scholarly journals Simulation of Heavy Rainfall Events during Retreat Phase of Summer Monsoon Season over Parts of Andhra Pradesh

2012 ◽  
Vol 03 (04) ◽  
pp. 737-748 ◽  
Author(s):  
O. S. R. U. Bhanu Kumar ◽  
P. Suneetha ◽  
S. Ramalingeswara Rao ◽  
M. Satya Kumar
Keyword(s):  
Summer Monsoon ◽  
Andhra Pradesh ◽  
Heavy Rainfall ◽  
Monsoon Season ◽  
Summer Monsoon Season ◽  
Rainfall Events ◽  
Heavy Rainfall Events

scholarly journals Variations in O<sub>3</sub>, CO, and CH<sub>4</sub> over the Bay of Bengal during the summer monsoon season: Ship-borne measurements and model simulations

2016 ◽  
Author(s):  
Imran A. Girach ◽  
Narendra Ojha ◽  
Prabha R. Nair ◽  
Andrea Pozzer ◽  
Yogesh K. Tiwari ◽  
...  
Keyword(s):  
Summer Monsoon ◽  
Bay Of Bengal ◽  
Monsoon Season ◽  
Surface Ozone ◽  
Ozone Production ◽  
Spatial And Temporal Variability ◽  
Summer Monsoon Season ◽  
Rainfall Events ◽  
Model Simulations ◽  
Mixing Ratios

Abstract. We present ship-borne measurements of surface ozone, carbon monoxide and methane over the Bay of Bengal (BoB), the first time such measurements have been taken during the summer monsoon season, as a part of the Continental Tropical Convergence Zone (CTCZ) experiment during 2009. O3, CO, and CH4 mixing ratios exhibited significant spatial and temporal variability in the ranges of 8–54 nmol mol−1, 50–200 nmol mol−1, and 1.57–2.15 µmol mol−1, with means of 29.7 ± 6.8 nmol mol−1, 96 ± 25 nmol mol−1, and 1.83 ± 0.14 µmol mol−1, respectively. The average mixing ratios of trace gases over northern BoB (O3: 30 ± 7 nmol mol−1, CO: 95 ± 25 nmol mol−1, CH4: 1.86 ± 0.12 µmol mol−1), in airmasses from northern or central India, did not differ much from those over central BoB (O3: 27 ± 5 nmol mol−1, CO: 101 ± 27 nmol mol−1, CH4: 1.72 ± 0.14 µmol mol−1), in airmasses from southern India. Spatial variability is observed to be most significant for CH4. The ship-based observations, in conjunction with backward air trajectories and ground-based measurements over the Indian region, are analyzed to estimate a net ozone production of 1.5–4 nmol mol−1 day−1 in the outflow. Ozone mixing ratios over the BoB showed large reductions (by ~ 20 nmol mol−1) during four rainfall events. Temporal changes in the meteorological parameters, in conjunction with ozone vertical profiles, indicate that these low ozone events are associated with downdrafts of free-tropospheric ozone-poor airmasses. While the observed variations in O3 and CO are successfully reproduced using the Weather Research and Forecasting model with Chemistry (WRF-Chem), this model overestimates mean concentrations by about 20 %, generally overestimating O3 mixing ratios during the rainfall events. Analysis of the chemical tendencies from model simulations for a low-O3 event on August 10, 2009, captured successfully by the model, shows the key role of horizontal advection in rapidly transporting ozone-rich airmasses across the BoB. Our study fills a gap in the availability of trace gas measurements over the BoB, and when combined with data from previous campaigns, reveals large seasonal amplitude (~ 39 and ~ 207 nmol mol−1 for O3 and CO, respectively) over the northern BoB.

scholarly journals Spatiotemporal patterns of synchronous heavy rainfall events in East Asia during the Baiu season

2021 ◽  
Author(s):  
Frederik Wolf ◽  
Ugur Ozturk ◽  
Kevin Cheung ◽  
Reik V. Donner
Keyword(s):  
System Dynamics ◽  
East Asia ◽  
Heavy Rainfall ◽  
Spatial Organization ◽  
Monsoon Season ◽  
Spatiotemporal Patterns ◽  
Extreme Weather Events ◽  
Earth System ◽  
Rainfall Events ◽  
Heavy Rainfall Events

&lt;p&gt;Investigating the synchrony and interdependency of heavy rainfall occurrences is crucial to understand the underlying physical mechanisms and reduce physical and economic damages by improved forecasting strategies. In this context, studies utilizing functional network representations have recently contributed to significant advances in the understanding and prediction of extreme weather events.&lt;/p&gt;&lt;p&gt;To thoroughly expand on previous works employing the latter framework to the East Asian Summer Monsoon (EASM) system, we focus here on changes in the spatial organization of synchronous heavy precipitation events across the monsoon season (April to August) by studying the temporal evolution of corresponding network characteristics in terms of a sliding window approach. Specifically, we utilize functional climate networks together with event coincidence analysis for identifying and characterizing synchronous activity from daily rainfall estimates with &lt;span&gt;a spatial resolution of 0.25&amp;#176; &lt;/span&gt;between 1998 and 2018. Our results demonstrate that the formation of the Baiu front as a main feature of the EASM is reflected by a double-band structure of synchronous heavy rainfall with two centers north and south of the front. Although the two separated bands are strongly related to either low- or high-level winds which are commonly assumed to be independent, we provide evidence that it is rather their mutual interconnectivity that changes during the different phases of the EASM season in a characteristic way.&lt;/p&gt;&lt;p&gt;Our findings shed some new light on the interplay between tropical and extratropical factors controlling the EASM intraseasonal evolution, which could potentially help improving future forecasts of the Baiu onset in different regions of East Asia.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;Further details: F. Wolf, U. Ozturk, K. Cheung, R.V. Donner: Spatiotemporal patterns of synchronous heavy rainfall events in East Asia during the Baiu season. Earth System Dynamics (in review). Discussion Paper: Earth System Dynamics Discussions, (2020)&lt;/p&gt;

scholarly journals Study of rainfall features over Goa state during southwest monsoon season

MAUSAM ◽  
2021 ◽  
Vol 61 (2) ◽  
pp. 155-162
Author(s):  
S. M. METRI ◽  
KHUSHVIR SINGH
Keyword(s):  
Heavy Rainfall ◽  
Monsoon Season ◽  
Southwest Monsoon ◽  
Annual Rainfall ◽  
Statistical Parameters ◽  
Maximum Rainfall ◽  
Rainfall Events ◽  
Average Rainfall ◽  
Southwest Monsoon Season ◽  
Heavy Rainfall Events

In this paper the rainfall features at different raingauge stations of Goa state have been studied for the period of 30 years. The statistical parameters such as mean monthly rainfall, Standard Deviation and Coefficient of Variation have been computed for each raingauge station of Goa. Some heavy rainfall events during the period have also been studied. The study shows the significant rising trend of rainfall towards the eastern parts of Goa. Goa experiences an average rainfall of about 330 cm annually and around 90% of annual rainfall occurs during southwest monsoon season i.e. (June to September). Studies revealed that most of heavy rainfall events caused due to active off-shore trough and low pressure systems formed over southeast Arabian Sea. It has also come out from the study that the orography of Goa plays an important role in rainfall distribution. Valpoi receives maximum rainfall due to its orographic effect.

Synoptic characteristics of heavy rainfall events in pre-monsoon season in South China

2010 ◽  
Vol 27 (2) ◽  
pp. 315-327 ◽  
Author(s):  
Liji Wu ◽  
Ronghui Huang ◽  
Haiyan He ◽  
Yaping Shao ◽  
Zhiping Wen
Keyword(s):  
South China ◽  
Heavy Rainfall ◽  
Monsoon Season ◽  
Rainfall Events ◽  
Heavy Rainfall Events

scholarly journals Orographic effects on heavy rainfall events over northeastern Taiwan during the northeasterly monsoon season

2013 ◽  
Vol 122 ◽  
pp. 310-335 ◽  
Author(s):  
Ching-Sen Chen ◽  
Yuh-Lang Lin ◽  
Hui-Ting Zeng ◽  
Chih-Ying Chen ◽  
Che-Ling Liu
Keyword(s):  
Heavy Rainfall ◽  
Monsoon Season ◽  
Rainfall Events ◽  
Orographic Effects ◽  
Heavy Rainfall Events

scholarly journals Evaluation of heavy rainfall warning over India during summer monsoon season

MAUSAM ◽  
2021 ◽  
Vol 60 (4) ◽  
pp. 475-490
Author(s):  
M. MOHAPATRA ◽  
H. R. HATWAR ◽  
B. K. BANDYOPADHYAY ◽  
V. SUBRAHMANYAM
Keyword(s):  
Summer Monsoon ◽  
Heavy Rainfall ◽  
Monsoon Season ◽  
Weather Prediction ◽  
India Meteorological Department ◽  
Skill Score ◽  
Spatial And Temporal Variability ◽  
Summer Monsoon Season ◽  
Skill Scores ◽  
Heidke Skill Score

India Meteorological Department (IMD) issues heavy rainfall warning for a meteorological sub-division when the expected 24 hours rainfall over any rain gauge station in that sub-division is likely to be 64.5 mm or more. Though these warnings have been provided since the inception of IMD, a few attempts have been made for quantitative evaluation of these warnings.  Hence, a study is undertaken to verify the heavy rainfall warnings over 36 meteorological sub-divisions of India during monsoon months (June-September) and season as a whole. For this purpose, data of recent 5 years (2002-2006) has been taken into consideration. In this connection, the day when heavy rainfall is recorded over at least one station in a sub-division, has been considered as a heavy rainfall day for that sub-division.   There is a large spatial and temporal variability in skill scores of heavy rainfall warnings over India during summer monsoon season. Considering the monsoon season as a whole, the Heidke Skill Score (HSS) is relatively less (<0.20) over the regions with less frequent heavy rainfall like Lakshadweep, southeast peninsula, Vidarbha, Marathwada, Jammu & Kashmir, Arunachal Pradesh and Nagaland, Manipur, Mizoram & Tripura (NMMT). It is higher (> 0.50) over Konkan & Goa, Madhya Maharashtra and Gujarat region. There has been improvement in the forecast skill with gradual increase in the critical success index and Heidke skill score over the years mainly due to the Numerical Weather Prediction (NWP) models' guidance available to the forecasters. However, the false alarm rate and missing rate are still very high (> 0.50), especially over many sub-divisions of northwest India, southeast peninsula and NMMT.

Inconsistency in the frequency of rainfall events in the Indian summer monsoon season

2019 ◽  
Vol 39 (13) ◽  
pp. 4907-4923 ◽  
Author(s):  
Vinay K. Prathipati ◽  
Naidu C. V. ◽  
Prasanna Konatham
Keyword(s):  
Summer Monsoon ◽  
Indian Summer Monsoon ◽  
Monsoon Season ◽  
Summer Monsoon Season ◽  
Rainfall Events
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