46 research outputs found
Applications in GNSS water vapor tomography
Algebraic reconstruction algorithms are iterative algorithms that are used in many area including medicine, seismology or meteorology. These algorithms are known to be highly computational intensive. This may be especially troublesome for real-time applications or when processed by conventional low-cost personnel computers. One of these real time applications
is the reconstruction of water vapor images from Global Navigation Satellite System (GNSS) observations. The parallelization of algebraic reconstruction algorithms has the potential to diminish signi cantly the required resources permitting to obtain valid solutions in time to be used for nowcasting and forecasting weather models.
The main objective of this dissertation was to present and analyse diverse shared memory
libraries and techniques in CPU and GPU for algebraic reconstruction algorithms. It was concluded that the parallelization compensates over sequential implementations. Overall the GPU implementations were found to be only slightly faster than the CPU implementations, depending on the size of the problem being studied.
A secondary objective was to develop a software to perform the GNSS water vapor reconstruction using the implemented parallel algorithms. This software has been developed with success and diverse tests were made namely with synthetic and real data, the preliminary results shown to be satisfactory.
This dissertation was written in the Space & Earth Geodetic Analysis Laboratory (SEGAL) and was carried out in the framework of the Structure of Moist convection in high-resolution GNSS observations and models (SMOG) (PTDC/CTE-ATM/119922/2010) project funded by FCT.Algoritmos de reconstrução algébrica são algoritmos iterativos que são usados em muitas áreas
incluindo medicina, sismologia ou meteorologia. Estes algoritmos são conhecidos por serem bastante
exigentes computacionalmente. Isto pode ser especialmente complicado para aplicações
de tempo real ou quando processados por computadores pessoais de baixo custo. Uma destas
aplicações de tempo real é a reconstrução de imagens de vapor de água a partir de observações
de sistemas globais de navegação por satélite. A paralelização dos algoritmos de reconstrução
algébrica permite que se reduza significativamente os requisitos computacionais permitindo
obter soluções válidas para previsão meteorológica num curto espaço de tempo.
O principal objectivo desta dissertação é apresentar e analisar diversas bibliotecas e técnicas
multithreading para a reconstrução algébrica em CPU e GPU. Foi concluído que a paralelização
compensa sobre a implementações sequenciais. De um modo geral as implementações GPU
obtiveram resultados relativamente melhores que implementações em CPU, isto dependendo do
tamanho do problema a ser estudado. Um objectivo secundário era desenvolver uma aplicação
que realizasse a reconstrução de imagem de vapor de água através de sistemas globais de
navegação por satélite de uma forma paralela. Este software tem sido desenvolvido com sucesso
e diversos testes foram realizados com dados sintéticos e dados reais, os resultados preliminares
foram satisfatórios.
Esta dissertação foi escrita no Space & Earth Geodetic Analysis Laboratory (SEGAL) e foi realizada de acordo com o projecto Structure 01' Moist convection in high-resolution GNSS observations and models (SMOG) (PTDC / CTE-ATM/ 11992212010) financiado pelo FCT.Fundação para a Ciência e a Tecnologia (FCT
Compressive sensing reconstruction of 3D wet refractivity based on GNSS and InSAR observations
In this work, the reconstruction quality of an approach for neutrospheric water vapor tomography based on Slant Wet Delays (SWDs) obtained from Global Navigation Satellite Systems (GNSS) and Interferometric Synthetic Aperture Radar (InSAR) is investigated. The novelties of this approach are (1) the use of both absolute GNSS and absolute InSAR SWDs for tomography and (2) the solution of the tomographic system by means of compressive sensing (CS). The tomographic reconstruction is performed based on (i) a synthetic SWD dataset generated using wet refractivity information from the Weather Research and Forecasting (WRF) model and (ii) a real dataset using GNSS and InSAR SWDs. Thus, the validation of the achieved results focuses (i) on a comparison of the refractivity estimates with the input WRF refractivities and (ii) on radiosonde profiles. In case of the synthetic dataset, the results show that the CS approach yields a more accurate and more precise solution than least squares (LSQ). In addition, the benefit of adding synthetic InSAR SWDs into the tomographic system is analyzed. When applying CS, adding synthetic InSAR SWDs into the tomographic system improves the solution both in magnitude and in scattering. When solving the tomographic system by means of LSQ, no clear behavior is observed. In case of the real dataset, the estimated refractivities of both methodologies show a consistent behavior although the LSQ and CS solution strategies differ
Influence of station density and multi-constellation GNSS observations on troposphere tomography
Troposphere tomography, using multi-constellation observations from global
navigation satellite systems (GNSSs), has
become a novel approach for the three-dimensional (3-D) reconstruction of
water vapour fields. An analysis of the integration of four GNSSs (BeiDou,
GPS, GLONASS, and Galileo) observations is presented to investigate the
impact of station density and single- and multi-constellation GNSS
observations on troposphere tomography. Additionally, the optimal horizontal
resolution of the research area is determined in Hong Kong considering both
the number of voxels divided, and the coverage rate of discretized voxels
penetrated by satellite signals. The results show that densification of the
GNSS network plays a more important role than using multi-constellation GNSS
observations in improving the retrieval of 3-D atmospheric water vapour
profiles. The root mean square of slant wet delay (SWD) residuals derived
from the single-GNSS observations decreased by 16 % when the data from
the other four stations are added. Furthermore, additional experiments have
been carried out to analyse the contributions of different combined GNSS data
to the reconstructed results, and the comparisons show some interesting
results: (1) the number of iterations used in determining the weighting
matrices of different equations in tomography modelling can be decreased when
considering multi-constellation GNSS observations and (2) the reconstructed
quality of 3-D atmospheric water vapour using multi-constellation GNSS data
can be improved by about 11 % when compared to the SWD estimated with
precise point positioning, but this was not as high as expected.</p
An improved pixel-based water vapor tomography model
As an innovative use of Global Navigation Satellite System (GNSS), the GNSS
water vapor tomography technique shows great potential in monitoring
three-dimensional water vapor variation. Most of the previous studies employ
the pixel-based method, i.e., dividing the troposphere space into finite
voxels and considering water vapor in each voxel as constant. However, this
method cannot reflect the variations in voxels and breaks the continuity of
the troposphere. Moreover, in the pixel-based method, each voxel needs a
parameter to represent the water vapor density, which means that huge numbers
of parameters are needed to represent the water vapor field when the
interested area is large and/or the expected resolution is high. In order to
overcome the abovementioned problems, in this study, we propose an improved
pixel-based water vapor tomography model, which uses layered optimal
polynomial functions obtained from the European Centre for Medium-Range
Weather Forecasts (ECMWF) by adaptive training for water vapor retrieval.
Tomography experiments were carried out using the GNSS data collected from
the Hong Kong Satellite Positioning Reference Station Network (SatRef) from
25 March to 25 April 2014 under different scenarios. The tomographic results
are compared to the ECMWF data and validated by the radiosonde. Results show
that the new model outperforms the traditional one by reducing the
root-mean-square error (RMSE), and this improvement is more pronounced, at
5.88 % in voxels without the penetration of GNSS rays. The improved model
also has advantages in more convenient expression.</p
Influence of station density and multi-constellation GNSS observations on troposphere tomography
Troposphere tomography, using multi-constellation observations from global navigation satellite systems (GNSSs), has become a novel approach for the three-dimensional (3-D) reconstruction of water vapour fields. An analysis of the integration of four GNSSs (BeiDou, GPS, GLONASS, and Galileo) observations is presented to investigate the impact of station density and single- and multi-constellation GNSS observations on troposphere tomography. Additionally, the optimal horizontal resolution of the research area is determined in Hong Kong considering both the number of voxels divided, and the coverage rate of discretized voxels penetrated by satellite signals. The results show that densification of the GNSS network plays a more important role than using multi-constellation GNSS observations in improving the retrieval of 3-D atmospheric water vapour profiles. The root mean square of slant wet delay (SWD) residuals derived from the single-GNSS observations decreased by 16&amp;thinsp;% when the data from the other four stations are added. Furthermore, additional experiments have been carried out to analyse the contributions of different combined GNSS data to the reconstructed results, and the comparisons show some interesting results: (1) the number of iterations used in determining the weighting matrices of different equations in tomography modelling can be decreased when considering multi-constellation GNSS observations and (2) the reconstructed quality of 3-D atmospheric water vapour using multi-constellation GNSS data can be improved by about 11&amp;thinsp;% when compared to the SWD estimated with precise point positioning, but this was not as high as expected
A new approach for GNSS tomography from a few GNSS stations
The determination of the distribution of water vapor in the atmosphere plays
an important role in the atmospheric monitoring. Global Navigation Satellite
Systems (GNSS) tomography can be used to construct 3-D distribution of water
vapor over the field covered by a GNSS network with high temporal and spatial
resolutions. In current tomographic approaches, a pre-set fixed rectangular
field that roughly covers the area of the distribution of the GNSS signals on
the top plane of the tomographic field is commonly used for all tomographic
epochs. Due to too many unknown parameters needing to be estimated, the
accuracy of the tomographic solution degrades. Another issue of these
approaches is their unsuitability for GNSS networks with a low number of stations, as
the shape of the field covered by the GNSS signals is, in fact, roughly that of an
upside-down cone rather than the rectangular cube as the pre-set. In this
study, a new approach for determination of tomographic fields fitting the
real distribution of GNSS signals on different tomographic planes at
different tomographic epochs and also for discretization of the tomographic
fields based on the perimeter of the tomographic boundary on the plane and
meshing techniques is proposed. The new approach was tested using three
stations from the Hong Kong GNSS network and validated by comparing the
tomographic results against radiosonde data from King's Park Meteorological
Station (HKKP) during the one month period of May 2015. Results indicated
that the new approach is feasible for a three-station GNSS network
tomography. This is significant due to the fact that the conventional
approaches cannot even solve a network tomography from a few stations
Comparisons between the WRF data assimilation and the GNSS tomography technique in retrieving 3-D wet refractivity fields in Hong Kong
Water vapor plays an important role in various scales of weather processes.
However, there are limited means to accurately describe its three-dimensional
(3-D) dynamical changes. The data assimilation technique and the Global
Navigation Satellite System (GNSS) tomography technique are two of the
limited means. Here, we conduct an interesting comparison between the GNSS
tomography technique and the Weather Research and Forecasting Data
Assimilation (WRFDA) model (a
representative of the data assimilation models) in retrieving wet
refractivity (WR) in the Hong Kong area during a wet period and a dry period.
The GNSS tomography technique is used to retrieve WR from the GNSS slant wet
delays. The WRFDA is used to
assimilate the zenith tropospheric delay to improve the background data. The
radiosonde data are used to validate the WR derived from the GNSS tomography,
the WRFDA output, and the background data. The root mean square
(rms) of the WR
derived from the tomography results, the WRFDA output, and the background
data are 6.50, 4.31, and 4.15 mm km−1 in the wet period. The rms
becomes 7.02, 7.26, and 6.35 mm km−1 in the dry period. The lower
accuracy in the dry period is mainly due to the sharp variation of WR in the
vertical direction. The results also show that assimilating GNSS ZTD into the
WRFDA only slightly improves the accuracy of the WR and that the WRFDA WR is
better than the tomographic WR in most cases. However, in a special
experimental period when the water vapor is highly concentrated in the lower
troposphere, the tomographic WR outperforms the WRFDA WR in the lower
troposphere. When we assimilate the tomographic WR in the lower troposphere
into the WRFDA, the retrieved WR is improved.</p