Updated Scheme for Data Assimilation of Adaptive Observation And Application for Typhoon Forecasts Over the Western North Pacific

2022 ◽  
Author(s):  
Siqi Chen ◽  
Yu Zhang ◽  
Jianjun Xu ◽  
Guiling Ye ◽  
Zebin Lu
Keyword(s):  
Data Assimilation ◽  
North Pacific ◽  
Western North Pacific ◽  
Adaptive Observation

scholarly journals Biological data assimilation for parameter estimation of a phytoplankton functional type model for the western North Pacific

2017 ◽  
Author(s):  
Yasuhiro Hoshiba ◽  
Takafumi Hirata ◽  
Masahito Shigemitsu ◽  
Hideyuki Nakano ◽  
Taketo Hashioka ◽  
...  
Keyword(s):  
Data Assimilation ◽  
North Pacific ◽  
Western North Pacific ◽  
Marine Ecosystem ◽  
Ecosystem Model ◽  
Biological Data ◽  
Physiological Parameters ◽  
Biological Parameters ◽  
Ecosystem Models ◽  
Marine Ecosystem Model

Abstract. Ecosystem models are used to understand ecosystem dynamics and ocean biogeochemical cycles and require optimum physiological parameters to best represent biological behaviours. These physiological parameters are often tuned up empirically, while ecosystem models have evolved to increase the number of physiological parameters. We developed a three-dimensional (3D) lower trophic level marine ecosystem model known as the Nitrogen, Silicon and Iron regulated Marine Ecosystem Model (NSI-MEM) and employed biological data assimilation using a micro-genetic algorithm to estimate 23 physiological parameters for two phytoplankton functional types in the western North Pacific. The approach used a one-dimensional emulator that referenced satellite data. The 3D NSI-MEM with biological parameters optimised by assimilation improved the timing of a modelled plankton bloom in the subarctic and subtropical regions compared to models without data assimilation. Furthermore, the model was able to simulate not only surface concentrations of phytoplankton but also subsurface maximum concentrations of phytoplankton. Our results show that surface data assimilation of biological parameters from two observatory stations benefits the representation of vertical plankton distribution in the western North Pacific.

scholarly journals Impact of Enhanced Satellite-Derived Atmospheric Motion Vector Observations on Numerical Tropical Cyclone Track Forecasts in the Western North Pacific during TPARC/TCS-08

2011 ◽  
Vol 50 (11) ◽  
pp. 2309-2318 ◽  
Author(s):  
Howard Berger ◽  
Rolf Langland ◽  
Christopher S. Velden ◽  
Carolyn A. Reynolds ◽  
Patricia M. Pauley
Keyword(s):  
Tropical Cyclone ◽  
Data Assimilation ◽  
North Pacific ◽  
Western North Pacific ◽  
Forecast Errors ◽  
Cyclone Track ◽  
Tropical Cyclone Track ◽  
Rapid Scan ◽  
Atmospheric Motion ◽  
The Impact

AbstractEnhanced atmospheric motion vectors (AMVs) produced from the geostationary Multifunctional Transport Satellite (MTSAT) are assimilated into the U.S. Navy Operational Global Atmospheric Prediction System (NOGAPS) to evaluate the impact of these observations on tropical cyclone track forecasts during the simultaneous western North Pacific Ocean Observing System Research and Predictability Experiment (THORPEX) Pacific Asian Regional Campaign (TPARC) and the Tropical Cyclone Structure—2008 (TCS-08) field experiments. Four-dimensional data assimilation is employed to take advantage of experimental high-resolution (space and time) AMVs produced for the field campaigns by the Cooperative Institute for Meteorological Satellite Studies. Two enhanced AMV datasets are considered: 1) extended periods produced at hourly intervals over a large western North Pacific domain using routinely available MTSAT imagery and 2) limited periods over a smaller storm-centered domain produced using special MTSAT rapid-scan imagery. Most of the locally impacted forecast cases involve Typhoons Sinlaku and Hagupit, although other storms are also examined. On average, the continuous assimilation of the hourly AMVs reduces the NOGAPS tropical cyclone track forecast errors—in particular, for forecasts longer than 72 h. It is shown that the AMVs can improve the environmental flow analyses that may be influencing the tropical cyclone tracks. Adding rapid-scan AMV observations further reduces the NOGAPS forecast errors. In addition to their benefit in traditional data assimilation, the enhanced AMVs show promise as a potential resource for advanced objective data-targeting methods.

scholarly journals Argo data assimilation and its effect on climate state estimation and forecasting in the western North Pacific using a coupled model

2015 ◽  
Vol 120 (4) ◽  
pp. 2636-2654 ◽  
Author(s):  
Shiro Nishikawa ◽  
Yoichi Ishikawa ◽  
Shuhei Masuda ◽  
Yoshihisa Hiyoshi ◽  
Yuji Sasaki ◽  
...  
Keyword(s):  
Data Assimilation ◽  
State Estimation ◽  
North Pacific ◽  
Western North Pacific ◽  
Coupled Model ◽  
Argo Data

scholarly journals Biological data assimilation for parameter estimation of a phytoplankton functional type model for the western North Pacific

Ocean Science ◽  
2018 ◽  
Vol 14 (3) ◽  
pp. 371-386 ◽  
Author(s):  
Yasuhiro Hoshiba ◽  
Takafumi Hirata ◽  
Masahito Shigemitsu ◽  
Hideyuki Nakano ◽  
Taketo Hashioka ◽  
...  
Keyword(s):  
Data Assimilation ◽  
North Pacific ◽  
Western North Pacific ◽  
Marine Ecosystem ◽  
Ecosystem Model ◽  
Biological Data ◽  
Physiological Parameters ◽  
Ecosystem Dynamics ◽  
Ecosystem Models ◽  
Marine Ecosystem Model

Abstract. Ecosystem models are used to understand ecosystem dynamics and ocean biogeochemical cycles and require optimum physiological parameters to best represent biological behaviours. These physiological parameters are often tuned up empirically, while ecosystem models have evolved to increase the number of physiological parameters. We developed a three-dimensional (3-D) lower-trophic-level marine ecosystem model known as the Nitrogen, Silicon and Iron regulated Marine Ecosystem Model (NSI-MEM) and employed biological data assimilation using a micro-genetic algorithm to estimate 23 physiological parameters for two phytoplankton functional types in the western North Pacific. The estimation of the parameters was based on a one-dimensional simulation that referenced satellite data for constraining the physiological parameters. The 3-D NSI-MEM optimized by the data assimilation improved the timing of a modelled plankton bloom in the subarctic and subtropical regions compared to the model without data assimilation. Furthermore, the model was able to improve not only surface concentrations of phytoplankton but also their subsurface maximum concentrations. Our results showed that surface data assimilation of physiological parameters from two contrasting observatory stations benefits the representation of vertical plankton distribution in the western North Pacific.

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