| FY2011 Annual Report of Data Assimilation and Predictability Studies for Improving Tropical Cyclone Intensity Forecasts |
30 Sep 2011 |
10 pages |
| Authors:
Takemasa Miyoshi; Craig Bishop; Eugenia Kalnay; Kayo Ide; MARYLAND UNIV COLLEGE PARK DEPT OF ATMOSPHERIC AND OCEANIC SCIENCE
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 | This project aims to understand and improve the forecast of Tropical Cyclone (TC) lifecycle evolution and intensity, focusing on both large-scale environment and mesoscale phenomena in the TC system, which are major components responsible for intensity change. Two major challenges in TC intensity forecasting are the general lack of observations in the vicinity of TCs and the adaptive representation of the forecast error covariance. This project attempts to address both ... |
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| Data Assimilation and Predictability Studies for Improving Tropical Cyclone Intensity Forecasts |
30 Sep 2011 |
10 pages |
| Authors:
Takemasa Miyoshi; Eugenia Kalnay; Kayo Ide; Craig Bishop; MARYLAND UNIV COLLEGE PARK DEPT OF ATMOSPHERIC AND OCEANIC SCIENCE
|
| This project aims to understand and improve the forecast of Tropical Cyclone (TC) lifecycle evolution and intensity, focusing on both large-scale environment and mesoscale phenomena in the TC system, which are major components responsible for intensity change. Two major challenges in TC intensity forecasting are the general lack of observations in the vicinity of TCs and the adaptive representation of the forecast error covariance. This project attempts to address both ... |
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| Data Assimilation and Predictability Studies for Improving Tropical Cyclone Intensity Forecasts |
Jan 2010 |
9 pages |
| Authors:
Takemasa Miyoshi; Eugenia Kalnay; Kayo Ide; Craig Bishop; MARYLAND UNIV COLLEGE PARK DEPT OF ATMOSPHERIC AND OCEANIC SCIENCE
|
| This project aims to understand and improve the forecast of Tropical Cyclone (TC) lifecycle evolution and intensity, focusing on both large-scale environment and mesoscale phenomena in the TC system, which are major components responsible for intensity change. Two major challenges in TC intensity forecasting are the general lack of observations in the vicinity of TCs and the adaptive representation of the forecast error covariance. This project attempts to address both ... |
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| Multi-Model Ensemble Approaches to Data Assimilation Using the 4D-Local Ensemble Transform Kalman Filter |
Jan 2010 |
3 pages |
| Authors:
Kayo Ide; MARYLAND UNIV COLLEGE PARK
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| Uncertainties in the numerical prediction using a computational model of a physical system arise from two primary sources: i) errors within the model itself; and ii) imperfect knowledge of (a) the initial conditions to start the model and (b) boundary conditions and the forcing that is required to run the model. One way to examine these uncertainties is the multi-model approach, i.e., to compare results from multiple models. However, the ... |
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| Balance and Ensemble Kalman Filter Localization Techniques |
2010 |
38 pages |
| Authors:
Steven J. Greybush; Eugenia Kalnay; Takemasa Miyoshi; Kayo Ide; Brian R. Hunt; MARYLAND UNIV COLLEGE PARK
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| In Ensemble Kalman Filter data assimilation, localization modifies the error covariance matrices to suppress the influence of distant observations, removing spurious long distance correlations. In addition to allowing efficient parallel implementation, this takes advantage of the atmosphere's lower dimensionality in local regions. There are two primary methods for localization. In B-localization, the background error covariance matrix elements are reduced by a Schur product so that correlations between grid points that ... |
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| An Operational Technology for Assimilating Lagrangian Data Based on Dynamical Systems Techniques |
30 Sep 2008 |
6 pages |
| Authors:
Kayo Ide; CALIFORNIA UNIV LOS ANGELES INST OF GEOPHYSICS AND PLANETARY PHYSICS
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| Much data in the ocean is Lagrangian in nature. Its full use in ocean prediction could advance significantly the Navy's ability both to predict ocean conditions and to assess the optimal strategies for deploying Lagrangian observational devices and their associated sensors. The development of a fully operational, integrated data assimilation scheme will afford such a naval predictive capacity in fixed ocean regions that can be comprehensively surveyed by Lagrangian measuring ... |
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