Hydrometeorological Modeling Study of Tropical Cyclone Phet in the Arabian Sea in 2010

Haggag, Mohammed; Badry, Hesham

doi:10.4236/acs.2012.22018

Cited by 12 publications

(9 citation statements)

References 22 publications

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“…Haggag and Badry used WRF model to study the atmospheric circulation and to reproduce the heavy rainfall over Oman on 4 June 2010 that triggers flash floods. All experiments resulted in analogous cyclone track and intensity that well conform to the observations [7].…”

Section: Introductionsupporting

confidence: 82%

Evaluation of Weather Research and Forecasting Model (WRF) Simulations over Middle East

Roomi

2018

MJS

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The Weather Research and Forecasting model (WRF) is an atmospheric simulation system designed for both research and operational applications. This worldwide used model requires a sophisticated modeling experience and computing skills. In this study, WRF model was used to predict many atmospheric parameters based on the initial conditions extracted from NOMADS data sets. The study area is basically the region surrounded by the longitudes and latitudes: 15o-75o E and 10.5o-45o N which typically includes the Middle East region. The model was installed on Linux platform with a grid size of 10 km in the X and Y directions. A low pressure trough was tracked in its movement from west to east via the Middle East during the period from 1 to 7 January 2010 as a case study of the WRF model. MATLAB and NCAR Command Language (NCL) were used to display the model output. To evaluate the forecasted parameters and patterns, some comparisons were made between the predicted and actual weather charts. Wind speeds and directions in the prognostic and actual charts of 700 hPa were in agreement. However, the predicted values of geopotential heights in WRF are somewhat overestimate the actual ones. This may be attributed to the differences in the data sources and data analysis methods of the two data agencies, NOMADS and ECMWF.

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Section: Introductionsupporting

confidence: 82%

Evaluation of Weather Research and Forecasting Model (WRF) Simulations over Middle East

Roomi

2018

MJS

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“…However, the associated damage due to tropical cyclones especially over the coastal regions is subjected to the understanding of various disciplines, for example communications, hydrometeorology, geography, socioeconomic structure of the regions (basins), and administrative promptness. Though the cyclones, Nargis and Phet, are of category 4 and their official forecasts (Saito et al , ; Haggag and Badry, ) are of comparable accuracy, the damages associated with them were very different. The associated number of deaths and destruction vary from basin to basin and relating the real‐time forecast with coastal vulnerability (Brakenridge et al , ) is therefore another spectrum of research.…”

Section: Resultsmentioning

confidence: 99%

Track and intensity forecast of tropical cyclones over the North Indian Ocean with multilayer feed forward neural nets

et al. 2014

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Precise forecasting of the track and intensity of tropical cyclones remains one of the top priorities for the meteorological community. In the present study multilayer feed forward neural nets with different architectures are developed to identify the best neural net for forecasting the track and intensity of tropical cyclones over the North Indian Ocean (NIO) with 6, 12 and 24 h lead time. Forecast errors are estimated with each neural net. The result reveals that the neural net architecture 1 (NNA 1) with 10 input layers, 2 hidden layers, 5 hidden nodes and 2 output layers provides the best forecast for both the track and the intensity of the tropical cyclones over NIO. Two cyclones of the same category in Saffir Simpson Hurricane Scale, namely Nargis and Phet, that occurred over the Bay of Bengal and the Arabian Sea of the NIO basin are considered in the present study for validation. The result reveals that the prediction errors (%) with NNA 1 model in estimating the intensity of the cyclones Nargis and Phet during the validation are 3. 37, 8.29 and 9.74 as well as 6.38, 11.26 and 18.72 with 6, 12 and 24 h lead time, respectively. The mean track errors for 6, 12 and 24 h forecasts are observed to be 45, 69 and 89 km for cyclone Nargis and 54, 87 and 98 km for cyclone Phet. NNA 1 model is observed to perform better than NNA 2 and NNA 3 models and the existing numerical models.

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“…Leading to this period, the impact of Cyclone Phet in Fujairah emirates in 2010 should have served as a learning curve for the UAE to improve warning systems. The cyclone which hit Fujairah during the early hours of 4 June 2010 was the worst cyclone recorded in the country and the second strongest tropical cyclone ever recorded in the Arabian Gulf (Haggag and Badry, 2012). The cyclone's heavy rainfall triggered flash floods, causing enormous damage to the infrastructure.…”

Section: Context Of Natural Hazards In the Uaementioning

confidence: 99%

“…According to Haggag and Badry (2012), the lack of coordination between emergency agencies for response was covert, but the lack of EWS for warning the community leading to the landfall disaster was evident. These two case studies in the emirates of Abu Dhabi and Fujairah provide the context for the limited community awareness of hazard risks in the UAE and the lack of EWS deployment for natural hazards.…”

Section: Context Of Natural Hazards In the Uaementioning

confidence: 99%

Integrated framework for early warning system in UAE

Alhmoudi

Aziz

2016

IJDRBE

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Purpose The impacts and costs of natural disasters on people, properties and environment are often severe when these occur on a large scale and with no warning system in place. The lack of deployment of an early warning system (EWS), low risk and hazard knowledge and impact of natural hazard experienced by some communities in the UAE have emphasised the need for more effective EWSs. This work focuses on developing an integrated framework for EWSs for communities prone to the impact of natural hazards to reduce their vulnerability and improve emergency management arrangements in the UAE. Design/methodology/approach The essential elements of effective EWS were identified through literature review to develop an integrated framework for EWS. Semi-structured interviews and questionnaires were also used to identify and confirm hindering factors to deployment of effective EWSs in Abu Dhabi and Fujairah Emirates, while areas that require further development were also identified through this means. Findings The outcome of this research revealed that the warning for natural hazards in the UAE lacked the required elements for effective EWS, whereas the elements which are present are insufficient to mitigate the impacts of natural hazards. The information in this work emphasises the need to improve two elements, and to develop the other two essential elements of EWS in the UAE. Originality/value The outcome of this research revealed that the warning for natural hazards in the UAE lacked the required elements for effective EWS, whereas the elements which are present are insufficient to mitigate the impacts of natural hazards. The information in this work emphasises the need to improve two elements and to develop the other two essential elements of EWS in the UAE.

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Hydrometeorological Modeling Study of Tropical Cyclone Phet in the Arabian Sea in 2010

Cited by 12 publications

References 22 publications

Evaluation of Weather Research and Forecasting Model (WRF) Simulations over Middle East

Evaluation of Weather Research and Forecasting Model (WRF) Simulations over Middle East

Track and intensity forecast of tropical cyclones over the North Indian Ocean with multilayer feed forward neural nets

Integrated framework for early warning system in UAE

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