Joint interpolation of multi-sensor sea surface geophysical fields using non-local and statistical priors

This work addresses the joint analysis of multi-source and multi-resolution remote sensing data for the interpolation of high-resolution geophysical fields. As case-study application, we consider the interpolation of sea surface temperature fields. We propose a novel statistical model, which combines two key features: an exemplar-based prior and second-order statistical priors. The exemplar-based prior, referred to as a non-local prior, exploits similarities between local patches (small field regions) to interpolate missing data areas from previously observed exemplars. This non-local prior also sets an explicit conditioning between the multi-sensor data. Two complementary statistical priors, namely a prior on the spatial covariance and a prior on the marginal distribution of the high-resolution details, are considered as sea surface geophysical fields are expected to depict specific spectral and marginal features in relation to the underlying turbulent ocean dynamics. We report experiments on both synthetic data and real SST data. These experiments demonstrate the contributions of the proposed combination of non-local and statistical priors to interpolate visually-consistent and geophysically-sound SST fields from multi-source satellite data. We further discuss the key features and parameterizations of this model as well as its relevance with respect to classical interpolation techniques

Interpolation de données manquantes dans des séquences multi-modales d'images géophysiques satellitaires

Session "Articles"National audienceCet article étudie l'estimation conjointe de données manquantes et de champs de déplacements dans des séquences multimodales d'observations satellitaires géophysiques. La complexité de la tâche est liée au taux élevé de données manquantes (entre 20% et 90%) pour des observations journalières de haute résolution et la reconstruction de structures fines en accord avec la dynamique sous jacente. Nous avons développé une méthode basée sur l'assimilation variationnelle de données pour des séries multimodales et multi-résolutions. A l'aide de données synthétiques et de données réelles de la surface océanique, une évaluation numérique et qualitative démontre l'apport de deux composantes clés du modèle proposé: la fusion d'informations multimodales à partir d'une contrainte géométrique basée sur les structures frontales, et la méthode d'assimilation variationnelle utilisant comme à priori dynamique un modèle d'advection-diffusion. Les expérimentations conduites montrent que de bonnes performances de reconstruction sont obtenues pour les observations hautes résolutions en dépit du pourcentage élevé de données manquante

Is the sky the limit? Performance of the revamped Swedish 1-m Solar Telescope and its blue- and red-beam re-imaging systems

We demonstrate that for data recorded with a solar telescope that uses adaptive optics and/or post-processing to compensate for many low- and high-order aberrations, the RMS granulation contrast is directly proportional to the Strehl ratio calculated from the residual (small-scale) wavefront error. We demonstrate that the wings of the high-order compensated PSF for SST are likely to extend to a radius of not more than about 2 arcsec, consistent with earlier conclusions drawn from straylight compensation of sunspot images. We report on simultaneous measurements of seeing and solar granulation contrast averaged over 2 sec time intervals at several wavelengths from 525 nm to 853.6 nm on the red-beam (CRISP beam) and wavelengths from 395 nm to 484 nm on the blue-beam (CHROMIS beam). These data were recorded with the Swedish 1-m Solar Telescope (SST) that has been revamped with an 85-electrode adaptive mirror and a new tip-tilt mirror, both of which were polished to exceptionally high optical quality. The highest 2-sec average image contrast measured in April 2015 through 0.3-0.9 nm interference filters at 525 nm, 557 nm, 630 nm and 853.5 nm with compensation only for the diffraction limited point spread function of SST is 11.8%, 11.8%, 10.2% and 7.2% respectively. Similarly, the highest 2-sec contrast measured at 395 nm, 400 nm and 484 nm in May 2016 through 0.37-1.3 nm filters is 16%, 16% and 12.5% respectively. The granulation contrast observed with SST compares favorably with that of other telescopes. Simultaneously with the above wideband red-beam data, we also recorded narrow-band continuum images with the CRISP imaging spectropolarimeter. We find that contrasts measured with CRISP are entirely consistent with the corresponding wide-band contrasts, demonstrating that any additional image degradation by the CRISP etalons and telecentric optical system is marginal or even insignificant.Comment: In press in Astronomy & Astrophysic

Spatial-Temporal Data Mining for Ocean Science: Data, Methodologies, and Opportunities

With the increasing amount of spatial-temporal~(ST) ocean data, numerous spatial-temporal data mining (STDM) studies have been conducted to address various oceanic issues, e.g., climate forecasting and disaster warning. Compared with typical ST data (e.g., traffic data), ST ocean data is more complicated with some unique characteristics, e.g., diverse regionality and high sparsity. These characteristics make it difficult to design and train STDM models. Unfortunately, an overview of these studies is still missing, hindering computer scientists to identify the research issues in ocean while discouraging researchers in ocean science from applying advanced STDM techniques. To remedy this situation, we provide a comprehensive survey to summarize existing STDM studies in ocean. Concretely, we first summarize the widely-used ST ocean datasets and identify their unique characteristics. Then, typical ST ocean data quality enhancement techniques are discussed. Next, we classify existing STDM studies for ocean into four types of tasks, i.e., prediction, event detection, pattern mining, and anomaly detection, and elaborate the techniques for these tasks. Finally, promising research opportunities are highlighted. This survey will help scientists from the fields of both computer science and ocean science have a better understanding of the fundamental concepts, key techniques, and open challenges of STDM in ocean

Mitigating masked pixels in climate-critical datasets

Remote sensing observations of the Earth's surface are frequently stymied by clouds, water vapour, and aerosols in our atmosphere. These degrade or preclude the measurementof quantities critical to scientific and, hence, societal applications. In this study, we train a natural language processing (NLP) algorithm with high-fidelity ocean simulations in order to accurately reconstruct masked or missing data in sea surface temperature (SST)--i.e. one of 54 essential climate variables identified by the Global Climate Observing System. We demonstrate that the Enki model repeatedly outperforms previously adopted inpainting techniques by up to an order-of-magnitude in reconstruction error, while displaying high performance even in circumstances where the majority of pixels are masked. Furthermore, experiments on real infrared sensor data with masking fractions of at least 40% show reconstruction errors of less than the known sensor uncertainty (RMSE < ~0.1K). We attribute Enki's success to the attentive nature of NLP combined with realistic SST model outputs, an approach that may be extended to other remote sensing variables. This study demonstrates that systems built upon Enki--or other advanced systems like it--may therefore yield the optimal solution to accurate estimates of otherwise missing or masked parameters in climate-critical datasets sampling a rapidly changing Earth.Comment: 21 pages, 6 main figure, 3 in Appendix; submitte

SST/CRISP Observations of Convective Flows in a Sunspot Penumbra

Context. Recent discoveries of intensity correlated downflows in the interior of a sunspot penumbra provide direct evidence for overturning convection, adding to earlier strong indications of convection from filament dynamics observed far from solar disk center, and supporting recent simulations of sunspots. Aims. Using spectropolarimetric observations obtained at a spatial resolution approaching 0'.'1 with the Swedish 1-m Solar Telescope (SST) and its spectropolarimeter CRISP, we investigate whether the convective downflows recently discovered in the C i line at 538.03 nm can also be detected in the wings of the Fe i line at 630.15 nm Methods. We make azimuthal fits of the measured LOS velocities in the core and wings of the 538 nm and 630 nm lines to disentangle the vertical and horizontal flows. To investigate how these depend on the continuum intensity, the azimuthal fits are made separately for each intensity bin. By using spatially high-pass filtered measurements of the LOS component of the magnetic field, the flow properties are determined separately for magnetic spines (relatively strong and vertical field) and inter-spines (weaker and more horizontal field). Results. The dark convective downflows discovered recently in the 538.03 nm line are evident also in the 630.15 nm line, and have similar strength. This convective signature is the same in spines and inter-spines. However, the strong radial (Evershed) outflows are found only in the inter-spines. Conclusions. At the spatial resolution of the present SST/CRISP data, the small-scale intensity pattern seen in continuum images is strongly related to a convective up/down flow pattern that exists everywhere in the penumbra. Earlier failures to detect the dark convective downflows in the interior penumbra can be explained by inadequate spatial resolution in the observed data.Comment: Revised and expanded by 2.5 pages. Fig. 14 adde

Machine Learning Approach to Retrieving Physical Variables from Remotely Sensed Data

Scientists from all over the world make use of remotely sensed data from hundreds of satellites to better understand the Earth. However, physical measurements from an instrument is sometimes missing either because the instrument hasn\u27t been launched yet or the design of the instrument omitted a particular spectral band. Measurements received from the instrument may also be corrupt due to malfunction in the detectors on the instrument. Fortunately, there are machine learning techniques to estimate the missing or corrupt data. Using these techniques we can make use of the available data to its full potential. We present work on four different problems where the use of machine learning techniques helps to extract more information from available data. We demonstrate how missing or corrupt spectral measurements from a sensor can be accurately interpolated from existing spectral observations. Sometimes this requires data fusion from multiple sensors at different spatial and spectral resolution. The reconstructed measurements can then be used to develop products useful to scientists, such as cloud-top pressure, or produce true color imagery for visualization. Additionally, segmentation and image processing techniques can help solve classification problems important for ocean studies, such as the detection of clear-sky over ocean for a sea surface temperature product. In each case, we provide detailed analysis of the problem and empirical evidence that these problems can be solved effectively using machine learning techniques

Opposite polarity field with convective downflow and its relation to magnetic spines in a sunspot penumbra

We discuss NICOLE inversions of Fe I 630.15 nm and 630.25 nm Stokes spectra from a sunspot penumbra recorded with the CRISP imaging spectropolarimeter on the Swedish 1-m Solar Telescope at a spatial resolution close to 0.15". We report on narrow radially extended lanes of opposite polarity field, located at the boundaries between areas of relatively horizontal magnetic field (the intra-spines) and much more vertical field (the spines). These lanes harbor convective downflows of about 1 km/s. The locations of these downflows close to the spines agree with predictions from the convective gap model (the "gappy penumbra") proposed six years ago, and more recent 3D MHD simulations. We also confirm the existence of strong convective flows throughout the entire penumbra, showing the expected correlation between temperature and vertical velocity, and having vertical RMS velocities of about 1.2 km/s.Comment: Accepted for publication in A&A (06-March-2013). Minor corrections made in this version