Robust extension of the simple sea-surface irradiance model to handle cloudy conditions for the global ocean using satellite remote sensing data

Sundararaman, Harish Kumar Kashtan; Shanmugam, Palanisamy; Nagamani, P. V.

doi:10.1016/j.asr.2022.10.009

Cited by 3 publications

(3 citation statements)

References 37 publications

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“…Table 4 lists remote sensing, weather station, geospatial, historical, sensor, radar, social media and mobile phone data that may help anticipate natural catastrophes by providing information on atmospheric conditions, land use, population movements, and other characteristics (Jiang et al, 2022). These data may be used in the development of models that have the capability of predicting the likelihood of natural catastrophes such as droughts, heatwaves, and wildfires (Kashtan Sundararaman et al, 2023). And also it might include past disasters' frequency, intensity, location, and environmental causes (Zhang et al, 2022a).…”

Section: Data Sources For Natural Disaster Forecastingmentioning

confidence: 99%

“…Remote sensing data Data collected by satellites or other remote sensors that provide information on land, ocean, and atmospheric conditions (Kashtan Sundararaman et al, 2023) Weather station data Data collected from ground-based weather stations that measure temperature, humidity, pressure, wind speed, and other weather variables (Han et al, 2023) Radar data Data collected by radar systems that can detect precipitation, wind speed, and direction (Wang et al, 2023a) Social media data Data collected from social media platforms that can provide real-time information on natural disasters and their impacts (Platania et al, 2022) Geospatial data Data that includes information on terrain, land use, population density, and infrastructure (Stokes and Seto, 2019) Historical data Data from past natural disasters that can be used to train machine learning models for forecasting future events (Jiang et al, 2022) Sensor data Data collected by sensors deployed in disaster-prone areas that can measure seismic activity, water levels, and other variables (Wang et al, 2023b) Mobile phone data Data collected from mobile phone networks that can provide information on population movements and density during disasters (Yabe et al, 2022) Frontiers in Environmental Science frontiersin.org 4 Benefits of using MLA's in disaster preparedness and response…”

Section: Data Source Descriptionmentioning

confidence: 99%

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Leveraging machine learning algorithms for improved disaster preparedness and response through accurate weather pattern and natural disaster prediction

Jain,

Dhupper,

Shrivastava

et al. 2023

Front. Environ. Sci.

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Globally, communities and governments face growing challenges from an increase in natural disasters and worsening weather extremes. Precision in disaster preparation is crucial in responding to these issues. The revolutionary influence that machine learning algorithms have in strengthening catastrophe preparation and response systems is thoroughly explored in this paper. Beyond a basic summary, the findings of our study are striking and demonstrate the sophisticated powers of machine learning in forecasting a variety of weather patterns and anticipating a range of natural catastrophes, including heat waves, droughts, floods, hurricanes, and more. We get practical insights into the complexities of machine learning applications, which support the enhanced effectiveness of predictive models in disaster preparedness. The paper not only explains the theoretical foundations but also presents practical proof of the significant benefits that machine learning algorithms provide. As a result, our results open the door for governments, businesses, and people to make wise decisions. These accurate predictions of natural catastrophes and emerging weather patterns may be used to implement pre-emptive actions, eventually saving lives and reducing the severity of the damage.

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Section: Data Sources For Natural Disaster Forecastingmentioning

confidence: 99%

Section: Data Source Descriptionmentioning

confidence: 99%

Leveraging machine learning algorithms for improved disaster preparedness and response through accurate weather pattern and natural disaster prediction

Jain,

Dhupper,

Shrivastava

et al. 2023

Front. Environ. Sci.

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“…Performance assessment of the P b opt , DIPP and DRPP models was done based on the most common statistical metrics such as mean relative error (MRE), Pearson-correlation coefficient (PCC), mean absolute error (MAE), root mean square error (RMSE) and mean net bias (MNB) [33], [61]. These metrics are defined as…”

Section: Performance Assessmentmentioning

confidence: 99%

Depth-Resolved and Depth-Integrated Primary Productivity Estimates From In-Situ and Satellite Data in the Global Ocean

Sundararaman

Shanmugam

2023

IEEE Access

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Estimates of marine phytoplankton primary productivity (PP) from satellite remote sensing observations are potentially used to assess global carbon budgets, biogeochemical response, pools and fluxes of carbon and its spatial and temporal variations due to ocean-atmospheric oscillations under climate change. According to the recent studies, satellite-based vertically integrated global PP products have significant uncertainties due to the limitations of the past models, challenges in deriving the appropriate parameters that account for the variation of PP with seasons and provinces, and specify the vertical structure of phytoplankton biomass from satellite observation data and scarcity of in-situ vertical profile data. To overcome these issues, we developed a depth-resolved and depth-integrated model to estimate PP for global oceanic waters. It comprises the depth-resolved primary productivity (DRPP) and satellitebased depth-integrated primary productivity (DIPP) parameterizations to accurately estimate the magnitude and variability of PP in the global ocean. These parameterization algorithms require knowledge of the relative chlorophyll-specific carbon fixation rate (P b rel ) and maximum chlorophyll-specific carbon fixation rate within the water-column P b opt in order to derive the spatial and temporal patterns of DRPP and DIPP. To estimate the chlorophyll-specific maximum rate of carbon fixation at a depth equal to z (P b z ), two different P b rel algorithms were developed based on the relative values of i) the subsurface photosynthetically available radiation (PAR rel ) and ii) the optical depth at depth z (ζ z ). Furthermore, a sensitivity analysis was conducted to understand the effect of sea-surface temperature (SST), sea-surface chlorophyll concentration (SCHL) and sea-surface photosynthetically available radiation (SPAR) on the photo-physiological parameter P b opt . These physical, biological and optical parameters were used to obtain accurate P b opt estimates. The model based on the SST-SCHL-SPAR (P b opt (SSTCP)) produced more accurate P b opt estimates than the global Vertically Generalised Productivity Model (P b opt (VGPM )). Comparison of the model results with in-situ measurement data demonstrated that the ζ z -based DRPP algorithm (DRPP(ζ z )) yields more accurate results than the PAR rel -based DRPP algorithm (DRPP(PAR rel )). This study also investigates the spatial and temporal patterns in MODIS-Aqua-derived P b opt and DIPP products and the impacts of climate-driven perturbations on the global ocean PP due to the La Niña and El Niño phenomenon during 2010 and 2015. INDEX TERMS Primary productivity (PP), maximum carbon fixation rate, depth-resolved PP, depthintegrated PP, ocean color remote sensing, global ocean.

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Estimates of the global ocean surface dissolved oxygen and macronutrients from satellite data

Kashtan Sundararaman,

Shanmugam

2024

Remote Sensing of Environment

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Robust extension of the simple sea-surface irradiance model to handle cloudy conditions for the global ocean using satellite remote sensing data

Cited by 3 publications

References 37 publications

Leveraging machine learning algorithms for improved disaster preparedness and response through accurate weather pattern and natural disaster prediction

Leveraging machine learning algorithms for improved disaster preparedness and response through accurate weather pattern and natural disaster prediction

Depth-Resolved and Depth-Integrated Primary Productivity Estimates From In-Situ and Satellite Data in the Global Ocean

Estimates of the global ocean surface dissolved oxygen and macronutrients from satellite data

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