Variation Of Precipitable Water Vapor Research Articles

A segmented grid model for vertical adjustment of precipitable water vapor in China

Precipitable Water Vapor (PWV) is vital in climate research, monitoring in troposphere stability and weather disasters. This study investigates the detailed vertical variation of PWV in China, utilizing data from the 5th generation European Centre for Medium-Range Weather Forecasts (ECMWF). A PWV segmented vertical adjustment grid model (CPWV-H) was developed based on a sliding window algorithm, with a spatial resolution of 0.5° × 0.5° and a temporal resolution of one day. The performance of the CPWV-H model was evaluated using multi-source PWV data and compared with the conventional empirical model (EPWV-H) and the recently released C-PWVC1 model. Results show that the CPWV-H model has superior accuracy and stability. Using ERA5 PWV profiles as a reference, the CPWV-H model has a mean Bias of −0.45 mm and a mean RMSE of 1.24 mm, improving by 21.52 % over the EPWV-H model and 31.11 % over the C-PWVC1 model. With radiosonde profiles as a reference, it has a mean Bias of −0.10 mm and a mean RMSE of 2.86 mm, improving by 7.14 % and 13.86 % over the EPWV-H and C-PWVC1 models, respectively. Consequently, the CPWV-H model exhibits enhanced stability and vertical adjustment accuracy in China, offering significant utility for the fusion of multi-source water vapor data, comparative analysis, and climate research.

Spatio‐Temporal Characteristics and Variability of GNSS‐Derived Atmospheric Precipitable Water Vapor From 2010 to 2023 in Fujian Province, China

AbstractAtmospheric precipitable water vapor (PWV) is a crucial factor affecting precipitation and the atmospheric environment. To quantitatively investigate the spatio‐temporal distribution and characteristics of the PWV in Fujian, China, its diurnal and seasonal variations are analyzed based on the global navigation satellite system (GNSS) data from 2010 to 2023, ground meteorological observations, meteorological sounding data and ERA5 data. Moreover, the spatio‐temporal characteristics of the monthly PWV are assessed by using the empirical orthogonal function (EOF), the Mann‐Kendall test and the sliding t‐test. The results indicate that the ground‐based GNSS PWV data is able to reveal the distribution and variation of PWV over Fujian. Specifically, the diurnal distribution of the PWV varies remarkably with time, and the PWV in the eastern coastal areas is generally higher than in the western mountainous areas. The seasonal variation characteristics of the PWV are consistent with the atmospheric circulation variations, with the largest amount of PWV in summer, followed by spring, autumn and winter. Moreover, the EOF spatial modes show that the PWV distributions are different in the eastern coastal areas and inland mountainous areas. The oscillation intensity of the PWV strengthens from the northwest to the southeast, and the corresponding time series of the PWV displays apparent seasonal variations. The observed precipitation is inconsistent with the PWV and is more affected by local terrain and thermodynamic conditions. The Mann‐Kendall test and the sliding t‐test indicate that the PWV over Fujian has not undergone abrupt changes during the past 13 years, but there is a possibility of sudden changes in the future.

Significant Increase in African Water Vapor over 2001–2020

Atmospheric water vapor is not only a key element of the global hydrological cycle but also the most abundant greenhouse gas. The phase transition and transportation of water vapor are essential for maintaining global energy balance and regulating hydrological processes. However, due to insufficient meteorological observational data, climate research in Africa faces significant limitations despite its substantial contribution to changes in global precipitable water vapor (PWV). In this study, we used MODIS near-infrared (NIR) PWV products and Berkeley temperature data to depict the spatial–temporal variability in PWV across Africa from 2001 to 2020. The results reveal a significant increasing trend in PWV over Africa, with an increase of 0.0158 cm/year. Nearly 99.96% of Africa shows an increase in PWV, with 88.95% of these areas experiencing statistically significant changes, particularly in central regions of Africa. The increase in PWV is more pronounced in high-value months compared to low-value months. The equatorial region of the Congo Basin exhibits higher PWV, which gradually decreases as latitude increases. Despite significant warming (0.0162 °C/year) in Africa, there is no consistent positive correlation between temperature and water vapor. A positive relationship between PWV and temperature is observed in western Africa, while a negative relationship is noted in eastern and southern Africa on an annual scale. Additionally, an increasing trend in precipitation (4.6669 mm/year) is observed, with a significant positive correlation between PWV and precipitation across most of Africa, although this relationship varies by month. These findings provide valuable insights into the comprehension of the hydrothermal variation in Africa amidst climate warming.

Revealing the water vapor transport during the Henan “7.20” heavy rainstorm based on ERA5 and Real-Time GNSS

In July 2021, a heavy rainstorm was sweeping across Henan Province, causing geological disasters such as floods, mudslides, and landslides, which seriously threatened the safety of human life and property. Precipitable water vapor (PWV) is related to the occurrence and scale of rainfall. Here, based on Global Navigation Satellite System (GNSS) observations, in-situ meteorological files (GMET), ephemeris products, ERA5 data, and weather station data, the relationship between PWV and rainstorm from July 1st to 30th was studied. The results show that GMET and ERA5 in July 2021 have high consistency in some stations, with a root mean square error (RMSE) for temperature below 1.6 °C, for pressure below 0.5 hPa, and for relative humidity below 9 %. During the week before the heavy rainstorm, the temperature dropped remarkably and the temperature difference decreased, while the relative humidity increased and the relative humidity difference decreased. Compared with ERA5 PWV, the RMSE of GNSS PWV retrieved using real-time ephemeris is 3.238 mm. Different from the normal rainfall, we found that the PWV variation during the Henan rainstorm experienced a unique “accumulation” period. We also observed a clear correlation between PWV and the rainstorm, both temporally and spatially. In addition, the PWV in the severely damaged area was 20 mm higher than the average value of the past decade. Ten days after the rainstorm, the surface of this area had subsided by 1.5–3 mm. Finally, we found that the topography of Henan, the low vortex, the north-biased subtropical high, and the double typhoons all played a role in the successful transport and deposition of water vapor.

Multiscale Spatiotemporal Variations of GNSS-Derived Precipitable Water Vapor over Yunnan

The geographical location of Yunnan province is at the upstream area of water vapor transportation from the Bay of Bengal and the South China Sea to inland China. Understanding the spatiotemporal variations of water vapor over this region holds significant importance. We utilized the Global Navigation Satellite System (GNSS) data collected from 12 stations situated in Yunnan, which are part of the Crustal Movement Observation Network of China, to retrieve hourly precipitable water vapor (PWV) data from 2011 to 2022. The retrieved PWV data at Station KMIN were evaluated by the nearby radiosonde data, and the results show that the mean bias and RMS of the differences between the two datasets are 0.08 and 1.78 mm, respectively. Average PWV values at these stations are in the range of 11.77 to 33.53 mm, which decrease from the southwest to the north of Yunnan and are negatively correlated with the stations’ heights and latitudes. Differences between average PWV in the wet season and dry season range from 12 to 27 mm. These differences tend to increase as the average PWV increases. The yearly rates of PWV variations, averaging 0.18 mm/year, are all positive for the stations, indicating a year-by-year increase in water vapor. The amplitudes of the PWV annual cycles are 9.75–20.94 mm. The spatial variation of these amplitudes is similar to that of the average PWV over the region. Generally, monthly average PWV values increase from January to July and decrease from July to December, and the growth rate is less than the decline rate. Average diurnal PWV variations show unimodal PWV distributions over the course of the day at the stations except Station YNRL, where bimodal PWV distribution was observed.

Spatiotemporal distribution and impact factors of GNSS-PWV in China based on climate region

Water vapor plays a pivotal role in the intricate processes of energy exchange and climate dynamics. However, a comprehensive understanding of the spatial distribution and temporal variability of water vapor in distinct climatic regions of China is still limited due to the intricate interplay of complex topography and diverse climatic conditions. This study investigates the multiscale variability of water vapor using 10-year 1-hour data of precipitable water vapor (PWV) from 248 observation stations of the Crustal Movement Observation Network of China (CMONOC). Significant variations in PWV distribution among China's climate regions are observed. Average PWV values vary considerably across regions, with the Tropical and Subtropical Monsoon Climate (TSMC) having the highest mean PWV of 32.09 mm, followed by the Midlatitude Monsoon Climate (MMC) with 15.70 mm, the Humid Continental Climate (HCC) with 9.26 mm, and the Plateau Climate (PC) with 7.66 mm. Seasons exhibit summer concentration and winter reduction across regions. Most stations exhibit increasing PWV trends, while a few show decreasing trends. The amplitude of annual PWV variations is higher in TSMC and MMC regions with a range from 8 to 22 mm, compared to HCC and PC regions with a range from 2 to 15 mm. Semiannual variations range from 0 to 6 mm in each region. PWV negatively correlates with latitude, altitude and pressure, particularly in the TSMC. Positive correlations with temperature exist in all regions, with varying strengths. Thermodynamic factors primarily impact PWV, while dynamic factors have secondary influence, equally significant in the PC region.

An improved typhoon monitoring model based on precipitable water vapor and pressure

The potential of monitoring the movement of typhoons using the precipitable water vapor (PWV) has been confirmed. However, monitoring the movement of typhoon is focused on PWV, making it difficult to describe the movement of a typhoon in detail minutely and resulting in insufficient accuracy. Hence, based on PWV and meteorological data, we propose an improved typhoon monitoring mode. First, the European Centre for Medium-Range Weather Forecasts Reanalysis 5-derived PWV (ERA5-PWV) and the Global Navigation Satellite System-derived PWV (GNSS-PWV) were compared with the reference radiosonde PWV (RS-PWV). Then, using the PWV and atmospheric parameters derived from ERA5, we discussed the anomalous variations of PWV, pressure (P), precipitation, and wind speed during different typhoons. Finally, we compiled a list of critical factors related to typhoon movement, PWV and P. We developed an improved multi-factor typhoon monitoring mode (IMTM) with different models (i.e., IMTM-I and IMTM-II) in different cases with a higher density of GNSS observation or only Numerical Weather Prediction (NWP) data. The IMTM was evaluated through the reference movement speeds of HATO and Mangkhut from the China Meteorological Observatory Typhoon Network (CMOTN). The results show that the root mean square (RMS) of the IMTM-I is 1.26 km/h based on ERA5-P and ERA5-PWV, and the absolute bias values are mostly within 2 km/h. Compared with the models considering the single factor ERA5-P/ERA5-PWV, the RMS of the IMTM-I is improved by 26.3% and 38.5%, respectively. The IMTM-II model manifests a residual of only 0.35 km/h. Compared with the single-factor model based on GNSS-PWV/P, the residual of the IMTM-II model is reduced by 90.8% and 84.1%, respectively. These results propose that the typhoon movement monitoring approach combining PWV and P has evident advantages over the single-factor model and is expected to supplement traditional typhoon monitoring.

Investigating the ERA5-Based PWV Products and Identifying the Monsoon Active and Break Spells with Dense GNSS Sites in Guangxi, China

Precipitable water vapor (PWV) with high precision and high temporal resolution estimated by Global Navigation Satellite System (GNSS) is widely used in atmospheric research and weather forecasting. However, most previous works are not consensual concerning the characteristics of the PWV at different time scales and the identification of active and break spells during summ er monsoon climate in Guangxi, China. Taking radiosonde (RS) observations as reference, a strong correlation (R > 0.97) exists between GNSS PWV and RS PWV with a mean root mean square error (RMSE) of 2.68 mm. The annual, seasonal, monthly, and diurnal PWV variations of three years (2017, 2018 and 2020) over Guangxi in were comprehensively investigated using 104 GNSS stations and the fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) Atmospheric Reanalysis (ERA5). The mean annual bias and RMSE between GNSS PWV and ERA5 PWV are −1.04 mm and 2.63 mm, respectively. The monthly bias and RMSE range are −0.77 to 3.87 mm, 1.32 to 4.45 mm, and the daily range is −1.41 to 1.07 mm and 1.11 to 5.02 mm, respectively. Additionally, the adopted average standardized rainfall anomaly criteria also identified 7/7/3 active spells and 5/3/7 break spells during the summer monsoon (June–September) from 2017 to 2020, respectively. During the three-year period, the daily amplitude ranges for active spells varied from 1.41 to 2.49 mm, 0.69 to 5.4 mm, and 0.88 to 1.41 mm, while the ranges for break spells were 2.45 to 6.76 mm, 1.66 to 8.17 mm, and 1.48 to 2.99 mm, respectively. The results show a superior performance of GNSS PWV compared to ERA5 PWV in Guangxi, and the maximum, minimum and occurrence time of PWV anomaly vary slightly with the season and the topography of stations. Despite temperature primarily exhibiting a negative correlation with rainfall, acting as a dampener, a positive correlation remains evident between PWV and rainfall. Therefore, densely distributed GNSS stations exhibit excellent capabilities in quantifying atmospheric water vapor and facilitating real-time monitoring of small and medium-scale weather phenomena.

Variations of precipitable water vapor in sandstorm season determined from GNSS data: the case of China’s Wuhai

In recent years, the Global Navigation Satellite System (GNSS) has witnessed rapid development. However, during the sandstorm season, the precipitable water vapor (PWVGNSS) determined from the GNSS data produces large fluctuations due to the influence of particulate matter, which can indirectly reflect the change in particulate matter concentration. To study the variations of PWVGNSS during the sandstorm season, daily data of PWVGNSS, particulate matter (PM10), and precipitation in Wuhai from 2017 to 2021 were used in this study. The principal components of PWV residual (PWVRPC) were obtained by using the least-squares linear fitting, singular spectrum analysis, and least-squares spectral analysis on PWVGNSS. The principal components of PM10 (PM10PC) were obtained by using least squares linear fitting and singular spectrum analysis for PM10. This study performed a correlation analysis of PWVRPC with PM10PC and precipitation data. The results showed a strong correlation between PWVRPC and PM10PC, with a correlation coefficient greater than 0.6. However, it was found that the correlation between PWVRPC and precipitation was not significant. This indicates that during the sandstorm season, PM10 affects PWV determined from GNSS data.Graphical

Determination and assessment of GNSS-derived precipitable water vapor in Indonesia using Ina-CORS

This work is the first attempt to investigate the accuracy and reliability of the Ina-CORS (Indonesian Continuously Operating Reference Stations) for meteorological applications. We used the standard procedures to determine and assess the precipitable water vapor (PWV) obtained from the Global Navigation Satellite System (GNSS) measurements. The GNSS-derived zenith tropospheric delay (ZTD) was estimated using the GAMIT software, and it was then converted to PWV using surface pressure measurements and the water vapor weighted mean temperature. The results were compared against those derived from the fifth generation of the European Centre for Medium-Range Weather Forecasts (ECMWF ERA5) data.Comparisons against the ECMWF ERA5 data reveal the following findings. Values for the mean bias and root mean square deviation (RMSD) of the GNSS-derived ZTD over the period 2011–2020 are −6.6 mm and 14.1 mm, respectively, representing the total percentage error of about 0.4%. The total errors from the surface pressure measurements and the calculations of water vapor weighted mean temperature introduce errors to the PWV values with the magnitude less than 1 mm. The GNSS-derived PWV values over Indonesia are comparable with those derived from the ECMWF ERA5 data. The mean bias and RMSD of PWV values are −0.60 mm and 2.59 mm, respectively. In addition, spatio-temporal variations of the GNSS-derived PWV can capture the main atmospheric features that are consistent with the other results deduced from long-term rainfall data. One of the features is the annual and semiannual cycles of PWV that largely correspond to the annual migration of the Asian-Australian monsoon system. Based on these results, we can conclude that the Ina-CORS is capable enough for accurate monitoring of the Indonesian weather and climate variations.

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.

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.

Seasonal Difference of the Spatio-Temporal Variation of Precipitable Water Vapor in China

Seasonal Difference of the Spatio-Temporal Variation of Precipitable Water Vapor in China

A combined model based on data decomposition and multi-model weighted optimization for precipitable water vapor forecasting

Water shortage is a major problem facing the world. Artificial precipitation enhancement is an effective way to improve precipitation conversion rate, but the selection of artificial precipitation enhancement operation timing is the main difficulty, and the precipitable water vapor(PWV) is a major index. The variation of PWV is nonlinear and unstable due to complex factors, especially in the Qilian mountains in the northeastern part of the Qinghai-Tibet Plateau, so it is difficult to predict it accurately. Therefore, based on the analysis of the observed data of microwave radiometer in Qilian Mountains, a new combined model is constructed which considers both data decomposition and prediction of several single models in this research. In the data preprocessing stage, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) technique is used to decompose and de-noise the PWV sequence. In the prediction stage, four neural network with unique characteristics, back propagation neural network (BPNN), long short term memroy (LSTM), bidirectional gated recurrent unit (BiGRU) and temporal convolutional network (TCN), are selected to predict the decomposed data respectively. A variant of gray Wolf optimization algorithm (IGWO) is used to determine the optimal weight of the model, and finally the comprehensive predicted value is obtained by weighting calculation. Through the analysis of experimental results, in the longest 15-step prediction, compared with CEEMDAN-BP, CEEMDAN-LSTM, CEEMDAN-BiGRU, CEEMDAN-TCN, the prediction accuracy can be improved by 54.17%, 35.05%, 22.38%, 23.86%, respectively. Other step size prediction also achieves the highest prediction accuracy.

Assessing the impact of variations in atmospheric water vapour content over Nigeria from GNSS measurements

This study analyses the meteorological impact of the variability of precipitable water vapour (PWV) retrieved from ground-based global navigation satellite system (GNSS) stations over Nigeria from 2013 to 2014; these measurements represent the foremost probe of GNSS PWV distribution and variability over Nigeria. In this study, GNSS PWV daily estimates were grouped into monthly and seasonal averages; the variations in the monthly and seasonal estimates of GNSS PWV were characterized and correlated with different weather events that are regarded as good climate change indicators. The results revealed that the spatiotemporal changes in PWV content are largely subjugated by the effects of latitude, topographical features, the seasons and the continental air masses. Our study shows that there is a very strong seasonal interplay among the GNSS PWV, relative humidity, rainfall and cloud estimates. In addition, GNSS PWV and total electron content (TEC) estimates show an opposite relationship; the semi-diurnal relationship between GNSS PWV and TEC is stronger than the seasonal relationship. The seasonal relation among GNSS PWV, temperature and wind speed appears weak, while very strong interplay exists among the GNSS PWV, sun spot number and total solar radiation estimates. Our results confirm that GNSS PWV is a good pointer for weather forecasting/monitoring and fit for climate monitoring if available on a longer time scale. Finally, we recommend the densification of the GNSS network in Nigeria, as this will enable 3D profiling of PWV, thereby providing more information on GNSS PWV time series, which is needed for long-term climatology.

Long-term variations in precipitable water vapor and temperature at Lenghu Site

Context. A high-quality astronomical observing site, the Lenghu site, was recently discovered on the Tibetan Plateau. The statistical analysis of site quality monitor data collected so far have indicated that the precipitable water vapor (PWV) is lower than 2 mm for 55% of the night. The nighttime temperature is also very stable; the median of the intranight variation amplitude is only 2.4 °C. Aims. The long-term trend of the PWV and temperature variations, which is essential for future facilities operating at infrared, millimeter, and submillimeter wavelengths, is investigated in this work. Methods. Here we used the atmospheric reanalysis datasets of the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) and ERA5, the fifth major atmospheric reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ECMWF), as well as the measurements from the weather station at the site to conduct a long-term (22 yr) comparative analysis of PWV and temperature at the Lenghu site. Results. The weighted annual mean nighttime temperature and PWV increase at rates of 0.17 °C decade−1 and 0.12 ~ 0.13 mm decade−1, respectively. The nighttime temperature and PWV slightly both decrease during the winter with rates of −0.04 °C decade−1 and −0.05 ~ −0.07 mm decade−1, respectively. Conclusions. These results indicate that the variation patterns of PWV and temperature at the Lenghu site are quite stable, especially during the winter; it is projected that the nighttime average PWV will be below 1 mm and the nighttime average temperature will be below −13 °C toward the end of this century. These conditions are ideal for large optical, infrared, millimeter, and submillimeter facilities where great scientific discoveries will be made that address the ultimate questions of humankind.

Revisiting mechanisms of the Mesoamerican Midsummer drought

Observations show that the seasonal cycle of precipitation in parts of southern Mexico and Central America exhibits a bimodal signal, known as the Midsummer drought (MSD), but there is no consensus on which processes are most relevant for the two-peak structure of the rainy season. This paper evaluates three hypotheses that could explain the MSD: the SST cloud-radiative feedback, the solar declination angle and the Caribbean Low-Level Jet (CLLJ) moisture transport hypotheses. Model experiments produced by the Met Office Hadley Centre (MOHC) for CMIP6 as well as ERA5 reanalysis data are used to critically assess the predictions of each hypothesis. The simulations capture the double peak signal of precipitation well and reasonably simulate the spatial and temporal variations of the MSD and other relevant climate features such as the CLLJ. Evidence from our analysis suggests that the Eastern Pacific SSTs do not increase in late summer in ERA5 data and only slightly increase in the simulations. More importantly, the Eastern Pacific SST variability in ERA5 and in the model experiments cannot explain the differences in the seasonality of precipitation. The net shortwave radiation at the surface shows a two-peak seasonal cycle; however, this behaviour appears to result from a strong anti-correlation of the incoming shortwave and convective activity due to cloud radiative-effects. There was no evidence found by this study of a causal link in which absorption of shortwave energy forces precipitation variations, as suggested by the solar declination angle hypothesis. The moisture convergence, CLLJ and the precipitable water vapor variations best explain the characteristics of the observed and simulated MSD, particularly for the onset of the MSD. The diagnosed variations of moisture convergence, which are synchronous with the timing of the MSD, point to a dynamic mechanism in which the low-level inflow from the Caribbean is more important for the MSD than other radiative mechanisms.

GPS Measurements of Precipitable Water Vapor Can Improve Survey Calibration: A Demonstration from KPNO and the Mayall z-band Legacy Survey

Dual-band Global Positioning Satellite (GPS) measurements of precipitable water vapor (PWV) at the Kitt Peak National Observatory predict the overall per-image sensitivity of the Mayall z-band Legacy Survey (MzLS). The per-image variation in the brightness of individual stars is strongly correlated with the measured PWV and the color of the star. Synthetic stellar spectra through TAPAS transmission models successfully predict the observed PWV-induced photometric variation. We find that PWV absorption can be well approximated by a linear relationship with (airmass × PWV)0.6 and present an update on the traditional treatment in the literature. The MzLS zero-point sensitivity in electrons s−1 varies with a normalized-mean absolute deviation of 61 mmag. PWV variation accounts 23 mmag of this zero-point variation. The MzLS per-image absolute sensitivity decreases by 40 mmag per effective mm of PWV. The overall gray offset portion of this variation is corrected by the calibration to a reference catalog. But the relative calibration error between blue (r − z < 0.5 mag) versus red (1.2 mag < r − z) stars increases by 0.3–2 mmag per effective mm of PWV. We argue that GPS systems provide more precise PWV measurements than using differential measurements of stars of different colors and recommend that observatories install dual-band GPS as a low-maintenance, low-cost, auxiliary calibration system. We extend our results of the need for well-calibrated PWV measurements by presenting the calculations of the PWV photometric impact on three science cases of interest: stellar photometry, supernova cosmology, and quasar identification and variability.

Seasonal and annual variations of the GPS-based precipitable water vapor over Sumatra, Indonesia

This study investigates the spatial distribution of seasonal and annual as well as semi-annual variations of the precipitable water vapor (PWV) over Sumatra Island, Indonesia, as revealed from a dense network of the Global Positioning System (GPS) receivers. We have analyzed the GPS-based PWV time series at 57 stations during the period of 2004 to 2020, and we revealed some features that have not yet been reported in detail by previous investigators using rainfall data. A notable feature is the presence of the seasonal PWV gradient in the northwest-southeast direction, which exist all times of the year. This gradient represents the annual migration of the Asian-Australian monsoon system, which migrates back and forth in the northwest-southeast direction. In addition, this gradient can also show the periodic changes between the wet and dry seasons over the region. The other feature is the presence of a clear demarcation line around the latitude of 2oS, separating Sumatra Island into the annual and semi-annual regimes. While the semi-annual variation tends to be concentrated in the northern side of 2oS, the annual one dominates the region in the southern side of 2oS. It is also revealed that the stations near the demarcation line are most likely to receive rainfall for most of the year.

Temporal Analysis of GNSS-Based Precipitable Water Vapor during Rainy Days over the Philippines from 2015 to 2017

Precipitable water vapor (PWV) is a parameter used to estimate water vapor content in the atmosphere. In this study, estimates of PWV from PIMO, PLEG and PPPC global navigation satellite system (GNSS) stations are evaluated regarding the PWV obtained from its collocated radiosonde (RS) stations. GNSS PWV were highly correlated with RS PWV (R ~ 0.97). Mean bias error (MBE) between −0.18 mm and −13.39 mm, and root mean square error (RMSE) between 1.86 mm and 2.29 mm showed a good agreement between GNSS PWV and RS PWV. The variations of PWV are presented. Daily variations of PWV conformed to the daily data of rainfall which agrees to the climate types of Quezon City (Type I), Legaspi (Type II), and Puerto Princesa (Type III) based on the Coronas climate classification. Moreover, PWV monthly variation at all sites is high from May to October (~62 mm) and low from November to April (~57 mm). The relationship between PWV and rainfall at all stations showed positive correlation coefficients between +0.49 to +0.83. Meanwhile, it is observed that when PWV is high (low), its variability is low (high). This study shows the potential of GNSS to study water vapor and its contribution to weather analysis.