Distribution Of Atmospheric Water Vapor Research Articles

An Observed Relationship Between Satellite‐Estimated Transmittance and Ground‐Estimated Water Vapor: Implications for High‐Temporal‐Resolution Water Vapor Retrieval From Non‐Geostationary Satellite Measurements

AbstractThe magnitude of integrated water vapor (IWV) varies considerably in the spatial‐temporal domain, which demonstrates the significance of high‐spatiotemporal‐resolution IWV observations for atmospheric water vapor distribution monitoring, both locally and globally. Unlike previously published algorithms based on data fusion, an empirical retrieval algorithm is for the first time proposed to directly retrieve high‐temporal‐resolution IWV estimates from near‐infrared radiance observations of the non‐geostationary Ocean and Land Color Instrument (OLCI). The retrieval algorithm is developed based on an observed regression relationship between satellite‐based OLCI‐estimated transmittance and ground‐based Global Navigation Satellite System (GNSS)‐estimated IWV in the temporal domain. The results show that all newly retrieved IWV estimates have an overall good consistency with ground‐based IWV from additional GNSS‐sensed measurements, indicating the feasibility of the retrieval approach. The performance of the retrieval algorithm is acceptable and satisfactory when compared with that of IWV retrievals listed in previous studies.

A review of atmospheric water vapor lidar calibration methods

AbstractAtmospheric water vapor is a crucial factor in the Earth's water cycle. As an important greenhouse gas, changes in the spatio‐temporal distribution of atmospheric water vapor can contribute to the occurrence of various extreme weather phenomena. Lidar, with its high spatial and temporal resolutions, has great potential for applications in water vapor profile detection. Raman lidar and differential absorption lidar (DIAL) have been successfully used to detect atmospheric water vapor. System calibration is crucial to ensure that the measured profile accurately represents the concentration profile of atmospheric water vapor. Choosing an effective system calibration method can ensure the accuracy of long‐term lidar measurements. This paper reviews the latest progress and applications of atmospheric water vapor lidar calibration in recent years. The basic principles of Raman lidar and DIAL calibration are introduced. Various methods and benefits of system calibration are discussed. Raman lidar has three commonly used calibration methods: external calibration, internal calibration, and hybrid calibration methods. The most commonly used method is external calibration based on radiosondes. DIAL is usually implemented with an advantageous self‐calibration method. Finally, potential development directions for atmospheric water vapor lidar and calibration technology are discussed.This article is categorized under: Science of Water > Methods Engineering Water > Methods Human Water > Value of Water

Ground-Based Remote Sensing of Atmospheric Water Vapor Using High-Resolution FTIR Spectrometry

Understanding the distribution of atmospheric water vapor (H2O) is crucial for global warming studies and climate change mitigation. In this study, we retrieved the ground layer, tropospheric and total columns of H2O using ground-based high-resolution Fourier transform infrared spectrometry (FTIR). The H2O total columns are obtained from near-infrared (NIR) and mid-infrared (MIR) spectra, and the ground layer and tropospheric H2O columns are retrieved from the MIR spectrum. The total columns of H2O retrieved from NIR and MIR have a good consistency (R = 0.989). Additionally, the ground layer H2O columns have a similar seasonal variation to total columns and tropospheric columns but have a higher seasonal amplitude. The ground layer H2O columns are close to the total columns and tropospheric columns in winter; however, in summer, the average difference between the ground layer and total columns and the value between the ground layer and tropospheric columns are large. This is mostly due to temperature variation. The temperature has a linear response to H2O, and the relationship between surface temperature and ln(XH2O) values in the ground layer, the entire atmosphere and the troposphere show a significantly positive correlation, and the correlation coefficient R is 0.893, 0.882 and 0.683, respectively. Furthermore, we selected the HYSPLIT model to simulate the back trajectories of air parcels in the four seasons in Hefei and find that the air mass transport has a significant impact on the local H2O change. These results demonstrate that ground-based high-resolution FTIR technology has high accuracy and precision in observing the vertical distribution and seasonal changes of H2O in different atmospheres.

The Spatial Distribution of Atmospheric Water Vapor Based on Analytic Hierarchy Process and Genetic Algorithm

The Spatial Distribution of Atmospheric Water Vapor Based on Analytic Hierarchy Process and Genetic Algorithm

Overview towards improved understanding of the mechanisms leading to heavy precipitation in the western Mediterranean: lessons learned from HyMeX

Abstract. Heavy precipitation (HP) constitutes a major meteorological threat in the western Mediterranean (WMed). Every year, recurrent events affect the area with fatal consequences for infrastructure and personal losses. Despite this being a well-known issue widely investigated in the past, open questions still remain. Particularly, the understanding of the underlying mechanisms and the modeling representation of the events must be improved. One of the major goals of the Hydrological Cycle in the Mediterranean Experiment (HyMeX; 2010–2020) has been to advance knowledge on this topic. In this article, we present an overview of the most recent lessons learned from HyMeX towards an improved understanding of the mechanisms leading to HP in the WMed. The unique network of instruments deployed as well as the use of finer model resolutions and coupled models provided an unprecedented opportunity to validate numerical model simulations, develop improved parameterizations, and design high-resolution ensemble modeling approaches and sophisticated assimilation techniques across scales. All in all, HyMeX, and particularly the science team heavy precipitation, favored the evidencing of theoretical results, the enrichment of our knowledge on the genesis and evolution of convection in a complex topography environment, and the improvement of precipitation forecasts. Illustratively, the intervention of cyclones and warm conveyor belts in the occurrence of heavy precipitation has been pointed out, and the crucial role of the spatiotemporal distribution of atmospheric water vapor for the understanding and accurate forecast of the timing and location of deep convection has been evidenced, as has the complex interaction among processes across scales. The importance of soil and ocean conditions and the interactions among systems were highlighted, and such systems were specifically developed in the framework of HyMeX to improve the realism of weather forecasts. Furthermore, the benefits of cross-disciplinary efforts within HyMeX have been a key asset in bringing our knowledge about heavy precipitation in the Mediterranean region a step forward.

LEO Constellation-Augmented Multi-GNSS for 3D Water Vapor Tomography

Global navigation satellite systems (GNSS) water vapor tomography is an important technique to obtain the three-dimensional distribution of atmospheric water vapor. The rapid development of low Earth orbit (LEO) constellations has led to a richer set of observations, which brings new expectations for water vapor tomography. This paper analyzes the influence of LEO constellation-augmented multi-GNSS(LCAMG)on the tomography, in terms of ray distribution, tomography accuracy, and horizontal resolution, by simulating LEO constellation data. The results show that after adding 288 LEO satellites to GNSS, the 30-min ray distribution effect of GNSS can be achieved in 10 min, which can effectively shorten the observation time by 66.7%. In the 10-min observation time, the non-repetitive effective observation value of LCAMG is 2.38 times that of GNSS, and the accuracy is 1.27% higher than that of GNSS. Compared with GNSS and the global positioning system (GPS), at a horizontal resolution of 13 × 14, the proportion of empty voxels in LCAMG reduces by 5.22% and 22.53%, respectively.

A new integrated method of GNSS and MODIS measurements for tropospheric water vapor tomography

Global Navigation Satellite System (GNSS) tomography has proved its potential for sensing the three-dimensional (3D) distribution of atmospheric water vapor. Although the standard tomography model has improved, the geometry defect of GNSS acquisition remains a key problem and thus becomes the focus of this research. Thanks to the availability of high-resolution Moderate Resolution Imaging Spectroradiometer (MODIS) precipitable water vapor (PWV) maps, the integration of GNSS and MODIS measurements for tropospheric tomography system is introduced and discussed to address this problem. A methodology based on the node tomography model is developed to exploit the MODIS signals, which overcomes the disadvantage of the standard voxel model in combining the MODIS observations. Three experimental schemes based on MODIS data and simultaneous GNSS observations over the Xuzhou area are implemented to validate the proposed approach. The results show that the mean number of newly crossed voxels (i.e., punctured only by the MODIS signals) increases from 2 in the top layer to 24 in the first layer. Consequently, the average number of total crossed voxels is increased from 272 to 447, which highlights the contribution of the MODIS observations to pass through the empty voxels. Besides, the mean number of tomographic observation equations is enhanced by 35.71% when the MODIS signals are combined. Two-dimensional (2D) profiles of water vapor from radiosonde and 3D distributions of it from ERA5 data are considered to validate the proposed method. It is noted that when using the proposed approach, the mean root-mean-square error (RMSE) of the 2D tomographic profiles is decreased by a value of about 1.06 g/m3 and the overall accuracy of the 3D water vapor distribution is improved by 30.40%. Both the 2D and 3D validations demonstrate the satisfactory performance of the new integrated method to optimize the tomographic water vapor field.

Detection of land-surface-induced atmospheric water vapor patterns

Abstract. Finding observational evidence of land surface and atmosphere interactions is crucial for understanding the spatial and temporal evolution of the boundary layer, as well as for model evaluation, and in particular for large-eddy simulation (LES) models. In this study, the influence of a heterogeneous land surface on the spatial distribution of atmospheric water vapor is assessed. Ground-based remote sensing measurements from a scanning microwave radiometer (MWR) are used in a long-term study over 6 years to characterize spatial heterogeneities in integrated water vapor (IWV) during clear-sky conditions at the Jülich ObservatorY for Cloud Evolution (JOYCE). The resulting deviations from the mean of the scans reveal a season- and direction-dependent IWV that is visible throughout the day. Comparisons with a satellite-derived spatial IWV distribution show good agreement for a selection of satellite overpasses during convective situations but no clear seasonal signal. With the help of a land use type classification and information on the topography, the main types of regions with a positive IWV deviation were determined to be agricultural fields and nearby open pit mines. Negative deviations occurred mainly above elevated forests and urban areas. In addition, high-resolution large-eddy simulations (LESs) are used to investigate changes in the water vapor and cloud fields for an altered land use input.

Martian dust storm impact on atmospheric H2O and D/H observed by ExoMars Trace Gas Orbiter.

Global dust storms on Mars are rare1,2 but can affect the Martian atmosphere for several months. They can cause changes in atmospheric dynamics and inflation of the atmosphere3, primarily owing to solar heating of the dust3. In turn, changes in atmospheric dynamics can affect the distribution of atmospheric water vapour, with potential implications for the atmospheric photochemistry and climate on Mars4. Recent observations of the water vapour abundance in the Martian atmosphere during dust storm conditions revealed a high-altitude increase in atmospheric water vapour that was more pronounced at high northern latitudes5,6, as well as a decrease in the water column at low latitudes7,8. Here we present concurrent, high-resolution measurements of dust, water and semiheavywater (HDO) at the onset of a global dust storm, obtained by the NOMAD and ACS instruments onboard the ExoMars Trace Gas Orbiter. We report the vertical distribution of the HDO/H2O ratio (D/H) from the planetary boundary layer up to an altitude of 80 kilometres. Our findings suggest that before the onset of the dust storm, HDO abundances were reduced to levels below detectability at altitudes above 40 kilometres. This decrease in HDO coincided with the presence of water-ice clouds. During the storm, an increase in the abundance of H2O and HDO was observed at altitudes between 40 and 80 kilometres. We propose that these increased abundances may be the result of warmer temperatures during the dust storm causing stronger atmospheric circulation and preventing ice cloud formation, which may confine water vapour to lower altitudes through gravitational fall and subsequent sublimation of ice crystals3. The observed changes in H2O and HDO abundance occurred within a few days during the development of the dust storm, suggesting a fast impact of dust storms on the Martian atmosphere.

Assessments of GMI-Derived Precipitable Water Vapor Products over the South and East China Seas Using Radiosonde and GNSS

Satellite remote sensing of the atmospheric water vapor distribution over the oceans is essential for both weather and climate studies. Satellite onboard microwave radiometer is capable of measuring the water vapor over the oceans under all weather conditions. This study assessed the accuracies of precipitable water vapor (PWV) products over the south and east China seas derived from the Global Precipitation Measurement Microwave Imager (GMI), using radiosonde and GNSS (Global Navigation Satellite System) located at islands and coasts as truth. PWV measurements from 14 radiosonde and 5 GNSS stations over the period of 2014–2017 were included in the assessments. Results show that the GMI 3-day composites have an accuracy of better than 5 mm. A further evaluation shows that RMS (root mean square) errors of the GMI 3-day composites vary greatly in the range of 3∼14 mm at different radiosonde/GNSS sites. GMI 3-day composites show very good agreements with radiosonde and GNSS measured PWVs with correlation coefficients of 0.896 and 0.970, respectively. The application of GMI products demonstrates that it is possible to reveal the weather front, moisture advection, transportation, and convergence during the Meiyu rainfall. This work indicates that the GMI PWV products can contribute to various studies such as climate change, hydrologic cycle, and weather forecasting.

Comparison of precipitable water vapor derived from AIRS and SPM measurements and its correlation with surface temperature of 29 synoptic stations over Iran

Distribution of atmospheric water vapor is highly variable in time and space across the Earth; the knowledge of its distribution is essential in climate studies. In Iran, atmospheric water vapor measurements using conventional ground-based methods such as radiosondes are limited. In this paper, a comparison of precipitable water vapor (PWV) estimated by a ground-based sunphotometer (SPM) with atmospheric infrared sounder (AIRS) satellite sensor is presented over Zanjan, a city in northwest Iran for a period from December 2009 to December 2013. The PWV of SPM and AIRS have a good agreement (R2 = 93%). The seasonal bias between PWV estimated by SPM and AIRS (PWV SPM - PWV AIRS) over Zanjan city is −3.35, −5.15, −2.27, −0.95 mm during spring, summer, autumn, and winter, respectively. Average of AIRS PWV over Iran shows that the largest amount of PWV occurs along the seas (Caspian Sea, Oman Sea, and the Persian Gulf) and its lowest value happens in high altitude mountainous regions of the country. Also, correlation coefficients between AIRS PWV and surface temperature of 29 synoptic stations are calculated over Iran from September 2002 to December 2016. The average of correlation coefficients over the stations is 0.73.

SPATIO-TEMPORAL ESTIMATION OF INTEGRATED WATER VAPOUR OVER THE MALAYSIAN PENINSULA DURING MONSOON SEASON

Abstract. This paper provides the precise information on spatial-temporal distribution of water vapour that was retrieved from Zenith Path Delay (ZPD) which was estimated by Global Positioning System (GPS) processing over the Malaysian Peninsular. A time series analysis of these ZPD and Integrated Water Vapor (IWV) values was done to capture the characteristic on their seasonal variation during monsoon seasons. This study was found that the pattern and distribution of atmospheric water vapour over Malaysian Peninsular in whole four years periods were influenced by two inter-monsoon and two monsoon seasons which are First Inter-monsoon, Second Inter-monsoon, Southwest monsoon and Northeast monsoon.

The Evolution of Springtime Water Vapor Over Beijing Observed by a High Dynamic Raman Lidar System: Case Studies

Raman lidar is an effective technique to retrieve the vertical distribution of atmospheric water vapor. For the first time, we present water vapor profiles retrieved by a high dynamic Raman lidar system over the Beijing area for representative cases in spring 2014, within the framework of the Aerosol Multi-wavelength Polarization Lidar Experiment project. In springtime, water vapor content over Beijing is generally low but with a strong daily variability. Its evolution is strongly coupled with winds and aerosols, with clouds also exerting a distinct impact. Northwesterly winds is found to be the most important factor impacting the temporal variability of water vapor mixing ratio (WVMR), and WVMR is found to be negatively correlated with wind speed. Moreover, we find that clouds tend to cause significant increases in the standard deviation of WMVR measurement, and relative humidity sharply increase below the cloud base. During a typical pollution episode, water vapor strongly covaries with aerosols due to hygroscopic growth effect and transport mechanism. Both water vapor and aerosols exhibit the highest variability within the planetary boundary layer (PBL), where the development and dissipation of haze mainly occur. Within the PBL, water vapor and aerosol concentration demonstrate different evolution features at different altitudes during the haze process, with a delayed increase and early decrease for higher altitudes. Back trajectory analysis using the hybrid single-particle Lagrangian trajectory model indicates that this phenomenon is most likely associated with different sources of the air mass at different altitudes.

Detecting Water Vapor Variability during Heavy Precipitation Events in Hong Kong Using the GPS Tomographic Technique

AbstractWater vapor has a strong influence on the evolution of heavy precipitation events due to the huge latent heat associated with the phase change process of water. Accurate monitoring of atmospheric water vapor distribution is thus essential in predicting the severity and life cycle of heavy rain. This paper presents a systematic study on the application of tomographic solutions to investigate water vapor variations during heavy precipitation events. Using global positioning system (GPS) observations, the wet refractivity field was constructed at a temporal resolution of 30 min for three heavy precipitation events occurring in Hong Kong, China, in 2010–14. The zenith wet delay (ZWD) is shown to be a good indicator in observing the water vapor evolution in heavy rain events. The variabilities of water vapor at five altitude layers (<1000, 1000–2000, 2000–3000, 3000–5000, and >5000 m) were examined. It revealed that water vapor above 3000 m has larger fluctuation than that under 3000 m, though it accounts for only 10%–25% of the total amount of water vapor. The relative humidity fields derived from tomographic results revealed moisture variation, accumulation, saturation, and condensation during the heavy rain events. The water vapor variabilities observed by tomography have been validated using European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis and radiosonde data. The results positively demonstrated the potential of using water vapor tomographic technique for detecting and monitoring the evolution of heavy rain events.

Maximally Using GPS Observation for Water Vapor Tomography

GPS-based water vapor tomography has been proved to be a cost-effective means of obtaining spatial and temporal distribution of atmospheric water vapor. In previous studies, the tomography height is empirically selected without considering the actual characteristics of the local water vapor distribution, and most existing studies only consider the signals passing from the top boundary of the tomography area. Therefore, the observed signals coming out from the side face of the tomography area are excluded as ineffective information, which not only reduces the utilization rate of signals used but also decreases the number of voxels crossed by rays. This becomes the research point of this paper, which studies the possibility of selecting a reasonable tomography boundary and using signals passing from the side face of the tomography area. This paper first tries to determine the tomography height based on the local atmospheric physical property using many years of radiosonde data, and 8 km is selected as the tomography boundary in Hong Kong. The second part focuses on superimposing the signals penetrating from the side face of the tomography area to tomography modeling by introducing a scale factor that is able to determine the water vapor content of each signal with the part that belongs in the tomography area. Finally, a tomography experiment is carried out based on data provided by the Satellite Positioning Reference Station Network (SatRef) in Hong Kong to validate the proposed method. Experimental result demonstrates that the utilization rate of the signal used and the number of voxels crossed by rays are both increased by 30.32% and 12.62%, respectively. The comparison of tomographic integrated water vapor (IWV) derived from different schemes with that from radiosonde and ECMWF data shows that the RMS error of the proposed method (4.1 and 5.1 mm) is smaller than that of the previous method (5.1 and 5.6 mm). In addition, the tomographic water vapor densities derived from different schemes is also compared with those of by radiosonde and ECMWF; the statistical result over the experimental period shows that the proposed method has an average RMS error of 1.23 and 2.12 g/m3, respectively, which is superior to the previous method at 1.60 and 2.43 g/m3, respectively.

Optimization of GPS water vapor tomography technique with radiosonde and COSMIC historical data

Abstract. The near-real-time high spatial resolution of atmospheric water vapor distribution is vital in numerical weather prediction. GPS tomography technique has been proved effectively for three-dimensional water vapor reconstruction. In this study, the tomography processing is optimized in a few aspects by the aid of radiosonde and COSMIC historical data. Firstly, regional tropospheric zenith hydrostatic delay (ZHD) models are improved and thus the zenith wet delay (ZWD) can be obtained at a higher accuracy. Secondly, the regional conversion factor of converting the ZWD to the precipitable water vapor (PWV) is refined. Next, we develop a new method for dividing the tomography grid with an uneven voxel height and a varied water vapor layer top. Finally, we propose a Gaussian exponential vertical interpolation method which can better reflect the vertical variation characteristic of water vapor. GPS datasets collected in Hong Kong in February 2014 are employed to evaluate the optimized tomographic method by contrast with the conventional method. The radiosonde-derived and COSMIC-derived water vapor densities are utilized as references to evaluate the tomographic results. Using radiosonde products as references, the test results obtained from our optimized method indicate that the water vapor density accuracy is improved by 15 and 12 % compared to those derived from the conventional method below the height of 3.75 km and above the height of 3.75 km, respectively. Using the COSMIC products as references, the results indicate that the water vapor density accuracy is improved by 15 and 19 % below 3.75 km and above 3.75 km, respectively.

Microwave radiometer observations of interannual water vapor variability and vertical structure over a tropical station

AbstractThe intraseasonal and interannual characteristics and the vertical distribution of atmospheric water vapor from the tropical coastal station Thiruvananthapuram (TVM) located in the southwestern region of the Indian Peninsula are examined from continuous multiyear, multifrequency microwave radiometer profiler (MRP) measurements. The accuracy of MRP for precipitable water vapor (PWV) estimation, particularly during a prolonged monsoon period, has been demonstrated by comparing with the PWV derived from collocated GPS measurements based on regression model between PWV and GPS wet delay component which has been developed for TVM station. Large diurnal and intraseasonal variations of PWV are observed during winter and premonsoon seasons. There is large interannual PWV variability during premonsoon, owing to frequent local convection and summer thunderstorms. During monsoon period, low interannual PWV variability is attributed to the persistent wind from the ocean which brings moisture to this coastal station. However, significant interannual humidity variability is seen at 2 to 6 km altitude, which is linked to the monsoon strength over the station. Prior to monsoon onset over the station, the specific humidity increases up to 5–10 g/kg in the altitude region above 5 km and remains consistently so throughout the active spells.

Sensitivity of soil moisture initialization for decadal predictions under different regional climatic conditions in Europe

ABSTRACTThe impact of soil initialization is investigated through perturbation simulations with the regional climate model Consortium for Small‐scale Modelling (COSMO) and the Climate Limited‐area Modelling (COSMO–CLM). The focus of the investigation is to assess the sensitivity of simulated extreme periods, dry and wet, to soil moisture initialization in different climatic regions over Europe and to establish the necessary spin‐up time within the framework of decadal predictions for these regions. Sensitivity experiments consisted of a reference simulation from 1968 to 1999 and five simulations from 1972 to 1983. The Effective Drought Index (EDI) is used to select and quantify drought status in the reference run to establish the simulation time period for the sensitivity experiments. Different soil initialization procedures are investigated. The sensitivity of the decadal predictions to soil moisture initial conditions is investigated through the analysis of water cycle components' (WCC) variability. In an episodic timescale, the local effects of soil moisture on the boundary layer and the propagated effects on the large‐scale dynamics are analysed. The results show: (1) COSMO–CLM reproduces the observed features of the drought index. (2) Soil moisture initialization exerts a relevant impact on WCC, e.g. precipitation distribution and intensity. (3) Regional characteristics strongly impact the response of the WCC. Precipitation and evapotranspiration deviations are larger for humid regions. (4) The initial soil conditions (wet/dry), the regional characteristics (humid/dry) and the annual period (wet/dry) play a key role in the time that soil needs to restore quasi‐equilibrium and the impact on the atmospheric conditions. Humid areas, and for all regions, a humid initialization, exhibit shorter spin‐up times, also soil reacts more sensitive when initialized during dry periods. (5) The initial soil perturbation may markedly modify atmospheric pressure field, wind circulation systems and atmospheric water vapour distribution affecting atmospheric stability conditions, thus modifying precipitation intensity and distribution even several years after the initialization.

Voxel-optimized regional water vapor tomography and comparison with radiosonde and numerical weather model

Water vapor tomography has been developed as a powerful tool to model spatial and temporal distribution of atmospheric water vapor. Global navigation satellite systems (GNSS) water vapor tomography refers to the 3D structural construction of tropospheric water vapor using a large number of GNSS signals that penetrate the tomographic modeling area from different positions. The modeling area is usually discretized into a number of voxels. A major issue involved is that some voxels are not crossed by any GNSS signal rays, resulting in an undetermined solution to the tomographic system. To alleviate this problem, the number of voxels crossed by GNSS signal rays should be as large as possible. An important way to achieve this is to optimize the geographic distribution of tomographic voxels. We propose an approach to optimize voxel distribution in both vertical and horizontal domains. In the vertical domain, water vapor profiles derived from radiosonde data are exploited to identify the maximum height of tomography and the optimal vertical resolution. In the horizontal domain, the optimal horizontal distribution of voxels is obtained by searching the maximum number of ray-crossing voxels in both latitude and longitude directions. The water vapor tomography optimization procedures are implemented using GPS water vapor data from the Hong Kong Satellite Positioning Reference Station Network. The tomographic water vapor fields solved from the optimized tomographic voxels are evaluated using radiosonde data and a numerical weather prediction non-hydrostatic model (NHM) obtained for the Hong Kong station. The comparisons of tomographic integrated water vapor (IWV) with the radiosonde and NHM IWV show that RMS errors of their differences are 1.41 and 3.09 mm, respectively. Moreover, the tomographic water vapor density results are compared with those of radiosonde and NHM. The RMS error of the density differences between tomography and radiosonde data is 1.05 $$\mathrm{g/m}^{3}$$ . For the comparison between tomography and NHM, an overall RMS error of $$1.43\,\mathrm{g/m^{3}}$$ is achieved.

GNSS troposphere tomography based on two-step reconstructions using GPS observations and COSMIC profiles

Abstract. Traditionally, balloon-based radiosonde soundings are used to study the spatial distribution of atmospheric water vapour. However, this approach cannot be frequently employed due to its high cost. In contrast, GPS tomography technique can obtain water vapour in a high temporal resolution. In the tomography technique, an iterative or non-iterative reconstruction algorithm is usually utilised to overcome rank deficiency of observation equations for water vapour inversion. However, the single iterative or non-iterative reconstruction algorithm has their limitations. For instance, the iterative reconstruction algorithm requires accurate initial values of water vapour while the non-iterative reconstruction algorithm needs proper constraint conditions. To overcome these drawbacks, we present a combined iterative and non-iterative reconstruction approach for the three-dimensional (3-D) water vapour inversion using GPS observations and COSMIC profiles. In this approach, the non-iterative reconstruction algorithm is first used to estimate water vapour density based on a priori water vapour information derived from COSMIC radio occultation data. The estimates are then employed as initial values in the iterative reconstruction algorithm. The largest advantage of this approach is that precise initial values of water vapour density that are essential in the iterative reconstruction algorithm can be obtained. This combined reconstruction algorithm (CRA) is evaluated using 10-day GPS observations in Hong Kong and COSMIC profiles. The test results indicate that the water vapor accuracy from CRA is 16 and 14% higher than that of iterative and non-iterative reconstruction approaches, respectively. In addition, the tomography results obtained from the CRA are further validated using radiosonde data. Results indicate that water vapour densities derived from the CRA agree with radiosonde results very well at altitudes above 2.5 km. The average RMS value of their differences above 2.5 km is 0.44 g m−3.