A New Strategy for Deep Wide-Field High Resolution Optical Imaging

We propose a new strategy for obtaining enhanced resolution (FWHM = 0.12 arcsec) deep optical images over a wide field of view. As is well known, this type of image quality can be obtained in principle simply by fast guiding on a small (D = 1.5m) telescope at a good site, but only for target objects which lie within a limited angular distance of a suitably bright guide star. For high altitude turbulence this 'isokinetic angle' is approximately 1 arcminute. With a 1 degree field say one would need to track and correct the motions of thousands of isokinetic patches, yet there are typically too few sufficiently bright guide stars to provide the necessary guiding information. Our proposed solution to these problems has two novel features. The first is to use orthogonal transfer charge-coupled device (OTCCD) technology to effectively implement a wide field 'rubber focal plane' detector composed of an array of cells which can be guided independently. The second is to combine measured motions of a set of guide stars made with an array of telescopes to provide the extra information needed to fully determine the deflection field. We discuss the performance, feasibility and design constraints on a system which would provide the collecting area equivalent to a single 9m telescope, a 1 degree square field and 0.12 arcsec FWHM image quality.Comment: 46 pages, 22 figures, submitted to PASP, a version with higher resolution images and other supplementary material can be found at http://www.ifa.hawaii.edu/~kaiser/wfhr

Time-consistent estimators of 2D/3D motion of atmospheric layers from pressure images

In this paper, we face the challenging problem of estimation of time-consistent layer motion fields at various atmospheric depths. Based on a vertical decomposition of the atmosphere, we propose three different dense motion estimator relying on multi-layer dynamical models. In the first method, we propose a mass conservation model which constitutes the physical background of a multi-layer dense estimator. In the perspective of adapting motion analysis to atmospheric motion, we propose in this method a two-stage decomposition estimation scheme. The second method proposed in this paper relying on a 3D physical model for a stack of interacting layers allows us to recover a vertical motion information. In the last method, we use the exact shallow-water formulation of the Navier-Stokes equations to control the motion evolution across the sequence. This is done through a variational approach derived from data assimilation principle which combines the dynamical model and the pressure difference observations obtained from satellite images. The three methods use sparse pressure difference image observations derived from top of cloud images and classification maps. The proposed approaches are validated on synthetic example and applied to real world meteorological satellite image sequences

Pressure image assimilation for atmospheric motion estimation

The complexity of dynamical laws governing 3D atmospheric flows associated with incomplete and noisy observations makes the recovery of atmospheric dynamics from satellite images sequences very difficult. In this report, we face the challenging problem of estimating physical sound and time consistent horizontal motion fields at various atmospheric depths for a whole image sequence. Based on a vertical decomposition of the atmosphere, we propose two dynamically consistent atmospheric motion estimators relying on different multi-layer dynamical models. Both estimators use a framework derived from data assimilation and are applied on noisy and incomplete pressure difference observations derived from satellite images. In the first model, dense pressure difference maps are reconstructed according to a shallow-water model on each cloud layer. While performing this reconstruction, the variational process estimates the average horizontal wind fields of the multi-layer model. The second model relies on a simplified vorticity-divergence form of the previous multi-layer shallow-water model. In this case, average horizontal motion fields are estimated for each layer without reconstructing pressure maps. While the simplified model is not as precise as the exact shallow-water model, the latter estimator exploits finer spatio-temporal image structures and succeeds in characterizing motion at smaller spatial scales. The performance of both methods is assessed on synthetic examples and on real world meteorological satellite image sequences

Real-time wind-field reconstruction from LiDAR measurements using a dynamic wind model and state estimation.

The use of light detection and ranging (LiDAR) instruments offers many potential benefits to the wind energy industry. Although much effort has been invested in developing such instruments, the fact remains that they provide limited spatio-temporal velocity measurements of the wind-field. Moreover, LiDAR measurements only provide the radial (line-of-sight) velocity component of the wind, making it difficult to precisely determine wind magnitude and direction, owing to the so-called `cyclops' dilemma. Motivated by a desire to extract more information from typical LiDAR data, this paper aims to show that it is possible to accurately estimate, in a real-time fashion, the radial and tangential velocity components of the wind field. We show how such reconstructions can be generated through the synthesis of an Unscented Kalman Filter that employs a low-order dynamic model of the wind to estimate the unmeasured velocities within the wind-field, using repeated measurement updates from typical nacelle-mounted LiDAR instruments. This approach is validated upon synthetic data generated from Large Eddy Simulations (LES) of the atmospheric boundary layer. The accuracy of the wind-field estimates are validated across a variety of beam configurations, look directions, atmospheric stabilities and imperfect measurement conditions. The main outcome of this paper is a technique that offers the potential to accurately reconstruct wind-fields from LiDAR data, overcoming the cyclops dilemma in the process. The ultimate aim of this research is to provide reliable gust detection warning systems to offshore construction workers, in addition to accurate wind-field estimates for use in preview turbine pitch control systems

Cloud base height estimates from sky imagery and a network of pyranometers

Cloud base height (CBH) is an important parameter for physics-based high resolution solar radiation modeling. In sky imager-based forecasts, a ceilometer or stereographic setup is needed to derive the CBH; otherwise erroneous CBHs lead to incorrect physical cloud velocity and incorrect projection of cloud shadows, causing solar power forecast errors due to incorrect shadow positions and timing of shadowing events. In this paper, two methods to estimate cloud base height from a single sky imager and distributed ground solar irradiance measurements are proposed. The first method (Time Series Correlation, denoted as “TSC”) is based upon the correlation between ground-observed global horizontal irradiance (GHI) time series and a modeled GHI time series generated from a sequence of sky images geo-rectified to a candidate set of CBH. The estimated CBH is taken as the candidate that produces the highest correlation coefficient. The second method (Geometric Cloud Shadow Edge, denoted as “GCSE”) integrates a numerical ramp detection method for ground-observed GHI time series with solar and cloud geometry applied to cloud edges in a sky image. CBH are benchmarked against a collocated ceilometer and stereographically estimated CBH from two sky imagers for 15 min median-filtered CBHs. Over 30 days covering all seasons, the TSC method performs similarly to the GCSE method with nRMSD of 18.9% versus 20.8%. A key limitation of both proposed methods is the requirement of sufficient variation in GHI to enable reliable correlation and ramp detection. The advantage of the two proposed methods is that they can be applied when measurements from only a single sky imager and pyranometers are available

Three-Dimensional Motion Estimation of Atmospheric Layers From Image Sequences

International audienceIn this paper, we address the problem of estimating three-dimensional motions of a stratified atmosphere from satellite image sequences. The analysis of three-dimensional atmospheric fluid flows associated with incomplete observation of atmospheric layers due to the sparsity of cloud systems is very difficult. This makes the estimation of dense atmospheric motion field from satellite images sequences very difficult. The recovery of the vertical component of fluid motion from a monocular sequence of image observations is a very challenging problem for which no solution exists in the literature. Based on a physically sound vertical decomposition of the atmosphere into cloud layers of different altitudes, we propose here a dense motion estimator dedicated to the extraction of three-dimensional wind fields characterizing the dynamics of a layered atmosphere. Wind estimation is performed over the complete three-dimensional space using a multi-layer model describing a stack of dynamic horizontal layers of evolving thickness, interacting at their boundaries via vertical winds. The efficiency of our approach is demonstrated on synthetic and real sequences