scholarly journals Complex Network Approach for Detecting Tropical Cyclones

2021 ◽  
Author(s):  
Shraddha Gupta ◽  
Niklas Boers ◽  
Florian Pappenberger ◽  
Jürgen Kurths
Keyword(s):  
Tropical Cyclones ◽  
Complex Network ◽  
Time Scales ◽  
North Atlantic Ocean ◽  
Southern Oscillation ◽  
Spatial Scales ◽  
Volcanic Eruptions ◽  
North Indian Ocean ◽  
Network Approach ◽  
Climate Networks

Abstract Tropical cyclones (TCs) are one of the most destructive natural hazards that pose a serious threat to society around the globe, particularly to those in the coastal regions. In this work, we study the temporal evolution of the regional weather conditions in relation to the occurrence of TCs using climate networks. Climate networks encode the interactions among climate variables at different locations on the Earth's surface, and in particular, time-evolving climate networks have been successfully applied to study different climate phenomena at comparably long time scales, such as the El Ni~no Southern Oscillation, different monsoon systems, or the climatic impacts of volcanic eruptions. Here, we develop and apply a complex network approach suitable for the investigation of the relatively short-lived TCs. We show that our proposed methodology has the potential to identify TCs and their tracks from mean sea level pressure (MSLP) data. We use the ERA5 reanalysis MSLP data to construct successive networks 20 of overlapping, short-length time windows for the regions under consideration, where we focus on the north Indian Ocean and the tropical north Atlantic Ocean. We compare the spatial features of various topological properties of the network, and the spatial scales involved, in the absence and presence of a cyclone. We find that network measures such as degree and clustering exhibit significant signatures of TCs and have striking similarities with their tracks. The study of network topology over time scales relevant to TCs allows us to obtain useful insights into the individual local signature of changes in the flow structure of the regional climate system.

scholarly journals Complex network approach for detecting tropical cyclones

2021 ◽  
Author(s):  
Shraddha Gupta ◽  
Niklas Boers ◽  
Florian Pappenberger ◽  
Jürgen Kurths
Keyword(s):  
Sea Level ◽  
Tropical Cyclones ◽  
Complex Network ◽  
Time Scales ◽  
Southern Oscillation ◽  
Volcanic Eruptions ◽  
Network Approach ◽  
Sea Level Pressure ◽  
Level Pressure ◽  
Climate Networks

AbstractTropical cyclones (TCs) are one of the most destructive natural hazards that pose a serious threat to society, particularly to those in the coastal regions. In this work, we study the temporal evolution of the regional weather conditions in relation to the occurrence of TCs using climate networks. Climate networks encode the interactions among climate variables at different locations on the Earth’s surface, and in particular, time-evolving climate networks have been successfully applied to study different climate phenomena at comparably long time scales, such as the El Niño Southern Oscillation, different monsoon systems, or the climatic impacts of volcanic eruptions. Here, we develop and apply a complex network approach suitable for the investigation of the relatively short-lived TCs. We show that our proposed methodology has the potential to identify TCs and their tracks from mean sea level pressure (MSLP) data. We use the ERA5 reanalysis MSLP data to construct successive networks of overlapping, short-length time windows for the regions under consideration, where we focus on the north Indian Ocean and the tropical north Atlantic Ocean. We compare the spatial features of various topological properties of the network, and the spatial scales involved, in the absence and presence of a cyclone. We find that network measures such as degree and clustering exhibit significant signatures of TCs and have striking similarities with their tracks. The study of the network topology over time scales relevant to TCs allows us to obtain crucial insights into the effects of TCs on the spatial connectivity structure of sea-level pressure fields.

A Complex Network approach for studying Tropical Cyclones

2021 ◽  
Author(s):  
Shraddha Gupta ◽  
Niklas Boers ◽  
Florian Pappenberger ◽  
Jürgen Kurths
Keyword(s):  
Tropical Cyclones ◽  
Complex Network ◽  
Time Scales ◽  
Spatial Scales ◽  
Time Windows ◽  
North Indian Ocean ◽  
Network Approach ◽  
Weather System ◽  
Complex Network Theory ◽  
Climate Networks

<p>Complex network theory provides a powerful framework to study the collective dynamics of the interacting units that constitute a complex system. Functional climate network analysis has been widely applied to study the evolution of climate phenomena such as the South American Monsoon and El Niño which occur over seasonal to (inter-)annual time scales. In this work, we use an evolving climate network approach for the study of tropical cyclones (TCs), which are highly localized extreme weather phenomena occurring over very short time scales (typically 3-10 days). We construct time-evolving climate networks of overlapping short-length (10-14 days) time windows using ERA5 reanalysis mean sea level pressure. We focus on studying the dynamics of the cyclones in the North Indian Ocean and the tropical Atlantic Ocean TC basins. We compute topological measures such as degree centrality as well as the local and global clustering coefficients for successive networks during the cyclone season. We find that, during a TC, the network undergoes a characteristic spatial reorganization in a way that localized structures with high clustering and low degree emerge along the TC track. We also compare the spatial scales involved in the regional weather system in the absence and presence of a TC, within the time span of the network. Our results show that weather variability at daily time scales, and in particular tropical cyclones, can be captured effectively by evolving climate networks.</p>

Study of Tropical Cyclones in the North Indian Ocean basin using Percolation in Climate Networks

2020 ◽  
Author(s):  
Shraddha Gupta ◽  
Jürgen Kurths ◽  
Florian Pappenberger
Keyword(s):  
Indian Ocean ◽  
Tropical Cyclones ◽  
Ocean Basin ◽  
North Indian Ocean ◽  
The European Union ◽  
Horizon 2020 ◽  
The North ◽  
Research And Innovation ◽  
Interacting Systems ◽  
Climate Networks

<p>Every point on the Earth’s surface is a dynamical system which behaves in a complex way while interacting with other dynamical systems. Network theory captures this feature of climate to study the collective behaviour of these interacting systems giving new insights into the problem. Recently, climate networks have been a promising approach to the study of climate phenomena such as El Niño, Indian monsoon, etc. These phenomena, however, occur over a long period of time. Weather phenomena such as tropical cyclones (TCs) that are relatively short-lived, destructive events are a major concern to life and property especially for densely populated coastlines such as in the North Indian Ocean (NIO) basin. Here, we study TCs in the NIO basin by constructing climate networks using the ERA5 Sea Surface Temperature and Air temperature at 1000 hPa. We analyze these networks using the percolation framework for the post-monsoon (October-November-December) season which experiences a high frequency of TCs every year. We find significant signatures of TCs in the network structure which appear as abrupt discontinuities in the percolation-based parameters during the period of a TC. This shows the potential of climate networks towards forecasting of tropical cyclones.</p><p> </p><p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 813844.</p>

Seasonal forecasting of tropical cyclones in the North Indian Ocean region: the role of El Niño-Southern Oscillation

2019 ◽  
Vol 54 (3-4) ◽  
pp. 1571-1589 ◽  
Author(s):  
Md Wahiduzzaman ◽  
Eric C. J. Oliver ◽  
Simon J. Wotherspoon ◽  
Jing-Jia Luo
Keyword(s):  
Indian Ocean ◽  
Tropical Cyclones ◽  
El Nino ◽  
Southern Oscillation ◽  
North Indian Ocean ◽  
Seasonal Forecasting ◽  
Indian Ocean Region ◽  
The North ◽  
Ocean Region

scholarly journals Subannual to Interannual Variabilities of SST in the North Atlantic Ocean

2020 ◽  
Vol 33 (13) ◽  
pp. 5547-5564
Author(s):  
Xiaofan Li ◽  
Zeng-Zhen Hu ◽  
Bohua Huang
Keyword(s):  
Atlantic Ocean ◽  
Time Scales ◽  
North Atlantic ◽  
Time Scale ◽  
North Atlantic Ocean ◽  
Southern Oscillation ◽  
The North ◽  
Sst Variability ◽  
The North Atlantic ◽  
The North Atlantic Oscillation

AbstractBased on observational data, this work examines the multi-time-scale feature of the sea surface temperature (SST) variability averaged in the whole North Atlantic Ocean (to be referred to as NASST), as well as its time-scale-dependent connections with El Niño–Southern Oscillation (ENSO) and the North Atlantic Oscillation (NAO). Traditionally, the NASST index is used to characterize the SST trend and multidecadal variability in the North Atlantic. This study found that superimposed on a prominent long-term trend, NASST is nonnegligible at subannual and interannual time scales, compared with that at decadal to multidecadal time scales. Spatially, the interannual variation of NASST is characterized by a horseshoe-like pattern of the SST anomaly (SSTA) in the North Atlantic. It is mainly a lagged response to ENSO through the atmospheric bridge, and NAO plays a secondary role. At the subannual time scale, both ENSO and NAO play a role in generating the fluctuations of NASST and a horseshoe-like pattern in the North Atlantic. Nevertheless, both the ENSO- and NAO-driven variations only explain a small fraction of the variances in both the interannual and subannual time scales. Thus, other factors unrelated to ENSO or NAO may play a more important role. The associated thermodynamical processes are similar at the two time scales; however, the dynamical processes have a significant contribution to the subannual component, but not to the interannual component. Thus, the SSTA averaged in the North Atlantic as a whole varies at different time scales and is associated with different mechanisms.

scholarly journals Tropical cyclone frequency in the north Indian Ocean in relation to southern oscillation phenomenon

MAUSAM ◽  
2022 ◽  
Vol 52 (3) ◽  
pp. 511-514
Author(s):  
O. P. SINGH ◽  
TARIQ MASOOD ALI KHAN ◽  
MD. SAZEDUR RAHMAN
Keyword(s):  
Tropical Cyclone ◽  
Indian Ocean ◽  
Tropical Cyclones ◽  
Bay Of Bengal ◽  
Arabian Sea ◽  
Southern Oscillation ◽  
North Indian Ocean ◽  
Tropical Cyclone Frequency ◽  
The North ◽  
Cyclone Frequency

The present paper deals with the influence of Southern Oscillation (SO) on the frequency of tropical cyclones in the north Indian Ocean. The results show that during the negative phase of SO the frequency of tropical cyclones and depressions over the Bay of Bengal and the Arabian Sea diminishes in May which is most important pre-monsoon cyclone month. The correlation coefficient between the frequency of cyclones and depressions and the Southern Oscillation Index (SOI) is +0.3 which is significant at 99% level. Post-monsoon cyclone frequency in the Bay of Bengal during November shows a significant positive correlation with SOl implying that it also decreases during the negative phase of SO. Thus there is a reduction in the tropical cyclone frequency over the Bay of Bengal during both intense cyclone months May and November in EI-Nino/Southern Oscillation (ENSO) epochs. Therefore it would not be correct to say that ENSO has no impact on the cyclogenesis in the north Indian Ocean. It is true that ENSO has no significant impact on the frequency of cyclones in the Arabian Sea. ENSO also seems to affect the rate of intensification of depressions to cyclone stage. The rate of intensification increases in May and diminishes in November in the north Indian Ocean during ENSO. The results are based on the analysis of monthly frequencies of tropical cyclones and depressions and SOI for the 100 year period from 1891-1990.

scholarly journals Seasonal Estimates and Uncertainties of Snow Accumulation from CloudSat Precipitation Retrievals

Atmosphere ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 363
Author(s):  
George Duffy ◽  
Fraser King ◽  
Ralf Bennartz ◽  
Christopher G. Fletcher
Keyword(s):  
Time Scales ◽  
Snow Water Equivalent ◽  
Spatial Scales ◽  
Snow Accumulation ◽  
High Latitudes ◽  
Percentage Points ◽  
Snow Water ◽  
Time Periods ◽  
Predicted Values ◽  
Good Agreement

CloudSat is often the only measurement of snowfall rate available at high latitudes, making it a valuable tool for understanding snow climatology. The capability of CloudSat to provide information on seasonal and subseasonal time scales, however, has yet to be explored. In this study, we use subsampled reanalysis estimates to predict the uncertainties of CloudSat snow water equivalent (SWE) accumulation measurements at various space and time resolutions. An idealized/simulated subsampling model predicts that CloudSat may provide seasonal SWE estimates with median percent errors below 50% at spatial scales as small as 2° × 2°. By converting these predictions to percent differences, we can evaluate CloudSat snowfall accumulations against a blend of gridded SWE measurements during frozen time periods. Our predictions are in good agreement with results. The 25th, 50th, and 75th percentiles of the percent differences between the two measurements all match predicted values within eight percentage points. We interpret these results to suggest that CloudSat snowfall estimates are in sufficient agreement with other, thoroughly vetted, gridded SWE products. This implies that CloudSat may provide useful estimates of snow accumulation over remote regions within seasonal time scales.

scholarly journals Are We Doing ‘Systems’ Research? An Assessment of Methods for Climate Change Adaptation to Hydrohazards in a Complex World

2019 ◽  
Vol 11 (4) ◽  
pp. 1163 ◽  
Author(s):  
Melissa Bedinger ◽  
Lindsay Beevers ◽  
Lila Collet ◽  
Annie Visser
Keyword(s):  
Climate Change ◽  
Human Nature ◽  
Climate Change Adaptation ◽  
Time Scales ◽  
Spatial Scales ◽  
Local Context ◽  
Multiple Time Scales ◽  
Future Research ◽  
Multiple Time ◽  
Systems Research

Climate change is a product of the Anthropocene, and the human–nature system in which we live. Effective climate change adaptation requires that we acknowledge this complexity. Theoretical literature on sustainability transitions has highlighted this and called for deeper acknowledgment of systems complexity in our research practices. Are we heeding these calls for ‘systems’ research? We used hydrohazards (floods and droughts) as an example research area to explore this question. We first distilled existing challenges for complex human–nature systems into six central concepts: Uncertainty, multiple spatial scales, multiple time scales, multimethod approaches, human–nature dimensions, and interactions. We then performed a systematic assessment of 737 articles to examine patterns in what methods are used and how these cover the complexity concepts. In general, results showed that many papers do not reference any of the complexity concepts, and no existing approach addresses all six. We used the detailed results to guide advancement from theoretical calls for action to specific next steps. Future research priorities include the development of methods for consideration of multiple hazards; for the study of interactions, particularly in linking the short- to medium-term time scales; to reduce data-intensivity; and to better integrate bottom–up and top–down approaches in a way that connects local context with higher-level decision-making. Overall this paper serves to build a shared conceptualisation of human–nature system complexity, map current practice, and navigate a complexity-smart trajectory for future research.

scholarly journals Scale-dependent intrinsic entropies of complex time series

2016 ◽  
Vol 374 (2065) ◽  
pp. 20150204 ◽  
Author(s):  
Jia-Rong Yeh ◽  
Chung-Kang Peng ◽  
Norden E. Huang
Keyword(s):  
Time Series ◽  
Time Scales ◽  
Spatial Scales ◽  
Multiple Time Scales ◽  
Multiple Time ◽  
Complex Time ◽  
Multi Scale ◽  
Mode Decomposition ◽  
Simulated Time ◽  
Simulated Time Series

Multi-scale entropy (MSE) was developed as a measure of complexity for complex time series, and it has been applied widely in recent years. The MSE algorithm is based on the assumption that biological systems possess the ability to adapt and function in an ever-changing environment, and these systems need to operate across multiple temporal and spatial scales, such that their complexity is also multi-scale and hierarchical. Here, we present a systematic approach to apply the empirical mode decomposition algorithm, which can detrend time series on various time scales, prior to analysing a signal’s complexity by measuring the irregularity of its dynamics on multiple time scales. Simulated time series of fractal Gaussian noise and human heartbeat time series were used to study the performance of this new approach. We show that our method can successfully quantify the fractal properties of the simulated time series and can accurately distinguish modulations in human heartbeat time series in health and disease.

Extended Prediction of North Indian Ocean Tropical Cyclones

2012 ◽  
Vol 27 (3) ◽  
pp. 757-769 ◽  
Author(s):  
James I. Belanger ◽  
Peter J. Webster ◽  
Judith A. Curry ◽  
Mark T. Jelinek
Keyword(s):  
Tropical Cyclone ◽  
Indian Ocean ◽  
Tropical Cyclones ◽  
North Indian Ocean ◽  
Ensemble Prediction ◽  
Tropical Cyclone Genesis ◽  
Lead Times ◽  
Prediction System ◽  
Ensemble Prediction System ◽  
The North

Abstract This analysis examines the predictability of several key forecasting parameters using the ECMWF Variable Ensemble Prediction System (VarEPS) for tropical cyclones (TCs) in the North Indian Ocean (NIO) including tropical cyclone genesis, pregenesis and postgenesis track and intensity projections, and regional outlooks of tropical cyclone activity for the Arabian Sea and the Bay of Bengal. Based on the evaluation period from 2007 to 2010, the VarEPS TC genesis forecasts demonstrate low false-alarm rates and moderate to high probabilities of detection for lead times of 1–7 days. In addition, VarEPS pregenesis track forecasts on average perform better than VarEPS postgenesis forecasts through 120 h and feature a total track error growth of 41 n mi day−1. VarEPS provides superior postgenesis track forecasts for lead times greater than 12 h compared to other models, including the Met Office global model (UKMET), the Navy Operational Global Atmospheric Prediction System (NOGAPS), and the Global Forecasting System (GFS), and slightly lower track errors than the Joint Typhoon Warning Center. This paper concludes with a discussion of how VarEPS can provide much of this extended predictability within a probabilistic framework for the region.

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