Results 1 - 10
of
19
Ensemble Optimal Interpolation: multivariate properties
- in the Gulf of Mexico, Tellus
"... (Manuscript submitted on the 26/09/2007) High-resolution models can reproduce mesoscale dynamics and the variability in the Gulf of Mexico (GOM), but cannot provide accurate locations of currents without data assimilation (DA). We use the computationally cheap Ensemble Optimal Inter-polation (EnOI) ..."
Abstract
-
Cited by 14 (3 self)
- Add to MetaCart
(Show Context)
(Manuscript submitted on the 26/09/2007) High-resolution models can reproduce mesoscale dynamics and the variability in the Gulf of Mexico (GOM), but cannot provide accurate locations of currents without data assimilation (DA). We use the computationally cheap Ensemble Optimal Inter-polation (EnOI) in conjunction with the HYCOM model for assimilating altimetry data. The covariance matrix extracted from a historical ensemble, is 3-dimensional and multivariate. This study shows that the multivariate correlations with Sea Level Anomaly are coherent with the known dynamics of the area at two locations: the cen-tral part of the GOM, and the upper slope of the northern shelf. The correlations in the first location are suitable for an eddy forecasting system, but the correlations in the second location show some limitations due to seasonal variability. The multivariate relationships between variables are reasonably linear, as assumed by the EnOI. Our DA set-up produces little noise that is dampened within two days, when the model is pulled strongly towards observations. Part of it is caused by density perturbations in the isopycnal layers, or artificial caballing. The DA system is demonstrated for a realistic case of Loop Current eddy shedding, namely Eddy Yankee (2006). 1
Data Assimilation of Coupled Fluid Flow and Geomechanics via Ensemble Kalman Filter
, 2009
"... ..."
Assimilation of Sea Level Observations for Multi-Decadal Regional Ocean Model Simulations for the North Sea Assimilation of Sea Level Observations for Multi-Decadal Regional Ocean Model
"... (Institute for Coastal Research) ..."
Seasonal-to-decadal predictions with the ensemble Kalman filter and the Norwegian Earth System Model: a twin experiment
, 2013
"... Here, we firstly demonstrate the potential of an advanced flow dependent data assimilation method for performing seasonal-to-decadal prediction and secondly, reassess the use of sea surface temperature (SST) for initialisation of these forecasts. We use the Norwegian Climate Prediction Model (NorCPM ..."
Abstract
- Add to MetaCart
(Show Context)
Here, we firstly demonstrate the potential of an advanced flow dependent data assimilation method for performing seasonal-to-decadal prediction and secondly, reassess the use of sea surface temperature (SST) for initialisation of these forecasts. We use the Norwegian Climate Prediction Model (NorCPM), which is based on the Norwegian Earth System Model (NorESM) and uses the deterministic ensemble Kalman filter to assimilate observations. NorESM is a fully coupled system based on the Community Earth System Model version 1, which includes an ocean, an atmosphere, a sea ice and a land model. A numerically efficient coarse resolution version of NorESM is used. We employ a twin experiment methodology to provide an upper estimate of predictability in our model framework (i.e. without considering model bias) of NorCPM that assimilates synthetic monthly SST data (EnKF-SST). The accuracy of EnKF-SST is compared to an unconstrained ensemble run (FREE) and ensemble predictions made with near perfect (i.e. microscopic SST perturbation) initial conditions (PERFECT). We perform 10 cycles, each consisting of a 10-yr assimilation phase, followed by a 10-yr prediction. The results indicate that EnKF-SST improves sea level, ice concentration, 2 m atmospheric temperature, precipitation and 3-D hydrography compared to FREE. Improvements for the hydrography are largest near the surface and are retained for longer periods at depth. Benefits in salinity are retained
Author manuscript, published in "8th international Geostatistics Congress, Santiago: Chili (2008)" ADDITIVITY, METALLURGICAL RECOVERY, AND GRADE
, 2013
"... Proper estimation of the metallurgical recovery is very important for the assessment of the economical value of a mining business. As this quantity is nonadditive, it is not possible to model its spatial variability or to perform its estimation directly. In the present work, we first recall that add ..."
Abstract
- Add to MetaCart
Proper estimation of the metallurgical recovery is very important for the assessment of the economical value of a mining business. As this quantity is nonadditive, it is not possible to model its spatial variability or to perform its estimation directly. In the present work, we first recall that additivity can concern the intrinsic nature of point-support quantities, and not only the usual support effect encountered in data sets mixing samples with different sizes. Then, on a practical study based on sulphide copper data, we show when non-additive practices have an impact on the results and when they do not. Finally, we open a discussion where we explain how to proceed to assess metallurgical recovery.
Towards a Dynamic Data Driven Application System for Wildfire Simulation
"... Abstract. We report on an ongoing effort to build a Dynamic Data Driven Application System (DDDAS) for short-range forecast of wildfire behavior from real-time weather data, images, and sensor streams. The system should change the forecast when new data is received. The basic approach is to encapsul ..."
Abstract
- Add to MetaCart
(Show Context)
Abstract. We report on an ongoing effort to build a Dynamic Data Driven Application System (DDDAS) for short-range forecast of wildfire behavior from real-time weather data, images, and sensor streams. The system should change the forecast when new data is received. The basic approach is to encapsulate the model code and use an ensemble Kalman filter in time-space. Several variants of the ensemble Kalman filter are presented, for out-of-sequence data assimilation, hidden model states, and highly nonlinear problems. Parallel implementation and web-based visualization are also discussed. 1
