Ali M. M.

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M. M.

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Now showing 1 - 4 of 4
  • Article
    Estimation of ocean subsurface thermal structure from surface parameters : a neural network approach
    (American Geophysical Union, 2004-10-22) Ali, M. M. ; Swain, D. ; Weller, Robert A.
    Satellite remote sensing provides diverse and useful ocean surface observations. It is of interest to determine if such surface observations can be used to infer information about the vertical structure of the ocean's interior, like that of temperature profiles. Earlier studies used either sea surface temperature or dynamic height/sea surface height to infer the subsurface temperature profiles. In this study we have used neural network approach to estimate the temperature structure from sea surface temperature, sea surface height, wind stress, net radiation, and net heat flux, available from an Arabian Sea mooring from October 1994 to October 1995, deployed by the Woods Hole Oceanographic Institution. On the average, 50% of the estimations are within an error of ±0.5°C and 90% within ±1.0°C. The average RMS error between the estimated temperature profiles and in situ observations is 0.584°C with a depth-wise average correlation coefficient of 0.92.
  • Article
    Ocean observations in support of studies and forecasts of tropical and extratropical cyclones
    (Frontiers Media, 2019-07-29) Domingues, Ricardo ; Kuwano-Yoshida, Akira ; Chardon-Maldonado, Patricia ; Todd, Robert E. ; Halliwell, George R. ; Kim, Hyun-Sook ; Lin, I.-I. ; Sato, Katsufumi ; Narazaki, Tomoko ; Shay, Lynn Keith ; Miles, Travis ; Glenn, Scott ; Zhang, Jun A. ; Jayne, Steven R. ; Centurioni, Luca R. ; Le Hénaff, Matthieu ; Foltz, Gregory R. ; Bringas, Francis ; Ali, M. M. ; DiMarco, Steven F. ; Hosoda, Shigeki ; Fukuoka, Takuya ; LaCour, Benjamin ; Mehra, Avichal ; Sanabia, Elizabeth ; Gyakum, John R. ; Dong, Jili ; Knaff, John A. ; Goni, Gustavo J.
    Over the past decade, measurements from the climate-oriented ocean observing system have been key to advancing the understanding of extreme weather events that originate and intensify over the ocean, such as tropical cyclones (TCs) and extratropical bomb cyclones (ECs). In order to foster further advancements to predict and better understand these extreme weather events, a need for a dedicated observing system component specifically to support studies and forecasts of TCs and ECs has been identified, but such a system has not yet been implemented. New technologies, pilot networks, targeted deployments of instruments, and state-of-the art coupled numerical models have enabled advances in research and forecast capabilities and illustrate a potential framework for future development. Here, applications and key results made possible by the different ocean observing efforts in support of studies and forecasts of TCs and ECs, as well as recent advances in observing technologies and strategies are reviewed. Then a vision and specific recommendations for the next decade are discussed.
  • Article
    Heat content of the Arabian Sea Mini Warm Pool is increasing
    (John Wiley & Sons, 2015-11-18) Nagamani, P. V. ; Ali, M. M. ; Goni, Gustavo J. ; Udaya Bhaskar, T. V. S. ; McCreary, Julian P. ; Weller, Robert A. ; Rajeevan, Madhavan Nair ; Gopalakrishna, V. V. ; Pezzullo, John C.
    Sea surface temperature in the Arabian Sea Mini Warm Pool has been suggested to be one of the factors that affects the Indian summer monsoon. In this paper, we analyze the annual ocean heat content (OHC) of this region during 1993–2010, using in situ data, satellite observations, and a model simulation. We find that OHC increases significantly in the region during this period relative to the north Indian Ocean, and propose that this increase could have caused the decrease in Indian Summer Monsoon Rainfall that occurred at the same time.
  • Article
    Estimation of mixed-layer depth from surface parameters
    (Sears Foundation for Marine Research, 2006-09) Swain, D. ; Ali, M. M. ; Weller, Robert A.
    Mixed layer depth (MLD) is an important oceanographic parameter. However, the lack of direct observations of MLD hampers both specification and investigation of its spatial and temporal variability. An important alternative to direct observation would be the ability to estimate MLD from surface parameters easily available from satellites. In this study, we demonstrate estimation of MLD using Artificial Neural Network methods and surface meteorology from a surface mooring in the Arabian Sea. The estimated MLD had a root mean square error of 7.36 m and a coefficient of determination (R2) of 0.94. About 67% (91%) of the estimates lie within ± 5 m (± 10 m) of the MLD determined from temperature sensors on the mooring.