50 anos de sinergia entre geodésia espacial e meteorologia: do erro no posicionamento GNSS a aplicações de previsão de precipitação de curtíssimo prazo

The neutral atmosphere (or troposphere) causes refraction in radio frequency signals, which results in errors in Global Navigation Satellite Systems (GNSS) measurements. In meteorology, this effect can represent important measurements of the concentration of atmospheric constituents, especially in regions where conventional high-altitude atmospheric sounding (radiosondes) cannot be performed. There are two GNSS techniques used for this. In the first one, GNSS receivers are located on terrestrial stations that provide estimates of the vertically integrated moisture content (Precipitable Water Vapor - PWV). In the second case, receivers are in space platforms, which obtains profiles of atmospheric pressure, temperature and humidity, known as GNSS radio occultation. These measurements have significant potential for nowcasting applications (30 minutes in advance) of extreme precipitation events (>35 mm). This paper presents a review of the state of the art in the synergy between Geodesy and Meteorology for modeling the neutral atmosphere (neutrosphere), its effect on GNSS positioning and in the estimation of atmospheric constituents, and their applications. Furthermore, it offers the improvements and new challenges developed in modeling the delay for high accuracy positioning.A atmosfera neutra (ou troposfera) causa refração nos sinais de radiofrequência, que resulta em erros nas medidas do Global Navigation Satellite Systems (GNSS) empregadas no posicionamento geodésico. Já para a Meteorologia esse efeito pode representar medidas importantes da concentração dos constituintes atmosféricos, principalmente em regiões onde não se pode realizar sondagem atmosférica convencional, por meio de radiossondas acopladas a balões. Duas técnicas GNSS podem ser empregadas para isso. A primeira utiliza receptores em estações terrestres que fornecem estimativas do conteúdo integrado verticalmente de umidade na atmosfera neutra (Precipitable Water Vapor - PWV). A segunda, com receptores localizados em plataformas espaciais, com os quais obtém perfis atmosféricos de pressão, temperatura e umidade, na técnica conhecida como Rádio-ocultação GNSS. Essas medidas têm um potencial significativo para aplicações em previsões de curtíssimo prazo (30 minutos) de eventos extremos de precipitação (>35 mm). O objetivo principal deste artigo é realizar uma revisão do estado da arte da sinergia entre a Geodésia e a Meteorologia na modelagem da atmosfera neutra (neutrosfera), seu efeito no posicionamento GNSS e na estimativa dos constituintes atmosféricos e suas aplicações. Além disso, apresenta os aprimoramentos e novos desafios desenvolvidos na modelagem do atraso para o posicionamento de alta acurácia

Observing geometry effects on a Global Navigation Satellite System (GNSS)-based water vapor tomography solved by least squares and by compressive sensing

In this work, the effect of the observing geometry on the tomographic reconstruction quality of both a regularized least squares (LSQ) approach and a compressive sensing (CS) approach for water vapor tomography is compared based on synthetic Global Navigation Satellite System (GNSS) slant wet delay (SWD) estimates. In this context, the term “observing geometry” mainly refers to the number of GNSS sites situated within a specific study area subdivided into a certain number of volumetric pixels (voxels) and to the number of signal directions available at each GNSS site. The novelties of this research are (1) the comparison of the observing geometry\u27s effects on the tomographic reconstruction accuracy when using LSQ or CS for the solution of the tomographic system and (2) the investigation of the effect of the signal directions\u27 variability on the tomographic reconstruction. The tomographic reconstruction is performed based on synthetic SWD data sets generated, for many samples of various observing geometry settings, based on wet refractivity information from the Weather Research and Forecasting (WRF) model. The validation of the achieved results focuses on a comparison of the refractivity estimates with the input WRF refractivities. The results show that the recommendation of Champollion et al. (2004) to discretize the analyzed study area into voxels with horizontal sizes comparable to the mean GNSS intersite distance represents a good rule of thumb for both LSQ- and CS-based tomography solutions. In addition, this research shows that CS needs a variety of at least 15 signal directions per site in order to estimate the refractivity field more accurately and more precisely than LSQ. Therefore, the use of CS is particularly recommended for water vapor tomography applications for which a high number of multi-GNSS SWD estimates are available

4D GPS water vapor tomography: new parameterized approaches

Water vapor is a key variable in numerical weather prediction, as it plays an important role in atmospheric processes. Nonetheless, the distribution of water vapor in the atmosphere is observed with a coarse resolution in time and space compared to the resolution of numerical weather models. GPS water vapor tomography is one of the promising methods to improve the resolution of water vapor measurements. This paper presents new parameterized approaches for the determination of water vapor distribution in the troposphere by GPS. We present the methods and give first results validating the approaches. The parameterization of voxels (volumetric pixels) by trilinear and spline functions in ellipsoidal coordinates are introduced in this study. The evolution in time of the refractivity field is modeled by a Kalman filter with a temporal resolution of 30 s, which corresponds to the available GPS-data rate. The algorithms are tested with simulated and with real data from more than 40 permanent GPS receiver stations in Switzerland and adjoining regions covering alpine areas. The investigations show the potential of the new parameterized approaches to yield superior results compared to the non parametric classical one. The accuracy of the tomographic result is quantified by the inter-quartile range (IQR), which is decreased by 10-20% with the new approaches. Further, parameterized voxel solutions have a substantially smaller maximal error than the non parameterized ones. Simulations show a limited ability to resolve vertical structures above the top station of the network with GPS tomograph

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

Beyond Mapping Functions and Gradients

Mapping functions and gradients in GNSS and VLBI applications were introduced in the sixties and seventies to model the microwave propagation delays in the troposphere, and they were proven to be the perfect tools for these applications. In this work, we revisit the physical and mathematical basis of these tools in the context of meteorology and climate applications and propose an alternative approach for the wet delay part. This alternative approach is based on perturbation theory, where the base case is an exponential decay of the wet refractivity with altitude. The perturbation is modeled as a set of orthogonal functions in space and time, with the ability to separate eddy-scale variations of the wet refractivity