| 1. |
- Rahaman, Hasibur, et al.
(author)
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Improved ocean analysis for the Indian Ocean
- 2019
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In: Journal of operational oceanography. Publisher. - : Taylor & Francis. - 1755-876X .- 1755-8778. ; 12:1, s. 16-33
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Journal article (peer-reviewed)abstract
- The National Centers for Environmental Prediction (NCEP) and the Indian National Centre for Ocean Information Services (INCOIS) produce global ocean analyses based on the Global Ocean Data Assimilation System (GODAS). This system uses a state of the art ocean general circulation model named moduler ocean model (MOM) and the 3D-Variational (3DVar) data assimilation technique. In this study we have evaluated the INCOIS-GODAS operational analysis products with an upgrade of the physical model from MOM4p0d to MOM4p1. Two experiments were performed with same atmospheric forcing fields:(i) using MOM4p0d (GODAS_p0), and (ii) using MOM4p1 (GODAS_p1). Observed temperature and salinity profiles were assimilated in both experiments. Validation with independent observations show improvement of sea surface temperature(SST), sea surface salinity (SSS) and surface currents in the new analysis GODAS_p1 as compared to the old analysis GODAS_p0.
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| 2. |
- Thandlam, Venugopal, Mr. 1987-, et al.
(author)
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Are We in the Right Path in Using Early Warning Systems?
- 2019
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In: Journal of Extreme Events. - : World Scientific. - 2345-7376 .- 2382-6339. ; 06:02
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Journal article (peer-reviewed)abstract
- This paper focusses on the recent tsunami in Indonesia and the factors led to the mass killing. We also discussed the failure of early warning systems, steps, methods, and technologies, in general, to improve the early warning systems in the future to mitigate the loss of lives and property during these impending disasters. We believe that this paper is timely as Indonesia has seen one of the worst tsunamis in recent years and the threat is still on. Hence, we stress the importance of improving and strengthening the existing early warning systems.
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| 3. |
- Thandlam, Venugopal, Mr. 1987-, et al.
(author)
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Evaluation of surface shortwave and longwave downwelling radiations over the global tropical oceans
- 2019
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In: SN Applied Sciences. - : Springer Science and Business Media LLC. - 2523-3963 .- 2523-3971. ; 1:10
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Journal article (peer-reviewed)abstract
- In the present study, daily downwelling shortwave (QS) and longwave radiation (QL) data from one satellite and two hybrid products have been evaluated using Global Tropical Moored Buoy Array during 2001–2009 in the tropical oceans. Daily satellite data are used from the Clouds and Earth’s Radiant Energy System (CERES) program. Data are obtained using Moderate Resolution Imaging Spectroradiometer (MODIS) (CM) aboard the Terra and Aqua satellites. Coordinated Ocean Research Experiments (CORE-II) and Tropical Flux data (TropFlux) are the other two hybrid products used in this study. The analysis shows that majority of QS observations as well as derived products lie in 200–300 Wm−2 range in all the three tropical oceans. Both QS and QL in all products overestimated the majority of the observations. Yet, they underestimated the lower (0–100 Wm−2) values in QS and higher (300–440 Wm−2) values in QL. Majority of the QL observations lie within 390–420 Wm−2 range, and CM slightly overestimated this observed distribution in the Pacific and the Atlantic Oceans. But, majority of the observations in the Indian Ocean lie within 420–450 Wm−2 range. This implies that the tropical Indian Ocean receives 30 Wm−2 more energy as compared to the tropical Pacific and the Atlantic in the form of downwelling longwave radiation. Daily observed QS shows dominant seasonal cycle over the central, the eastern Pacific and the eastern Atlantic. On the other hand, the western Pacific, the central Atlantic and the Indian Oceans show intraseasonal variations. All products show this variation with high root-mean-square error (RMSE) values (QS and QL) over the Indian Ocean than in the Pacific and the Atlantic Oceans. Downwelling radiation from CORE-II shows highest RMSE (for both QS and QL) with least correlation coefficient (CC), and TropFlux has lowest RMSE and highest CC among all products in all three tropical oceans. CM has intermediate values of standard deviation, CC and RMSE. These results are not seasonally dependent, since the seasonal statistics are consistent with seasonal changes. Assuming that the SST is only driven by the downwelling shortwave and longwave fluxes, the errors associated with monthly SST can be as large as 0.2–0.3 (0.1–0.2) °C associated with errors in QS (QL). Both QS and QL in CORE-II have lower spatial variability as compared to other datasets. QL in the tropical oceans shows seasonal spatial variability determined by intertropical convergence zone positions. This variability does not change significantly over the Pacific and the Atlantic Oceans. The summer and winter monsoon patterns in the Indian Ocean guide the QL variability. Opposite to QS, higher QL values have lower variability. Thus, this study aims at finding better radiation dataset to use in the numerical models and deduce that satellite data could be an alternative to existing reanalysis products.
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| 4. |
- Thandlam, Venugopal, Mr. 1987-, et al.
(author)
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Quantifying the role of antecedent Southwestern Indian Ocean capacitance on the summer monsoon rainfall variability over homogeneous regions of India
- 2023
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In: Scientific Reports. - : Springer Nature. - 2045-2322. ; 13:1
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Journal article (peer-reviewed)abstract
- The role of ocean variability is at a focal point in improving the weather and climate forecasts at different spatial and temporal scales. We study the effect of antecedent southwestern Indian Ocean mean sea level anomaly (MSLA) and sea surface temperature anomalies (SSTA) as a proxy to upper ocean heat capacitance on all India summer monsoon rainfall (AISMR) during 1993–2019. SSTA and MSLA over the southwestern Indian Ocean (SWIO) have been influenced by El Niño-Southern Oscillation (ENSO), the impact of ENSO-induced SWIO variability was low on rainfall variability over several homogeneous regions. Rainfall over northeast (NE) and North India (EI) has been modulated by ENSO-induced SSTA and MSLA over SWIO, thus effecting the total AISMR magnitude. The ENSO-induced changes in heat capacitance (SSTA and MSLA) over SWIO during antecedent months has less impact on west coast of India, central India and North India (NI) rainfall variability. The long-term trend in pre-monsoonal SSTA and MSLA over SWIO shows decreasing rainfall trend over NI, NE, and EI in the recent time. Furthermore, the cooler (warmer) anomaly over the western Indian Ocean affects rainfall variability adversely (favourably) due to the reversal of the wind pattern during the pre-monsoon period. While SSTA and MSLA are increasing in the SWIO, large-scale variability of these parameters during preceding winter and pre-monsoon months combined with surface winds could impact the inter-annual AISMR variability over homogeneous regions of India. Similarly, from an oceanic perspective, the antecedent heat capacitance over SWIO on an inter-annual time scale has been the key to the extreme monsoon rainfall variability.
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| 5. |
- Thandlam, Venugopal, Mr. 1987-, et al.
(author)
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Quantifying uncertainties in CERES/MODIS Downwelling radiation fluxes in the global tropical oceans
- 2023
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In: Ocean-Land-Atmosphere Research. - : American Association for the Advancement of Science (AAAS). - 2771-0378. ; 2
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Journal article (peer-reviewed)abstract
- The Clouds and the Earth's Radiant Energy System program, which uses the Moderate Resolution Imaging Spectroradiometer (CM), has been updated with the launch of new satellites and the availability of newly upgraded radiation data. The spatial and temporal variability of daily averaged synoptic 1-degree CM version 3 (CMv3) (old) and version 4 (CMv4) (new) downwelling shortwave (QS) and longwave radiation (QL) data in the global tropical oceans spanning 30°S–30°N from 2000 to 2017 is investigated. Daily in situ data from the Global Tropical Moored Buoy Array were used to validate the CM data from 2000 to 2015. When compared to CMv3, both QS and QL in CMv4 show significant improvements in bias, root-mean-square error, and standard deviations. Furthermore, a long-term trend analysis shows that QS has been increasing by 1 W m−2 per year in the Southern Hemisphere. In contrast, the Northern Hemisphere has a −0.7 W m−2 annual decreasing trend. QS and QL exhibit similar spatial trend patterns. However, in the Indian Ocean, Indo-Pacific warm pool region, and Southern Hemisphere, QL spatial patterns in CMv3 and CMv4 differ with an opposite trend (0.5 W m−2). These annual trends in QS and QL could cause the sea surface temperature to change by −0.2 to 0.3 °C per year in the tropical oceans. These results stress the importance of accurate radiative flux data, and CMv4 can be an alternative to reanalysis or other model-simulated data.
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