Evaluation of surface temperature and pressure derived from MERRA-2 and ERA5 reanalysis datasets and their applications in hourly GNSS precipitable water vapor retrieval over China

Temperature and pressure play key roles in Global Navigation Satellite System(GNSS) precipitable water vapor(PWV) retrieval. The National Aeronautics and Space Administration(NASA) and European Center for Medium-Range Weather Forecasts(ECMWF) have released their latest reanalysis product: the modern-era retrospective analysis for research and applications, version 2(MERRA-2) and the fifthgeneration ECMWF reanalysis(ERA5), respectively. Based on the reanalysis data, we evaluate and analyze the accuracy of the surface temperature and pressure products in China using the the measured temperature and pressure data from 609 ground meteorological stations in 2017 as reference values.Then the accuracy of the two datasets and their performances in estimating GNSS PWV are analyzed. The PWV derived from the pressure and temperature products of ERA5 and MERRA-2 has high accuracy. The annual average biases of pressure and temperature for ERA5 are-0.07 hPa and 0.45 K, with the root mean square error(RMSE) of 0.95 hPa and 2.04 K, respectively. The annual average biases of pressure and temperature for MERRA-2 are-0.01 hPa and 0.38 K, with the RMSE of 1.08 h Pa and 2.66 K, respectively.The accuracy of ERA5 is slightly higher than that of MERRA-2. The two reanalysis data show negative biases in most regions of China, with the highest to lowest accuracy in the following order: the south,north, northwest, and Tibet Plateau. Comparing the GNSS PWV calculated using MERRA-2(GNSS MERRA-2 PWV) and ERA5(GNSS ERA5 PWV) with the radiosonde-derived PWV from 48 co-located GNSS stations and the measured PWV of the co-location radiosonde stations, it is found that the accuracy of GNSS ERA5 PWV is better than that of GNSS MERRA-2 PWV. These results show the different applicability of surface temperature and pressure products from MERRA-2 and ERA5 data, indicating that both have important applications in meteorological research and GNSS water vapor monitoring in China.

Decadal trends in precipitable water vapor over the Indus River Basin using ERA5 reanalysis data

Precipitable Water Vapor(PWV)constitutes a pivotal parameter within the domains of atmospheric science,and remote sensing due to its profound influence on Earth’s climate dynamics and weather patterns.It exerts a significant impact on atmospheric stability absorption and emission of radiation,thus engendering alterations in the Earth’s radiative equilibrium.As such,precise quantification of PWV holds the potential to enhance weather prognostication and fortify preparedness against severe meteorological phenomena.This study aimed to elucidate the spatial and temporal changes in seasonal and annual PWV across the Indus River Basin and its sub-basins using ERA5 reanalysis datasets.The present study used ERA5 PWV(entire atmospheric column),air temperature at 2 m(t2m)and 500 hPa(T_500hPa),evapotranspiration,and total cloud cover data from 1960 to 2021.Theil Sen slope estimator and Mann-Kendall test were used for trend analysis.Correlation and multiple regression methods were used to understand the association of PWV with other factors.The findings have unveiled the highest increase in mean PWV during the monsoon(0.40 mm/decade),followed by premonsoon(0.37 mm/decade),post-monsoon(0.27 mm/decade),and winter(0.19 mm/decade)throughout the study period.Additionally,the mean PWV exhibited the most pronounced positive trend in the sub-basin Lower Indus(LI),followed by Panjnad(P),Kabul(K),and Upper Indus(UI)across all seasons,except winter.Annual PWV has also risen in the Indus basin and its sub-basins over the last six decades.PWV exhibits a consistent upward trend up to an elevation of 3500 m within the basin which is most pronounced during the monsoon season,followed by the pre-monsoon.The escalating PWV within the basin is reasonably ascribed to increasing air temperatures,augmented evapotranspiration,and heightened cloud cover.These findings hold potential utility for pertinent authorities engaged in water resource management and planning.

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.

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

This study analyzes the spatial and temporal distribution characteristics of seasonal precipitable water vapor (PWV) in China between 1979 and 2008. To achieve this, the observed temperature dew point difference and atmospheric pressure at various altitudes of 102 radiosonde stations were utilized. The analysis involved calculating and examining the PWV variations across the different seasons in the study period. The results are illustrated as follows: 1) The annual mean and seasonal mean PWV over China is characterized by decreasing from southeast to northwest. The PWV has obvious seasonal features. It is the least in winter, which is mainly affected by latitude and altitude, and the most in summer, which is mainly affected by the monsoon. It is the medium in spring and autumn, with more in autumn than in spring. 2) The spatial distribution pattern of four seasonal PWV is approximately opposite to its variation coefficient distribution pattern, that is, the monsoon (non-monsoon) areas with more (less) PWV have a smaller (larger) variation amplitude. 3) The distribution pattern of four seasonal PWV shows a consistent distribution pattern in the whole region and the winter characteristics are the most significant. The abnormal variation of PWV shows consistent interdecadal oscillation, and it exhibits an obvious phase transition around 2002 when the PWV has an increasing shift in winter, spring, and summer, while it is more complicated in autumn.

The GNSS PWV retrieval using non-observation meteorological parameters based on ERA5 and its relation with precipitation

The pressure and temperature significantly influence precipitable water vapor(PWV) retrieval. Global Navigation Satellite System(GNSS) PWV retrieval is limited because the GNSS stations lack meteorological sensors. First, this article evaluated the accuracy of pressure and temperature in 68 radiosonde stations in China based on ERA5 Reanalysis data from 2015 to 2019 and compared them with GPT3model. Then, the accuracy of pressure and temperature calculated by ERA5 were estimated in 5 representative IGS stations in China. And the PWV calculated by these meteorological parameters from ERA5(ERA5-PWV) were analyzed. Finally, the relation between ERA5-PWV and precipitation was deeply explored using wavelet coherence analysis in IGS stations. These results indicate that the accuracy of pressure and temperature of ERA5 is better than the GPT3 model. In radiosonde stations, the mean BIAS and MAE of pressure and temperature in ERA5 are-0.41/1.15 hpa and-0.97/2.12 K. And the mean RMSEs are 1.35 hpa and 2.87 K, which improve 74.77% and 40.58% compared with GPT3 model. The errors of pressure and temperature of ERA5 are smaller than the GPT3 model in bjfs, hksl and wuh2, and the accuracy of ERA5-PWV is improved by 18.77% compared with the GPT3 model. In addition, there is a significant positive correlation between ERA5-PWV and precipitation. And precipitation is always associated with the sharp rise of ERA5-PWV, which provides important references for rainfall prediction.

Antecedent Precipitation Index to Estimate Soil Moisture and Correlate as a Triggering Process in the Occurrence of Landslides

Landslides are highly dangerous phenomena that occur in different parts of the world and pose significant threats to human populations. Intense rainfall events are the main triggering process for landslides in urbanized slope regions, especially those considered high-risk areas. Various other factors contribute to the process;thus, it is essential to analyze the causes of such incidents in all possible ways. Soil moisture plays a critical role in the Earth’s surface-atmosphere interaction systems;hence, measurements and their estimations are crucial for understanding all processes involved in the water balance, especially those related to landslides. Soil moisture can be estimated from in-situ measurements using different sensors and techniques, satellite remote sensing, hydrological modeling, and indicators to index moisture conditions. Antecedent soil moisture can significantly impact runoff for the same rainfall event in a watershed. The Antecedent Precipitation Index (API) or “retained rainfall,” along with the antecedent moisture condition from the Natural Resources Conservation Service, is generally applied to estimate runoff in watersheds where data is limited or unavailable. This work aims to explore API in estimating soil moisture and establish thresholds based on landslide occurrences. The estimated soil moisture will be compared and calibrated using measurements obtained through multisensor capacitance probes installed in a high-risk area located in the mountainous region of Campos do Jordão municipality, São Paulo, Brazil. The API used in the calculation has been modified, where the recession coefficient depends on air temperature variability as well as the climatological mean temperature, which can be considered as losses in the water balance due to evapotranspiration. Once the API is calibrated, it will be used to extrapolate to the entire watershed and consequently estimate soil moisture. By utilizing recorded mass movements and comparing them with API and soil moisture, it will be possible to determine thresholds, thus enabling anticipation of landslide occurrences.

Analysis of Heavy Precipitation Event in Northeast China from August 1 to 5, 2023

This study delves into the multiple weather systems and their interaction mechanisms that caused the severe rainfall event in Northeast China in early August 2023. The analysis reveals that the atmospheric circulation in the mid-to-high latitudes of the Eurasian continent exhibited a significant “two troughs and two ridges” structure, with Northeast China located precisely in the peripheral region of the subtropical high, significantly influenced by its marginal airflows. Additionally, the residual circulation of Typhoon “Doksuri” interacting with the subtropical high and upper-level troughs significantly increased the rainfall intensity and duration in the region. In particular, the continuous and powerful transport of the southwest jet provided the necessary moisture and unstable conditions for the generation and development of convective systems. The rainfall event resulted in nearly 40,000 people affected and crop damage covering an area of approximately 4000 hectares, demonstrating the severity of extreme weather. The study emphasizes that strengthening meteorological monitoring and early warning systems, as well as formulating and improving emergency response mechanisms, are crucial for reducing potential disaster losses caused by heavy rainfall. Future research can further explore the interaction mechanisms among weather systems, limitations of data sources, and the connection between long-term trends of heavy rainfall events and global climate change.

Response of soil water dynamics to precipitation years under different vegetation types on the northern Loess Plateau, China

Implementation of the Grain-for-Green project has led to rapid land cover changes and resulted in a significantly increased vegetation cover on the Loess Plateau of China during the past few decades. The main objective of this study was to examine the responses of soil water dynamics under four typical vegetation types against precipitation years. Soil water contents (SWCs) were measured in 0–4.0 m profiles on a hillslope under the four vegetation types of shrub, pasture, natural fallow and crop in a re-vegetated catchment area from April to October in normal (2010), dry (2011), wet (2014) and extremely wet (2013) years. The results indicated that precipitation and vegetation types jointly controlled the soil water temporal dynamics and profile characteristics in the study region. SWCs in 0–4.0 m profiles of the four vegetation types were ranked from high to low as crop>fallow>pasture>shrub and this pattern displayed a temporal stability over the four years. In the extremely wet year, SWC changes occurred in the 0–2.0 m layer under shrub and pasture while the changes further extended to the depth of 4.0-m deep layers under fallow and crop. In the other three years, SWCs changes mainly occurred in the 0–1.0 m layer and kept relatively stable in the layers deeper than 1.0 m for all the four vegetation types. The interannual variation in soil depth of SWCs was about 0–2.0 m for shrub and pasture, about 0–3.4 m for fallow and about 0–4.0 m for crop, respectively. The dried soil layers formed at the depths of 1.0, 0.6, 1.6 and 0.7 m under shrub, and 1.0, 1.0, 2.0 and 0.9 m under pasture, respectively in 2010, 2011, 2013 and 2014. The infiltrated rainwater mostly stayed in the 0–1.0 m layer and hardly supplied to soil depth >1.0 m in normal, dry and wet years. Even in the extremely wet year of 2013, rainwater recharge depth did not exceed 2.0 m under shrub and pasture. This implied that soil desiccation was difficult to remove in normal, dry and wet years, and soil desiccation could be removed in 1.0–2.0 m soil layers even in the extremely wet year under shrub and pasture. The results indicated that the natural fallow was the best vegetation type for achieving sustainable utilization of soil water and preventing soil desiccation.

Precipitation isotopes in the Tianshan Mountains as a key to water cycle in arid central Asia

The Tianshan Mountains is a wet island in arid central Asia, and precipitation amount across the mountains is much larger than that in the surrounding low-lying areas. To investigate the regional water cycle in arid central Asia, stable isotope composition in precipitation has received increased attention during the past decades. This paper reviewed current knowledge of observed and simulated stable isotope ratios in precipitation across the Tianshan Mountains. The temperature effect of stable isotopes in precipitation has been widely accepted in arid central Asia and can be applied to paleoclimate reconstruction using ice cores. The seasonality of precipitation isotopically enriched in summer months and depleted in winter months is usually attributed to westerly-dominated moisture, but different trajectory paths to the northern and southern slopes of the Tianshan Mountains can still be modelled. The proportional contribution and its uncertainty of surface evaporation and transpiration to local precipitation can be estimated using the isotope approach, and transpiration plays a dominant role in recycled moisture for oasis sites. The impact of below-cloud evaporation on precipitation stable isotopes on the southern slope is usually larger than that on the northern slope.

Assessment of Soil Water Content in Field with Antecedent Precipitation Index and Groundwater Depth in the Yangtze River Estuary

To better understand soil moisture dynamics in the Yangtze River Estuary （YRE） and predict its variation in a simple way, a field monitoring experiment was carried out along the north branch of the Yangtze River, where seawater intrusion was strong and salt-water variation is one of the limiting factors of local agriculture. In present paper, relation between antecedent precipitation index （API） and soil water content is studied, and effects of groundwater depth on soil water content was analyzed. A relatively accurate prediction result of soil water content was reached using a neural network model. The impact analysis result showed that the variation of the API was consistent with soil water content and it displayed significant correlations with soil water content in both 20 and 50 cm soil layer, and higher correlation was observed in the layer of 20 cm. Groundwater impact analysis suggested that soil moisture was affected by the depth of groundwater, and was affected more greatly by groundwater at depth of 50 cm than that at 20 cm layer. By introducing API, groundwater depth and temperature together, a BP artificial network model was established to predict soil water content and an acceptable agreement was achieved. The model can be used for supplementing monitoring data of soil water content and predicting soil water content in shallow groundwater areas, and can provide favorable support for the research of water and salt transport in estuary area.

Use of Total Precipitable Water Classification of A Priori Error and Quality Control in Atmospheric Temperature and Water Vapor Sounding Retrieval

This study investigates the use of dynamic a priori error information according to atmospheric moistness and the use of quality controls in temperature and water vapor profile retrievals from hyperspectral infrared （IR） sounders. Temperature and water vapor profiles are retrieved from Atmospheric InfraRed Sounder （AIRS） radiance measurements by applying a physical iterative method using regression retrieval as the first guess. Based on the dependency of first-guess errors on the degree of atmospheric moistness, the a priori first-guess errors classified by total precipitable water （TPW） are applied in the AIRS physical retrieval procedure. Compared to the retrieval results from a fixed a priori error, boundary layer moisture retrievals appear to be improved via TPW classification of a priori first-guess errors. Six quality control （QC） tests, which check non-converged or bad retrievals, large residuals, high terrain and desert areas, and large temperature and moisture deviations from the first guess regression retrieval, are also applied in the AIRS physical retrievals. Significantly large errors are found for the retrievals rejected by these six QCs, and the retrieval errors are substantially reduced via QC over land, which suggest the usefulness and high impact of the QCs, especially over land. In conclusion, the use of dynamic a priori error information according to atmospheric moistness, and the use of appropriate QCs dealing with the geographical information and the deviation from the first-guess as well as the conventional inverse performance are suggested to improve temperature and moisture retrievals and their applications.

Analysis of the Relationship between the Cloud Water Path and Precipitation Intensity of Mature Typhoons in the Northwest Pacific Ocean

The relationship between precipitation intensity and cloud water in typhoon systems remains unclear.This study combined time-and space-synchronized precipitation and spectral data obtained by the Precipitation Radar(PR)as well as the Visible and Infrared Scanner(VIRS)onboard the TRMM satellite,to overcome the limitations of precipitation properties and cloud parameters not being synchronized in previous studies.A merged dataset of near-surface rain rate(RR)and corresponding cloud water path(CWP)was established and used to analyze the potential correlation between cloud microphysical properties and precipitation,to deepen our understanding of the evolution of cloud to rain.In addition,25 collocated satellite overpasses of mature typhoon cases in the Northwest Pacific Ocean from 1998 to 2012 were obtained,and the relationships between the CWP and RR of 144515 pixels were analyzed in detail.The results show that the CWP and RR of mature typhoon systems with different precipitation types,precipitation cloud phases,and vertical depths of precipitation can be fitted by a notable sigmoid function,which may be useful for estimating CWP and parameterizing precipitation in models.Furthermore,the relationship was applied and tested with an independent sample to show that RR is a significant indicator of CWP.

Meteorological applications of precipitable water vapor measurements retrieved by the national GNSS network of China

In this study, the Global Navigation Satellite System （GNSS） network of China is discussed, which can be used to monitor atmospheric precipitable water vapor （PWV）. By the end of 2013, the network had 952 GNSS sites, including 260 belonging to the Crustal Movement Observation Network of China （CMONOC） and 692 belonging to the China Meteorological Administration GNSS network （CMAGN）. Additionally, GNSS observation collecting and data processing procedures are presented and PWV data quality control methods are investigated. PWV levels as determined by GNSS and radiosonde are compared. The results show that GNSS estimates are generally in good agreement with measurements of radio- sondes and water vapor radiometers （WVR）. The PWV retrieved by the national GNSS network is used in weather forecasting, assimilation of data into numerical weather prediction models, the validation of PWV estimates by radiosonde, and plum rain monitoring. The network is also used to monitor the total ionospheric electron content.

Moisture Analysis of a Squall Line Case Based on Precipitable Water Vapor Data from a Ground-Based GPS Network in the Yangtze River Delta

A squall line swept eastward across the area of the Yangtze River Delta and produced gusty winds and heavy rain from the afternoon to the evening of 24 August 2002. In this papers the roles of moisture in the genesis and development of the squall line were studied. Based on the precipitable water vapor （PWV） data from a ground-based GPS network over the Yangtze River Delta in China, plus data from a Pennsylvania State University/National Atmospheric Center （PSU/NCAR） mesoscale model （MM5） simulation, initialized by three-dimensional variational （3D-VAR） assimilation of the PWV data, some interesting features are revealed. During the 12 hours prior to the squall line arriving in the Shanghai area, a significant increase in PWV indicates a favorable moist environment for a squall line to develop. The vertical profile of the moisture illustrates that it mainly increased in the middle levels of the troposphere, and not at the surface. Temporal variation in PWV is a better precursor for squall line development than other surface meteorological parameters. The characteristics of the horizontal distribution of PWV not only indicated a favorable moist environment, but also evolved a cyclonic wind field for a squall line genesis and development. The ＂＋2 mm＂ contours of the three-hourly PWV variation can be used successfully to predict the location of the squall line two hours later.

Variational Assimilation of GPS Precipitable Water Vapor and Hourly Rainfall Observations for a Meso-βScale Heavy Precipitation Event During the 2002 Mei-Yu Season

Recent advances in Global Positioning System （GPS） remote sensing technology allow for a direct estimation of the precipitable water vapor （PWV） from delayed signals transmitted by GPS satellites, which can be assimilated into numerical models with four-dimensional variational （4DVAR） data assimilation. A mesoscale model and its 4DVAR system are used to access the impacts of assimilating GPS-PWV and hourly rainfall observations on the short-range prediction of a heavy rainfall event on 20 June 2002. The heavy precipitation was induced by a sequence of meso-β-scale convective systems （MCS） along the mei-yu front in China. The experiments with GPS-PWV assimilation cluster and also eliminated the erroneous rainfall successfully simulated the evolution of the observed MCS systems found in the experiment without 4DVAR assimilation. Experiments with hourly rainfall assimilation performed similarly both on the prediction of MCS initiation and the elimination of erroneous systems, however the MCS dissipated much sooner than it did in observations. It is found that the assimilation-induced moisture perturbation and mesoscale low-level jet are helpful for the MCS generation and development. It is also discovered that spurious gravity waves may post serious limitations for the current 4DVAR algorithm, which would degrade the assimilation efficiency, especially for rainfall data. Sensitivity experiments with different observations, assimilation windows and observation weightings suggest that assimilating GPS-PWV can be quite effective, even with the assimilation window as short as 1 h. On the other hand, assimilating rainfall observations requires extreme cautions on the selection of observation weightings and the control of spurious gravity waves.

Seasonal dynamics of soil water content in the typical vegetation and its response to precipitation in a semi-arid area of Chinese Loess Plateau

Soil water content is a key limiting factor for vegetation growth in the semi-arid area of Chinese Loess Plateau and precipitation is the main source of soil water content in this area.To further understand the impact of vegetation types and environmental factors such as precipitation on soil water content,we continuously monitored the seasonal dynamics in soil water content in four plots(natural grassland,Caragana korshinskii,Armeniaca sibirica and Pinus tabulaeformis)in Chinese Loess Plateau.The results show that the amplitude of soil water content fluctuation decreases with an increase in soil depth,showing obvious seasonal variations.Soil water content of artificial vegetation was found to be significantly lower than that of natural grassland,and most precipitation events have difficulty replenishing soil water content below a depth of 40 cm.Spring and autumn are the key seasons for replenishment of soil water by precipitation.Changes in soil water content are affected by precipitation,vegetation types,soil evaporation and other factors.The interception effect of vegetation on precipitation and the demand for water consumption by transpiration are the key factors affecting the efficiency of soil water replenishment by precipitation in this area.Due to artificial vegetation plantation in this area,soil will face a water deficit crisis in the future.

The Impact of Precipitation on Drinking Water Resources in the Sagarmatha National Park (Mt. Everest Region), Nepal

This study focuses on the link between precipitation, the bacteriological characteristics, and the physical parameters of drinking water sources from 2016 to 2018 in the Sagarmatha National Park (Mt. Everest region), Nepal. Surface water shows a positive correlation between bacteria content, altitude and corresponding temperature, whereas water from springs shows no correlation between bacteria content and altitude and corresponding temperature. Correlation between precipitation data and both pH and conductivity suggests a link between drinking water quality and precipitation whereby high precipitation rates result in increased contamination of both surface water and springs used for drinking water. This data also indicates that during periods of low precipitation, water handling is likely to contribute to water contamination. These results highlight vulnerability to climate change as melting glacial ice and changing precipitation patterns are key factors for safe drinking water.

Evaluation of ocean tide loading effects on GPS-estimated precipitable water vapour in Turkey

Global Positioning System （GPS） has been widely used to estimate the total zenith tropo- spheric delay （ZTD） and precipitable water vapour （PWV） for weather prediction and at- mospheric research as a continuous and all-weather technique. However, estimations of ZTD and PWV are subject to effects of geophysical models with large uncertainties, particularly imprecise ocean tide models of inland seas in Turkey. In this paper, GPS data from Jan. 1, 2010 to Dec. 31, 2011 are processed using GAMIT/GLOBK at four co-located GPS stations （ISTN, ERZR, SAMN, and IZMI） with Radiosonde from the Turkish Met-Office together with several nearby IGS stations. Four widely used ocean tide models are adopted to evaluate their effects on GPS-estimated PWV, such as IERS recommended FES2004, NAO99b, CSR4.0 and GOT00. Five different strategies are taken without ocean tide model and with four ocean tide models, respectively, which are used to evaluate ocean tide models effects on GPS-estimated PWV through comparing with co-located Radiosonde. Results showed that ocean tide models have greatly affected the estimation of the pre- cipitable water vapour at stations near coasts. The ocean tide model FES2004 gave the best results when compared to Radiosonde with ＋2.12 mm in PWV at stations near coastline. While other ocean tides models agree each other at millimeter level in PWV. However, at inland GPS stations, ocean tide models have less effect on GPS-estimated PWV.

Prediction of wax precipitation region in wellbore during deep water oil well testing

During deep water oil well testing, the low temperature environment is easy to cause wax precipitation, which affects the normal operation of the test and increases operating costs and risks. Therefore, a numerical method for predicting the wax precipitation region in oil strings was proposed based on the temperature and pressure fields of deep water test string and the wax precipitation calculation model. And the factors affecting the wax precipitation region were analyzed. The results show that: the wax precipitation region decreases with the increase of production rate, and increases with the decrease of geothermal gradient, increase of water depth and drop of water-cut of produced fluid, and increases slightly with the increase of formation pressure. Due to the effect of temperature and pressure fields, wax precipitation region is large in test strings at the beginning of well production. Wax precipitation region gradually increases with the increase of shut-in time. These conclusions can guide wax prevention during the testing of deep water oil well, to ensure the success of the test.

On the Variability of Charleston South Carolina Winds, Atmospheric Temperatures, Water Levels, Waves and Precipitation

Atmospheric winds, air temperatures, water levels, precipitation and oceanic waves in the Charleston South Carolina (SC) coastal zone are evaluated for their intrinsic, internal variability over temporal scales ranging from hours to multi-decades. The purpose of this study was to bring together a plethora of atmospheric and coastal ocean state variable data in a specific locale, to assess temporal variabilities and possible relationships between variables. The questions addressed relate to the concepts of weather and climate. Data comprise the basis of this study. The overall distributions of atmospheric and coastal oceanic state variable variability, including wind speed, direction and kinematic distributions and state variable amplitudes over a variety of time scales are assessed. Annual variability is shown to be highly variable from year to year, making arithmetic means mathematically tractable but physically meaningless. Employing empirical and statistical methodologies, data analyses indicate the same number of intrinsic, internal modes of temporal variability in atmospheric temperatures, coastal wind and coastal water level time series, ranging from hours to days to weeks to seasons, sub-seasons, annual, multi-year, decades, and centennial time scales. This finding demonstrates that the atmosphere and coastal ocean in a southeastern U.S. coastal city are characterized by a set of similar frequency and amplitude modulated phenomena. Kinematic hodograph descriptors of atmospheric winds reveal coherent rotating and rectilinear particle motions. A mathematical statistics-based wind to wave-to-wave algorithm is developed and applied to offshore marine buoy data to create an hour-by-hour forecast capability from 1 to 24 hours;with confidence levels put forward. This affects a different approach to the conventional deterministic model forecasting of waves.