Impact of SAPHIR radiances on the simulation of tropical cyclones over the Bay of Bengal using NCMRWF hybrid-4DVAR assimilation and forecast system

2020 ◽  
Vol 129 (1) ◽  
Author(s):  
Devanil Choudhury ◽  
Ankur Gupta ◽  
S Indira Rani ◽  
John P George
Keyword(s):  
Tropical Cyclones ◽  
Bay Of Bengal ◽  
Forecast System

scholarly journals Gravity Wave Behavior in Lower Stratosphere During Tropical Cyclones Over the Bay of Bengal

Radio Science ◽  
2018 ◽  
Vol 53 (11) ◽  
pp. 1356-1367 ◽  
Author(s):  
Gargi Rakshit ◽  
Soumyajyoti Jana ◽  
Animesh Maitra
Keyword(s):  
Gravity Wave ◽  
Tropical Cyclones ◽  
Bay Of Bengal ◽  
Lower Stratosphere

scholarly journals Intensidade do vento de dois ciclones tropicais obtida por diferentes conjuntos de dados

2020 ◽  
Vol 13 (1) ◽  
pp. 067
Author(s):  
Christie Andre Souza ◽  
Michelle Simões Reboita
Keyword(s):  
Tropical Cyclones ◽  
The Other ◽  
Data Sets ◽  
Global Forecast System ◽  
Forecast System ◽  
Wind Intensity

Os ciclones tropicais quando atingem ventos com intensidade igual ou superior a 119 km/h desenvolvem uma estrutura conhecida como olho em seu centro. Já os ventos mais intensos do sistema são encontrados imediatamente após o olho. Num estudo recente para os ciclones Haiyan e Haima foi levantada a questão da qualidade dos dados do Global Forecast System (GFS) em representar os ventos uma vez que os ventos máximos apareceram no olho do sistema. Diante disso, esse estudo tem como objetivo avaliar como diferentes conjuntos de dados (GFS, ERA5, ERA-Interim e CCMP) representam os ventos nesses dois ciclones tropicais. A ERA5 e o GFS mostram ventos mais intensos nos ciclones do que os outros dois conjuntos de dados. Todos, exceto o GFS, mostram claramente ventos mais fracos no olho dos ciclones.  Wind intensity of two tropical cyclones obtained by different data sets A B S T R A C TWhen the tropical cyclones reach winds with intensity equal or higher than 119 km/h, they develop a structure known as the eye at its center. The strongest winds in the system are found immediately after the eye. In a recent study for Haiyan and Haima cyclones, the question of the quality of the Global Forecast System (GFS) data in representing the winds once the maximum winds appeared in the system eye was raised. Therefore, this study aims to evaluate how different data sets (GFS, ERA5, ERA-Interim and CCMP) represent the winds in these two tropical cyclones. ERA5 and GFS show cyclones with more intense winds than the other datasets. Except the GFS, the other data clearly show weaker winds in the cyclone eye.Keywords: analyzes; cyclones; meteorology; reanalysis

scholarly journals High-Resolution Ensemble HFV3 Forecasts of Hurricane Michael (2018): Rapid Intensification in Shear

2020 ◽  
Vol 148 (5) ◽  
pp. 2009-2032 ◽  
Author(s):  
Andrew T. Hazelton ◽  
Xuejin Zhang ◽  
Sundararaman Gopalakrishnan ◽  
William Ramstrom ◽  
Frank Marks ◽  
...  
Keyword(s):  
Tropical Cyclones ◽  
Early Stage ◽  
Vertical Wind Shear ◽  
Vertical Shear ◽  
Rapid Intensification ◽  
Forecast System ◽  
The Mean ◽  
Cubed Sphere ◽  
Environmental Prediction ◽  
The Relationship

Abstract The FV3GFS is the current operational Global Forecast System (GFS) at the National Centers for Environmental Prediction (NCEP), which combines a finite-volume cubed sphere dynamical core (FV3) and GFS physics. In this study, FV3GFS is used to gain understanding of rapid intensification (RI) of tropical cyclones (TCs) in shear. The analysis demonstrates the importance of TC structure in a complex system like Hurricane Michael, which intensified to a category 5 hurricane over the Gulf of Mexico despite over 20 kt (10 m s−1) of vertical wind shear. Michael’s RI is examined using a global-nest FV3GFS ensemble with the nest at 3-km resolution. The ensemble shows a range of peak intensities from 77 to 159 kt (40–82 m s−1). Precipitation symmetry, vortex tilt, moisture, and other aspects of Michael’s evolution are compared through composites of stronger and weaker members. The 850–200-hPa vertical shear is 22 kt (11 m s−1) in the mean of both strong and weak members during the early stage. Tilt and moisture are two distinguishing factors between strong and weak members. The relationship between vortex tilt and humidification is complex, and other studies have shown both are important for sheared intensification. Here, it is shown that tilt reduction leads to upshear humidification and is thus a driving factor for intensification. A stronger initial vortex and early evolution of the vortex also appear to be the key to members that are able to resist the sheared environment.

scholarly journals Author Correction: On the processes influencing rapid intensity changes of tropical cyclones over the Bay of Bengal

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Saiprasanth Bhalachandran ◽  
R. Nadimpalli ◽  
K. K. Osuri ◽  
F. D. Marks ◽  
S. Gopalakrishnan ◽  
...  
Keyword(s):  
Tropical Cyclones ◽  
Bay Of Bengal ◽  
Intensity Changes

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

scholarly journals Effects of Tropical Cyclones on Sea Surface Salinity in the Bay of Bengal Based on SMAP and Argo Data

Water ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 2975
Author(s):  
Huabing Xu ◽  
Rongzhen Yu ◽  
Danling Tang ◽  
Yupeng Liu ◽  
Sufen Wang ◽  
...  
Keyword(s):  
Tropical Cyclones ◽  
Bay Of Bengal ◽  
Vertical Mixing ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Ekman Pumping ◽  
Surface Salinity ◽  
Argo Data ◽  
Before And After ◽  
The Impact

This paper uses the Argo sea surface salinity (SSSArgo) before and after the passage of 25 tropical cyclones (TCs) in the Bay of Bengal from 2015 to 2019 to evaluate the sea surface salinity (SSS) of the Soil Moisture Active Passive (SMAP) remote sensing satellite (SSSSMAP). First, SSSArgo data were used to evaluate the accuracy of the 8-day SMAP SSS data, and the correlations and biases between SSSSMAP and SSSArgo were calculated. The results show good correlations between SSSSMAP and SSSArgo before and after TCs (before: SSSSMAP = 1.09SSSArgo−3.08 (R2 = 0.69); after: SSSSMAP = 1.11SSSArgo−3.61 (R2 = 0.65)). A stronger negative bias (−0.23) and larger root-mean-square error (RMSE, 0.95) between the SSSSMAP and SSSArgo were observed before the passage of 25 TCs, which were compared to the bias (−0.13) and RMSE (0.75) after the passage of 25 TCs. Then, two specific TCs were selected from 25 TCs to analyze the impact of TCs on the SSS. The results show the significant SSS increase up to the maximum 5.92 psu after TC Kyant (2016), which was mainly owing to vertical mixing and strong Ekman pumping caused by TC and high-salinity waters in the deep layer that were transported to the sea surface. The SSSSMAP agreed well with SSSArgo in both coastal and offshore waters before and after TC Roanu (2016) and TC Kyant (2016) in the Bay of Bengal.

scholarly journals Impact of ocean mixed‐layer depth initialization on the simulation of tropical cyclones over the Bay of Bengal using the WRF‐ARW model

2019 ◽  
Vol 27 (1) ◽  
Author(s):  
Viswanadhapalli Yesubabu ◽  
Vijaya Kumari Kattamanchi ◽  
Naresh Krishna Vissa ◽  
Hari Prasad Dasari ◽  
Vijaya Bhaskara Rao Sarangam
Keyword(s):  
Tropical Cyclones ◽  
Mixed Layer ◽  
Bay Of Bengal ◽  
Mixed Layer Depth ◽  
Ocean Mixed Layer ◽  
Layer Depth ◽  
Arw Model ◽  
Ocean Mixed Layer Depth

scholarly journals The response of ocean parameters to tropical cyclones in the Bay of Bengal

2019 ◽  
Vol 145 (724) ◽  
pp. 3320-3332 ◽  
Author(s):  
Nanda Kishore Reddy Busireddy ◽  
Kumar Ankur ◽  
Krishna Kishore Osuri ◽  
Sanikommu Sivareddy ◽  
Dev Niyogi
Keyword(s):  
Tropical Cyclones ◽  
Bay Of Bengal

Climatological characteristics of Bay of Bengal tropical cyclones: 1972–2017

2019 ◽  
Vol 139 (1-2) ◽  
pp. 615-629 ◽  
Author(s):  
Pankaj Bhardwaj ◽  
Omvir Singh
Keyword(s):  
Tropical Cyclones ◽  
Bay Of Bengal
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