scholarly journals Promises in Air‐Sea Fully Coupled Data Assimilation for Future Hurricane Prediction

2018 ◽  
Vol 45 (23) ◽  
Author(s):  
Fuqing Zhang ◽  
Kerry Emanuel
Keyword(s):  
Data Assimilation ◽  
Hurricane Prediction ◽  
Coupled Data Assimilation ◽  
Fully Coupled

scholarly journals Impact of an observational time window on coupled data assimilation: simulation with a simple climate model

2017 ◽  
Vol 24 (4) ◽  
pp. 681-694 ◽  
Author(s):  
Yuxin Zhao ◽  
Xiong Deng ◽  
Shaoqing Zhang ◽  
Zhengyu Liu ◽  
Chang Liu ◽  
...  
Keyword(s):  
Data Assimilation ◽  
General Circulation ◽  
Climate Model ◽  
Time Window ◽  
Coupled Model ◽  
Circulation Model ◽  
Scale Interactions ◽  
Climate Signals ◽  
Coupled Data Assimilation ◽  
Climate Analysis

Abstract. Climate signals are the results of interactions of multiple timescale media such as the atmosphere and ocean in the coupled earth system. Coupled data assimilation (CDA) pursues balanced and coherent climate analysis and prediction initialization by incorporating observations from multiple media into a coupled model. In practice, an observational time window (OTW) is usually used to collect measured data for an assimilation cycle to increase observational samples that are sequentially assimilated with their original error scales. Given different timescales of characteristic variability in different media, what are the optimal OTWs for the coupled media so that climate signals can be most accurately recovered by CDA? With a simple coupled model that simulates typical scale interactions in the climate system and twin CDA experiments, we address this issue here. Results show that in each coupled medium, an optimal OTW can provide maximal observational information that best fits the characteristic variability of the medium during the data blending process. Maintaining correct scale interactions, the resulting CDA improves the analysis of climate signals greatly. These simple model results provide a guideline for when the real observations are assimilated into a coupled general circulation model for improving climate analysis and prediction initialization by accurately recovering important characteristic variability such as sub-diurnal in the atmosphere and diurnal in the ocean.

scholarly journals Consequences of different air-sea feedbacks on ocean using MITgcm and MERRA-2 forcing: Implications for coupled data assimilation systems

2018 ◽  
Vol 132 ◽  
pp. 91-111 ◽  
Author(s):  
Ehud Strobach ◽  
Andrea Molod ◽  
Gael Forget ◽  
Jean-Michel Campin ◽  
Chris Hill ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Coupled Data Assimilation

scholarly journals Assessment of ocean analysis and forecast from an atmosphere–ocean coupled data assimilation operational system

Ocean Science ◽  
2019 ◽  
Vol 15 (5) ◽  
pp. 1307-1326 ◽  
Author(s):  
Catherine Guiavarc'h ◽  
Jonah Roberts-Jones ◽  
Chris Harris ◽  
Daniel J. Lea ◽  
Andrew Ryan ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Weather Forecasting ◽  
Current System ◽  
Weather Prediction ◽  
Surface Ocean ◽  
Weakly Coupled ◽  
Ocean Prediction ◽  
Coupled Data Assimilation ◽  
Ocean Forecasting ◽  
Prediction Systems

Abstract. The development of coupled atmosphere–ocean prediction systems with utility on short-range numerical weather prediction (NWP) and ocean forecasting timescales has accelerated over the last decade. This builds on a body of evidence showing the benefit, particularly for weather forecasting, of more correctly representing the feedbacks between the surface ocean and atmosphere. It prepares the way for more unified prediction systems with the capability of providing consistent surface meteorology, wave and surface ocean products to users for whom this is important. Here we describe a coupled ocean–atmosphere system, with weakly coupled data assimilation, which was operationalised at the Met Office as part of the Copernicus Marine Environment Service (CMEMS). We compare the ocean performance to that of an equivalent ocean-only system run at the Met Office and other CMEMS products. Sea surface temperatures in particular are shown to verify better than in the ocean-only systems, although other aspects including temperature profiles and surface currents are slightly degraded. We then discuss the plans to improve the current system in future as part of the development of a “coupled NWP” system at the Met Office.

scholarly journals Application of a local attractor dimension to reduced space strongly coupled data assimilation for chaotic multiscale systems

2020 ◽  
Vol 27 (1) ◽  
pp. 51-74 ◽  
Author(s):  
Courtney Quinn ◽  
Terence J. O'Kane ◽  
Vassili Kitsios
Keyword(s):  
Data Assimilation ◽  
Accurate Estimate ◽  
Variable Number ◽  
Frobenius Norm ◽  
Accurate Analysis ◽  
Attractor Dimension ◽  
Strongly Coupled ◽  
Cross Domain ◽  
Local Attractor ◽  
Coupled Data Assimilation

Abstract. The basis and challenge of strongly coupled data assimilation (CDA) is the accurate representation of cross-domain covariances between various coupled subsystems with disparate spatio-temporal scales, where often one or more subsystems are unobserved. In this study, we explore strong CDA using ensemble Kalman filtering methods applied to a conceptual multiscale chaotic model consisting of three coupled Lorenz attractors. We introduce the use of the local attractor dimension (i.e. the Kaplan–Yorke dimension, dimKY) to prescribe the rank of the background covariance matrix which we construct using a variable number of weighted covariant Lyapunov vectors (CLVs). Specifically, we consider the ability to track the nonlinear trajectory of each of the subsystems with different variants of sparse observations, relying only on the cross-domain covariance to determine an accurate analysis for tracking the trajectory of the unobserved subdomain. We find that spanning the global unstable and neutral subspaces is not sufficient at times where the nonlinear dynamics and intermittent linear error growth along a stable direction combine. At such times a subset of the local stable subspace is also needed to be represented in the ensemble. In this regard the local dimKY provides an accurate estimate of the required rank. Additionally, we show that spanning the full space does not improve performance significantly relative to spanning only the subspace determined by the local dimension. Where weak coupling between subsystems leads to covariance collapse in one or more of the unobserved subsystems, we apply a novel modified Kalman gain where the background covariances are scaled by their Frobenius norm. This modified gain increases the magnitude of the innovations and the effective dimension of the unobserved domains relative to the strength of the coupling and timescale separation. We conclude with a discussion on the implications for higher-dimensional systems.

scholarly journals On Temporal Scale Separation in Coupled Data Assimilation with the Ensemble Kalman Filter

2020 ◽  
Vol 179 (5-6) ◽  
pp. 1161-1185 ◽  
Author(s):  
Maxime Tondeur ◽  
Alberto Carrassi ◽  
Stephane Vannitsem ◽  
Marc Bocquet
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Temporal Scale ◽  
Scale Separation ◽  
Coupled Data Assimilation

scholarly journals Ocean Data Assimilation and Initialization Procedure for the Coupled GFDL/URI Hurricane Prediction System

2005 ◽  
Vol 22 (12) ◽  
pp. 1918-1932 ◽  
Author(s):  
Aleksandr Falkovich ◽  
Isaac Ginis ◽  
Stephen Lord
Keyword(s):  
Data Assimilation ◽  
Rhode Island ◽  
Field Experiments ◽  
Feature Modeling ◽  
Geophysical Fluid Dynamics Laboratory ◽  
Prediction System ◽  
Modeling Approach ◽  
Ocean Data Assimilation ◽  
Hurricane Prediction ◽  
Initialization Procedure

Abstract A new ocean data assimilation and initialization procedure is presented. It was developed to obtain more realistic initial ocean conditions, including the position and structure of the Gulf Stream (GS) and Loop Current (LC), in the Geophysical Fluid Dynamics Laboratory/University of Rhode Island (GFDL/URI) coupled hurricane prediction system used operationally at the National Centers for Environmental Prediction. This procedure is based on a feature-modeling approach that allows a realistic simulation of the cross-frontal temperature, salinity, and velocity of oceanic fronts. While previous feature models used analytical formulas to represent frontal structures, the new procedure uses the innovative method of cross-frontal “sharpening” of the background temperature and salinity fields. The sharpening is guided by observed cross sections obtained in specialized field experiments in the GS. The ocean currents are spun up by integrating the ocean model for 2 days, which was sufficient for the velocity fields to adjust to the strong gradients of temperature and salinity in the main thermocline in the GS and LC. A new feature-modeling approach was also developed for the initialization of a multicurrent system in the Caribbean Sea, which provides the LC source. The initialization procedure is demonstrated for coupled model forecasts of Hurricane Isidore (2002).

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