An ocean data assimilation system in the Indian Ocean and west Pacific Ocean

2015 ◽  
Vol 32 (11) ◽  
pp. 1460-1472 ◽  
Author(s):  
Changxiang Yan ◽  
Jiang Zhu ◽  
Jiping Xie
Keyword(s):  
Pacific Ocean ◽  
Indian Ocean ◽  
Data Assimilation ◽  
West Pacific ◽  
West Pacific Ocean ◽  
Ocean Data Assimilation ◽  
The Indian Ocean ◽  
Data Assimilation System ◽  
Assimilation System

Ensemble based regional ocean data assimilation system for the Indian Ocean: Implementation and evaluation

2019 ◽  
Vol 143 ◽  
pp. 101470
Author(s):  
Balaji Baduru ◽  
Biswamoy Paul ◽  
Deep Sankar Banerjee ◽  
Sivareddy Sanikommu ◽  
Arya Paul
Keyword(s):  
Indian Ocean ◽  
Data Assimilation ◽  
Ocean Data Assimilation ◽  
The Indian Ocean ◽  
Data Assimilation System ◽  
Assimilation System

Evaluation of the Global Ocean Data Assimilation System at INCOIS: The Tropical Indian Ocean

2013 ◽  
Vol 69 ◽  
pp. 123-135 ◽  
Author(s):  
M. Ravichandran ◽  
D. Behringer ◽  
S. Sivareddy ◽  
M.S. Girishkumar ◽  
Neethu Chacko ◽  
...  
Keyword(s):  
Indian Ocean ◽  
Data Assimilation ◽  
Tropical Indian Ocean ◽  
Global Ocean ◽  
Ocean Data Assimilation ◽  
Data Assimilation System ◽  
Assimilation System

The imprints of Indian Ocean Monsoon and West Pacific Monsoon on the spatial and temporal patterns of forest fires in Yunnan, Southwest China

2020 ◽  
Author(s):  
Zehao Shen ◽  
Lingxiao Ying
Keyword(s):  
Pacific Ocean ◽  
Indian Ocean ◽  
Forest Fires ◽  
Southwest China ◽  
Fire Regime ◽  
Warm Pool ◽  
Burnt Area ◽  
West Pacific ◽  
West Pacific Ocean ◽  
The Indian Ocean

<p>Wildfire is a widespread natural disturbance and internal ecological process, critical in shaping ecosystem structure and function across scales. The Indo-China Peninsula and its surrounding areas is a global hotspot of fires initiated by natural and anthropogenic drives. Studies indicated that both the Indian Ocean monsoon and the Pacific Ocean monsoon significantly influence the climate in this region, and the precipitation seasonality regulated by monsoon is a critical driver of prevalent wildfires. However, the relative importance of the two monsoon systems on the terrestrial ecosystems in this region, specifically via their effects on vegetation burnings, has rarely been estimated. Yunnan Province in Southwest China comprises the northeast corner of this region, and shares the intensive impacts of the two monsoon systems in terms of the characteristics of climate and wildfire activity. The present study integrated multiple data sources of the forest fires during 2003~2015 in Yunnan, detected the spatial and interannual variations of the fire occurrence and burnt area, and related the fire activities with the dynamics of the Indian Ocean Monsoon (IOM) and West Pacific Ocean Monsoon (WPOM). The monthly time sequence analysis of the forest fire events in Yunnan Province showed that, a significant, synchronous teleconnection can be detected between the forest fire dynamics and Indian Ocean Warm Pool intensity, while an opposite temporal pace was revealed for the West Pacific Ocean Warm Pool. During the study period, IOM dominated the wildfire seasonality in Yunnan in eight years, in contrast to the dominance of WPOM in five years. A borderline can roughly divides Yunnan into the west and the east climatic regions, which were dominated by IOM and WPOM, respectively. Humidity and the forest area ratio were the dominant factors for the mean annual fire number and burnt area in the IOM affected region; but in the WPOM region, rural road density was the most important factor. It was suggested that the fire regime of the IOM region was climate-driven for fire number and fuel-driven for burnt area, while the fire regime was dominant with human activities in the WPOM region in Yunnan</p>

scholarly journals Impact of Multiple Altimeter Data and Mean Dynamic Topography in a Global Analysis and Forecasting System

2019 ◽  
Vol 36 (7) ◽  
pp. 1255-1266 ◽  
Author(s):  
Mathieu Hamon ◽  
Eric Greiner ◽  
Pierre-Yves Le Traon ◽  
Elisabeth Remy
Keyword(s):  
Data Assimilation ◽  
Sea Surface ◽  
Global Ocean ◽  
Altimeter Data ◽  
Dynamic Topography ◽  
Mean Dynamic Topography ◽  
Ocean Data Assimilation ◽  
The Mean ◽  
Data Assimilation System ◽  
Assimilation System

AbstractSatellite altimetry is one of the main sources of information used to constrain global ocean analysis and forecasting systems. In addition to in situ vertical temperature and salinity profiles and sea surface temperature (SST) data, sea level anomalies (SLA) from multiple altimeters are assimilated through the knowledge of a surface reference, the mean dynamic topography (MDT). The quality of analyses and forecasts mainly depends on the availability of SLA observations and on the accuracy of the MDT. A series of observing system evaluations (OSEs) were conducted to assess the relative importance of the number of assimilated altimeters and the accuracy of the MDT in a Mercator Ocean global 1/4° ocean data assimilation system. Dedicated tools were used to quantify impacts on analyzed and forecast sea surface height and temperature/salinity in deeper layers. The study shows that a constellation of four altimeters associated with a precise MDT is required to adequately describe and predict upper-ocean circulation in a global 1/4° ocean data assimilation system. Compared to a one-altimeter configuration, a four-altimeter configuration reduces the mean forecast error by about 30%, but the reduction can reach more than 80% in western boundary current (WBC) regions. The use of the most recent MDT updates improves the accuracy of analyses and forecasts to the same extent as assimilating a fourth altimeter.

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