scholarly journals Preferred Intra-seasonal Circulation Patterns of the Indian Summer Monsoon and Active-Break Cycles

2021 ◽  
Author(s):  
David Martin Straus
Keyword(s):  
Cluster Analysis ◽  
Indian Ocean ◽  
Bay Of Bengal ◽  
Bootstrap Method ◽  
Principal Component ◽  
Central India ◽  
Boreal Summer ◽  
Transition Matrices ◽  
Seasonal Circulation ◽  
Strong Convection

Abstract Intra-Seasonal circulation regimes are identified from a cluster analysis of 5-day mean (pentad) anomaly fields of 850 hPa horizontal winds (u,v)from the ERA-Interim reanalysis for the boreal summer season (120 days starting 01June for the years 1979 - 2018) over the broad Indian region (50 o -100 o E; 5 o S - 35 o N). The anomalies are formed with respect to a parabolic (in time) seasonal cycle computed separately for each year, thus filtering out periods of greater than 240 days. The k-means method was applied in the phase space of the leading 6 (12) principal component modes, which explain 65% (78%) of the space-time variance, yielding k clusters. The degree of clustering is significant when compared to synthetic data sets for any value of k > 3. The transition matrices for k=4 and k=5 establish that the system is most likely to stay in the same cluster from one pentad to the next, but that the significant transitions (with 95% confidence level using a modified bootstrap method) form a cycle. The similarity between the cycle as depicted from 4 or 5 clusters is established by composites of 850 hPa (u,v,), 200 hPa divergence, 500 hPa vorticity and vertical pressure velocity, and daily rainfall over India: Strong convection (with large positive divergence and vorticity) over the subtropical Indian Ocean, moves to the central Bay of Bengal and over central India, then subsequently to the northern Bay of Bengal and west Bengal, and then further north into the Himalayas. The Indian rainfall composites show a similar cycle. The phases in which strong convection is seen over central and northern India are seen for about 60% of the time for both k=4 and k=5 analyses. However the 4 cluster analysis also shows a preferred transition in which the convection moves equatorward from central India. The number of complete cycles (including a return to the starting cluster) found in the 40 years of data is 7 in the 4-cluster analysis, while the number of times the system undergoes four (three) consecutive legs of the cycle is 16 (31). Fewer instances of complete cycles are found for 5 clusters (only 3), but sequences of five, four and three consecutive legs occur 10, 11 and 28 times respectively. Composites of the tropics-wide vertically integrated diabatic heating (estimated from ERA5 reanalyses) reproduce the characteristics of the boreal summer intra-seasonal oscillation, with northwest-to-southeast oriented bands of heating moving northward from the tropical Indian Ocean into the subtropics. This depiction of the active-break cycle is particularly useful for diagnosing the cycle in short-range forecasts: as long as pentad anomalies can be formed, they can be assigned to one of the observed clusters described in this paper without the need for further time-filtering.

scholarly journals Preferred intra-seasonal circulation patterns of the Indian summer monsoon and active-break cycles

2021 ◽  
Author(s):  
David M. Straus
Keyword(s):  
Cluster Analysis ◽  
Bay Of Bengal ◽  
Synthetic Data ◽  
Central India ◽  
Precipitable Water ◽  
Boreal Summer ◽  
Data Sets ◽  
Seasonal Circulation ◽  
Strong Convection ◽  
Northern Bay Of Bengal

AbstractIntra-Seasonal circulation regimes are identified from a cluster analysis of 5-day mean anomaly fields of 850 hPa horizontal winds from the ERA-Interim reanalysis for the boreal summer season (June–Sept. for 1979–2018) over the region (50°–100° E; 5° S–35° N). The k-means method was applied to the leading 6 principal components yielding k clusters. The degree of clustering is significant compared to synthetic data sets for any value of $$k > 3$$ k > 3 . The circulation is most likely to stay in the same cluster from one pentad to the next; significant transitions (with 95% confidence level) form a cycle. The similarity between the cycle depicted from 4 or 5 clusters and the active-break cycle, as well as the 45-day oscillation, is established by composites of 850 hPa winds, 200 hPa divergence, 500 hPa vorticity and vertical pressure velocity, precipitable water, diabatic heating and rainfall over India: Strong convection over the subtropical Indian Ocean moves to the central Bay of Bengal and central India, subsequently to the northern Bay of Bengal and west Bengal, and then further north into the Himalayas. We also find preferred transitions in which the convection moves equatorward from central India. The number of complete cycles found in 40 summers is 7 in the 4-cluster analysis. The number of times the system undergoes four (three) consecutive legs of the cycle is 16 (31). For 5 clusters only 3 complete cycles are found. sequences of five, four and three consecutive legs occur 10, 11 and 28 times, respectively.

scholarly journals Principal component analysis of monthly mean Area surface temperature over the Arabian Sea, Bay of Bengal and north Indian Ocean for two Contrasting sets of monsoon years

MAUSAM ◽  
2021 ◽  
Vol 44 (1) ◽  
pp. 69-76
Author(s):  
T. K. BALAKRISHNAN ◽  
A. K. JASWAL ◽  
S.S.. SINGH ◽  
H. N. SRIVASTAVA
Keyword(s):  
Indian Ocean ◽  
Bay Of Bengal ◽  
Arabian Sea ◽  
Monsoon Rainfall ◽  
Equatorial Indian Ocean ◽  
Principal Component ◽  
North Indian Ocean ◽  
Orthogonal Function ◽  
The North ◽  
Extreme North

The spatial distribution and temporal variation of the monthly mean SSTA over the Arabian Sea, Bay of Bengal and the north Indian Ocean were investigated for a set of contrasting years of monsoon over the period 1961-80 for months April through July using Empirical Orthogonal Function (EOF) technique with a view to identify regions that are significantly related to the monsoon rainfall. Over 75% of the total variance is, explained by the first mode EOF. SSTA over the north and northeast Arabian Sea during pre-monsoon months were found to be possible indicators of the ensuing monsoon activity. The higher eigen vectors in May over northeast Arabian Sea may signal good monsoon and vice versa. In June there is a marked contrast in the distribution of SST over the Arabian Sea between the two sets of the years the eastern Arabian Sea IS warmer for the deficient monsoon years while the entire Arabian Sea except over the extreme north Arabian Sea is cool during good monsoon years. There is formation of SSTA over the equatorial Indian Ocean area close to Indonesian island commencing from May which is more marked in June and is positively correlated with seasonal rainfall activity over India.  

scholarly journals Indo-Pacific Sea Surface Temperature Perturbations Associated with Intraseasonal Oscillations of Tropical Convection

2007 ◽  
Vol 20 (13) ◽  
pp. 3056-3082 ◽  
Author(s):  
Jean Philippe Duvel ◽  
Jérôme Vialard
Keyword(s):  
Indian Ocean ◽  
Bay Of Bengal ◽  
Arabian Sea ◽  
Large Scale ◽  
Surface Wind ◽  
Mode Analysis ◽  
Boreal Summer ◽  
Northwest Pacific ◽  
The West ◽  
The Indian Ocean

Abstract Since the ISV of the convection is an intermittent phenomenon, the local mode analysis (LMA) technique is used to detect only the ensemble of intraseasonal events that are well organized at large scale. The LMA technique is further developed in this paper in order to perform multivariate analysis given patterns of SST and surface wind perturbations associated specifically with these intraseasonal events. During boreal winter, the basin-scale eastward propagation of the convective perturbation is present only over the Indian Ocean Basin. The intraseasonal SST response to convective perturbations is large and recurrent over thin mixed layer regions located north of Australia and in the Indian Ocean between 5° and 10°S. By contrast, there is little SST response in the western Pacific basin and no clear eastward propagation of the convective perturbation. During boreal summer, the SST response is large over regions with thin mixed layers located north of the Bay of Bengal, in the Arabian Sea, and in the China Sea. The northeastward propagation of the convective perturbation over the Bay of Bengal is associated with a standing oscillation of the SST and the surface wind between the equator and the northern part of the bay. In fact, many intraseasonal events mostly concern a single basin, suggesting that the interbasin organization is not a necessary condition for the existence of coupled intraseasonal perturbations of the convection. The perturbation of the surface wind tends to be larger to the west of the large-scale convective perturbation (like for a Gill-type dynamical response). For eastward propagating perturbations, the cooling due to the reinforcement of the wind (i.e., surface turbulent heat flux) thus generally lags the radiative cooling due to the reduction of the surface solar flux by the convective cloudiness. This large-scale Gill-type response of the surface wind also cools the surface to the west of the basin (northwest Arabian Sea and northwest Pacific Ocean), even if the convection is locally weak. An intriguing result is a frequently occurring small delay between the maximum surface wind and the minimum SST. Different explanations are invoked, like a rapid surface cooling due to the vanishing of an ocean warm layer (diurnal surface warming due to solar radiation in low wind conditions) as soon as the wind increases.

scholarly journals Unusual Central Indian Drought of Summer Monsoon 2008: Role of Southern Tropical Indian Ocean Warming

2010 ◽  
Vol 23 (19) ◽  
pp. 5163-5174 ◽  
Author(s):  
Suryachandra A. Rao ◽  
Hemantkumar S. Chaudhari ◽  
Samir Pokhrel ◽  
B. N. Goswami
Keyword(s):  
Global Warming ◽  
Indian Ocean ◽  
Summer Monsoon ◽  
Bay Of Bengal ◽  
Monsoon Rainfall ◽  
Equatorial Indian Ocean ◽  
Central India ◽  
Tropical Indian Ocean ◽  
Indian Ocean Warming ◽  
Normal Rainfall

Abstract While many of the previous positive Indian Ocean dipole (IOD) years were associated with above (below)-normal monsoon rainfall over central (southern) India during summer monsoon months [June–September (JJAS)], the IOD event in 2008 is associated with below (above)-normal rainfall in many parts of central (southern peninsular) India. Because understanding such regional organization is a key for success in regional prediction, using different datasets and atmospheric model simulations, the reasons for this abnormal behavior of the monsoon in 2008 are explored. Compared to normal positive IOD events, sea surface temperature (SST) and rainfall in the southern tropical Indian Ocean (STIO) in JJAS 2008 were abnormally high. Downwelling Rossby waves and oceanic heat advection played an important role in warming SST abnormally in the STIO. It was also found that the combined influence of a linear warming trend in the tropical Indian Ocean and warming associated with the IOD have resulted in abnormal warming of the STIO. This abnormal SST warming resulted in enhancement of convection in the southwest tropical Indian Ocean and forced anticyclonic circulation anomalies over the Bay of Bengal and central India, leading to suppressed rainfall over this region in JJAS 2008. The above mechanism is tested by conducting several model sensitivity experiments with an atmospheric general circulation model (AGCM). These experiments confirmed that the subsidence over central India and the Bay of Bengal was forced mainly by the anomalous warming in the STIO region driven by coupled ocean–atmosphere processes. This study provides the first evidence of combined Indian Ocean warming, associated with global warming, and IOD-related warming influence on Indian summer monsoon rainfall. The combined influence may force below-normal rainfall over central India by inducing strong convection in the STIO region. The conventional seesaw in convection between the Indian subcontinent and the eastern equatorial Indian Ocean may shift to the central equatorial Indian Ocean and the Bay of Bengal if the central Indian Ocean consistently warms in the global warming scenario.

Classification of Observations through Combination of the Dimension Reduction and the Cluster Analysis

2017 ◽  
Vol 7 (8) ◽  
pp. 30
Author(s):  
Hyeuk Kim
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Unsupervised Learning ◽  
Principal Component ◽  
Component Analysis ◽  
Baseball Players ◽  
Partitioning Around Medoids ◽  
Different Characteristics

Unsupervised learning in machine learning divides data into several groups. The observations in the same group have similar characteristics and the observations in the different groups have the different characteristics. In the paper, we classify data by partitioning around medoids which have some advantages over the k-means clustering. We apply it to baseball players in Korea Baseball League. We also apply the principal component analysis to data and draw the graph using two components for axis. We interpret the meaning of the clustering graphically through the procedure. The combination of the partitioning around medoids and the principal component analysis can be used to any other data and the approach makes us to figure out the characteristics easily.

Quality evaluation of tugaksheeree samples by ATR-FTIR spectroscopy using multicomponent analysis

2020 ◽  
Vol 10 ◽  
Author(s):  
Nikunj D. Patel ◽  
Niranjan S. Kanaki
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Quality Evaluation ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Attenuated Total Reflectance ◽  
Chemometric Methods ◽  
Total Reflectance ◽  
Fingerprint Region ◽  
Ftir Technique

Background: Numerous Ayurvedic formulations contains tugaksheeree as key ingredient. Tugaksheereeis the starch gained from the rhizomes of two plants, Curcuma angustifoliaRoxb. (Zingiberaceae) and Marantaarundinacea (MA) Linn. (Marantaceae). Objective: The primary concerns in quality assessment of Tugaksheeree occur due to adulteration or substitution. Method: In current study, Fourier transform infrared (FTIR) technique with attenuated total reflectance (ATR) facility was used to evaluate tugaksheeree samples. Total 10 different samples were studied and transmittance mode was kept to record the spectra devoid of pellets of KBR. Further treatment was given with multi component tools by considering fingerprint region of the spectra. Multivariate analysis was performed by various chemometric methods. Result: Multi component methods like Principal Component Analysis (PCA), and Hierarchical Cluster Analysis (HCA)were used to discriminate the tugaksheeree samples using Minitab software. Conclusion: This method can be used as a tool to differentiate samples of tugaksheeree from its adulterants and substitutes.

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