Ground-based Global Positioning System Observations Research Articles

Correlation Analysis between Precipitation and Precipitable Water Vapor over China Based on 1999–2015 Ground-Based GPS Observations

Abstract Correlation analysis between precipitable water vapor (PWV) and precipitation over China was conducted by combining high-quality PWV data based on 1999–2015 ground-based global positioning system (GPS) observations with the measurements at matched meteorological stations in the same period. The mean correlation coefficient at all the stations is approximately 0.73, indicating that there is a significant positive correlation between PWV content and precipitation measurements, and the comparison of correlation among different climate types suggests that the distribution characteristics of the correlation coefficients are distinctively related to different climate types. There is also some positive correlation between PWV and precipitation long-term trends, with the correlation coefficients of monthly anomalies ranging generally from 0.2 to 0.6. Furthermore, the intensity of both PWV and precipitation extremes shows a long-term upward trend overall, with the most-intense events showing more significant increases. The extreme precipitation–temperature scaling rate of changes can reach above Clausius–Clapeyron (CC) scaling, whereas that of the extreme PWV-temperature is sub-CC overall, with regional differences in the specific scaling values. The correlation analysis in this work is of great significance for long-term climate analysis and extreme weather understanding, which provides a valuable reference for better utilizing the advantages of PWV data to carry out the studies above. Significance Statement Atmospheric water vapor is crucial to the climate system, especially in the context of global warming, and accurate knowledge of the correlation between precipitable water vapor (PWV) and precipitation is of great significance for long-term climate analysis and extreme precipitation weather forecasting. We take full advantage of the long-term homogeneity of ground-based GPS to conduct long-term correlation analysis between GPS-derived PWV and precipitation over China. Results show a significant positive correlation between them, and the degree of correlation is related to different climate types. The correlation of monthly anomalies is also positive, and, over the long-term, both water vapor and precipitation extremes have been increasing in intensity, with more significant increases occurring in the most-intense events. Extreme precipitation might increase beyond thermodynamic expectations, whereas PWV increases below expectations.

Assessment of machine learning techniques for prediction of integrated water vapor using meteorological data

Weather and Climatological studies are very important in assessing atmospheric conditions like storms and cyclones. Integrated water vapor (IWV) is an important greenhouse gas in the atmosphere responsible for the Earth's radiative balance. Global Positioning System (GPS) observations have been used for monitoring the IWV variability. The IWV estimations are carried out using ground-based GPS observations at Hyderabad (17.4°N, 78.46°E), India using GAMIT software. GAMIT is GPS analysis software developed by MIT, USA. It takes input as GPS observation data containing pseudo ranges, navigation data containing ephemeris, clock errors, g-files with orbital information, and meteorological data like pressure, temperature, and relative humidity to calculate IWV. However, estimating IWV for forecasting applications is impossible with a GPS system. This paper introduces a methodology to predict IWV during normal days and severe cyclonic events using machine learning (ML) techniques. Rational quadratic Gaussian process regression (RQ-GPR) and neural network (NN) algorithms are considered for identifying suitable ML prediction algorithms over tropical conditions. Meteorological surface data like Pressure, Temperature, and relative humidity are given as input to the machine learning models. The IWV values computed from GPS are compared with the model's predicted values. RQ-GPR model is showing good accuracy with the IWV values computed from GPS against the NN model. The correlation coefficient (ρ) achieved for RQ-GPR is 0.93, and 0.86 is obtained for the NN model.
 The RMSE (Root Mean Square Error) of the predicted IWV value with RQ-GPR is better than the NN model. We have obtained mean square error (MSE) and mean absolute error (MAE) as 18.146 kg/m2 and 3.0762 kg/m2 for RQ-GPR and 27.509 kg/m2 and 3.9102 kg/m2 for the NN model which is showing RQ-GPR is a suitable model for forecasting applications. The HUDHUD cyclonic event that occurred in October 2014 is considered for testing the proposed ML algorithms. RQ-GPR model has better results in the Prediction of IWV than the NN model. The RMSE value obtained is 2.837 kg/m2 for RQ-GPR and 3.327 kg/m2 obtained from the NN model. The results indicate that the RQ-GPR model has more accuracy than the other IWV prediction models. The prediction results are helpful for meteorology, weather, and climatology studies and useful to improve the accuracy of the regional numerical weather prediction models.

Comparison of seasonal and longitudinal variation of daytime MSTID activity using GPS observation and GAIA simulations

The total electron content (TEC) data derived from the GAIA (Ground-to-topside model of Atmosphere Ionosphere for Aeronomy) is used to study the seasonal and longitudinal variation of occurrence of medium-scale traveling ionospheric disturbances (MSTIDs) during daytime (09:00–15:00 LT) for the year 2011 at eight locations in northern and southern hemispheres, and the results are compared with ground-based Global Positioning System (GPS)-TEC. To derive TEC variations caused by MSTIDs from the GAIA (GPS) data, we obtained detrended TEC by subtracting 2-h (1-h) running average from the TEC, and calculated standard deviation of the detrended TEC in 2 h (1 h). MSTID activity was defined as a ratio of the standard deviation to the averaged TEC. Both GAIA simulation and GPS observations data show that daytime MSTID activities in the northern and southern hemisphere (NH and SH) are higher in winter than in other seasons. From the GAIA simulation, the amplitude of the meridional wind variations, which could be representative of gravity waves (GWs), shows two peaks in winter and summer. The winter peak in the amplitude of the meridional wind variations coincides with the winter peak of the daytime MSTIDs, indicating that the high GW activity is responsible for the high MSTID activity. On the other hand, the MSTID activity does not increase in summer. This is because the GWs in the thermosphere propagate poleward in summer, and equatorward in winter, and the equatorward-propagating GWs cause large plasma density perturbations compared to the poleward-propagating GWs. Longitudinal variation of daytime MSTID activity in winter is seen in both hemispheres. The MSTID activity during winter in the NH is higher over Japan than USA, and the MSTID activity during winter in the SH is the highest in South America. In a nutshell, GAIA can successfully reproduce the seasonal and longitudinal variation of the daytime MSTIDs. This study confirms that GWs cause the daytime MSTIDs in GAIA and amplitude and propagation direction of the GWs control the noted seasonal variation. GW activities in the middle and lower atmosphere cause the longitudinal variation.

Comparison of Vertical Surface Deformation Estimates Derived From Space‐Based Gravimetry, Ground‐Based GPS, and Model‐Based Hydrologic Loading Over Snow‐Dominated Watersheds in the United States

AbstractSpatiotemporal variability in Earth's terrestrial water storage (TWS) causes changes in surface deformation. The potential for using ground‐based Global Positioning System (GPS) vertical displacement observations for estimating TWS is explored through a comparison of vertical displacements derived from space‐based gravimetric retrievals, ground‐based GPS, and model‐based hydrologic estimates. The study presented here focuses on two snow‐dominated basins in the Western United States for the years 2003–2016. Seasonal variations are observed in the vertical displacements derived from all three data sets, and the variation is coherent with the changes in hydrologic loading. Good consistency is observed between any two of the three data sets with gravimetric retrievals and hydrologic model estimates providing the highest level of agreement (i.e., all examined stations with correlation coefficient R > 0.70). Vertical displacements derived from gravimetric retrievals and ground‐based GPS yielded R > 0.70 for more than 89% of the stations. In addition, it is found that both GPS‐derived and space‐based, gravimetry‐derived vertical displacements clearly reflected the impact of climate variation (i.e., heavy precipitation during 2010–2011 winter followed by prolonged drought). Vertical displacements derived from the hydrologic model highlighted the relatively large precipitation convergence phase during late 2010 to early 2011 at some stations but not the prolonged drought that followed. The results indicate that ground‐based GPS observations of vertical displacement have the capability to capture variations in TWS changes, which can be systematically merged in conjunction with Gravity Recovery and Climate Experiment (GRACE) into a land surface model to improve TWS estimates in a follow‐up study.

Features of High-Latitude Ionospheric Irregularities Development as Revealed by Ground-Based GPS Observations, Satellite-Borne GPS Observations and Satellite In Situ Measurements over the Territory of Russia during the Geomagnetic Storm on March 17–18, 2015

The dynamic picture of the response of the high- and mid-latitude ionosphere to the strong geomagnetic disturbances on March 17–18, 2015, has been studied with ground-based and satellite observations, mainly, by transionospheric measurements of delays of GPS (Global Positioning System) signals. The advantages of the joint use of ground-based GPS measurements and GPS measurements on board of the Swarm Low-Earth-Orbit satellite mission for monitoring of the appearance of ionospheric irregularities over the territory of Russia are shown for the first time. The results of analysis of ground-based and space-borne GPS observations, as well as satellite, in situ measurements, revealed large-scale ionospheric plasma irregularities observed over the territory of Russia in the latitude range of 50°–85° N during the main phase of the geomagnetic storm. The most intense ionospheric irregularities were detected in the auroral zone and in the region of the main ionospheric trough (MIT). It has been found that sharp changes in the phase of the carrier frequency of the navigation signal from all tracked satellites were recorded at all GPS stations located to the North from 55° MLAT. The development of a deep MIT was related to dynamic processes in the subauroral ionosphere, in particular, with electric fields of the intense subauroral polarization stream. Analysis of the electron and ion density values obtained by instruments on board of the Swarm and DMSP satellites showed that the zone of highly structured auroral ionosphere extended at least to heights of 850–900 km.

Validation of GOME-2/Metop total column water vapour with ground-based and in situ measurements

Abstract. The total column water vapour product from the Global Ozone Monitoring Experiment-2 on board Metop-A and Metop-B satellites (GOME-2/Metop-A and GOME-2/Metop-B) produced by the Satellite Application Facility on Ozone and Atmospheric Chemistry Monitoring (O3M SAF) is compared with co-located radiosonde observations and global positioning system (GPS) retrievals. The validation is performed using recently reprocessed data by the GOME Data Processor (GDP) version 4.7. The time periods for the validation are January 2007–July 2013 (GOME-2A) and December 2012–July 2013 (GOME-2B). The radiosonde data are from the Integrated Global Radiosonde Archive (IGRA) maintained by the National Climatic Data Center (NCDC). The ground-based GPS observations from the COSMIC/SuomiNet network are used as the second independent data source. We find a good general agreement between the GOME-2 and the radiosonde/GPS data. The median relative difference of GOME-2 to the radiosonde observations is −2.7 % for GOME-2A and −0.3 % for GOME-2B. Against the GPS, the median relative differences are 4.9 % and 3.2 % for GOME-2A and B, respectively. For water vapour total columns below 10 kg m−2, large wet biases are observed, especially against the GPS retrievals. Conversely, at values above 50 kg m−2, GOME-2 generally underestimates both ground-based observations.

Analysis of meteorological variables in the Australasian region using ground- and space-based GPS techniques

Results of analysis of meteorological variables (temperature and moisture) in the Australasian region using the global positioning system (GPS) radio occultation (RO) and GPS ground-based observations verified with in situ radiosonde (RS) data are presented. The potential of using ground-based GPS observations for retrieving column integrated precipitable water vapour (PWV) over the Australian continent has been demonstrated using the Australian ground-based GPS reference stations network. Using data from the 15 ground-based GPS stations, the state of the atmosphere over Victoria during a significant weather event, the March 2010 Melbourne storm, has been investigated, and it has been shown that the GPS observations has potential for monitoring the movement of a weather front that has sharp moisture contrast. Temperature and moisture variability in the atmosphere over various climatic regions (the Indian and the Pacific Oceans, the Antarctic and Australia) has been examined using satellite-based GPS RO and in situ RS observations. Investigating recent atmospheric temperature trends over Antarctica, the time series of the collocated GPS RO and RS data were examined, and strong cooling in the lower stratosphere and warming through the troposphere over Antarctica has been identified, in agreement with outputs of climate models. With further expansion of the Global Navigation Satellite Systems (GNSS) system, it is expected that GNSS satellite- and ground-based measurements would be able to provide an order of magnitude larger amount of data which in turn could significantly advance weather forecasting services, climate monitoring and analysis in the Australasian region.

Temporal and Spatial Ionospheric Variations of 20 April 2013 Earthquake in Yaan, China

In this letter, we investigate the ionospheric variations associated with the Yaan earthquake that occurred on April 20, 2013 in China by using the total electron content (TEC) derived from ground-based Global Positioning System observations and a global ionosphere map (GIM). Geomagnetic and solar activities are taken into account. First, we focus on the coseismic ionospheric disturbances of the earthquake. The time period of the variations is about 15 min after the seismic rupture, and the maximum amplitude is about 0.1 TEC units. We then examine the preseismic ionospheric anomalies by the TEC values from the GIM and the electron density $(Ne)$ values reconstructed by computerized ionospheric tomography. Temporal variations show that the TEC and $Ne$ values simultaneously increased on April 5–8, 2013, which are 12–15 days before. This increase is possibly related to the earthquake. Spatial analysis shows that anomalies tend to appear around the epicenter and their conjugate points.

Capturing the Signature of Severe Weather Events in Australia Using GPS Measurements

Rapid developments in satellite positioning, navigation, and timing have revolutionized surveying and mapping practice and significantly influenced the way people live and society operates. The advent of new generation global navigation satellite systems (GNSS) has heralded an exciting future for not only the GNSS community, but also many other areas that are critical to our society at large. With the rapid advances in space-based technologies and new dedicated space missions, the availability of large scale and dense contemporary GNSS networks such as regional continuously operating reference station (CORS) networks and the developments of new algorithms and methodologies, the ability of using space geodetic techniques to remotely sense the atmosphere (i.e., the troposphere and ionosphere) has dramatically improved. Real time GNSS-derived atmospheric variables with a high spatio-temporal resolution have become an important new source of measurements for meteorology, particularly for extreme weather events since water vapour (WV), as the most abundant element of greenhouse gas and accounting for $\sim $ 70% of global warming, is under-sampled in current meteorological and climate observing systems. This study investigates the emerging area of GNSS technology for near real-time monitoring and forecasting of severe weather and climate change research. This includes both ground-based global positioning system (GPS)-derived precipitable water vapour (PWV) estimation and four-dimensional (4-D) tomographic modeling for wet refractivity fields. Two severe weather case studies were used to investigate the signature of GPS-derived PWV and wet refractivity derived from the 4-D GPS tomographic model under the influence of severe mesoscale convective systems (MCSs). GPS observations from the Victorian state-wide CORS network, i.e., GPSnet, in Australia were used. Results showed strong spatial and temporal correlations between the variations in the ground-based GPS-derived PWV and the passage of the severe MCS. This indicates that the GPS method can complement conventional meteorological observations for the studying, monitoring, and potentially predicting of severe weather events. The advantage of using the ground-based GPS technique is that it can provide continuous observations for the storm passage with high temporal and spatial resolution. Results from these two case studies also suggest that GPS-derived PWV can resolve the synoptic signature of the dynamics and offer precursors to severe weather, and the tomographic technique has the potential to depict the three-dimensional (3-D) signature of wet refractivity for the convective and stratiform processes evident in MCS events. This research reveals the potential of using GNSS-derived PWV to strengthen numerical weather prediction (NWP) models and forecasts, and the potential for GNSS-derived PWV and wet refractivity fields to enhance early detection and sensing of severe weather.

Precipitable Water Vapor Estimates in the Australian Region from Ground-Based GPS Observations

We present a comparison of atmospheric precipitable water vapor (PWV) derived from ground-based global positioning system (GPS) receiver with traditional radiosonde measurement and very long baseline interferometry (VLBI) technique for a five-year period (2008–2012) using Australian GPS stations. These stations were selectively chosen to provide a representative regional distribution of sites while ensuring conventional meteorological observations were available. Good agreement of PWV estimates was found between GPS and VLBI comparison with a mean difference of less than 1 mm and standard deviation of 3.5 mm and a mean difference and standard deviation of 0.1 mm and 4.0 mm, respectively, between GPS and radiosonde measurements. Systematic errors have also been discovered during the course of this study, which highlights the benefit of using GPS as a supplementary atmospheric PWV sensor and calibration system. The selected eight GPS sites sample different climates across Australia covering an area of approximately 30° NS/EW. It has also shown that the magnitude and variation of PWV estimates depend on the amount of moisture in the atmosphere, which is a function of season, topography, and other regional climate conditions.

Research on the regional short-term ionospheric delay modeling and forecasting methodology for mid-latitude area

The modeling and forecasting of the ionospheric delay is crucial in Global Positioning System (GPS) applications. In this study, a mathematical model that combines the polynomial function with B-spline function is put forward to estimate the regional ionospheric delay. In order to increase the forecasting accuracy, an integration forecasting method based on Holt---Winter model and Markov model is proposed to forecast the regional ionospheric delay. The Holt---Winter model is used to forecast the periodicity of vertical total electron content (VTEC), and the Markov model is employed to refine the residuals produced by the Holt---Winter model and forecast the fluctuation of VTEC. The two approaches are applied to real data obtained from the ground-based GPS observations of Jiangsu Continuously Operating Reference Stations. The results illustrate the effectiveness of the 2-h interval ionospheric delay modeling method, and the feasibility of the short-term ionospheric delay forecasting method for mid-latitude area. The applicability of the methodology during the ionospheric storms in the low-latitude area is also discussed.

Preliminary Design for near Real-Time GPS Meteorology over Peninsular Malaysia (G-MeM)

This paper presents a conceptual approach for near real time Global Positioning System (GPS) meteorology in Malaysia using combined space- and ground-based GPS observations. Data from a single GPS station is utilised to derive wet refractivities for comparison with radio occultation (RO). This study shows that the wet refractivities from the ground-based GPS present similar patterns and better correlation with the radiosonde data than that from the space-based GPS RO data at altitudes between 0 - 5 km. Similarly, the wet refractivities derived from RO are more highly correlated with the radiosonde data than the ground-based GPS at altitudes above 5 km. The residual Nwvary from -9.25 - 21.136 N-unit at 00 h UTC for the ground-based GPS while for the GPS RO, it is -19 - 9.259 N-unit at 00 h UTC.

Ionospheric Electron Concentration Imaging Using Combination of LEO Satellite Data With Ground-Based GPS Observations Over China

Space-based global positioning system (GPS) data onboard low Earth orbit satellites are used together with ground-based GPS observations to simultaneously image ionospheric electron density (IED), and it aims to improve the spatial resolution of ionospheric tomography. High-precision dual-frequency GPS data from the Crustal Movement Observation Network of China and several International GPS Service stations and the Constellation Observation System for Meteorology Ionosphere and Climate (COSMIC) are used to invert a time series of the IED profiles over China and surroundings. Some retrieved electron density profiles are compared with the results from COSMIC occultation data processed by COSMIC Data Analysis and Archival Center, and the peak density in F2 (NmF2) is verified by ionosonde data. Experimental results demonstrate the imaging effectiveness due to COSMIC data used in the inversion, and the potential benefits using COSMIC data in such system are also discussed.

The Validation of AIRS Retrievals of Integrated Precipitable Water Vapor Using Measurements from a Network of Ground-Based GPS Receivers over the Contiguous United States

Abstract A robust and easily implemented verification procedure based on the column-integrated precipitable water (IPW) vapor estimates derived from a network of ground-based global positioning system (GPS) receivers has been used to assess the quality of the Atmospheric Infrared Sounder (AIRS) IPW retrievals over the contiguous United States. For a period of six months from April to October 2004, excellent agreement has been realized between GPS-derived IPW estimates and those determined from AIRS, showing small monthly bias values ranging from 0.5 to 1.5 mm and root-mean-square (rms) differences of 4 mm or less. When the spatial (latitude–longitude) window for the GPS and AIRS matchup observations is reduced from the initial ½° by ½° to ¼° by ¼°, the rms differences are reduced. Analysis revealed that the observed IPW biases between the instruments are strongly correlated to the reported surface pressure differences between the GPS and AIRS observational points. Adjusting the AIRS IPW values to account for the surface pressure discrepancies resulted in significant reductions of the bias between GPS and AIRS. A similar reduction can be obtained by comparing only (GPS–AIRS) match-up pairs for which the corresponding surface pressure differences are 0.5 mb or less. The comparisons also revealed that the AIRS IPW tends to be relatively dry in moist atmospheres (when IPW values >40 mm) but wetter in dry cases (when IPW values <10 mm). This is consistent with the documented bias of satellite measurements toward the first guess used in retrieval algorithms. However, additional study is needed to verify whether the AIRS water vapor retrieval process is the source of the discrepancies. It is shown that the IPW bias and rms differences have a seasonal dependency, with a maximum in summer (bias ∼ 1.2 mm, rms ∼ 4.14 mm) and minimum in winter (bias < −0.5 mm, rms ∼ 3 mm).

Retrieval of Moisture from Simulated GPS Slant-Path Water Vapor Observations Using 3DVAR with Anisotropic Recursive Filters

Abstract Anisotropic recursive filters are implemented within a three-dimensional variational data assimilation (3DVAR) framework to efficiently model the effect of flow-dependent background error covariance. The background error covariance is based on an estimated error field and on the idea of Riishøjgaard. In the anisotropic case, the background error pattern can be stretched or flattened in directions oblique to the alignment of the grid coordinates and is constructed by applying, at each point, six recursive filters along six directions corresponding, in general, to a special configuration of oblique lines of the grid. The recursive filters are much more efficient than corresponding explicit filters used in an earlier study and are therefore more suitable for real-time numerical weather prediction. A set of analysis experiments are conducted at a mesoscale resolution to examine the effectiveness of the 3DVAR system in analyzing simulated global positioning system (GPS) slant-path water vapor observations from ground-based GPS receivers and observations from collocated surface stations. It is shown that the analyses produced with recursive filters are at least as good as those with corresponding explicit filters. In some cases, the recursive filters actually perform better. The impact of flow-dependent background errors modeled using the anisotropic recursive filters is also examined. The use of anisotropic filters improves the analysis, especially in terms of finescale structures. The analysis system is found to be effective in the presence of typical observational errors. The sensitivity of isotropic and anisotropic recursive-filter analyses to the decorrelation scales is also examined systematically.

Unusual ionospheric effects observed during the intense 28 October 2003 solar flare in the Brazilian sector

Abstract. The 28 October 2003 solar flare (X-ray Class X17.2) was one of the most intense solar flares observed in the recent past. In the present investigation we show the unusual ionospheric effects observed in the Brazilian sector during this solar flare, using both the ionospheric sounding observations obtained at the UNIVAP stations: Palmas (7–10.2° S, 48.2° W, dip lat. 5.5° S) and Sao Jose dos Campos (23.2° S, 45.9° W, dip lat. 17.6° S), Brazil; and ground-based global positioning system (GPS) data obtained at the "Instituto Brasileiro de Geografia e Estatística" (IBGE) stations: Imperatriz (5.5° S, 47.5° W, dip lat. 2.9° S), Brasilia (15.9° S, 47.9° W, dip lat. 11.7° S), Presidente Prudente (22.3° S, 51.4° W, dip lat. 14.9° S), and Porto Alegre (30.1° S, 51.1° W, dip lat. 20.7° S), Brazil; on two consecutive days, viz., 27 (without solar flare) and 28 (with solar flare) October 2003. It should be mentioned that the vertical total electron content (VTEC) from the GPS observations obtained during the solar flare showed an unusual simultaneous increase in the VTEC values at about 11:00 UT at all four stations associated with the solar flare EUV enhancements and lasted for about 3 h. However, no ionograms were obtained at any of the two UNIVAP stations for a period of about 1 h between about 11:00 to 12:00 UT. Before 11:00 UT (from about 10:45 UT) and after 12:00 UT (to about 16:00 UT), the ionograms were only partial, with the low frequency end missing. During this intense solar flare, hard X-rays (1 to 10 A), as observed by the GOES 12 satellite, were ejected by the Sun during a long period (several hours), with peak radiation at about 11:10 UT. These hard X-ray radiations can penetrate further into the ionosphere, causing an increase in ionization in the lower part of ionosphere (D-region). In this way, the lack of ionograms or partial ionograms, which indicates no echoes or partial echoes of the transmitted digital ionosonde signals, are related to intense absorption, resulting in complete or partial fade-out of radio signals at the lower ionospheric heights. The partial and complete radio fade-out observed in the Brazilian sector for more than six hours must have resulted in considerable difficulties to the radio station operating agencies using ionospheric radio transmissions.

Retrieval of Moisture from Slant-Path Water Vapor Observations of a Hypothetical GPS Network Using a Three-Dimensional Variational Scheme with Anisotropic Background Error

Abstract A three-dimensional variational (3DVAR) scheme is developed for retrieving three-dimensional moisture in the atmosphere from slant-path measurements of a hypothetical ground-based global positioning system (GPS) observation network. It is assumed that the observed data are in the form of slant-path water vapor (SWV), which is the integrated water vapor along the slant path between the ground receiver and the GPS satellite. The inclusion of a background in the analysis overcomes the under-determinedness problem. An explicit Gaussian-type spatial filter is used to model the background error covariances that can be anisotropic. As a unique aspect of this study, an anisotropic spatial filter based on flow-dependent background error structures is implemented and tested and the filter coefficients are derived from either true background error field or from the increment of an intermediate analysis that is obtained using an isotropic filter. In the latter case, an iterative procedure is involved. A set of experiments is conducted to test the new scheme with hypothetical GPS observations for a dryline case that occurred during the 2002 International H2O Project (IHOP_2002) field experiment. Results illustrate that this system is robust and can properly recover three-dimensional mesoscale moisture structures from GPS SWV data and surface moisture observations. The analysis captures major features in water vapor associated with the dryline even when an isotropic spatial filter is used. The analysis is further improved significantly by the use of flow-dependent background error covariances modeled by an anisotropic spatial filter. Sensitivity tests show that surface moisture observations are important for the analysis near ground, and more so when flow-dependent background error covariances are not used. Vertical filtering is necessary for obtaining accurate analysis increments. The retrieved moisture field remains reasonably accurate when the surface moisture observations and GPS SWV data contain errors of typical magnitudes. The positive impact of flow-dependent background error covariances increases when the density of ground-based GPS receiver stations decreases.

Monitoring of Integrated Water Vapour from ground-based GPS observations and their assimilation in a limited-area NWP model

Detailed short-range numerical weather prediction (NWP) requires observational information of good quality and with high spatial/temporal resolution. Due to various limitations, important quantities such as tropospheric water vapour are often inadequately covered by conventional observations from radiosondes or by meteorological satellite systems. The Global Positioning System (GPS) is developing into a powerful source of humidity information for fine-scale regional models. Since May 2000, the GeoForschungsZentrum in Potsdam has been processing the data from a dense network of German GPS ground stations in near-real time. The final product, Integrated Water Vapour, has been monitored at the Deutscher Wetterdienst, and preliminary assimilation experiments have been carried out to test the impact of the new observations in the operational non-hydrostatic limited-area NWP model.

Measuring and modeling the slant wet delay with GPS and the ECMWF NWP model

We describe the data fusion of ground-based Global Positioning System (GPS) slant total delay values and ray-traced slant dry delay values from the European Centre for Medium-Range Weather Forecasts (ECMWF) model to determine wet delay values for elevations down to −0.51°. They have a magnitude up to several meters and provide valuable information on the distribution of water vapor. The slant wet delay values for one GPS station and one GPS satellite are determined with the Bernese Software V4.2 using a modified elevation dependent weighting scheme to handle GPS observations below the horizon. We compare the eigenvectors of the wet delay correlation matrix to predictions from the Treuhaft–Lanyi (TL) model and find a good agreement for the three largest eigenvalues representing the major wet delay fluctuations. This result shows that the TL-model describes certain aspects of refractivity fluctuations within the troposphere accurately. We use it to propose a new model for ground-based slant wet delay values at very low elevations. In addition to the zenith delay value the model contains so-called fluctuation parameters. The model is optimal in the sense that, depending on the number of fluctuation parameters, the slant wet delay can be modeled with an elevation independent accuracy.

Validation of line‐of‐sight water vapor measurements with GPS

We present a direct comparison of nonisotropic, integrated water vapor measurements between a ground‐based Global Positioning System (GPS) receiver and a water vapor radiometer (WVR). These line‐of‐sight water vapor observations are made in the straight line path between a ground station and a GPS satellite. GPS double‐difference observations are processed, and the residual line‐of‐sight water vapor delays are extracted from the double‐difference residuals. These water vapor delays contain the nonisotropic component of the integrated water vapor signal. The isotropic component is represented by the zenith precipitable water vapor measurement and can be scaled to a specific elevation angle based on a mapping function. The GPS observations are corrected for station‐dependent errors using site‐specific multipath maps. The resulting measurements are validated using a WVR which pointed in the direction of the observed satellites. The double‐difference technique used to make these water vapor observations does not depend on accurate satellite clock estimates. Therefore it is especially well suited for near‐real‐time application in weather prediction and allows for sensing atmospheric structure that is below the noise level of current satellite and receiver clock errors. This paper describes the analysis technique and provides precision estimates for the GPS‐measured nonisotropic water vapor as a function of elevation angle for use with data assimilation systems.