GPS Precipitable Water Research Articles

On the Collective Importance of Model Physics and Data Assimilation on Mesoscale Convective System and Precipitation Forecasts over Complex Terrain

Abstract Forecasting mesoscale convective systems (MCSs) and precipitation over complex terrain is an ongoing challenge even for convective-permitting numerical models. Here, we show the value of combining mesoscale constraints to improve short-term MCS forecasts for two events during the North American monsoon season in 2013, including the following: 1) the initial specification of moisture, via GPS-precipitable water vapor (PWV) data assimilation (DA); 2) kinematics via modification of cumulus parameterization; and 3) microphysics via modification of cloud microphysics parameterization. A total of five convective-permitting Weather Research and Forecasting (WRF) Model experiments is conducted for each event to elucidate the impact of these constraints. Results show that combining GPS-PWV DA with a modified Kain–Fritsch scheme and double-moment microphysics provides relatively the best forecast of both North American monsoon MCSs and convective precipitation in terms of timing, location, and intensity relative to available precipitation and cloud-top temperature observations. Additional examination on the associated reflectivity, vertical wind field, equivalent potential temperature, and hydrometeor distribution of MCS events show the added value of each individual constraint to forecast performance. Significance Statement Forecasting thunderstorm clouds and rain over mountainous regions is challenging because of limitations in having radar and rain gauges and in resolving physical drivers in forecast models. We examine the value of considering all possible constraints by incorporating moisture into these models, and correcting physics in the model treatment of cumulus and cloud microphysics parameterizations. This study demonstrates that assimilating moisture and using modified Kain–Fritsch and double-moment microphysics schemes provides the best thunderstorm cloud and rain forecasts in terms of timing, location, and intensity. Each correction improves key properties of these storms such as vertical wind, along with distribution of water in various phases. We highlight the need to improve our efforts on effectively integrating these constraints into current and future forecasts.

Performance assessment of weighted mean temperature models derived from AIRS and ERA5 reanalysis for calculating GPS precipitable water vapor in the thailand region

Performance assessment of weighted mean temperature models derived from AIRS and ERA5 reanalysis for calculating GPS precipitable water vapor in the thailand region

Development of an empirical model for weighted mean temperature over North Indian region

Weighted mean temperature ( T m ) is an important parameter in the process of GPS precipitable water vapour (PWV) estimation. GPS PWV is obtained by multiplying a proportionality constant in zenith wet delay (ZWD). This proportionality constant depends on parameter T m . The parameter T m depends on surface temperature which varies from one place to another. When this variation is large, a more specific T m is required for a region for accurate PWV estimation. The topography of the northern Indian region is quite complex and the Himalayan mountains play a vital role in several atmospheric processes in this region. Thus a regional T m model is required for this region which should be developed with observational input over the foothills of northwestern Himalayas. In the present study, a regional weighted mean temperature model is developed over this region using five years of radiosonde observations from 2016 to 2020 over five stations New Delhi, Patiala, Dehradun, Jammu, and Srinagar. Site-specific models are also developed over these locations. Their accuracies are assessed with respect to existing site-specific, regional and global models and radiosonde observations. The results show that differences between developed regional model and existing regional and global linear models are less than 3 °K. The developed regional and site specific models have the T m differences of the order of 2 °K to 3 °K which is less than the difference between other models and radiosonde observations. Site-specific models provide slightly better accuracies (less than 1°K) when compared with the developed regional model. PWV is estimated using both the models and an average difference of less than 1 mm is found when compared with radiosonde observations. The developed regional model has better PWV accuracy than any other existing model in this region. Hence, both site-specific and regional models developed in this study can be used for further PWV estimation in the north Indian region.

Variations in GPS precipitable water vapor and rainfall during the 2006–2019 Mei-yu season in Taiwan

The Mei-yu season contributes 26.3% of the annual precipitation and provides important water resources to Taiwan. GPS meteorology derives continuous, real-time and high spatial coverage of perceptible water vapor (PWV), which supports weather forecasting for rainfall indicators. This study analyzes the long-term spatial–temporal changes in GPS PWV and rainfall data during the Mei-yu season from 2006 to 2019. The daily average data show that the Mei-yu season extended in recent years. Both daily rainfall and PWV increase in mid-May and rainfall reaches a maximum in early June, while PWV maintains stable in June. The yearly results show that the trend of the accumulated rainfall in the Mei-yu season is flat, and that PWV increases by 13% over 14 years, which may be due to global warming as the trend of temperature increases by 8%. Spatially, PWV is significantly higher in southern Taiwan during the Mei-yu season, and rainfall increased the most in both the central and southern mountain regions.

The Impact of Assimilating GPS Precipitable Water Vapor in Convective-Permitting WRF-ARW on North American Monsoon Precipitation Forecasts over Northwest Mexico

AbstractWe assess the impact of GPS precipitable water vapor (GPS-PWV) data assimilation (DA) on short-range North American monsoon (NAM) precipitation forecasts, across 38 days with weak synoptic forcing, during the NAM GPS Hydrometeorological Network field campaign in 2017 over northwest Mexico. Utilizing an ensemble-based data assimilation technique, the GPS-PWV data retrieved from 18 observation sites are assimilated every hour for 12 hours into a 30-member ensemble convective-permitting (2.5 km) Advanced Research version of the Weather Research and Forecasting (WRF-ARW) model. As the assimilation of the GPS-PWV improves the initial condition of WRF by reducing the root mean square error and bias of PWV across 1200-1800 UTC, this also leads to an improvement in capturing nocturnal convection of mesoscale convective systems (MCSs; after 0300 UTC) and to an increase by 0.1 mm h-1 in subsequent precipitation during the 0300-0600 UTC period relative to no assimilation of the GPS-PWV (NODA) over the area with relatively more observation sites. This response is consistent with observed precipitation from the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement Final Precipitation product. Moreover, compared to the NODA, we find that the GPS-PWV DA decreases cloud top temperature, increases most unstable convective available energy and surface dewpoint temperature, and thus creates a more favorable condition for convective organization in the region.

On the suitability of ERA5 in hourly GPS precipitable water vapor retrieval over China

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

天山山区夏季强降水过程GPS可降水量演变特征分析

天山山区夏季强降水过程GPS可降水量演变特征分析

A new global grid model for the determination of atmospheric weighted mean temperature in GPS precipitable water vapor

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

Possibility of GPS precipitable water vapour for reservoir inflow forecasting

AbstractWe investigated the possibility of using GPS precipitable water vapour (GPS-PWV) for forecasting reservoir inflow. The correlations between monthly GPS-PWV and the inflow of two reservoirs were examined and the relationship tested, using a group method of data handling (GMDH) type neural network algorithm. The daily and monthly reservoir inflows were directly proportional to daily and monthly GPS-PWV trends. Peak reservoir inflow, however, shifted from the peak averages for GPS-PWV. A strong relationship between GPS-PWV and inflow was confirmed by high R2 values, high coefficients of correlation, and acceptable mean absolute errors (MAE) of both the daily and monthly models. The Ubon Ratana reservoir model had a monthly MAE of 54.19·106 m3 and a daily MAE of 5.40·106 m3. By comparison, the Lumpow reservoir model had a monthly MAE of 25.65·106 m3 and a daily MAE of 2.62·106 m3. The models using GPS-PWV as input data responded to extreme inflow better than traditional variables such that reservoir inflow could be predicted using GPS-PWV without using actual inflow and rainfall data. GPS-PWV, thus, represents a helpful tool for regional and national water management. Further research including more reservoirs is needed to confirm this preliminary finding.

Multiscale Variations of Precipitable Water over China Based on 1999–2015 Ground-Based GPS Observations and Evaluations of Reanalysis Products

Abstract The dense ground-based GPS provides a good tool to study water vapor distribution and multiscale variations, especially for linear trends on the interannual scale and short-term variations on the diurnal scale. It can also serve as an independent data source to evaluate performances of reanalyses. In this study, the 6-hourly precipitable water (PW) products at more than 260 GPS stations over China from 1999 to 2015 were analyzed and eight commonly used reanalyses, including 20CR version 2 (20CRv2), CFSR, ERA-Interim, JRA-25, JRA-55, MERRA, NCEP–NCAR, and NCEP–DOE AMIP-II, were evaluated. The climatological annual mean GPS PW distribution over China roughly shows a decreasing trend from southeast to northwest, with the largest annual and semiannual amplitudes in the lower reaches of the Yangtze River and mideastern China, respectively, and the smallest values in the Tibetan Plateau and southwestern China. All reanalyses (except for 20CRv2) can generally reproduce well the climatological annual mean PW (within 20%), annual amplitudes (within 20%), and semiannual amplitudes (within 20% except in the tropical monsoon region), but they all show wet biases in the Tibetan Plateau. Diurnal variation amplitudes reproduced by all reanalysis products are smaller than amplitudes estimated from GPS observations over China as a whole, and none of the reanalyses can capture the diurnal phases correctly. PW linear trends at most GPS stations in the recent 16 years are insignificant or with absolute values smaller than 0.10 mm yr−1. However, because of the assimilation of the unhomogenized radiosonde humidity data, most reanalyses show artificial decreasing PW trends (except in 20CRv2 and CFSR).

Precipitable water characteristics during the 2013 Colorado flood using ground-based GPS measurements

Abstract. During 9–16 September 2013, the Front Range region of Colorado experienced heavy rainfall that resulted in severe flooding. Precipitation totals for the event exceeded 450 mm, damages to public and private properties were estimated to be over USD 2 billion, and nine lives were lost. This study analyzes the characteristics of precipitable water (PW) surrounding the event using 10 years of high-resolution GPS PW data in Boulder, Colorado, which was located within the region of maximum rainfall. PW in Boulder is dominated by seasonal variability with an average summertime maximum of 36 mm. In 2013, the seasonal PW maximum extended into early September and the September monthly mean PW exceeded the 99th percentile of climatology with a value 25 % higher than the 40-year climatology. Prior to the flood, around 18:00 UTC on 8 September, PW rapidly increased from 22 to 32 mm and remained around 30 mm for the entire event as a result of the nearly saturated atmosphere. The frequency distribution of September PW for Boulder is typically normal, but in 2013 the distribution was bimodal due to a combination of above-average PW values from 1 to 15 September and much drier conditions from 16 to 30 September. The above-normal, near-saturation PW values during the flood were the result of large-scale moisture transport into Colorado from the Tropical Eastern Pacific and the Gulf of Mexico. This moisture transport was the product of a stagnating cutoff low over the southwestern United States working in conjunction with an anticyclone located over the southeastern United States. A blocking ridge located over the Canadian Rocky Mountains kept both of the synoptic features in place over the course of several days, which helped to provide continuous moisture to the storm, thus enhancing the accumulated precipitation totals.

The Use of Ground-Based GPS Precipitable Water Measurements over China to Assess Radiosonde and ERA-Interim Moisture Trends and Errors from 1999 to 2015

Abstract Global positioning system (GPS) data from over 260 ground-based permanent stations in China covering the period from 1 March 1999 to 30 April 2015 were used to estimate precipitable water (PW) above each site with an accuracy of about 0.75 mm. Four types of radiosondes (referred to as GZZ2, GTS1, GTS1-1, and GTS1-2) were used in China during this period. Instrumentation type changes in radiosonde records were identified by comparing PW calculated from GPS and radiosonde data. Systematic errors in different radiosonde types introduced significant biases to the estimated PW trends at stations where more than one radiosonde type was used. Estimating PW trends from reanalysis products (ERA-Interim), which assimilate the unadjusted radiosonde humidity data, resulted in an artificial downward PW trend at almost all stations in China. The statistically significant GPS PW trends are predominantly positive, consistent in sign with the increase in moisture expected from the Clausius–Clapeyron relation due to a global temperature increase. The standard deviations of the differences between ERA-Interim and GPS PW in the summer were 3 times larger than the observational error of GPS PW, suggesting that potentially significant improvements to the reanalysis could be achieved by assimilating denser GPS PW observations over China. This work, based on an entirely independent GPS PW dataset, confirms previously reported significant differences in radiosonde PW trends when using corrected data. Furthermore, the dense geographical coverage of the all-weather GPS PW observations, especially in remote areas in western China, provides a valuable resource for calibrating regional trends in reanalysis products.

Observation System Experiments with the Hourly Updating Rapid Refresh Model Using GSI Hybrid Ensemble–Variational Data Assimilation

Abstract A set of observation system experiments (OSEs) over three seasons using the hourly updated Rapid Refresh (RAP) numerical weather prediction (NWP) assimilation–forecast system identifies the importance of the various components of the North American observing system for 3–12-h RAP forecasts. Aircraft observations emerge as the strongest-impact observation type for wind, relative humidity (RH), and temperature forecasts, permitting a 15%–30% reduction in 6-h forecast error in the troposphere and lower stratosphere. Major positive impacts are also seen from rawinsondes, GOES satellite cloud observations, and surface observations, with lesser but still significant impacts from GPS precipitable water (PW) observations, satellite atmospheric motion vectors (AMVs), and radar reflectivity observations. A separate experiment revealed that the aircraft-related RH forecast improvement was augmented by 50% due specifically to the addition of aircraft moisture observations. Additionally, observations from en route aircraft and those from ascending or descending aircraft contribute approximately equally to the overall forecast skill, with the strongest impacts in the respective layers of the observations. Initial results from these OSEs supported implementation of an improved assimilation configuration of boundary layer pseudoinnovations from surface observations, as well as allowing the assimilation of satellite AMVs over land. The breadth of these experiments over the three seasons suggests that observation impact results are applicable to general forecasting skill, not just classes of phenomena during limited time periods.

Effect of Enhanced Satellite-Derived Atmospheric Motion Vectors on Numerical Weather Prediction in East Asia Using an Adjoint-Based Observation Impact Method

Abstract When producing forecasts by integrating a numerical weather prediction model from an analysis, not all observations assimilated into the analysis improve the forecast. Therefore, the impact of particular observations on the forecast needs to be evaluated quantitatively to provide relevant information about the impact of the observing system. One way to assess the observation impact is to use an adjoint-based method that estimates the impact of each assimilated observation on reducing the error of the forecast. In this study, the Weather Research and Forecasting Model and its adjoint are used to evaluate the impact of several types of observations, including enhanced satellite-derived atmospheric motion vectors (AMVs) that were made available during observation campaigns for two typhoons: Sinlaku and Jangmi, which both formed in the western North Pacific during September 2008. Without the assimilation of enhanced AMV data, radiosonde observations and satellite radiances show the highest total observation impact on forecasts. When enhanced AMVs are included in the assimilation, the observation impact of AMVs is increased and the impact of radiances is decreased. The highest ratio of beneficial observations comes from GPS Precipitable Water (GPSPW) without the assimilation of enhanced AMVs. Most observations express a ratio of approximately 60%. Enhanced AMVs improve forecast fields when tracking the typhoon centers of Sinlaku and Jangmi. Both the model background and the analysis are improved by the continuous cycling of enhanced AMVs, with a greater reduction in forecast error along the background trajectory than the analysis trajectory. Thus, while the analysis–forecast system is improved by assimilating these observations, the total observation impact is smaller as a result of the improvement.

Numerical simulation of rainfall with assimilation of conventional and GPS observations over north of Iran

&lt;p&gt;In this work, the effect of assimilation of synoptic, radiosonde and ground-based GPS precipitable water vapor (PWV) data has been investigated on the short-term prediction of precipitation, vertical relative humidity and PWV fields over north of Iran. We selected two rainfall events (i.e. February 1, 2014, and September 17, 2014) caused by synoptic systems affecting the southern coasts of the Caspian Sea. These systems are often associated with a shallow and cold high pressure located over Russia that extends towards the southern Caspian Sea. The three dimensional variational (3DVAR) data assimilation system of the weather research and forecasting (WRF) model is used in two rainfall cases. In each case, three numerical experiments, namely CTRL, CONVDA and GPSCONVDA, are performed. The CTRL experiment uses the global analysis as the initial and boundary conditions of the model. In the second experiment, surface and radiosonde observations are inserted into the model. Finally, the GPSCONVDA experiment uses the GPS PWV data in the assimilation process in addition to the conventional observations. It is found that in CONVDA experiment, the mean absolute error (MAE) of the accumulated precipitation is reduced about 5 and 13 percent in 24h model simulation of February and September cases, respectively, when compared to CTRL. Also, the results in both cases suggest that the assimilation of GPS data has the greatest impact on model PWV simulations, with maximum root mean squares error (RMSE) reduction of 0.7 mm. In the GPSCONVDA experiment, comparison of the vertical profiles of 12h simulated relative humidity with the corresponding radiosonde observations shows a slight improvement in the lower levels.&lt;/p&gt;

Global Water Vapor Trend from 1988 to 2011 and Its Diurnal Asymmetry Based on GPS, Radiosonde, and Microwave Satellite Measurements

Abstract This study analyzes trends in precipitable water (PW) over land and ocean from 1988 to 2011, the PW–surface temperature Ts relationship, and their diurnal asymmetry using homogenized radiosonde data, microwave satellite observations, and ground-based global positioning system (GPS) measurements. It is found that positive PW trends predominate over the globe, with larger magnitudes over ocean than over land. The PW trend is correlated with surface warming spatially over ocean with a pattern correlation coefficient of 0.51. The PW percentage increase rate normalized by Ts expressed as is larger and closer to the rate implied by the Clausius–Clapeyron (C–C) equation over ocean than over land. The 2-hourly GPS PW data show that the PW trend from 1995 to 2011 is larger at night than during daytime. Nighttime PW monthly anomalies correlate positively and significantly with nighttime minimum temperature Tmin at all stations, but this is not true for daytime PW and maximum temperature Tmax. The ratio of relative PW changes with Tmin () is larger and closer to the C–C equation’s implied value of ~7% K−1 than . This suggests that the relationship between PW and Ts at night is a better indicator of the water vapor feedback than that during daytime, when clouds and other factors also influence Ts.

A Comprehensive Analysis of a Heavy Precipitation Event in Chengdu Plain (China) Based on Ground-Based GPS

This study utilized the ground-based GPS water vapor monitoring network in the Chengdu Plain (102.9°–104.9°E, 30.1°–31.4°N), alongside radiosonde data and National Center for Environmental Prediction and National Center for Atmospheric Research (NCEP/NCAR) reanalysis data, to conduct a comprehensive analysis of a heavy precipitation event in this region in 2008. Correlations were found between the GPS precipitable water vapor (GPS-PWV) variations, the actual precipitation in the region, and the physical mechanism for the GPS-PWV variations. The research results indicate that the variation trends in precipitable water vapor had a significant correlation with actual precipitation. The precipitable water vapor increased and decreased significantly before and after the precipitation event, respectively. The residence time of precipitable water vapor at high levels was correlated with the duration of actual precipitation to some extent. The maximum value of the precipitation intensity lagged behind the precipitable water vapor peak, which brought forward precipitation to a certain degree. A strong ascending motion of the air was linked to increases in PWV, and the intensity of the ascending motion was strongly correlated with GPS-PWV. Different atmospheric thermodynamic conditions also had a notable effect on GPS-PWV variations.

On the inclusion of GPS precipitable water vapour in the nowcasting of rainfall

Abstract. The temporal behaviour of precipitable water vapour (PWV) retrieved from GPS delay data is analysed in a number of case studies of intense precipitation in the Lisbon area, in the period 2010–2012 and in a continuous annual cycle of 2012 observations. Such behaviour is found to correlate positively with the probability of precipitation, especially in cases of severe rainfall. The evolution of the GPS PWV in a few stations is analysed by a least-squares fitting of a broken line tendency, made by a temporal sequence of ascents and descents over the data. It is found that most severe rainfall events occur in descending trends after a long ascending period and that the most intense events occur after steep ascents in PWV. A simple algorithm, forecasting rain in the 6 h after a steep ascent of the GPS PWV in a single station, is found to produce reasonable forecasts of the occurrence of precipitation in the nearby region, without significant misses in what concerns larger rain events, but with a substantial amount of false alarms. It is suggested that this method could be improved by the analysis of 2-D or 3-D time-varying GPS PWV fields or by its joint use with other meteorological data relevant to nowcast precipitation.

National Weather Service Forecasters Use GPS Precipitable Water Vapor for Enhanced Situational Awareness during the Southern California Summer Monsoon

Abstract During the North American Monsoon, low-to-midlevel moisture is transported in surges from the Gulf of California and Eastern Pacific Ocean into Mexico and the American Southwest. As rising levels of precipitable water interact with the mountainous terrain, severe thunderstorms can develop, resulting in flash floods that threaten life and property. The rapid evolution of these storms, coupled with the relative lack of upper-air and surface weather observations in the region, make them difficult to predict and monitor, and guidance from numerical weather prediction models can vary greatly under these conditions. Precipitable water vapor (PW) estimates derived from continuously operating ground-based GPS receivers have been available for some time from NOAA’s GPS-Met program, but these observations have been of limited utility to operational forecasters in part due to poor spatial resolution. Under a NASA Advanced Information Systems Technology project, 37 real-time stations were added to NOAA’s GPS-Met analysis providing 30-min PW estimates, reducing station spacing from approximately 150 km to 30 km in Southern California. An 18–22 July 2013 North American Monsoon event provided an opportunity to evaluate the utility of the additional upper-air moisture observations to enhance National Weather Service (NWS) forecaster situational awareness during the rapidly developing event. NWS forecasters used these additional data to detect rapid moisture increases at intervals between the available 1–6-h model updates and approximately twice-daily radiosonde observations, and these contributed tangibly to the issuance of timely flood watches and warnings in advance of flash floods, debris flows, and related road closures.

2014년 강원 폭설동안 GPS 가강수량 탐측

2014년 강원 폭설동안 GPS 가강수량 탐측