We have studied the possibilities of the stellar occultation method in monitoring the global dist... more We have studied the possibilities of the stellar occultation method in monitoring the global distribution of stratospheric ozone. In this paper we discuss the tomographic inversion problem of stellar occultations. We present different techniques and compare them using the characteristic properties of GOMOS instrument.
International Journal for Uncertainty Quantification
We propose a statistical approach to estimate emissions of isolated pointlike sources by NO2 trop... more We propose a statistical approach to estimate emissions of isolated pointlike sources by NO2 tropospheric column concentrations satellite observations. The approach is data driven; in addition to the satellite measurements it only uses available wind data and a rudimentary model for the NOx chemistry. We construct interpolated fields of the satellite observations using Gaussian random fields, which allows for a more flexible fitting of data than the more standard Gaussian plume regressions. They enable producing uncertainty quantification, even with partly obscured or missing observations. The Gaussian random field surfaces provide continuous surfaces of the satellite observations along which flux integrals are computed to simplify the problem from two-dimensional satellite observations to one-dimensional fluxes. The emission estimates are then obtained by a simple model that combines the flux and chemistry. Extensive uncertainty quantification is implemented at every step of the es...
In the present study the impact of household size together with behavioral alterations caused by ... more In the present study the impact of household size together with behavioral alterations caused by parasite on malaria transmission is investigated using a discrete agent-based model of mosquito host-seeking behavior earlier calibrated against experimental data. The rate of transmission was shown to decrease with the typical size of household. Depending on the assumptions, one can observe different thresholds for a significant decrease of the disease reproduction number.
Background Increasingly complex models have been developed to characterize the transmission dynam... more Background Increasingly complex models have been developed to characterize the transmission dynamics of malaria. The multiplicity of malaria transmission factors calls for a realistic modelling approach that incorporates various complex factors such as the effect of control measures, behavioural impacts of the parasites to the vector, or socio-economic variables. Indeed, the crucial impact of household size in eliminating malaria has been emphasized in previous studies. However, increasing complexity also increases the difficulty of calibrating model parameters. Moreover, despite the availability of much field data, a common pitfall in malaria transmission modelling is to obtain data that could be directly used for model calibration. Methods In this work, an approach that provides a way to combine in situ field data with the parameters of malaria transmission models is presented. This is achieved by agent-based stochastic simulations, initially calibrated with hut-level experimental...
Quarterly Journal of the Royal Meteorological Society, 2019
In ensemble weather prediction systems, ensemble spread is generated using uncertainty representa... more In ensemble weather prediction systems, ensemble spread is generated using uncertainty representations for initial and boundary values as well as for model formulation. The ensuing ensemble spread is thus regulated through what we call ensemble spread parameters. The task is to specify the parameter values such that the ensemble spread corresponds to the prediction skill of the ensemble mean – a prerequisite for a reliable prediction system. In this paper, we present an algorithmic approach suitable for this task consisting of a differential evolution algorithm with filter likelihood providing evidence. The approach is demonstrated using an idealized ensemble prediction system based on the Lorenz–Wilks system. Our results suggest that it might be possible to optimize the spread parameters without manual intervention.
International Journal for Numerical Methods in Fluids, 2016
SummaryKalman filter is a sequential estimation scheme that combines predicted and observed data ... more SummaryKalman filter is a sequential estimation scheme that combines predicted and observed data to reduce the uncertainty of the next prediction. Because of its sequential nature, the algorithm cannot be efficiently implemented on modern parallel compute hardware nor can it be practically implemented on large‐scale dynamical systems because of memory issues. In this paper, we attempt to address pitfalls of the earlier low‐memory approach described in and extend it for parallel implementation. First, we describe a low‐memory method that enables one to pack covariance matrix data employed by the Kalman filter into a low‐memory form by means of certain quasi‐Newton approximation. Second, we derive parallel formulation of the filtering task, which allows to compute several filter iterations independently. Furthermore, this leads to an improvement of estimation quality as the method takes into account the cross‐correlations between consequent system states. We experimentally demonstrate...
Journal of Quantitative Spectroscopy and Radiative Transfer, 2015
Abstract In recent decades, the use of nanomaterials has become very common. Different nanomateri... more Abstract In recent decades, the use of nanomaterials has become very common. Different nanomaterials are being used in over 1600 consumer products. Nanomaterials have been defined as having at least one dimension in the range of 1−100 nm. Such materials often have unique properties. Despite some warnings of applying bulk optical constants for nano size materials, stated already in 1980s, bulk constants are still commonly used in the light scattering measurements of nano size particles. Titanium dioxide is one of the materials that is manufactured and used as an engineered nanomaterial in increasing quantities. Due to the aforementioned facts, it is quite crucial for successful research and production of nanoparticles to find out the dependence of the refractive index function (RIF) of the material on its crystal size. We have earlier performed several ab initio computations for obtaining the dependence of the RIF of TiO 2 on the crystal or on the cluster size, for particles of size up to ca. 2 nm. Extending the calculations to greater sizes has turned out to be infeasible due to the unbearable increase in computational time. However, in this study we show how the crystal-size-dependent-RIF (CS-RIF), for both rutile and anatase can be modeled from measured extinction or turbidity spectra of samples with varying crystal and particle sizes. For computing the turbidity spectrum, we constructed a model including primary crystals whose distributions were parameterized by mean and standard deviation, and also including aggregates consisting of mean sized primary particles, parameterized just by mean aggregate size. Mainly because of the long computing times Mie calculation was used in the computation of extinction spectra. However, in practical process applications, the obtained RIF will be used together with the T-matrix method. We constructed the RIFs used in the model using generalized oscillator model (GOM) as expanded to crystal size dependence. The unknown parameters of the model were solved using nonlinear least squares estimation. When the crystal size becomes smaller than the bulk size the shape of the estimated CS-RIFs reveal two distinct regions for both rutile and anatase. In the first region, starting apparently already from ca. 200 nm, the height of both the real part and the imaginary part of CS-RIF decreases on crystal diameter. However, the band gap remains constant. In the second region, starting when the crystal diameter is decreased to ca. 3 nm, a blue shift starts to increase the band gap. The band gap dependence on crystal size is quite consistent with the existing experimental values. Consequently, it is of great importance to use CS-RIF in light scattering measurements for nanoparticle size determination. Neglecting this, the smaller particles in the size distribution will have too small values, already for sub-micrometer particles, naturally distorting also the mean value. To our knowledge, this is the first time ever that a CS-RIF from bulk to 1 nm size is determined for any material.
A new approach was recently introduced for the task of estimation of parameters of chaotic dynami... more A new approach was recently introduced for the task of estimation of parameters of chaotic dynamical systems. Here we apply the method for stochastic differential equation (SDE) systems. It turns out that the basic version of the approach does not identify such systems. However, a modification is presented that enables efficient parameter estimation of SDE models. We test the approach with basic SDE examples, compare the results to those obtained by usual state-space filtering methods, and apply it to more complex cases where the more traditional methods are no more available.
Reliable estimation of parameters of chaotic dynamical systems is a long standing problem importa... more Reliable estimation of parameters of chaotic dynamical systems is a long standing problem important in numerous applications. We present a robust method for parameter estimation and uncertainty quantification that requires neither the knowledge of initial values for the system nor good guesses for the unknown model parameters. The method uses a new distance concept recently introduced to characterize the variability of chaotic dynamical systems. We apply it to cases where more traditional methods, such as those based on state space filtering, are no more applicable. Indeed, the approach combines concepts from chaos theory, optimization and statistics in a way that enables solving problems considered as 'intractable and unsolved' in prior literature. We illustrate the results with a large number of chaotic test cases, and extend the method in ways that increase the accuracy of the estimation results.
We have studied the possibilities of the stellar occultation method in monitoring the global dist... more We have studied the possibilities of the stellar occultation method in monitoring the global distribution of stratospheric ozone. In this paper we discuss the tomographic inversion problem of stellar occultations. We present different techniques and compare them using the characteristic properties of GOMOS instrument.
International Journal for Uncertainty Quantification
We propose a statistical approach to estimate emissions of isolated pointlike sources by NO2 trop... more We propose a statistical approach to estimate emissions of isolated pointlike sources by NO2 tropospheric column concentrations satellite observations. The approach is data driven; in addition to the satellite measurements it only uses available wind data and a rudimentary model for the NOx chemistry. We construct interpolated fields of the satellite observations using Gaussian random fields, which allows for a more flexible fitting of data than the more standard Gaussian plume regressions. They enable producing uncertainty quantification, even with partly obscured or missing observations. The Gaussian random field surfaces provide continuous surfaces of the satellite observations along which flux integrals are computed to simplify the problem from two-dimensional satellite observations to one-dimensional fluxes. The emission estimates are then obtained by a simple model that combines the flux and chemistry. Extensive uncertainty quantification is implemented at every step of the es...
In the present study the impact of household size together with behavioral alterations caused by ... more In the present study the impact of household size together with behavioral alterations caused by parasite on malaria transmission is investigated using a discrete agent-based model of mosquito host-seeking behavior earlier calibrated against experimental data. The rate of transmission was shown to decrease with the typical size of household. Depending on the assumptions, one can observe different thresholds for a significant decrease of the disease reproduction number.
Background Increasingly complex models have been developed to characterize the transmission dynam... more Background Increasingly complex models have been developed to characterize the transmission dynamics of malaria. The multiplicity of malaria transmission factors calls for a realistic modelling approach that incorporates various complex factors such as the effect of control measures, behavioural impacts of the parasites to the vector, or socio-economic variables. Indeed, the crucial impact of household size in eliminating malaria has been emphasized in previous studies. However, increasing complexity also increases the difficulty of calibrating model parameters. Moreover, despite the availability of much field data, a common pitfall in malaria transmission modelling is to obtain data that could be directly used for model calibration. Methods In this work, an approach that provides a way to combine in situ field data with the parameters of malaria transmission models is presented. This is achieved by agent-based stochastic simulations, initially calibrated with hut-level experimental...
Quarterly Journal of the Royal Meteorological Society, 2019
In ensemble weather prediction systems, ensemble spread is generated using uncertainty representa... more In ensemble weather prediction systems, ensemble spread is generated using uncertainty representations for initial and boundary values as well as for model formulation. The ensuing ensemble spread is thus regulated through what we call ensemble spread parameters. The task is to specify the parameter values such that the ensemble spread corresponds to the prediction skill of the ensemble mean – a prerequisite for a reliable prediction system. In this paper, we present an algorithmic approach suitable for this task consisting of a differential evolution algorithm with filter likelihood providing evidence. The approach is demonstrated using an idealized ensemble prediction system based on the Lorenz–Wilks system. Our results suggest that it might be possible to optimize the spread parameters without manual intervention.
International Journal for Numerical Methods in Fluids, 2016
SummaryKalman filter is a sequential estimation scheme that combines predicted and observed data ... more SummaryKalman filter is a sequential estimation scheme that combines predicted and observed data to reduce the uncertainty of the next prediction. Because of its sequential nature, the algorithm cannot be efficiently implemented on modern parallel compute hardware nor can it be practically implemented on large‐scale dynamical systems because of memory issues. In this paper, we attempt to address pitfalls of the earlier low‐memory approach described in and extend it for parallel implementation. First, we describe a low‐memory method that enables one to pack covariance matrix data employed by the Kalman filter into a low‐memory form by means of certain quasi‐Newton approximation. Second, we derive parallel formulation of the filtering task, which allows to compute several filter iterations independently. Furthermore, this leads to an improvement of estimation quality as the method takes into account the cross‐correlations between consequent system states. We experimentally demonstrate...
Journal of Quantitative Spectroscopy and Radiative Transfer, 2015
Abstract In recent decades, the use of nanomaterials has become very common. Different nanomateri... more Abstract In recent decades, the use of nanomaterials has become very common. Different nanomaterials are being used in over 1600 consumer products. Nanomaterials have been defined as having at least one dimension in the range of 1−100 nm. Such materials often have unique properties. Despite some warnings of applying bulk optical constants for nano size materials, stated already in 1980s, bulk constants are still commonly used in the light scattering measurements of nano size particles. Titanium dioxide is one of the materials that is manufactured and used as an engineered nanomaterial in increasing quantities. Due to the aforementioned facts, it is quite crucial for successful research and production of nanoparticles to find out the dependence of the refractive index function (RIF) of the material on its crystal size. We have earlier performed several ab initio computations for obtaining the dependence of the RIF of TiO 2 on the crystal or on the cluster size, for particles of size up to ca. 2 nm. Extending the calculations to greater sizes has turned out to be infeasible due to the unbearable increase in computational time. However, in this study we show how the crystal-size-dependent-RIF (CS-RIF), for both rutile and anatase can be modeled from measured extinction or turbidity spectra of samples with varying crystal and particle sizes. For computing the turbidity spectrum, we constructed a model including primary crystals whose distributions were parameterized by mean and standard deviation, and also including aggregates consisting of mean sized primary particles, parameterized just by mean aggregate size. Mainly because of the long computing times Mie calculation was used in the computation of extinction spectra. However, in practical process applications, the obtained RIF will be used together with the T-matrix method. We constructed the RIFs used in the model using generalized oscillator model (GOM) as expanded to crystal size dependence. The unknown parameters of the model were solved using nonlinear least squares estimation. When the crystal size becomes smaller than the bulk size the shape of the estimated CS-RIFs reveal two distinct regions for both rutile and anatase. In the first region, starting apparently already from ca. 200 nm, the height of both the real part and the imaginary part of CS-RIF decreases on crystal diameter. However, the band gap remains constant. In the second region, starting when the crystal diameter is decreased to ca. 3 nm, a blue shift starts to increase the band gap. The band gap dependence on crystal size is quite consistent with the existing experimental values. Consequently, it is of great importance to use CS-RIF in light scattering measurements for nanoparticle size determination. Neglecting this, the smaller particles in the size distribution will have too small values, already for sub-micrometer particles, naturally distorting also the mean value. To our knowledge, this is the first time ever that a CS-RIF from bulk to 1 nm size is determined for any material.
A new approach was recently introduced for the task of estimation of parameters of chaotic dynami... more A new approach was recently introduced for the task of estimation of parameters of chaotic dynamical systems. Here we apply the method for stochastic differential equation (SDE) systems. It turns out that the basic version of the approach does not identify such systems. However, a modification is presented that enables efficient parameter estimation of SDE models. We test the approach with basic SDE examples, compare the results to those obtained by usual state-space filtering methods, and apply it to more complex cases where the more traditional methods are no more available.
Reliable estimation of parameters of chaotic dynamical systems is a long standing problem importa... more Reliable estimation of parameters of chaotic dynamical systems is a long standing problem important in numerous applications. We present a robust method for parameter estimation and uncertainty quantification that requires neither the knowledge of initial values for the system nor good guesses for the unknown model parameters. The method uses a new distance concept recently introduced to characterize the variability of chaotic dynamical systems. We apply it to cases where more traditional methods, such as those based on state space filtering, are no more applicable. Indeed, the approach combines concepts from chaos theory, optimization and statistics in a way that enables solving problems considered as 'intractable and unsolved' in prior literature. We illustrate the results with a large number of chaotic test cases, and extend the method in ways that increase the accuracy of the estimation results.
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