Comparison of high-resolution TRMM-based precipitation products during tropical cyclones in the North Indian Ocean

2011 ◽  
Vol 61 (2) ◽  
pp. 689-701 ◽  
Author(s):  
Satya Prakash ◽  
C. Mahesh ◽  
R. M. Gairola ◽  
P. K. Pal
Keyword(s):  
High Resolution ◽  
Indian Ocean ◽  
Tropical Cyclones ◽  
North Indian Ocean ◽  
The North

Tropical cyclones over the North Indian Ocean: experiments with the high-resolution global icosahedral grid point model GME

2017 ◽  
Vol 130 (1) ◽  
pp. 23-37 ◽  
Author(s):  
Yogesh V. Kumkar ◽  
P. N. Sen ◽  
Hemankumar S. Chaudhari ◽  
Jai-Ho Oh
Keyword(s):  
High Resolution ◽  
Indian Ocean ◽  
Tropical Cyclones ◽  
Grid Point ◽  
North Indian Ocean ◽  
Point Model ◽  
The North

Real-Time Track Prediction of Tropical Cyclones over the North Indian Ocean Using the ARW Model

2013 ◽  
Vol 52 (11) ◽  
pp. 2476-2492 ◽  
Author(s):  
Krishna K. Osuri ◽  
U. C. Mohanty ◽  
A. Routray ◽  
M. Mohapatra ◽  
Dev Niyogi
Keyword(s):  
High Resolution ◽  
Indian Ocean ◽  
Real Time ◽  
Tropical Cyclones ◽  
North Indian Ocean ◽  
Track Forecast ◽  
Forecast Errors ◽  
The North ◽  
Arw Model ◽  
The Impact

AbstractThe performance of the Advanced Research version of the Weather Research and Forecasting (ARW) model in real-time prediction of tropical cyclones (TCs) over the north Indian Ocean (NIO) at 27-km resolution is evaluated on the basis of 100 forecasts for 17 TCs during 2007–11. The analyses are carried out with respect to 1) basins of formation, 2) straight-moving and recurving TCs, 3) TC intensity at model initialization, and 4) season of occurrence. The impact of high resolution (18 and 9 km) on TC prediction is also studied. Model results at 27-km resolution indicate that the mean track forecast errors (skill with reference to persistence track) over the NIO were found to vary from 113 to 375 km (7%–51%) for a 12–72-h forecast. The model showed a right/eastward and slow bias in TC movement. The model is more skillful in track prediction when initialized at the intensity stage of severe cyclone or greater than at the intensity stage of cyclone or lower. The model is more efficient in predicting landfall location than landfall time. The higher-resolution (18 and 9 km) predictions yield an improvement in mean track error for the NIO Basin by about 4%–10% and 8%–24%, respectively. The 9-km predictions were found to be more accurate for recurving TC track predictions by ~13%–28% and 5%–15% when compared with the 27- and 18-km runs, respectively. The 9-km runs improve the intensity prediction by 15%–40% over the 18-km predictions. This study highlights the capabilities of the operational ARW model over the Indian monsoon region and the continued need for operational forecasts from high-resolution models.

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.

scholarly journals Effect of tropical cyclones on the Stratosphere-Troposphere Exchange observed using satellite observations over north Indian Ocean

2016 ◽  
Author(s):  
M. Venkat Ratnam ◽  
S. Ravindra Babu ◽  
S. S. Das ◽  
Ghouse Basha ◽  
B. V. Krishnamurthy ◽  
...  
Keyword(s):  
Water Vapor ◽  
Indian Ocean ◽  
Tropical Cyclones ◽  
Lower Stratosphere ◽  
North Indian Ocean ◽  
Satellite Observations ◽  
Upper Troposphere ◽  
North West ◽  
The North ◽  
The Impact

Abstract. Tropical cyclones play an important role in modifying the tropopause structure and dynamics as well as stratosphere-troposphere exchange (STE) process in the Upper Troposphere and Lower Stratosphere (UTLS) region. In the present study, the impact of cyclones that occurred over the North Indian Ocean during 2007–2013 on the STE process is quantified using satellite observations. Tropopause characteristics during cyclones are obtained from the Global Positioning System (GPS) Radio Occultation (RO) measurements and ozone and water vapor concentrations in UTLS region are obtained from Aura-Microwave Limb Sounder (MLS) satellite observations. The effect of cyclones on the tropopause parameters is observed to be more prominent within 500 km from the centre of cyclone. In our earlier study we have observed decrease (increase) in the tropopause altitude (temperature) up to 0.6 km (3 K) and the convective outflow level increased up to 2 km. This change leads to a total increase in the tropical tropopause layer (TTL) thickness of 3 km within the 500 km from the centre of cyclone. Interestingly, an enhancement in the ozone mixing ratio in the upper troposphere is clearly noticed within 500 km from cyclone centre whereas the enhancement in the water vapor in the lower stratosphere is more significant on south-east side extending from 500–1000 km away from the cyclone centre. We estimated the cross-tropopause mass flux for different intensities of cyclones and found that the mean flux from stratosphere to troposphere for cyclonic stroms is 0.05 ± 0.29 × 10−3 kg m−2 and for very severe cyclonic stroms it is 0.5 ± 1.07 × 10−3 kg m−2. More downward flux is noticed in the north-west and south-west side of the cyclone centre. These results indicate that the cyclones have significant impact in effecting the tropopause structure, ozone and water vapour budget and consequentially the STE in the UTLS region.

scholarly journals Impact of air-sea coupling on the simulation of tropical cyclones in the North Indian Ocean using a simple 3-D ocean model coupled to ARW

2016 ◽  
Vol 121 (16) ◽  
pp. 9400-9421 ◽  
Author(s):  
C. V. Srinivas ◽  
Greeshma M. Mohan ◽  
C. V. Naidu ◽  
R. Baskaran ◽  
B. Venkatraman
Keyword(s):  
Indian Ocean ◽  
Tropical Cyclones ◽  
Ocean Model ◽  
North Indian Ocean ◽  
The North

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>

Sign in / Sign up

Export Citation Format

Share Document