2,862 research outputs found
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
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