Dombrowsky, E., Bertino, L., Brassington, G., Chassignet, E., Davidson, F., Hurlburt, H., et al. (2009). GODAE Systems in Operation.
Oceanog., 22(3), 80–95.
Garcia-Pineda, O., MacDonald, I., Hu, C., Svejkovsky, J., Hess, M., Dukhovskoy, D., et al. (2013). Detection of Floating Oil Anomalies From the Deepwater Horizon Oil Spill With Synthetic Aperture Radar.
Goni, G., DeMaria, M., Knaff, J., Sampson, C., Ginis, I., Bringas, F., et al. (2009). Applications of Satellite-Derived Ocean Measurements to Tropical Cyclone Intensity Forecasting.
Oceanog., 22(3), 190–197.
Cabrera, V. E., D. Solis, and D. Letson. (2009). Optimal crop insurance under climate variability: contrasting insurer and farmer interests.
Transactions of the ASABE, 52(2), 623–631.
Cintra, R., Campos Velho, H., & Cocke, S. (2016). Multilayer Perceptron on data assimilation system applied to FSU global model..
Ahern, K. K. (2019).
Hurricane Boundary Layer Structure during Intensity Change: An Observational and Numerical Analysis.
Carstens, J. (2019).
Tropical Cyclogenesis from Self-aggregated Convection in Numerical Simulations of Rotating Radiative-convective Equilibrium. Florida State University - FCLA; ProQuest Dissertations & Theses Global.
Abstract: Organized convection is of critical importance in the tropical atmosphere. Recent advances in numerical modeling have revealed that moist convection can interact with its environment to transition from a quasi-random to organized state. This phenomenon, known as convective self-aggregation,is aided by feedbacks involving clouds, water vapor, and radiation that increase the spatial variance of column-integrated frozen moist static energy. Prior studies have shown self-aggregation to takeseveral different forms, including that of spontaneous tropical cyclogenesis in an environment of rotating radiative-convective equilibrium (RCE). This study expands upon previous work to address the processes leading to tropical cyclogenesis in this rotating RCE framework. More specifically,a three-dimensional, cloud-resolving numerical model is used to examine the self-aggregation of convection and potential cyclogenesis, and the background planetary vorticity is varied on an f-plane across simulations to represent a range of deep tropical and near-equatorial environments.Convection is initialized randomly in an otherwise homogeneous environment, with no background wind, precursor disturbance, or other synoptic-scale forcing.All simulations with planetary vorticity corresponding to latitudes from 10°to 20°generate intense tropical cyclones, with maximum wind speeds of 80 m s−1or above. Time to genesis varies widely, even within a five-member ensemble of 20°simulations, reflecting a potential degree of stochastic variability based in part on the initial random distribution of convection. Shared across this so-called “high-f” group is the emergence of a midlevel vortex in the days leading to genesis,which has dynamic and thermodynamic implications on its environment that facilitate the spinup of a low-level vortex. Tropical cyclogenesis is possible in this model even at values of Coriolis parameter as low as that representative of 1°. In these experiments, convection self-aggregates into a quasi-circular cluster, which then begins to rotate and gradually strengthen into a tropical storm, aided by near-surface inflow and shallow overturning radial circulations aloft within the aggregated cluster. Other experiments at these lower Coriolis parameters instead self-aggregate into an elongated band and fail to undergo cyclogenesis over the 100-day simulation. A large portion of this study is devoted to examining in greater detail the dynamic and thermodynamic evolution of cyclogenesis in these experiments and comparing the physical mechanisms to current theories.
Buchanan, S., Misra, V., & Bhardwaj, A. (2018). https://rmets.onlinelibrary.wiley.com/doi/10.1002/joc.5450.
International Journal of Climatology, 38(6), 2651–2661.
Abstract: The integrated kinetic energy (IKE) of a tropical cyclone (TC), a volume integration of the surface winds around the centre of the TC, is computed from a comprehensive surface wind (National Aeronautics and Space Administration’s (NASA) cross‐calibrated multi‐platform [CCMP]) analysis available over the global oceans to verify against IKE from wind radii estimates of extended best‐track data maintained by NOAA for the North Atlantic TCs. It is shown that CCMP surface wind analysis severely underestimates IKE largely from not resolving hurricane force winds for majority of the Atlantic TCs, under sampling short‐lived and small‐sized TCs. The seasonal cycle of the North Atlantic TC IKE also verifies poorly in the CCMP analysis. In this article we introduce proxy IKE (PIKE) based on the kinetic energy of the winds at the radius of the last closed isobar (ROCI), which shows promise for a wide range of TC sizes including the smaller‐sized TCs unresolved in the CCMP data set.
Ali, M. M. (2020). Is it high time to use ocean mean temperature for monsoon prediction?
Abstract: A monsoon is a seasonal reversal in the prevailing wind direction, that is usually initiated by the land sea temperature contrast. The Indian summer monsoon, for example, is triggered when the land gets heated up more than the surrounding sea during the summer creating a pressure gradient between the land and the sea. It is well known that the ocean thermal energy required for fueling monsoon circulations comes from the upper layer of the ocean (e.g. Venugopal et al. 2018). But such amount of energy does not come from the top thin layer represented by sea surface temperature (SST) alone. Nevertheless, often SST does not represent the thermal energy available in the upper ocean, although this parameter has been the only oceanographic input to the cyclone and monsoon atmospheric numerical and statistical models.
Kumar, V., Jana, S., Bhardwaj, A., Deepa, R., Sahu, S. K., Pradhan, P. K., et al. (2018). Greenhouse Gas Emission, Rainfall and Crop Production Over North-Western India.
TOECOLJ, 11(1), 47–61.
This study is based on datasets acquired from multi sources e.g. rain-gauges, satellite, reanalysis and coupled model for the region of Northwestern India. The influence of rainfall on crop production is obvious and direct. With the climate change and global warming, greenhouse gases are also showing an adverse impact on crop production. Greenhouse gases (e.g. CO2, NO2 and CH4) have shown an increasing trend over Northwestern Indian region. In recent years, rainfall has also shown an increasing trend over Northwestern India, while the production of rice and maize are reducing over the region. From eight selected sites, over Northwestern India, where rice and maize productions have reduced by 40%, with an increase in CO2, NO2 and CH4 gas emission by 5% from 1998 to 2011.
The correlation from one year to another between rainfall, gas emission and crop production was not very robust throughout the study period, but seemed to be stronger for some years than others.
Such trends and crop yield are attributed to rainfall, greenhouse gas emissions and to the climate variability.