scholarly journals Developing the 3-Dimensional Flow Model Used in the Data Assimilation System and Verification of Ocean Sequestration Technique of Carbon Dioxide.

2002 ◽  
pp. 67-79
Author(s):  
Kazuyuki HASEGAWA ◽  
Akira WADA ◽  
Kenji TAKANO ◽  
Ryosuke NISHIMURA
Keyword(s):  
Carbon Dioxide ◽  
Data Assimilation ◽  
Flow Model ◽  
3 Dimensional ◽  
Dimensional Flow ◽  
Ocean Sequestration ◽  
Data Assimilation System ◽  
Assimilation System

scholarly journals “Big Data Assimilation” Revolutionizing Severe Weather Prediction

2016 ◽  
Vol 97 (8) ◽  
pp. 1347-1354 ◽  
Author(s):  
Takemasa Miyoshi ◽  
Masaru Kunii ◽  
Juan Ruiz ◽  
Guo-Yuan Lien ◽  
Shinsuke Satoh ◽  
...  
Keyword(s):  
Big Data ◽  
Data Assimilation ◽  
Weather Prediction ◽  
Severe Weather ◽  
Weather System ◽  
3 Dimensional ◽  
Parallel Simulations ◽  
Convective Weather ◽  
Data Assimilation System ◽  
Assimilation System

Abstract Sudden local severe weather is a threat, and we explore what the highest-end supercomputing and sensing technologies can do to address this challenge. Here we show that using the Japanese flagship “K” supercomputer, we can synergistically integrate “big simulations” of 100 parallel simulations of a convective weather system at 100-m grid spacing and “big data” from the next-generation phased array weather radar that produces a high-resolution 3-dimensional rain distribution every 30 s—two orders of magnitude more data than the currently used parabolic-antenna radar. This “big data assimilation” system refreshes 30-min forecasts every 30 s, 120 times more rapidly than the typical hourly updated systems operated at the world’s weather prediction centers. A real high-impact weather case study shows encouraging results of the 30-s-update big data assimilation system.

scholarly journals CTDAS-Lagrange v1.0: a high-resolution data assimilation system for regional carbon dioxide observations

2018 ◽  
Vol 11 (8) ◽  
pp. 3515-3536 ◽  
Author(s):  
Wei He ◽  
Ivar R. van der Velde ◽  
Arlyn E. Andrews ◽  
Colm Sweeney ◽  
John Miller ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
High Resolution ◽  
Data Assimilation ◽  
Transport Model ◽  
Adjustment Method ◽  
Sensitivity Tests ◽  
Lagrange System ◽  
Data Assimilation System ◽  
Flux Adjustment ◽  
Assimilation System

Abstract. We have implemented a regional carbon dioxide data assimilation system based on the CarbonTracker Data Assimilation Shell (CTDAS) and a high-resolution Lagrangian transport model, the Stochastic Time-Inverted Lagrangian Transport model driven by the Weather Forecast and Research meteorological fields (WRF-STILT). With this system, named CTDAS-Lagrange, we simultaneously optimize terrestrial biosphere fluxes and four parameters that adjust the lateral boundary conditions (BCs) against CO2 observations from the NOAA ESRL North America tall tower and aircraft programmable flask packages (PFPs) sampling program. Least-squares optimization is performed with a time-stepping ensemble Kalman smoother, over a time window of 10 days and assimilating sequentially a time series of observations. Because the WRF-STILT footprints are pre-computed, it is computationally efficient to run the CTDAS-Lagrange system. To estimate the uncertainties in the optimized fluxes from the system, we performed sensitivity tests with various a priori biosphere fluxes (SiBCASA, SiB3, CT2013B) and BCs (optimized mole fraction fields from CT2013B and CTE2014, and an empirical dataset derived from aircraft observations), as well as with a variety of choices on the ways that fluxes are adjusted (additive or multiplicative), covariance length scales, biosphere flux covariances, BC parameter uncertainties, and model–data mismatches. In pseudo-data experiments, we show that in our implementation the additive flux adjustment method is more flexible in optimizing net ecosystem exchange (NEE) than the multiplicative flux adjustment method, and our sensitivity tests with real observations show that the CTDAS-Lagrange system has the ability to correct for the potential biases in the lateral BCs and to resolve large biases in the prior biosphere fluxes. Using real observations, we have derived a range of estimates for the optimized carbon fluxes from a series of sensitivity tests, which places the North American carbon sink for the year 2010 in a range from −0.92 to −1.26 PgC yr−1. This is comparable to the TM5-based estimates of CarbonTracker (version CT2016, -0.91±1.10 PgC yr−1) and CarbonTracker Europe (version CTE2016, -0.91±0.31 PgC yr−1). We conclude that CTDAS-Lagrange can offer a versatile and computationally attractive alternative to these global systems for regional estimates of carbon fluxes, which can take advantage of high-resolution Lagrangian footprints that are increasingly easy to obtain.

scholarly journals DASSO: a data assimilation system for the Southern Ocean that utilizes both sea-ice concentration and thickness observations

2021 ◽  
pp. 1-6
Author(s):  
Hao Luo ◽  
Qinghua Yang ◽  
Longjiang Mu ◽  
Xiangshan Tian-Kunze ◽  
Lars Nerger ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Southern Ocean ◽  
Sea Ice ◽  
Coupled Model ◽  
Sea Ice Concentration ◽  
Model Free ◽  
Antarctic Sea Ice ◽  
Ice Concentration ◽  
Data Assimilation System ◽  
Assimilation System

Abstract To improve Antarctic sea-ice simulations and estimations, an ensemble-based Data Assimilation System for the Southern Ocean (DASSO) was developed based on a regional sea ice–ocean coupled model, which assimilates sea-ice thickness (SIT) together with sea-ice concentration (SIC) derived from satellites. To validate the performance of DASSO, experiments were conducted from 15 April to 14 October 2016. Generally, assimilating SIC and SIT can suppress the overestimation of sea ice in the model-free run. Besides considering uncertainties in the operational atmospheric forcing data, a covariance inflation procedure in data assimilation further improves the simulation of Antarctic sea ice, especially SIT. The results demonstrate the effectiveness of assimilating sea-ice observations in reconstructing the state of Antarctic sea ice, but also highlight the necessity of more reasonable error estimation for the background as well as the observation.

scholarly journals Use of Microwave Radiances from Metop-C and Fengyun-3 C/D Satellites for a Northern European Limited-area Data Assimilation System

2021 ◽  
Author(s):  
Magnus Lindskog ◽  
Adam Dybbroe ◽  
Roger Randriamampianina
Keyword(s):  
Data Assimilation ◽  
Positive Impact ◽  
Weather Prediction ◽  
Implementation Process ◽  
Satellite System ◽  
Limited Area ◽  
Global Navigation Satellite ◽  
Microwave Radiances ◽  
Data Assimilation System ◽  
Assimilation System

AbstractMetCoOp is a Nordic collaboration on operational Numerical Weather Prediction based on a common limited-area km-scale ensemble system. The initial states are produced using a 3-dimensional variational data assimilation scheme utilizing a large amount of observations from conventional in-situ measurements, weather radars, global navigation satellite system, advanced scatterometer data and satellite radiances from various satellite platforms. A version of the forecasting system which is aimed for future operations has been prepared for an enhanced assimilation of microwave radiances. This enhanced data assimilation system will use radiances from the Microwave Humidity Sounder, the Advanced Microwave Sounding Unit-A and the Micro-Wave Humidity Sounder-2 instruments on-board the Metop-C and Fengyun-3 C/D polar orbiting satellites. The implementation process includes channel selection, set-up of an adaptive bias correction procedure, and careful monitoring of data usage and quality control of observations. The benefit of the additional microwave observations in terms of data coverage and impact on analyses, as derived using the degree of freedom of signal approach, is demonstrated. A positive impact on forecast quality is shown, and the effect on the precipitation for a case study is examined. Finally, the role of enhanced data assimilation techniques and adaptions towards nowcasting are discussed.

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