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Exploring the dynamics of capped inversion from sodar data
Abstract
The time evolution of planetary boundary layer can be studied from sodar data. Normally most of the sound wave transmitted above gets backscattered from a single inversion layer. However, during turbulent atmospheric conditions, there are multiple layers from where the signal gets backscattered with varying echo strengths. The present work describes a methodology for computer analysis of multilayered structures. Two distinct backscatter heights are extracted from observed echograms. This noisy measurement is filtered to estimate the layer heights using an a priori system model. The process and measurement noise covariances used by the filter algorithm are tuned from physical considerations to get good filter performance. Data from sodar installed at Maitree station of Antarctica with capping over an inversion layer is used to prove the concept.
Remote sensing techniques are applied to sodar data for providing information regarding the planetary boundary layer. The temperature inhomogeneity that results from convective mixing in lower atmosphere is responsible for the backscattering of sound wave transmitted by the sodar. The intensity of echo returns fluctuates with time in the mixing height zone due to convection. In this work, fractal dimension (FD) of the data is considered as a measure for classifying the mixing height zone. A method has been proposed to tackle the noisy sodar data without losing relevant meteorological information. A comparison of the computed FD for noise-prone sodar data sets of tropical urban settings with those of relatively noise-free Antarctic settings is reported in this context.
An episode of fog during 8-9 January 1996 over the Schirmacher Oasis of East Antarctica was investigated. It revealed that the fog was formed due to advection of southeasterly winds by high moisturecontaining northwesterly winds, blowing from the ocean over the cold ice shelf, which leads to the condensation of moisture, thereby forming fog for a duration of about 24 h. A monostatic acoustic sodar recorded the episode, which revealed the thickness of fog layer up to an altitude of about 300 to 400 m. Supplementary meteorological and radiosonde data were utilized to understand the mechanisms of the formation and dissipation of advection fog in this region. Supporting literature illustrates that this type of fog is an important source of water for photosynthesis, growth, reproduction and other metabolic activities of the poikilohydric microbiotic crust inhabiting the cold, arid and dry ecosystem of the coastal East Antarctic oases.
This paper compares a number of one-dimensional closure models for the planetary boundary layer (PBL) that are currently in use in large-scale atmospheric models. Using the results of a large-eddy simulation (LES) model as the standard of comparison, the PBL models are evaluated over a range of stratifications from free convective to neutral and a range of surface shear stresses. Capping inversion strengths for the convective cases range from weakly to strongly capped. Six prototypical PBL models are evaluated in this study, which focuses on the accuracy of the boundary-layer fluxes of momentum, heat, and two passive scalars. One scalar mimics humidity and the other is a top-down scalar entrained into the boundary layer from above. A set of measures based on the layer-averaged differences of these fluxes from the LES solutions is developed. In addition to the methodological framework and suite of LES solutions, the main result of the evaluation is the recognition that all of the examined PBL parameterizations have difficulty reproducing the entrainment at the top of the PBL, as given by the LES, in most parameter regimes. Some of the PBL models are relatively accurate in their entrainment flux in a subset of parameter regimes. The sensitivity of the PBL models to vertical resolution is explored, and substantive differences are observed in the performance of the PBL models, relative to LES, at low resolution typical of large scale atmospheric models.
Application of acoustic sounders (sodars) to study the atmospheric boundary layer (ABL) began in the early 1970s. During the last two decades the scope of sodars applications enlarged considerably. The proceedings of eleven symposia of the International Society for Remote Acoustic Sensing of the Atmosphere and Oceans (ISARS) are a unique collection of papers, where all directions of the sodar use since 1981 are presented. In this paper, a review of sodar applications to atmospheric research is presented based on materials published in these proceedings in the following fields: conditions of microwave and light propagation; regional climatology of the ABL; air pollution meteorology and weather forecast; mesoscale phenomena under stable and unstable stratification; micrometeorology; peculiarities of the ABL in remote and complex terrain.
For 20 years the biennial symposia organized by the International Society for Acoustic Remote Sensing of the Atmosphere and Oceans and Associated Techniques (ISARS) have been held in different countries. The original papers collected in the proceedings of the symposia reflect the development of ideas, equipment, and methods, plus results obtained in the area. The spectrum of themes discussed at the symposia is broad. More than 60% of the papers were devoted to the role of sodars in atmospheric boundary-layer research. This paper presents an overview of the development of sodar technique as elucidated in the proceedings of 11 ISARS symposia. Topics include (1) instrumentation, (2) methods of measuring different meteorological quantities and their turbulence characteristics, and (3) validating the methods by comparison of sodar data with in situ measurements.
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The sound detection and ranging (SODAR) system is an effective and active remote sensing tool to probe, in round-the-clock basis, the lower planetary boundary layer (LPBL) of the earth. The observations obtained from such SODAR systems can prove to be extremely useful for LPBL studies if classified and interpreted automatically. Manual interpretation of such data by an expert in this field is very tedious and error-prone. Hence, in this letter, we attempt to classify such data obtained from monostatic SODAR system, which is represented as echogram, by graph matching method. We initially convert the time domain's sample intensity variation, which is recorded as height versus time scale on echogram, to frequency domain for obtaining higher discriminating power of each sample of each class (as for example, thermal plume, inversion, etc.) of the echogram. For discriminating the individual class of the echogram, we use a measure of similarity. Thus, an expert system using graph matching method based on the frequency domain representation of each SODAR sample of each class is developed.
Sodar provides round-the-clock information about the atmospheric boundary layer (ABL). However, computer-based extraction of relevant ABL information from sodar data calls for techniques of image processing to remove the inherent noise. Kalman filter is employed as an alternative methodology to extract the ABL. Thus, it becomes possible to bypass the time-consuming image processing steps, making it faster for real time interpretation of atmospheric conditions from ABL. The technique compares well with the performance of the image processing technique.
The paper deals with estimation of noise covariance matrices in state and measurement equations of linear discrete-time stochastic dynamic systems. In the last decade several novel methods for noise covariance matrices estimation, which are based on state estimation techniques, have been proposed. Unfortunately, the novel methods have been compared mainly with classical methods proposed in the seventies only. The aim of the paper is to analyse identifiability of state noise parameters by means of the Bayesian approach and to summarise and compare the novel methods from both theoretical and numerical point of view.
Instead of piecemeal approaches for detecting specific patterns in the SODAR echograms, an integrated modular approach 'towards' automatic interpretation of the ABL structure patterns, as depicted in the SODAR facsimile records, is presented. Here we propose a unified approach where the user is at liberty to select a wide range of image processing and the pattern recognition techniques required to extract remotely-probed meteorological information from the closest geometric representation of the SODAR pattern boundary. The ultimate goal, part of which is already implemented and reported here, is to generate 'expert-like' interpretation of SODAR echograms.
Accurate estimation of mixing height is important, since it is an important parameter for lower atmospheric studies involving aerosol monitoring and pollutant dispersal models. Sodar happens to be one of the best instruments for monitoring the mixing height. But it suffers from the drawback of acoustic noise, which makes the measurement inaccurate. Conventional Kalman filter has been used to estimate atmospheric boundary layer by filtering the measurement noise involved in sodar data. But there are certain limitations of the accuracy available from conventional Kalman filter which may be overcome by proper adaptive design. The present work develops an adaptive scheme for estimation of mixing heights. It considers the selection of a proper meteorological system model from a bank of system models. Diurnal and seasonal changes in measurement noise statistics of the acoustic radar is taken into account by designing a fuzzy logic based adaptive scheme.
The mean and covariance of a Kalman filter residual are computed
for specific cases in which the Kalman filter model differs from a
linear model that accurately represents the true system (the truth
model). Multiple model adaptive estimation (MMAE) uses a bank of Kalman
filters, each with a different internal model, and a hypothesis testing
algorithm that uses the residuals from this bank of Kalman filters to
estimate the true system model. At most, only one Kalman filter model
will exactly match the truth model and will produce a residual whose
mean and standard deviation have already been analyzed. All of the other
filters use internal models that mismodel the true system. We compute
the effects of a mismodeled input matrix, output matrix, and state
transition matrix on these residuals. The computed mean and covariance
are compared with simulation results of flight control failures that
correspond to mismodeled input matrices and output matrices