Slant Water Vapor Research Articles

An Improved Tropospheric Tomographic Model Based on Artificial Neural Network

Global navigation satellite systems (GNSS) tropospheric tomography can be used to build a three-dimensional water vapor field. In traditional tomography, the signals crossing from the four sides of the tomographic region are not utilized. To make the best use of these valuable side-crossing signals, an improved tomographic model based on back propagation artificial neural network (BP-ANN) is proposed. In the new tomographic model, the inside part of the slant wet delay (SWD) of the side-crossing signal is divided into two sections: the isotropic and anisotropic components. The former is estimated by the zenith wet delay multiplied by the mapping function multiplied by an isotropic scale factor using a BP-ANN model, and the latter is estimated by horizontal gradients of the SWD multiplied by an anisotropic scale factor using an empirical model. The new tomographic model is experimentally evaluated using the HK CORS network measurements for the period of 21 days from 1 to 21 August 2019. Statistical results show that the root mean square error (RMSE) of slant water vapor reconstructed from the improved model is reduced to 1.35 from 2.85 mm of the traditional model. Compared with the traditional/height factor models, the percentages of the reduction in the RMSE of the tomographic result derived from the new model are 16%/9% and 22%/16%, respectively, using radiosonde and ERA5 data as references. These results suggest a good performance of the new model for GNSS tropospheric tomography.

GPS-Derived Slant Water Vapor for Cloud Monitoring in Singapore

This paper presents a GPS-derived slant water vapor technique for cloud monitoring in Singapore. The normalized slant wet delay (SWD) and slant water vapor (SWV) are introduced. The suitability of the normalized SWV over SWV for cloud monitoring is demonstrated, as it is not very sensitive to the satellite elevation angle. For better illustration and representation of the spatial distribution of the normalized SWV, the skyplot is discretized into different cells based on the azimuth and elevation angles to produce the spatial plot. The spatial plots are analyzed for cloud monitoring and compared alongside the sky images. The results show that the spatial plots of normalized SWV are generally consistent with the cloud formation observed in the sky images, hence demonstrating their usefulness for cloud monitoring. The probability distribution of the normalized SWV associated with cloudy and clear sky conditions is also analyzed, which shows that the mean values of normalized SWV associated with the former are higher. Finally, the time series of the normalized SWV is explored in relation to the solar irradiance. It is shown that the time series and spatial plots of normalized SWV are also consistent with the ratio of clear sky to measured irradiance.

Assessment of the Water Vapor Tomography Based on Four Navigation Satellite Systems and Their Various Combinations

With the gradual improvement of Galileo and the opening of BDS-3 services, water vapor tomography based on multi-GNSS can be effectively carried out to reconstruct three-dimensional water vapor distribution. In this paper, experiments in Hong Kong were conducted to analyze and assess the performances of GPS, BDS, GLONASS, and Galileo and their combinations in water vapor tomography. Numerical results show that the number of available signal rays varies widely in the four satellite systems, and the value can be increased by the combination of satellite systems; the combinations also increase the number of voxels crossed by signal rays, but this value is not directly related to the number of available signal rays; the number and distribution of the voxels with sufficient signal rays, which most closely related to the structure of the tomographic model, show no obvious differences in the four satellite systems and their combinations. Comparative results of slant water vapor (SWV) estimated by GNSS data and water vapor density derived from radiosonde data reveal that the differences in the water vapor tomography of the four satellite systems are small, and their combinations have limited improvement in the tomographic results.

Adaptive Algebraic Reconstruction Algorithms for GNSS Water Vapor Tomography

Objectives Global navigation satellite system(GNSS) tomography, characterized by reconstructing the three-dimensional distribution of the atmospheric water vapor, has proved its capacity for studying the extreme weather events. The ill-conditioned problem resulting from the GNSS acquisition geometry is the critical issue of the GNSS tomography system. Algebraic reconstruction techniques, with the advantages of simple iteration and fast convergence, has been widely applied to the tropospheric tomography. However, the error allocation principle based on the intersection length of GNSS rays with each ray-voxel affects the accuracy of tomographic results. An improved adaptive algebraic reconstruction techniques are proposed to address this problem. Methods In view of the unreasonable error allocation in the traditional algorithm, we suggest a new error allocation principle based on the variation of water vapor density(WVD) in voxels for the adaptive algorithms, in which the product of the intersection length and WVD is considered as a new principle to redistribute the difference between the GNSS slant water vapor(SWV)and the reconstructed SWV. Besides, the weight matrix model of elevation angles is introduced to the improved algorithm to optimize the tomographic results. Results The new algorithms are tested by measured data from Xuzhou continuously operating reference stations(CORS) network and radiosonde during July2016. Experimental results reveal that the WVD derived from the adaptive algorithms performs better than that of the general algorithms in root mean squared error(RMSE), standard deviation(STD) and mean absolute error(MAE), and the RMSE is decreased by 25.91%, 15.81%, and 24.64% for adaptive algebraic reconstruction technique(AART), adaptive multiplicative algebraic reconstruction technique(AMART)and adaptive simultaneous iterative reconstruction technique(ASIRT) respectively. Under the conditions of light rain, moderate rain and heavy rain, the adaptive algorithms retrieve better water vapor profile than the traditional ones. Conclusions Overall, the water vapor profile derived from the adaptive algorithms agrees with the radiosonde distribution better than the traditional algorithms, which reveals the advantage of the proposed method on optimizing the tomography solutions.

GNSS-RS Tomography: Retrieval of Tropospheric Water Vapor Fields Using GNSS and RS Observations

High spatiotemporal resolution atmospheric water vapor can be retrieved using the Global Navigation Satellite System (GNSS) tomography technique, in which the remained ill-posed problem of the tomography system resulting from the acquisition geometry is a vital issue to be addressed. Remote sensing (RS) water vapor data, with high-resolution and global coverage, show great potential for retrieval of slant water vapor (SWV) observations to improve the tomographic geometrical distribution. In this article, we develop a GNSS-RS (GNSS combining RS) tomography model to fully exploit the value of observation signals from GNSS and RS measurements. The two key factors of retrieving the RS SWV are performed by calibrating the original precipitable water vapor (PWV) images and adding the tropospheric horizontal gradients. The results reveal that when introducing the RS SWV observations into the tomography model, the acquisition geometry is significantly improved, with the average rate of voxels crossed by rays from 62% to 95% and the mean number of observation signals from 395 to 508 during the tomographic periods. Independent radiosonde data are used to validate the tomographic water vapor fields. The mean root-mean-square error (RMSE) and bias of the water vapor profiles derived from GNSS-RS solutions are decreased by 28% and 45% with respect to the GNSS-only results, respectively. Such improvements highlight that GNSS-RS troposphere tomography has significant potential to improve the reconstruction of the atmospheric water vapor fields.

Застосування процедури симуляції даних для задач GNSS-томографії тропосфери

The content and distribution of water vapor in the Earth’s atmosphere are related to various weather conditions and climatic processes, and are therefore important for understanding many meteorological phenomena. At the current stage of development and formation of Global Navigation Satellite Systems (GNSS), the distribution of water vapor content can be established using such observations from GNSS tomography, which, in turn, allows to study changes in the vertical profile of water vapor content in the Earth’s troposphere. In troposphere GNSS tomography, accurate information on the distribution of water vapor is obtained using integrated measurements, such as the water vapor content value in the slant direction (Slant Water Vapor, SWV). The essence of the problem of troposphere GNSS tomography is the solution of equations system, the number of which is limited by the number of satellites involved in observations. In this case, the functional relationship between observations and unknowns, in the pathways of GNSS signals through the troposphere, must be known in sufficient numbers. However, today there is a problem of lack of such information, which leads to the main problem of the troposphere GNSS-tomography method – overcoming the deficit of rank in the inversion of the original equation. This problem can be solved by increasing the number of satellite signals in a wide range of positions. The purpose of this work is to maximize the use ofGNSS signals inmodeling tomographic solutions based on data simulation. Method. Based on the developed method of multi-GNSS observations data processing by the PPP method, an algorithm of the procedure of simulation of additional satellites in tomographic modeling in order to overcome the problems of rank deficit is proposed. Results. The results of application of the data simulation procedure for the vertical profile of water vapor content in the Earth’s troposphere are presented based on the results of processingGNSS observations at the GANP station (Poprad, Slovakia) in the period from 31.05.2019 to 1.06.2019. Scientific novelty and practical significance. For the first time, an algorithm for the procedure of additional satellites simulation was proposed in order to overcome the problems of rank deficit in the tomographic modeling.

A new troposphere tomography algorithm with a truncation factor model (TFM) for GNSS networks

For previous studies of global navigation satellite system (GNSS) troposphere tomography, only the GNSS observations derived from ground-based stations located inside the tomographic area were considered; however, stations distributed outside the area of interest in a dense regional network were neglected. This wastes valuable GNSS data and decreases the number of voxels traveled by satellite rays. This becomes the focus of this work, which tries to use GNSS receivers located outside the tomographic region to participate in the establishment of a tomographic observation equation. A new troposphere tomography algorithm is proposed with a truncation factor model (TFM), while the ability of the TFM to calculate the sectional slant water vapor inside the tomographic area, derived from the receivers outside this area, has been verified. The proposed algorithm is validated using the observed data collected over 31 days from the continuously operating reference system network of Zhejiang Province, China. At elevation angle masks of 10°, the number of satellite rays used has increased by 21.27% while the number of voxels transited by satellite rays has increased by 13.97% from 65.44 to 79.23% when adopting the TFM. The compared result of integrated water vapor with those from radiosonde data reveals that the RMS error and bias of the proposed algorithm are 4.1 mm and 0.06 mm, respectively, while those of the conventional method are 4.8 mm and − 0.34 mm, respectively. Water vapor profile comparison also shows that the RMS error and bias of the proposed algorithm are superior with average values of 1.17 g m−3 and 0.02 g m−3 to that of the conventional algorithm with values of 1.44 g m−3 and 0.03 g m−3, respectively. The PWV differences between tomography and GAMIT further indicate a good performance of the proposed algorithm with the values of RMS error and bias of 8.7 mm and 0.5 mm, respectively, while those of the traditional method are 12.6 mm and 0.9 mm, respectively.

A New Method of GPS Water Vapor Tomography for Maximizing the Use of Signal Rays

The spatio-temporal distribution of atmospheric water vapor information can be obtained by global positioning system (GPS) water vapor tomography. GPS signal rays pass through the tomographic area from different boundaries because the scope of the research region (latitude, longitude, and altitude) is designated in the process of tomographic modeling, the influence of the geographic distribution of receivers, and the geometric location of satellite constellations. Traditionally, only signal rays penetrating the entire tomographic area are considered in the computation of water vapor information, whereas those passing through the sides are neglected. Therefore, the accuracy of the tomographic result, especially at the bottom of the area, does not reach its full potential. To solve this problem, this paper proposes a new method that simultaneously considers the discretized tomographic voxels and the troposphere outside the research area as unknown parameters. This method can effectively improve the utilization of existing GPS observations and increase the number of voxels crossed by satellite signals, especially by increasing the proportion of voxels penetrated. A tomographic experiment is implemented using GPS data from the Hong Kong Satellite Positioning Reference Station Network. Compared to the traditional method, the proposed method increases the number of voxels crossed by signal rays and the utilization of the observed data by 15.14% and 19.68% on average, respectively. Numerical results, including comparisons of slant water vapor (SWV), precipitable water vapor (PWV), and water vapor density profile, show that the proposed method is better than traditional methods. In comparison to the water vapor density profile, the root-mean-square error (RMS), mean absolute error (MAE), standard deviation (SD), and bias of the proposed method are 1.39, 1.07, 1.30, and −0.21 gm−3, respectively. For the SWV and PWV comparison, the RMS/MAE of the proposed method are 10.46/8.17 mm and 4.00/3.39 mm, respectively.

Troposphere Water Vapour Tomography: A Horizontal Parameterised Approach

Global Navigation Satellite System (GNSS) troposphere tomography has become one of the most cost-effective means to obtain three-dimensional (3-d) image of the tropospheric water vapour field. Traditional methods divide the tomography area into a number of 3-d voxels and assume that the water vapour density at any voxel is a constant during the given period. However, such behaviour breaks the spatial continuity of water vapour density in a horizontal direction and the number of unknown parameters needing to be estimated is very large. This is the focus of the paper, which tries to reconstruct the water vapor field using the tomographic technique without imposing empirical horizontal and vertical constraints. The proposed approach introduces the layered functional model in each layer vertically and only an a priori constraint is imposed for the water vapor information at the location of the radiosonde station. The elevation angle mask of 30° is determined according to the distribution of intersections between the satellite rays and different layers, which avoids the impact of ray bending and the error in slant water vapor (SWV) at low elevation angles on the tomographic result. Additionally, an optimal weighting strategy is applied to the established tomographic model to obtain a reasonable result. The tomographic experiment is performed using Global Positioning System (GPS) data of 12 receivers derived from the Satellite Positioning Reference Station Network (SatRef) in Hong Kong. The quality of the established tomographic model is validated under different weather conditions and compared with the conventional tomography method using 31-day data, respectively. The numerical result shows that the proposed method is applicable and superior to the traditional one. Comparisons of integrated water vapour (IWV) of the proposed method with that derived from radiosonde and European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim data show that the root mean square (RMS)/Bias of their differences are 3.2/−0.8 mm and 3.3/−1.7 mm, respectively, while the values of traditional method are 5.1/−3.9 mm and 6.3/−5.9 mm, respectively. Furthermore, the water vapour density profiles are also compared with radiosonde and ECMWF data, and the values of RMS/Bias error for the proposed method are 0.88/0.06 g/m3 and 0.92/−0.08 g/m3, respectively, while the values of the traditional method are 1.33/0.38 g/m3 and 1.59/0.40 g/m3, respectively.

A Method to Improve the Distribution of Observations in GNSS Water Vapor Tomography.

Water vapor is an important driving factor in the related weather processes in the troposphere, and its temporal-spatial distribution and change are crucial to the formation of cloud and rainfall. Global Navigation Satellite System (GNSS) water vapor tomography, which can reconstruct the water vapor distribution using GNSS observation data, plays an increasingly important role in GNSS meteorology. In this paper, a method to improve the distribution of observations in GNSS water vapor tomography is proposed to overcome the problem of the relatively concentrated distribution of observations, enable satellite signal rays to penetrate more tomographic voxels, and improve the issue of overabundance of zero elements in a tomographic matrix. Numerical results indicate that the accuracy of the water vapor tomography is improved by the proposed method when the slant water vapor calculated by GAMIT is used as a reference. Comparative results of precipitable water vapor (PWV) and water vapor density (WVD) profiles from radiosonde station data indicate that the proposed method is superior to the conventional method in terms of the mean absolute error (MAE), standard deviations (STD), and root-mean-square error (RMS). Further discussion shows that the ill-condition of tomographic equation and the richness of data in the tomographic model need to be discussed separately.

Retrieval of Slant Water Vapor Path and Slant Liquid Water from Microwave Radiometer Measurements during the DYNAMO Experiment

Observations during the Dynamics of the Madden–Julian Oscillation (DYNAMO) experiment focused on sensing atmospheric parameters, including vertical moisture profiles, cloud structure, precipitation processes, and planetary boundary layer properties, all of which are important for understanding and modeling the Madden–Julian Oscillation (MJO). These observations were performed using a variety of in-situ and remote sensors, including the S-band polarimetric and Ka-band (S-PolKa) radar, deployed by the National Center for Atmospheric Research (NCAR), and a colocated University of Miami microwave radiometer (UM-radiometer) operating at 23.8 and 30.0 GHz. These instruments sampled approximately the same volumes of the atmosphere at a variety of azimuth and elevation angles. The principal goal of this study is to develop a new retrieval strategy to estimate slant water vapor path (SWP) and slant liquid water (SLW) using UM-radiometer measurements from zenith to low elevation angles at a variety of azimuth angles. Retrievals of SWP along the radar signal path help to determine the error in radar reflectivity due to water vapor absorption. The retrieval algorithm has been developed using the vapor–liquid water ratio (VLWR) as well as both modeled and measured brightness temperatures for zenith to low elevation angles. Observation system simulation experiment (OSSE) results and measured radiosonde data have been used to determine that the retrieval uncertainty is less than 5% for integrated water vapor (IWV) and less than 12% for integrated liquid water (ILW). OSSE results for SWP show that the retrieval uncertainty is less than 8% at 5° elevation angle and less than 5% at 7° and 9°, while the mean difference between SWP retrieved from radiometer measurements and those retrieved from the S-PolKa radar during the DYNAMO campaign is less than 10% at 5° elevation angle and less than 7.5% at 7° and 9°. OSSE results for SLW show that the mean error is less than 24% for 5° elevation angle and less than 18% for 7° and 9°. Such retrievals of SWP and SLW help to characterize the distribution of water vapor and liquid water in the lower troposphere, which in turn may contribute to improvements in forecasting of convective initiation and precipitation.

Near real-time water vapor tomography using ground-based GPS and meteorological data: long-term experiment in Hong Kong

Abstract. Water vapor tomography is a promising technique for reconstructing the 4-D moisture field, which is important to the weather forecasting and nowcasting as well as to the numerical weather prediction. A near real-time 4-D water vapor tomographic system is developed in this study. GPS slant water vapor (SWV) observations are derived by a sliding time window strategy using double-difference model and predicted orbits. Besides GPS SWV, surface water vapor measurements are also assimilated as real time observations into the tomographic system in order to improve the distribution of observations in the lowest layers of tomographic grid. A 1-year term experiment in Hong Kong was carried out. The feasibility of the GPS data processing strategy is demonstrated by the good agreement between the time series of GPS-derived Precipitable Water Vapor (PWV) and radio-sounding-derived PWV with a bias of 0.04 mm and a root-mean-square error (RMSE) of 1.75 mm. Using surface humidity observations in the tomographic system, the bias and RMSE between tomography and radiosonde data are decreased by half in the ground level, but such improved effects weaken gradually with the rise of altitude until becoming adverse above 4000 m. The overall bias is decreased from 0.17 to 0.13 g m−3 and RMSE is reduced from 1.43 to 1.28 g m−3. By taking the correlation coefficient and RMSE between tomography and radiosonde individual profile as the statistical measures, quality of individual profile is also improved as the success rate of tomographic solution is increased from 44.44 to 63.82% while the failure rate is reduced from 55.56 to 36.18%.

Validation of tropospheric slant path delays derived from single and dual frequency GPS receivers

In order to increase the spatial resolution of tropospheric delays derived from GPS observations, existing GPS networks assembled from dual frequency (DF) receivers must be densified. For economic reasons, low‐cost single‐frequency (SF) receivers are considered for the densification. The Satellite‐specific Epoch‐differenced Ionospheric Delay model (SEID) was developed at the German Research Centre for Geosciences (GFZ) to derive the ionospheric corrections for SF GPS receivers. Those corrections allow synthesizing a L2 observable for SF receivers, and existing GPS processing packages can analyze the resulting observables (L1 and synthesized L2) using the same methodology as with DF receivers. The SEID model has already been successfully applied to tropospheric Zenith Total Delay (ZTD) and station coordinates estimation. To assess the possibility of densifying an existing GPS network with low‐cost SF GPS receivers, observations from 258 German DF GPS stations are treated as observations from SF GPS stations (only L1 GPS observations are used). While in a previous study ZTD products were validated, in this study Slant Total Delay (STD) and Slant Water Vapor (SWV) products, derived from SF data and the SEID model, are validated using tropospheric products derived from DF data, a Water Vapor Radiometer (WVR) and a numerical weather model.

Improvement of Rainfall Forecast by Assimilations of Ground-Based GPS Data and Radio Occultation Data

Impacts of three kinds of GPS-derived water vapor data, i.e., precipitable water vapor (PWV), slant water vapor (SWV) and radio occultation (RO) data, were investigated using the Meso 4D-Var system of Japan Meteorological Agency (JMA) for a heavy rainfall case on 16 July 2004. When PWV or SWV data were assimilated individually, water vapor in the rainfall region was increased and on the northern sides was decreased, and then the shape of the rainfall region became similar to the observed one. However, the reproduced rainfall amount remained smaller than the observed one. Compared with PWV, SWV made the horizontal contrast of water vapor larger. When RO data were assimilated, the low-level water vapor was increased so that the rainfall amount was largely increased. However, the rainfall region became wider than the observed one. When SWV and RO data were assimilated simultaneously, low-level water vapor in the rainfall region and on its southern side was increased, and then both shapes of rainfall region and of rainfall amount became similar to the observed ones.

3D water-vapor tomography with Shanghai GPS network to improve forecasted moisture field

The vertical structure of water vapor in atmosphere is one of the initial information of numerical weather forecast model. Because of the strong variation of water vapor in atmosphere and limited spatio-temporal solutions of traditional observation technique, the initial water vapor field of numerical weather forecast model can not accurately be described. At present, using GIPS slant observations to study water vapor profile is very popular in the world. Using slant water vapor(SWV) observations from Shanghai GIPS network,we diagnose the three-dimensional(3D) water vapor structure over Shanghai area firstly in China. In water vapor tomography, Gauss weighted function is used as horizontal constraint, the output of numerical forecast is used as apriori information, and boundary condition is also considered. For the problem without exact apriori weights for observations, estimation of variance components is introduced firstly in water vapor tomography to determine posteriori weights. Robust estimation is chosen for reducing the effect of blunders on solutions. For the descending characteristic of water vapor with height increasing, non-equal weights are used along vertical direction. Comparisons between tomography results and the profile provided by numerical model (MM5) show that the forecasted moisture fields of MM5 can be improved obviously by GPS slant water vapor. Using GIPS slant observations to study 3D structure of atmosphere in near real-time is very important for improving initial water vapor field of short-term weather forecast and enhancing the accuracy of numerical weather forecast.

The Meso-.GAMMA. scale Water Vapor Distribution Associated with a Thunderstorm Calculated from a Dense Network of GPS Receivers

The Tsukuba GPS Dense Net campaign that took place in the autumn of 2000 and in the summer of 2001 measured the meso-γ scale distribution of water vapor. As part of the campaign, 75 GPS receivers and 20 automatic meteorological observation systems (AMOS) recorded water vapor variations associated with a thunderstorm on 1 August 2001. The three-dimensional water vapor distribution in the area was estimated from slant water vapor (SWV) data derived from GPS receivers using tomographic methods. SWV is the total amount of water vapor per unit area between a GPS receiver on the ground and a GPS satellite. The SWV data used in this study were obtained with sufficient accuracy by carefully removing multi-path effects, phase center variations of the GPS antenna, and other error sources. SWV was converted to a value that was projected onto the vertical direction (VSWV), so that the influence of the elevation angle on the slant path was removed. VSWV values from adjacent receivers to individual satellites were strongly correlated with each other. Variations in VSWV depended on the GPS receiver positions relative to the developing or moving thunderstorm. Our results indicate that SWV data can provide useful information about the water vapor distribution in the vicinity of thunderstorms. Correlations between the variation of VSWV and the precipitable water vapor (PWV) distribution around the GPS receiver were also calculated. The directions of the large values of VSWV corresponded to regions of high PWV. The three-dimensional water vapor distribution, estimated tomographically, agreed well with Doppler radar-observed reflectivity. Regions of high water vapor near the surface occurred on the northern side of a region of intense reflectivity. The more humid regions above 3 km corresponded to regions where reflectivity increased. The water vapor distribution estimated from the GPS showed an increase of water vapor above a height of 1 km, which preceded the appearance of radar echoes by about 20 minutes during the thunderstorm formation.

Impacts of GPS-derived Water Vapor and Radial Wind Measured by Doppler Radar on Numerical Prediction of Precipitation

This study conducted data assimilation experiments using the operational mesoscale four-dimensional variational data assimilation (4D-Var DA) system of the Japan Meteorological Agency (JMA). Experiments investigated the impacts of GPS-derived water vapor and Doppler radar-derived radial wind (RW) on precipitation prediction for a heavy rain event on 21 July 1999. RW data were obtained from Doppler radars at Narita and Haneda airports. GPS data were obtained from the GPS Earth Observation Network (GEONET) of the Geographical Survey Institute (GSI). Both precipitable water vapor (PWV) and slant water vapor (SWV), which is the amount of water vapor integrated along the slant path between GPS receivers and GPS satellites, were derived from the GPS data. Because SWV contains three-dimensional water vapor distribution information (Seko et al. 2000), we anticipated that assimilating SWV data would more accurately reproduce the moist air inflow at lower layers. Comparisons between observed and model-predicted precipitation regions helped define the impacts of assimilating RW, PWV, and SWV into the model. If the assimilated data included only conventional meteorological data, the model yielded small precipitation regions over a mountainous area far from Tokyo. If the assimilated data included both GPS-derived water vapor data and conventional data, lowlevel inflow in the model was more humid and precipitation occurred along the low-level convergence zone. Because the predicted position of the convergence zone differed from observations, however, the position of the precipitation region was not reproduced correctly. When RW and conventional data were assimilated into the model, low-level northerly flow was reproduced in the northwest of Tokyo. This northerly flow intensified the low-level convergence where precipitation was observed, and the position of the forecasted precipitation was more similar to that of observations. In this model run, however, lowlevel inflow from the south was less humid than observed, and precipitation onset was delayed by 1 hour. If GPS-derived water vapor data, RW data, and conventional data were all simultaneously assimilated, the precipitation position was modeled correctly, and precipitation onset occurred as observed. Comparisons between the vertical cross sections of analyzed water vapor fields and first-guess water vapor fields helped measure the impact of data assimilation on the modeled water vapor distribution. When PWV, RW, and the conventional data were assimilated, water vapor on the windward side of the low-level inflow decreased. In contrast, water vapor in the low-level inflow did not decrease when SWV data were used, instead of PWV data. For this rainfall event, the assimilation of RW and GPS-derived water vapor data improved the precipitation prediction. Assimilation of SWV data improved the representation of the vertical water vapor distribution.

Telluric water vapor contamination of the Mount Wilson solar Doppler measurements

It has been shown that the solar line λ5250.2 (Fei) is weakly blended with a telluric line in the water vapor spectrum, and that magnetograms taken using this line are therefore inaccurate. We investigate the effects of this contamination on the Mount Wilson synoptic magnetograph data, which is based on λ5250.2. Using spectrum scans taken at Kitt Peak, we model the contamination and develop a procedure that would correct for it, whenever the slant water vapor along the line of sight to the Sun is known. As this information is not available for the data collected thus far at Mount Wilson, we use the variation of determined quantities with airmass to obtain an average, or first-order, correction. Concentrating on the fitted coefficients for the solar rotation, the correction is found to be very slight, ∼ 0.5%, raising the value for the A coefficient, averaged over the period 3 December, 1985 to 22 July, 1990, from 2.8289 to 2.8422 μrad s-1, The correction also removes a slight annual variation that has become discernible in the data collected since 1986.