scholarly journals APOLLO_NG – a probabilistic interpretation of the APOLLO legacy for AVHRR heritage channels

2015 ◽  
Vol 8 (10) ◽  
pp. 4155-4170 ◽  
Author(s):  
L. Klüser ◽  
N. Killius ◽  
G. Gesell
Keyword(s):  
Radiative Transfer ◽  
Probabilistic Approach ◽  
Retrieval Algorithm ◽  
Probabilistic Interpretation ◽  
Cloud Detection ◽  
Processing Scheme ◽  
Cloud Property ◽  
Cloud Processing ◽  
Wide Range ◽  
Physical Principles

Abstract. The cloud processing scheme APOLLO (AVHRR Processing scheme Over cLouds, Land and Ocean) has been in use for cloud detection and cloud property retrieval since the late 1980s. The physics of the APOLLO scheme still build the backbone of a range of cloud detection algorithms for AVHRR (Advanced Very High Resolution Radiometer) heritage instruments. The APOLLO_NG (APOLLO_NextGeneration) cloud processing scheme is a probabilistic interpretation of the original APOLLO method. It builds upon the physical principles that have served well in the original APOLLO scheme. Nevertheless, a couple of additional variables have been introduced in APOLLO_NG. Cloud detection is no longer performed as a binary yes/no decision based on these physical principles. It is rather expressed as cloud probability for each satellite pixel. Consequently, the outcome of the algorithm can be tuned from being sure to reliably identify clear pixels to conditions of reliably identifying definitely cloudy pixels, depending on the purpose. The probabilistic approach allows retrieving not only the cloud properties (optical depth, effective radius, cloud top temperature and cloud water path) but also their uncertainties. APOLLO_NG is designed as a standalone cloud retrieval method robust enough for operational near-realtime use and for application to large amounts of historical satellite data. The radiative transfer solution is approximated by the same two-stream approach which also had been used for the original APOLLO. This allows the algorithm to be applied to a wide range of sensors without the necessity of sensor-specific tuning. Moreover it allows for online calculation of the radiative transfer (i.e., within the retrieval algorithm) giving rise to a detailed probabilistic treatment of cloud variables. This study presents the algorithm for cloud detection and cloud property retrieval together with the physical principles from the APOLLO legacy it is based on. Furthermore a couple of example results from NOAA-18 are presented.

scholarly journals APOLLO_NG – a probabilistic interpretation of the APOLLO legacy for AVHRR heritage channels

2015 ◽  
Vol 8 (4) ◽  
pp. 4413-4449
Author(s):  
L. Klüser ◽  
N. Killius ◽  
G. Gesell
Keyword(s):  
Radiative Transfer ◽  
Probabilistic Approach ◽  
Retrieval Algorithm ◽  
Probabilistic Interpretation ◽  
Cloud Detection ◽  
Processing Scheme ◽  
Cloud Property ◽  
Cloud Processing ◽  
Wide Range ◽  
Physical Principles

Abstract. The cloud processing scheme APOLLO (Avhrr Processing scheme Over cLouds, Land and Ocean) has been in use for cloud detection and cloud property retrieval since the late 1980s. The physics of the APOLLO scheme still build the backbone of a range of cloud detection algorithms for AVHRR (Advanced Very High Resolution Radiometer) heritage instruments. The APOLLO_NG (APOLLO_NextGeneration) cloud processing scheme is a probabilistic interpretation of the original APOLLO method. While building upon the physical principles having served well in the original APOLLO a couple of additional variables have been introduced in APOLLO_NG. Cloud detection is not performed as a binary yes/no decision based on these physical principals but is expressed as cloud probability for each satellite pixel. Consequently the outcome of the algorithm can be tuned from clear confident to cloud confident depending on the purpose. The probabilistic approach allows to retrieving not only the cloud properties (optical depth, effective radius, cloud top temperature and cloud water path) but also their uncertainties. APOLLO_NG is designed as a standalone cloud retrieval method robust enough for operational near-realtime use and for the application with large amounts of historical satellite data. Thus the radiative transfer solution is approximated by the same two stream approach which also had been used for the original APOLLO. This allows the algorithm to be robust enough for being applied to a wide range of sensors without the necessity of sensor-specific tuning. Moreover it allows for online calculation of the radiative transfer (i.e. within the retrieval algorithm) giving rise to a detailed probabilistic treatment of cloud variables. This study presents the algorithm for cloud detection and cloud property retrieval together with the physical principles from the APOLLO legacy it is based on. Furthermore a couple of example results from on NOAA-18 are presented.

scholarly journals NWCSAF AVHRR Cloud Detection and Analysis Using Dynamic Thresholds and Radiative Transfer Modeling. Part I: Algorithm Description

2005 ◽  
Vol 44 (1) ◽  
pp. 39-54 ◽  
Author(s):  
Adam Dybbroe ◽  
Karl-Göran Karlsson ◽  
Anke Thoss
Keyword(s):  
Radiative Transfer ◽  
Land Surface ◽  
Short Range ◽  
Radiative Transfer Model ◽  
Actual State ◽  
Transfer Model ◽  
Cloud Detection ◽  
Cloud Type ◽  
Wide Range ◽  
Type Classification

Abstract New methods and software for cloud detection and classification at high and midlatitudes using Advanced Very High Resolution Radiometer (AVHRR) data are developed for use in a wide range of meteorological, climatological, land surface, and oceanic applications within the Satellite Application Facilities (SAFs) of the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT), including the SAF for Nowcasting and Very Short Range Forecasting Applications (NWCSAF) project. The cloud mask employs smoothly varying (dynamic) thresholds that separate fully cloudy or cloud-contaminated fields of view from cloud-free conditions. Thresholds are adapted to the actual state of the atmosphere and surface and the sun–satellite viewing geometry using cloud-free radiative transfer model simulations. Both the cloud masking and the cloud-type classification are done using sequences of grouped threshold tests that employ both spectral and textural features. The cloud-type classification divides the cloudy pixels into 10 different categories: 5 opaque cloud types, 4 semitransparent clouds, and 1 subpixel cloud category. The threshold method is fuzzy in the sense that the distances in feature space to the thresholds are stored and are used to determine whether to stop or to continue testing. They are also used as a quality indicator of the final output. The atmospheric state should preferably be taken from a short-range NWP model, but the algorithms can also run with climatological fields as input.

scholarly journals Uncertainty-Aware Deep Learning-Based Cardiac Arrhythmias Classification Model of Electrocardiogram Signals

Computers ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 82
Author(s):  
Ahmad O. Aseeri
Keyword(s):  
Deep Learning ◽  
Cardiac Arrhythmias ◽  
Large Scale ◽  
Clinical Decision Making ◽  
Probabilistic Approach ◽  
Classification Model ◽  
Gating Mechanism ◽  
Uncertainty Estimates ◽  
Wide Range

Deep Learning-based methods have emerged to be one of the most effective and practical solutions in a wide range of medical problems, including the diagnosis of cardiac arrhythmias. A critical step to a precocious diagnosis in many heart dysfunctions diseases starts with the accurate detection and classification of cardiac arrhythmias, which can be achieved via electrocardiograms (ECGs). Motivated by the desire to enhance conventional clinical methods in diagnosing cardiac arrhythmias, we introduce an uncertainty-aware deep learning-based predictive model design for accurate large-scale classification of cardiac arrhythmias successfully trained and evaluated using three benchmark medical datasets. In addition, considering that the quantification of uncertainty estimates is vital for clinical decision-making, our method incorporates a probabilistic approach to capture the model’s uncertainty using a Bayesian-based approximation method without introducing additional parameters or significant changes to the network’s architecture. Although many arrhythmias classification solutions with various ECG feature engineering techniques have been reported in the literature, the introduced AI-based probabilistic-enabled method in this paper outperforms the results of existing methods in outstanding multiclass classification results that manifest F1 scores of 98.62% and 96.73% with (MIT-BIH) dataset of 20 annotations, and 99.23% and 96.94% with (INCART) dataset of eight annotations, and 97.25% and 96.73% with (BIDMC) dataset of six annotations, for the deep ensemble and probabilistic mode, respectively. We demonstrate our method’s high-performing and statistical reliability results in numerical experiments on the language modeling using the gating mechanism of Recurrent Neural Networks.

scholarly journals Potential of Hyperspectral Thermal Infrared Spaceborne Measurements to Retrieve Ice Cloud Physical Properties: Case Study of IASI and IASI-NG

2020 ◽  
Vol 13 (1) ◽  
pp. 116
Author(s):  
Lucie Leonarski ◽  
Laurent C.-Labonnote ◽  
Mathieu Compiègne ◽  
Jérôme Vidot ◽  
Anthony J. Baran ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
Signal To Noise Ratio ◽  
Thermal Infrared ◽  
Radiative Transfer Model ◽  
Retrieval Algorithm ◽  
Transfer Model ◽  
Water Path ◽  
Ice Cloud ◽  
Ice Water Path ◽  
Ice Water

The present study aims to quantify the potential of hyperspectral thermal infrared sounders such as the Infrared Atmospheric Sounding Interferometer (IASI) and the future IASI next generation (IASI-NG) for retrieving the ice cloud layer altitude and thickness together with the ice water path. We employed the radiative transfer model Radiative Transfer for TOVS (RTTOV) to simulate cloudy radiances using parameterized ice cloud optical properties. The radiances have been computed from an ice cloud profile database coming from global operational short-range forecasts at the European Center for Medium-range Weather Forecasts (ECMWF) which encloses the normal conditions, typical variability, and extremes of the atmospheric properties over one year (Eresmaa and McNally (2014)). We performed an information content analysis based on Shannon’s formalism to determine the amount and spectral distribution of the information about ice cloud properties. Based on this analysis, a retrieval algorithm has been developed and tested on the profile database. We considered the signal-to-noise ratio of each specific instrument and the non-retrieved atmospheric and surface parameter errors. This study brings evidence that the observing system provides information on the ice water path (IWP) as well as on the layer altitude and thickness with a convergence rate up to 95% and expected errors that decrease with cloud opacity until the signal saturation is reached (satisfying retrievals are achieved for clouds whose IWP is between about 1 and 300 g/m2).

scholarly journals Scale dependence of cirrus heterogeneity effects. Part II: MODIS NIR and SWIR channels

2018 ◽  
Vol 18 (16) ◽  
pp. 12105-12121 ◽  
Author(s):  
Thomas Fauchez ◽  
Steven Platnick ◽  
Tamás Várnai ◽  
Kerry Meyer ◽  
Céline Cornet ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
Spatial Resolution ◽  
Thermal Infrared ◽  
Radiative Effects ◽  
Ice Clouds ◽  
Plane Parallel ◽  
Wide Range ◽  
The Impact ◽  
Side Illumination

Abstract. In a context of global climate change, the understanding of the radiative role of clouds is crucial. On average, ice clouds such as cirrus have a significant positive radiative effect, but under some conditions the effect may be negative. However, many uncertainties remain regarding the role of ice clouds on Earth's radiative budget and in a changing climate. Global satellite observations are particularly well suited to monitoring clouds, retrieving their characteristics and inferring their radiative impact. To retrieve ice cloud properties (optical thickness and ice crystal effective size), current operational algorithms assume that each pixel of the observed scene is plane-parallel and homogeneous, and that there is no radiative connection between neighboring pixels. Yet these retrieval assumptions are far from accurate, as real radiative transfer is 3-D. This leads to the plane-parallel and homogeneous bias (PPHB) plus the independent pixel approximation bias (IPAB), which impacts both the estimation of top-of-the-atmosphere (TOA) radiation and the retrievals. An important factor that determines the impact of these assumptions is the sensor spatial resolution. High-spatial-resolution pixels can better represent cloud variability (low PPHB), but the radiative path through the cloud can involve many pixels (high IPAB). In contrast, low-spatial-resolution pixels poorly represent the cloud variability (high PPHB), but the radiation is better contained within the pixel field of view (low IPAB). In addition, the solar and viewing geometry (as well as cloud optical properties) can modulate the magnitude of the PPHB and IPAB. In this, Part II of our study, we simulate TOA 0.86 and 2.13 µm solar reflectances over a cirrus uncinus scene produced by the 3DCLOUD model. Then, 3-D radiative transfer simulations are performed with the 3DMCPOL code at spatial resolutions ranging from 50 m to 10 km, for 12 viewing geometries and nine solar geometries. It is found that, for simulated nadir observations taken at resolution higher than 2.5 km, horizontal radiation transport (HRT) dominates biases between 3-D and 1-D reflectance calculations, but these biases are mitigated by the side illumination and shadowing effects for off-zenith solar geometries. At resolutions coarser than 2.5 km, PPHB dominates. For off-nadir observations at resolutions higher than 2.5 km, the effect that we call THEAB (tilted and homogeneous extinction approximation bias) due to the oblique line of sight passing through many cloud columns contributes to a large increase of the reflectances, but 3-D radiative effects such as shadowing and side illumination for oblique Sun are also important. At resolutions coarser than 2.5 km, the PPHB is again the dominant effect. The magnitude and resolution dependence of PPHB and IPAB is very different for visible, near-infrared and shortwave infrared channels compared with the thermal infrared channels discussed in Part I of this study. The contrast of 3-D radiative effects between solar and thermal infrared channels may be a significant issue for retrieval techniques that simultaneously use radiative measurements across a wide range of solar reflectance and infrared wavelengths.

scholarly journals Differences in ozone retrieval in MIPAS channels A and AB: a spectroscopic issue

2018 ◽  
Vol 11 (8) ◽  
pp. 4707-4723 ◽  
Author(s):  
Norbert Glatthor ◽  
Thomas von Clarmann ◽  
Gabriele P. Stiller ◽  
Michael Kiefer ◽  
Alexandra Laeng ◽  
...  
Keyword(s):  
Spectroscopic Data ◽  
Radiative Transfer Model ◽  
Retrieval Algorithm ◽  
Transfer Model ◽  
Forward Modelling ◽  
Hitran Database ◽  
Processing Scheme ◽  
Mixing Ratios ◽  
Level 1 ◽  
Standing Problem

Abstract. Discrepancies in ozone retrievals in MIPAS channels A (685–970 cm−1) and AB (1020–1170 cm−1) have been a long-standing problem in MIPAS data analysis, amounting to an interchannel bias (AB–A) of up to 8 % between ozone volume mixing ratios in the altitude range 30–40 km. We discuss various candidate explanations, among them forward model and retrieval algorithm errors, interchannel calibration inconsistencies and spectroscopic data inconsistencies. We show that forward-modelling errors as well as errors in the retrieval algorithm can be ruled out as an explanation because the bias can be reproduced with an entirely independent retrieval algorithm (GEOFIT), relying on a different forward radiative transfer model. Instrumental and calibration issues can also be refuted as an explanation because ozone retrievals based on balloon-borne measurements with a different instrument (MIPAS-B) and an independent level-1 data processing scheme produce a rather similar interchannel bias. Thus, spectroscopic inconsistencies in the MIPAS database used for ozone retrieval are practically the only reason left. To further investigate this issue, we performed retrievals using additional spectroscopic databases. Various versions of the HITRAN database generally produced rather similar channel AB–A differences. Use of a different database, namely GEISA-2015, led to similar results in channel AB, but to even higher ozone volume mixing ratios for channel A retrievals, i.e. to a reversal of the bias. We show that the differences in MIPAS channel A retrievals result from about 13 % lower air-broadening coefficients of the strongest lines in the GEISA-2015 database. Since the errors in line intensity of the major lines used in MIPAS channels A and AB are reported to be considerably lower than the observed bias, we posit that a major part of the channel AB–A differences can be attributed to inconsistent air-broadening coefficients as well. To corroborate this assumption we show some clearly inconsistent air-broadening coefficients in the HITRAN-2008 database. The interchannel bias in retrieved ozone amounts can be reduced by increasing the air-broadening coefficients of the lines in MIPAS channel AB in the HITRAN-2008 database by 6 %–8 %.

Multiscale unified prediction of size/scale and Hall-Petch effects in the mechanics of polycrystalline materials

2013 ◽  
Vol 22 (1-2) ◽  
pp. 67-71 ◽  
Author(s):  
George N. Frantziskonis
Keyword(s):  
Size Effects ◽  
Probabilistic Approach ◽  
Higher Dimensions ◽  
Spatial Dimension ◽  
Polycrystalline Materials ◽  
Material Failure ◽  
Average Grain Size ◽  
Wide Range ◽  
Physical Information ◽  
Polycrystalline Solids

AbstractMaterials show size effects in their strength, i.e., improved strength as size decreases. Size effects have been studied extensively at a wide range of scales, from atomistic to continuum. Size effects depend on the scale of reference, as the physics change with increasing or decreasing scale. The work reported herein concentrates at scales near the average grain size in polycrystalline solids, where they are examined in conjunction with Hall-Petch effects. It presents a process for isolating physical information on a problem at specific spatial or temporal scales and applies it to Hall-Petch and size effects in one spatial dimension, extendable to higher dimensions. Importantly, the scale-isolated information captures the interactions among scales. As material failure and Hall-Petch effects are highly stochastic, a probabilistic approach to the present work is more appropriate than a deterministic one.

scholarly journals A quantum probabilistic approach to Hecke algebras for 𝔭-adic PGL2

2018 ◽  
Vol 21 (03) ◽  
pp. 1850015
Author(s):  
Takehiro Hasegawa ◽  
Hayato Saigo ◽  
Seiken Saito ◽  
Shingo Sugiyama
Keyword(s):  
Inversion Formula ◽  
Probabilistic Approach ◽  
Hecke Algebras ◽  
Hecke Algebra ◽  
Quantum Probability ◽  
Probabilistic Interpretation ◽  
Fourier Inversion ◽  
Fourier Inversion Formula ◽  
The Subject

The subject of the present paper is an application of quantum probability to [Formula: see text]-adic objects. We give a quantum-probabilistic interpretation of the spherical Hecke algebra for [Formula: see text], where [Formula: see text] is a [Formula: see text]-adic field. As a byproduct, we obtain a new proof of the Fourier inversion formula for [Formula: see text].

scholarly journals Radioecological assessment of forage agricultural land in the southwestern areas of the Bryansk region based on a set of statistical models

2021 ◽  
Vol 30 (2) ◽  
pp. 38-49
Author(s):  
S.I. Spiridonov ◽  
◽  
V.V. Ivanov ◽  
I.E. Titov ◽  
V.E. Nushtaeva ◽  
...  
Keyword(s):  
Soil Contamination ◽  
Statistical Models ◽  
Agricultural Land ◽  
Sandy Loam ◽  
Probabilistic Approach ◽  
Point Of View ◽  
Content Standards ◽  
Organic Soils ◽  
137Cs Content ◽  
Wide Range

This paper presents a radioecological assessment of forage agricultural land in the southwestern districts of the Bryansk region based on data characterizing the variability of the radionuclides content in the soil. Concentration of 137Cs in forage was calculated taking into account the proba-bility distributions of 137Cs soil contamination density and the soil to plant transfer factor. The pro-cessing data of the radioecological survey has shown the soil contamination density with 137Cs of agricultural lands in the southwestern areas of the Bryansk region obeys a lognormal law. The authors have used statistical models and software modules for the radioecological assessment of forage lands. Risks of exceeding the 137Cs content standards in forage obtained on soils with different texture have been calculated. The limiting levels of contamination of pastures and hay-fields with 137Cs, ensuring compliance with the specified risks for forage, have been estimated. The lowest limiting soil contamination density is characteristic of organic soils, which can be con-sidered “critical” from the point of view of 137Cs intake into forage. The authors have predicted the time of remediation of forage lands in the southwestern districts of the Bryansk region in the ab-sence of protective measures based on a probabilistic approach. The time period during which the risk of forage contamination for sandy, sandy loam and clay loam soils will decrease to 10% varies for the areas under consideration in a wide range, not exceeding 64 years. It is concluded that it is advisable to substantiate the value of the acceptable risk of forage contamination, taking into account radiological and socio-economic aspects.

scholarly journals Comparison of measured brightness temperatures from SMOS with modelled ones from ORCHIDEE and H-TESSEL over the Iberian Peninsula

2015 ◽  
Vol 12 (12) ◽  
pp. 13019-13067
Author(s):  
A. Barella-Ortiz ◽  
J. Polcher ◽  
P. de Rosnay ◽  
M. Piles ◽  
E. Gelati
Keyword(s):  
Soil Moisture ◽  
Radiative Transfer ◽  
Iberian Peninsula ◽  
Land Surface ◽  
Function Analysis ◽  
Microwave Emission ◽  
Radiative Transfer Model ◽  
Retrieval Algorithm ◽  
Transfer Model ◽  
Brightness Temperatures

Abstract. L-Band radiometry is considered to be one of the most suitable techniques to estimate surface soil moisture by means of remote sensing. Brightness temperatures are key in this process, as they are the main input in the retrieval algorithm. The work exposed compares brightness temperatures measured by the Soil Moisture and Ocean Salinity (SMOS) mission to two different sets of modelled ones, over the Iberian Peninsula from 2010 to 2012. The latter were estimated using a radiative transfer model and state variables from two land surface models: (i) ORganising Carbon and Hydrology In Dynamic EcosystEms (ORCHIDEE) and (ii) Hydrology – Tiled ECMWF Scheme for Surface Exchanges over Land (H-TESSEL). The radiative transfer model used is the Community Microwave Emission Model (CMEM). A good agreement in the temporal evolution of measured and modelled brightness temperatures is observed. However, their spatial structures are not consistent between them. An Empirical Orthogonal Function analysis of the brightness temperature's error identifies a dominant structure over the South-West of the Iberian Peninsula which evolves during the year and is maximum in Fall and Winter. Hypotheses concerning forcing induced biases and assumptions made in the radiative transfer model are analysed to explain this inconsistency, but no candidate is found to be responsible for it at the moment. Further hypotheses are proposed at the end of the paper.

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