LS-SVRPWV). After the training step, the new model is used to estimate the spatio-temporal variation of PWV. The results of the LS-SVR model are compared and evaluated with the standard neural network (SNN), adaptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR), Saastamoinen, global pressure and temperature 3 (GPT3), voxel-based tomography (VBT) models, as well as with radiosonde measurements. Error evaluation of models has been done in control stations as well as by precise point positioning (PPP) method. For LS-SVR model, the averaged RMSE in the control stations of the north-west GPS network is 1.69 mm, while for the central Alborz GPS network, 1.88 mm is calculated. Also, the averaged relative error of LS-SVR model calculated in the Tabriz and Tehran radiosonde stations are 4.66 % and 6.12 %, respectively. The results of this paper show that the LS-SVR model has a very high capability in forecasting the spatio-temporal variation of PWV at the GPS network territory. The new model can be used for accurate estimation of PWV, meteorological applications and flood forecasting.

On the Impact of GPS Multipath Correction Maps and Post-Fit Residuals on Slant Wet Delays for Tracking Severe Weather Events

Climate change has increased the frequency and intensity of weather events with heavy precipitation, making communities worldwide more vulnerable to flash flooding. As a result, accurate fore- and nowcasting of impending excessive rainfall is crucial for warning and mitigating these hydro-meteorological hazards. The measurement of integrated water vapour along slant paths is made possible by ground-based global positioning system (GPS) receiver networks, delivering three-dimensional (3D) water vapour distributions at low cost and in real-time. As a result, these data are an invaluable supplementary source of knowledge for monitoring storm events and determining their paths. However, it is generally known that multipath effects at GPS stations have an influence on incoming signals, particularly at low elevations. Although estimates of zenith total delay and horizontal linear gradients make up the majority of the GPS products for meteorology to date, these products are not sufficient for understanding the full 3D distribution of water vapour above a station. Direct utilization of slant delays can address this lack of azimuthal information, although, at low elevations it is more prone to multipath (MP) errors. This study uses the convective storm event that happened on 27 July 2017 over Bulgaria, Greece, and Turkey, which caused flash floods and severe damage, to examine the effects of multipath-corrected slant wet delay (SWD) estimations on monitoring severe weather events. First, we reconstructed the one-way SWD by adding GPS post-fit phase residuals, describing the anisotropic component of the SWD. Because MP errors in the GPS phase observables can considerably impact SWD from individual satellites, we used an averaging technique to build station-specific MP correction maps by stacking the post-fit phase residuals acquired from a precise point positioning (PPP) processing strategy. The stacking was created by spatially organizing the residuals into congruent cells with an optimal resolution in terms of the elevation and azimuth at the local horizon.This enables approximately equal numbers of post-fit residuals to be distributed across each congruent cell. Finally, using these MP correction maps, the one-way SWD was improved for use in the weather event analysis. We found that the anisotropic component of the one-way SWD accounts for up to 20% of the overall SWD estimates. For a station that is strongly influenced by site-specific multipath error, the anisotropic component of SWD can reach up to 4.3 mm in equivalent precipitable water vapour. The result also showed that the spatio-temporal changes in the SWD as measured by GPS closely reflected the moisture field estimated from a numerical weather prediction model (ERA5 reanalysis) associated with this weather event.

Assessment of precipitable water vapor retrieved from precise point positioning with PPP-B2b service

Assessment of precipitable water vapor retrieved from precise point positioning with PPP-B2b service

A Climatology of Midlatitude Maritime Cloud Fraction and Radiative Effect Derived from the ARM ENA Ground-Based Observations

Abstract More than four years of ground-based measurements taken at the ARM Eastern North Atlantic (ENA) site between July 2015 and September 2019 have been collected and processed in this study. Monthly and hourly means of clear-sky, all-sky, total cloud fraction (CFT), and single-layered low (CFL) and high (CFH) clouds, the impacts of all scene types on the surface radiation budget (SRB), and their cloud radiative effects (CREs) have been examined. The annual averages of CFT, CFL, and CFH are 0.785, 0.342, and 0.123, respectively. The annual averages of the SW (LW) CREs for all sky, total, low, and high clouds are −56.7 (37.7), −76.6 (48.5), −73.7 (51.4), and −26.8 (13.9) W m−2, respectively, resulting in the NET CREs of −19.0, −28.0, −22.2, and −12.9 W m−2. Comparing the cloud properties and CREs at both ARM ENA and Southern Great Plains (SGP) sites, we found that the clear-sky downwelling SW and LW fluxes at the two sites are similar to each other due to their similar atmospheric background. Compared to SGP, the lower all-sky SW and higher LW fluxes at ENA are caused by its higher CFT and all-sky precipitable water vapor (PWV). With different low cloud microphysical properties and cloud condensation nuclei at the two sites, much higher cloud optical depth at SGP plays an important role in determining its lower SW flux, while Tb and PWV are important for downwelling LW flux at the surface. A sensitivity study has shown that the all-sky SW CREs at SGP are more sensitive to CFT (−1.07 W m−2 %−1) than at ENA (−0.689 W m−2 %−1), with the same conclusion for all-sky LW CREs (0.735 W m−2 %−1 at SGP vs 0.318 W m−2 %−1 at ENA). The results over the two sites shed new light on the impacts of clouds on the midlatitude surface radiation budgets, over both ocean and land.

GNSS-DERIVED PRECIPITABLE WATER VAPOR MODELING USING MACHINE LEARNING METHODS

Abstract. Atmospheric water vapor plays a vital role in phenomena related to the global hydrological cycle and climate changes, and its Spatio-temporal modeling and prediction help to identify and predict climatic phenomena. Accordingly, in this study, hourly precipitable water vapor (PWV) data sets for 27 stations receiving Global Navigation Satellite Systems (GNSS) observations in one month and machine learning methods were used to estimate PWV. Machine learning methods used in this study 1. Random Forest Regression (RFR) method 2. Extreme Gradient Boosting Regression (XGBR). The root mean square error (RMSE) in PWV estimation with the RFR method (RFR PWV) is 2.42 mm, and in PWV estimation with the XGBR method (XGBR PWV) is 2.75 mm, and the R-squared (R2) of the RFR method is 0.74, and for the XGBR method, these values are equal to 0.71. The obtained results show the efficiency and accuracy of both models in estimating PWV, which shows that machine learning methods have been able to recognize the behavior and changes of precipitable vapor in a small spatial and temporal interval. Although both ways had high accuracies, the RFR model performed slightly better and had better accuracy than the XGBR model.

GLOBAL POSITIONING SYSTEM PRECIPITABLE WATER VAPOR INTERPOLATION USING INVERSE MULTIQUADRIC, ARTIFICIAL NEURAL NETWORK AND INVERSE DISTANCE WEIGHTED

Abstract. Precipitable water vapor (PWV) is one of the most critical data in many meteorological departments. This component has great spatial and temporal changes, so the global positioning system (GPS) always seeks to increase the accuracy of estimating the water vapor component in the troposphere. The waves sent by the satellites of this system are delayed due to passing through atmospheric layers such as the troposphere. In this paper, interpolation methods are used to estimate precipitable water vapor. Inverse multiquadric (IMQ) interpolation which is based on radial basis functions, artificial neural network (ANN) method, and inverse distance weighted (IDW) which are the most common method of interpolation in meteorology. A region in North America with 23 GPS stations was randomly selected. Then, the interpolation of precipitable water vapor on a summer day is done using GPS data. The root mean square error value (RMSE) for the IMQ method was the lowest compared to other methods and was equal to 2.11 mm. Finally, using the IMQ interpolation method, a dense map of Precipitable water vapor changes in the troposphere layer is developed for the study area.

Investigating the Influence of Water Vapor on Heavy Rainfall Events in the Southern Korean Peninsula

In this study, we examined the influence of water vapor on heavy rainfall events over the complex mountainous terrain of the southern Korean Peninsula using rawinsonde and global navigation satellite system (GNSS) datasets from a mobile observation vehicle (MOVE). Results demonstrated that the prevailing southeasterly winds enhanced precipitation on the leeward side of the mountainous region. The probability of severe rainfall increased in the highest precipitable water vapor (PWV) bin (>60 mm). A lead–lag analysis demonstrated that the atmosphere remained moist for 1 h before and after heavy rainfall. The temporal behavior of PWV retrieved from the MOVE-GNSS data demonstrated that during Changma (the summer monsoon) (Case 1), heavy rainfall events experience a steep decrease after a long increasing trend in PWV. However, the most intense rainfall events occurred after a rapid increase in PWV along with a strong southwesterly water vapor flow during convective instability (Case 2), and they had consistently higher moisture and greater instability than those in Case 1 over the entire period. The results of this study can provide some insights to improve the predictability of heavy rainfall in the southern Korean Peninsula.

Performance of ERA5 data in retrieving precipitable water vapor over Hong Kong

When accurately retrieving atmospheric precipitable water vapor (PWV) using Global Positioning System (GPS) data, errors mainly originate from the effects of atmospheric weighted mean temperature (Tm) and ground meteorological data (e.g. surface pressure and temperature). This article assessed the accuracy of interpolated pressure and temperature from the European Centre for Medium-Range Weather Forecasts (ECMWF) fifth generation reanalysis dataset (ERA5), using actual meteorological data in the Hong Kong region from 2016 to 2020 as a reference.Radiosonde data were used to establish a linear equation (HKTm) between Tm and surface temperature (Ts), and a fitted equation between Tm, Ts, and water vapor pressure (HKTm-e). Then, these equations were evaluated using the Tm estimated by radiosonde with higher accuracy as a reference. On this basis, the PWV obtained from the measured meteorological data and BTm (GPS MET PWV) was compared with the PWV obtained from interpolated ERA5 meteorological data and HKTm-e (GPS ERA5 PWV), and the overall mean Bias and RMSE were −0.08 mm and 0.28 mm, respectively. In addition, PWV obtained from radiosonde data (RS PWV) was used to evaluate the accuracy of PWV obtained from ERA5 data (ERA5 PWV) and GPS ERA5 PWV. Then, the GPS ERA5 PWV was used to analyze the seasonal PWV variation and the effect of elevation on PWV in Hong Kong. The accuracy of ERA5 meteorological data is affected by the level of tropical cyclones in Hong Kong. Most of the differences are negative and the reason for the increased accuracy in temperature is explained based on the measured temperature and interpolated ERA5 temperature variations during all high-level tropical cyclones from 2016 to 2020. This article assessed the feasibility of using ERA5 data to replace the missing measured meteorological data during tropical cyclones. Based on the spatial variation of PWV as the tropical cyclone approaches Hong Kong, it is found that the spatial variation of PWV is related to the direction and location of the tropical cyclone.

Improving the Accuracy of MODIS Near-Infrared Water Vapor Product Under all Weather Conditions Based on Machine Learning Considering Multiple Dependence Parameters

We developed six machine learning based calibration models to improve the all-weather accuracy of precipitable water vapor (PWV) product from near-infrared (NIR) observations of the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument, i.e. MOD05 PWV. The six machine learning approaches are: Back Propagation Neural Network (BPNN), Gradient Boosting Decision Tree (GBDT), Generalized Regression Neural Network (GRNN), K-Nearest Neighbor (KNN), Multilayer Perceptron Neural Network (MLPNN), and eXtreme Gradient Boosting (XGBoost). The input of the models included MOD05 PWV, latitude, longitude, elevation, cloud, season, and solar zenith angle, in association with the quality of the MOD05 PWV product. PWV data measured from in-situ 453 Global Positioning System (GPS) stations in Australia in 2017 were utilized as the target water vapor data for model training. The validation results in Australia in 2018–2019 indicate that the models can significantly improve the all-weather quality (increased R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , reduced root-mean-square error (RMSE), and reduced mean bias (MB)) of the MOD05 PWV product, exhibiting a reduction in RMSE of 53.33% for BPNN, 55.25% for GBDT, 53.24% for GRNN, 37.81% for KNN, 54.98% for MLPNN, and 55.16% for XGBoost. The R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> was 0.58 ~ 0.79 and the MB was 0.12 mm ~ 0.72 mm, much better than the MOD05 all-weather PWV product (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0.26 and MB = -1.75 mm). Different from previous studies that focused on clear-sky conditions only, this work is the first one to enhance the quality of official MODIS NIR PWV products under all weather conditions, reducing the impact of clouds on PWV products.

An Estimation of Precipitable Water Vapour Around Surabaya City Using GIPSY-X

Precipitable water vapor is a crucial climatic parameter that indicates the available humidity in the atmosphere. Precipitable water vapor is produced by evaporation/evapotranspiration from the surface to the atmosphere and thus plays an important role in the water cycle. As an essential indicator of climate change, water vapor contributes significantly to the thermodynamics of the atmosphere. Zenith Total Delay can be calculated using the Precise Point Positioning strategy and all parameters. Using the parameter mapping function, Slant Tropospheric Delay can be converted to Zenith Total Delay, which is affected by temperature, pressure, and humidity. We processed the GNSS data from five stations surrounding Surabaya City using GipyX. Advanced processing generates five minutes of ZTD estimation on each station for five years of observation. A high correlation of ZTD estimated using GipsyX (corr = 0.88) with ZTD at meteorological rinex data of DARW station shows our estimation’s promising and dependable result. The ZTD time series shows consistent patterns on each station, showing the significant contribution of regional signals such as monsoon and ENSO.

A Novel Global Grid Model for Atmospheric Weighted Mean Temperature in Real-Time GNSS Precipitable Water Vapor Sounding

The atmospheric weighted mean temperature (Tm) is an important parameter in calculating the precipitable water vapor from Global Navigation Satellite System (GNSS) signals. As both GNSS positioning and GNSS precipitable water vapor detection require high-precision spatial and temporal resolutions for calculating Tm, high-precision modeling of Tm has gained widespread attention in recent years. The previous models for calculating Tm have the limitation of too many model parameters or single grid data. Therefore, this study presents a global high-precision Tm model (GGTm-H model) developed from the latest MERRA-2 (Modern-Era Retrospective Analysis for Research and Applications, version-2) atmospheric reanalysis data provided by United States National Aeronautics and Space Administration (NASA). The accuracy of GGTm-H model was verified by combining the MERRA-2 surface Tm data and 319 radiosonde data. The results highlighted that: (1) When the MERRA-2 Tm data was used as a reference value, the mean annual RMSE of the GGTm-H model was observed to be 2.72K. When compared with the Bevis model, GPT2w-5 and GPT2w-1 models, GGTm-H model showed an improvement of 1.5K, 0.33K, and 0.21K, respectively. (2) When the radiosonde data was used as a reference value, the mean bias and RMSE of the GGTm-H model were <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$-0.41$</tex-math></inline-formula> K and 3.82K, respectively. Compared with the other models, the GGTm-H model had the lowest mean annual bias and RMSE. The developed model does not consider any meteorological parameters while calculating Tm. Therefore, it has important applications in the real-time and high-precision monitoring of precipitable water vapor from GNSS signal.

Analyzing the Spatiotemporal Characteristics of Extreme Rainfall Using CAPE and GNSS-Derived ZTD Across China

The power-law relationship between precipitable water vapor (PWV)/convective available potential energy (CAPE), and extreme rainfall (ER) has been explored. However, the retrieval of PWV is reliant on the zenith total delay (ZTD) of the Global Navigation Satellite System (GNSS), and errors are introduced when converting ZTD to PWV. In this study, we propose the ZCER model, a comprehensive analysis model that integrates ZTD, CAPE, and ER, for investigating long-term variation in ER intensity and frequency, which expresses their close relationships in a novel approach and expands the application area of GNSS-derived ZTD. Daily ZTD, CAPE, and rainfall data were collected from 219 GNSS stations in China from 2011 to 2020 (10 years). Time- and frequency-domain information encapsulated in the three variables was extracted using wavelet coherence, proving that both ZTD and CAPE contributed to rainfall. The relationships between ZTD/log <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</sub> (CAPE) and log <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</sub> (ER) were investigated at the annual, seasonal, and monthly scales. The results revealed that the contribution of ZTD/CAPE to ER varied spatially and temporally. Furthermore, the synergistic contributions of ZTD and CAPE to ER were further investigated. Statistical results showed that CAPE and ZTD not only complemented each other on the geographical scales to ER in China but also on the seasonal and monthly scales. Moreover, the qualitative relationships between the ZTD, CAPE, and ER frequency were elucidated. Our findings validate the strong links between high ZTD, CAPE, and ER intensity and frequency in China on geographical and temporal scales.

Long-term changes in precipitable water vapour over India derived from satellite and reanalysis data for the past four decades (1980–2020)

PWV values are highest along the East Coast and lowest in the Western Himalaya. An increase in PWV is observed during the monsoon season, about 0.03–0.15 mm per year.

A Spatial PWV Retrieval Model Over Land for GCOM-W/AMSR2 Using Neural Network Method: A Case in the Western United States

Precipitable water vapor (PWV) is an important and active part of the atmosphere. As known, microwave PWV retrieval is well applied for the ocean but challenging over land. In this article, we creatively established a microwave PWV retrieval spatial model over land using the back-propagation (BP) neural network to combine the high precision ground-based Global Navigation Satellite System (GNSS) data and high spatial continuity satellite-borne data. Three-year data from 167 GNSS stations located in western America were utilized for training the network, and 40 untrained sites were selected as the test set. Root-mean-square error (RMSE) of the test set can reach 3.90 mm and 3.88 mm in the ascending (As) and descending (De) orbits. Then, we analyzed the influence of land cover types on the model over land. As a result, we found that stations located in the area with a single large-scale continuous land cover type had higher retrieval accuracy. In contrast, stations with diversified land cover types had lower precision. Furthermore, after fully considering the impact of land cover type, we performed an improved model with 61 stations on the single large-scale continuous grasslands, and the results of 8 stations as the test set showed that the RMSE could reach 3.41 and 3.31 mm in the As and De orbits. Compared with the spatial model established previously, the accuracy had been improved by about 13%. We think this is due to the stable physical properties (such as microwave emissivity) of the single large-scale continuous land cover type.

Comparisons of simulated radiation, surface wind stress and SST fields over tropical pacific by the GISS CMIP6 versions of global climate models with observations

This study compares the overall performance between versions 2.1 and 3 of National Aeronautics and Space Administration (NASA) Goddard Institute for Space Studies (GISS) global climate models (referred to as GISS-E2.1 and GISS-E3, respectively), in simulating the present-day Pacific climate using the CMIP6 protocol. Model physical representations and configurations are extensively changed from GISS-E2.1 to GISS-E3, which result in greatly reduced discrepancies, including ice water path (IWP), ice water content (IWC), radiative fluxes, surface wind stress (TAU), sea surface temperature (SST), precipitation (PR) and column water vapor (PRW), relative to satellite-based observational products over south Pacific oceans. Cloud only IWP (CIWP) shows the largest change, decreasing biases from ∼400 g kg−1 in GISS-E2.1 to 10–20 g kg−1 in GISS-E3. The combination of improved CIWP and the inclusion of snow in GISS-E3 may play roles on reducing overestimated outgoing longwave radiation, overestimated reflected shortwave at the top of atmosphere, and underestimated surface downward shortwave in GISS-E2.1. Both models’ intertropical convergence zones (ITCZs) are, however, located far too north of the equator, as found in radiative fluxes, PR and PRW but not in SST relative to observations. This introduces biases in TAU, PR and PRW over north flank of the equator and north Pacific. Over south Pacific, especially the trade wind regions, the improvements of radiation fluxes, SST, PR and PRW appear to be due to improved TAU associated with inclusion of snow-radiative effects. In particular, GISS-E3 reduces a longstanding too warm SST bias over trade-wind regions, from 4 K in GISS-E2.1 to within 0.5 K, and too cold SST bias over north Pacific Ocean. Although GISS-E3 shows improved geographic patterns of the simulated fields in particular over south Pacific oceans compared to GISS-E2.1, our results suggest that the location of ITCZ needs to be further improved.

海上可降水量観測のための船舶搭載GNSS解析設定の最適化に関する研究

海上可降水量観測のための船舶搭載GNSS解析設定の最適化に関する研究

Performance of the Semi-Empirical Precipitable Water Vapor Retrieval Algorithm Developed for Polarized Scanning Atmospheric Corrector (PSAC) in the Presence of Sensor Decay

Performance of the Semi-Empirical Precipitable Water Vapor Retrieval Algorithm Developed for Polarized Scanning Atmospheric Corrector (PSAC) in the Presence of Sensor Decay

A New Bottom-of-Atmosphere (BOA) Radiance-Based Hybrid Method for Estimating Clear-Sky Surface Longwave Upwelling Radiation From MODIS Data

Surface longwave upwelling radiation (SLUR) is a critical parameter for studying the water-energy balance between the land surface and atmosphere. In this study, we proposed a new hybrid method for estimating the clear-sky SLUR from the bottom-of-atmosphere (BOA) radiance (new BOA-hybrid method) of the Moderate Resolution Imaging Spectroradiometer (MODIS). There are two key parts in the developed new BOA-hybrid method. First, a physical four-channel algorithm was developed to estimate the atmospheric terms (atmospheric upwelling radiance and transmittance), which were used to calculate MODIS BOA radiance. Second, a linear model linking SLUR and MODIS BOA channel radiances was constructed using a large number of samples generated by extensive radiative transfer simulations. In situ measurements from twenty-seven sites in four flux networks were collected to validate the new BOA-hybrid method. The average bias and RMSE were 1.65 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and 16.23 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> for the new BOA-hybrid method, which was superior to the original BOA-hybrid method whose bias and RMSE were 3.52 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and 18.51 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and slightly better than the classical hybrid method that estimates SLUR using the top-of-atmosphere (TOA) radiance (TOA-hybrid method) whose bias and RMSE were -1.64 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and 17.44 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , respectively. The performance of the new and original BOA-hybrid methods was similar when the total precipitable water vapor (TPW) was less than 1.5 cm. However, under a large TPW (i.e., TPW>3 cm), especially in the case of a large viewing zenith angle (i.e., VZA>45°), the accuracy of the original BOA-hybrid method decreased significantly, while the physical four-channel algorithm could effectively improve the accuracy of atmospheric correction and the RMSE of the SLUR estimated by BOA-hybrid could be reduced by more than 10 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . In summary, the developed new BOA-hybrid method can estimate SLUR accurately and has great potential to produce a long-term high spatial resolution environmental data record of SLUR.

Long-Term Calibration of Satellite-Based All-Weather Precipitable Water Vapor Product From FengYun-3A MERSI Near-Infrared Bands From 2010 to 2017 in China

Long-Term Calibration of Satellite-Based All-Weather Precipitable Water Vapor Product From FengYun-3A MERSI Near-Infrared Bands From 2010 to 2017 in China