scholarly journals Hierarchical structure of the Madden-Julian oscillation in infrared brightness temperature revealed through nonlinear Laplacian spectral analysis

2012 ◽  
Author(s):  
Dimitrios Giannakis ◽  
Wen-wen Tung ◽  
Andrew J. Majda
Keyword(s):  
Spectral Analysis ◽  
Brightness Temperature ◽  
Hierarchical Structure ◽  
Madden Julian Oscillation ◽  
Infrared Brightness

Symmetric and Antisymmetric Convection Signals in the Madden–Julian Oscillation. Part I: Basic Modes in Infrared Brightness Temperature

2014 ◽  
Vol 71 (9) ◽  
pp. 3302-3326 ◽  
Author(s):  
Wen-wen Tung ◽  
Dimitrios Giannakis ◽  
Andrew J. Majda
Keyword(s):  
Brightness Temperature ◽  
Diurnal Cycle ◽  
Propagation Speed ◽  
Boreal Winter ◽  
Single Pair ◽  
Madden Julian Oscillation ◽  
Tropical Ocean ◽  
Symmetric Component ◽  
Toga Coare ◽  
Infrared Brightness

Abstract This work studies the significance of north–south asymmetry in convection associated with the 20–90-day Madden–Julian oscillation (MJO) propagating across the equatorial Indo-Pacific warm pool region. Satellite infrared brightness temperature data in the tropical belt for the period 1983–2006 were decomposed into components symmetric and antisymmetric about the equator. Using a recent nonlinear objective method called nonlinear Laplacian spectral analysis, modes of variability were extracted representing symmetric and antisymmetric features of MJO convection signals, along with a plethora of other modes of tropical convective variability spanning diurnal to interannual time scales. The space–time reconstruction of these modes during the 1992/93 Tropical Ocean Global Atmosphere Coupled Ocean–Atmosphere Response Experiment (TOGA COARE) period is described in detail. In particular, the boreal winter MJO emerges as a single pair of modes in both symmetric and antisymmetric convection signals. Both signals originate in the Indian Ocean around 60°E. They coexist for all significant MJO events with a varying degree of relative importance, which is affected by ENSO. The symmetric signals tend to be suppressed when crossing the Maritime Continent, while the antisymmetric signals are not as inhibited. Their differences in peak phase and propagation speed suggest fundamental differences in the underlying mechanisms. The multiscale interactions between the diurnal, MJO, and ENSO modes of convection were studied. It was found that the symmetric component of MJO convection appears out of phase with the symmetric component of the diurnal cycle, while the antisymmetric component of MJO convection is in phase with the antisymmetric diurnal cycle. The former relationship likely breaks down during strong El Niño events, and both relationships likely break down during prolonged La Niña events.

scholarly journals Efficient radiative transfer model for thermal infrared brightness temperature simulation in cloudy atmospheres

2020 ◽  
Vol 28 (18) ◽  
pp. 25730
Author(s):  
Wenwen Li ◽  
Feng Zhang ◽  
Yi-Ning Shi ◽  
Hironobu Iwabuchi ◽  
Mingwei Zhu ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
Brightness Temperature ◽  
Thermal Infrared ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Infrared Brightness

Spectral Synchronization Reveals Community Structure

2015 ◽  
Vol 719-720 ◽  
pp. 1198-1202
Author(s):  
Ming Yang Zhou ◽  
Zhong Qian Fu ◽  
Zhao Zhuo
Keyword(s):  
Spectral Analysis ◽  
Community Structure ◽  
Community Detection ◽  
Hierarchical Structure ◽  
Edge Density ◽  
Complex Structures ◽  
Detection Algorithms ◽  
Dimension Space ◽  
Local Synchronization ◽  
Better Than

Practical networks have community and hierarchical structure. These complex structures confuse the community detection algorithms and obscure the boundaries of communities. This paper proposes a delicate method which synthesizes spectral analysis and local synchronization to detect communities. Communities emerge automatically in the multi-dimension space of nontrivial eigenvectors. Its performance is compared to that of previous methods and applied to different practical networks. Our results perform better than that of other methods. Besides, it’s more robust for networks whose communities have different edge density and follow various degree distributions. This makes the algorithm a valuable tool to detect and analysis large practical networks with various community structures.

scholarly journals Assessing the Impact of the Covariance Localization Radius when Assimilating Infrared Brightness Temperature Observations Using an Ensemble Kalman Filter

2012 ◽  
Vol 140 (2) ◽  
pp. 543-561 ◽  
Author(s):  
Jason A. Otkin
Keyword(s):  
Kalman Filter ◽  
Brightness Temperature ◽  
Ensemble Kalman Filter ◽  
Short Range ◽  
Infrared Observations ◽  
Localization Radius ◽  
Temperature Observations ◽  
The Impact ◽  
Infrared Brightness ◽  
Cloud Analysis

A regional-scale Observing System Simulation Experiment is used to examine how changes in the horizontal covariance localization radius employed during the assimilation of infrared brightness temperature observations in an ensemble Kalman filter assimilation system impacts the accuracy of atmospheric analyses and short-range model forecasts. The case study tracks the evolution of several extratropical weather systems that occurred across the contiguous United States during 7–8 January 2008. Overall, the results indicate that assimilating 8.5-μm brightness temperatures improves the cloud analysis and forecast accuracy, but has the tendency to degrade the water vapor mixing ratio and thermodynamic fields unless a small localization radius is used. Vertical cross sections showed that varying the localization radius had a minimal impact on the shape of the analysis increments; however, their magnitude consistently increased with increasing localization radius. By the end of the assimilation period, the moisture, temperature, cloud, and wind errors generally decreased with decreasing localization radius and became similar to the Control case in which only conventional observations were assimilated if the shortest localization radius was used. Short-range ensemble forecasts showed that the large positive impact of the infrared observations on the final cloud analysis diminished rapidly during the forecast period, which indicates that it is difficult to maintain beneficial changes to the cloud analysis if the moisture and thermodynamic forcing controlling the cloud evolution are not simultaneously improved. These results show that although assimilation of infrared observations consistently improves the cloud field regardless of the length of the localization radius, it may be necessary to use a smaller radius to also improve the accuracy of the moisture and thermodynamic fields.

scholarly journals Kernel-driven model fitting of multi-angle thermal infrared brightness temperature and its application

2012 ◽  
Vol 30 (4) ◽  
pp. 361-365 ◽  
Author(s):  
Jing-Jing PENG ◽  
Qiang LIU ◽  
Qin-Huo LIU ◽  
Jia-Hong LI ◽  
Hong-Zhang MA ◽  
...  
Keyword(s):  
Brightness Temperature ◽  
Model Fitting ◽  
Thermal Infrared ◽  
Infrared Brightness

Thermal sea ice classification during the MOSAiC expedition

2021 ◽  
Author(s):  
Linda Thielke ◽  
Marcus Huntemann ◽  
Gunnar Spreen ◽  
Stefan Hendricks ◽  
Arttu Jutila ◽  
...  
Keyword(s):  
Heat Flux ◽  
Sea Ice ◽  
Surface Temperature ◽  
Brightness Temperature ◽  
Ice Age ◽  
The Arctic ◽  
Ice Thickness ◽  
Atmospheric Measurements ◽  
Data Set ◽  
Infrared Brightness

<p>The MOSAiC expedition took place in the Arctic from September 2019 to October 2020 while having measurements under, in, and above sea ice for a complete annual cycle. Airborne thermal infrared imaging was conducted during 41 helicopter survey flights along the MOSAiC drift track. We analyze the infrared brightness temperature of snow, sea ice, and ocean water surfaces from October 2019 until May 2020 from the airborne measurements. While the snow-covered sea ice appears very cold, thin ice and open water are significantly warmer. These surface types will be considered with particular attention because they dominate the heat exchange between the ocean, ice, and atmosphere during wintertime. This influences the Arctic Climate and becomes even more important in the currently changing Arctic, where the sea ice gets thinner, moves faster, and breaks up easier. After georeferencing and merging the recorded images to a mosaic, we can provide maps of infrared brightness temperatures in a high spatial resolution of 1 m for each flight. The spatial range of the maps varies from local (~5 km) up to regional (~30 km). This data set provides a basis to study the spatial and temporal variability of sea ice characteristics in the Arctic winter. We derive the physical surface temperature from the brightness temperature, surface emissivity, and downwelling radiation from the sky or clouds. Using the surface temperature, we calculate the heat flux from a local up to a regional scale based on thermodynamic assumptions and atmospheric measurements on the ice floe. From more complex thermodynamic simulations, we estimate ice thickness and ice age based on the airborne measured surface temperatures. The model calculates for each surface temperature a specific ice thickness and heat flux based on the knowledge about the surface’s thermodynamic history. The simulated ice thickness allows a sea ice classification which is compared to our first classification approach which deals with the flight's temperature distribution only. In the future, we will investigate the sub-footprint scale variability of ice surface temperature and thin ice thickness for satellite data, e.g. MODIS and Sentinel-3.</p>

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