scholarly journals Reduction of Spatially Structured Errors in Wide-Swath Altimetric Satellite Data Using Data Assimilation

2019 ◽  
Vol 11 (11) ◽  
pp. 1336 ◽  
Author(s):  
Sammy Metref ◽  
Emmanuel Cosme ◽  
Julien Le Sommer ◽  
Nora Poel ◽  
Jean-Michel Brankart ◽  
...  

The Surface Water and Ocean Topography (SWOT) mission is a next generation satellite mission expected to provide a 2 km-resolution observation of the sea surface height (SSH) on a two-dimensional swath. Processing SWOT data will be challenging because of the large amount of data, the mismatch between a high spatial resolution and a low temporal resolution, and the observation errors. The present paper focuses on the reduction of the spatially structured errors of SWOT SSH data. It investigates a new error reduction method and assesses its performance in an observing system simulation experiment. The proposed error-reduction method first projects the SWOT SSH onto a subspace spanned by the SWOT spatially structured errors. This projection is removed from the SWOT SSH to obtain a detrended SSH. The detrended SSH is then processed within an ensemble data assimilation analysis to retrieve a full SSH field. In the latter step, the detrending is applied to both the SWOT data and an ensemble of model-simulated SSH fields. Numerical experiments are performed with synthetic SWOT observations and an ensemble from a North Atlantic, 1/60° simulation of the ocean circulation (NATL60). The data assimilation analysis is carried out with an ensemble Kalman filter. The results are assessed with root mean square errors, power spectrum density, and spatial coherence. They show that a significant part of the large scale SWOT errors is reduced. The filter analysis also reduces the small scale errors and allows for an accurate recovery of the energy of the signal down to 25 km scales. In addition, using the SWOT nadir data to adjust the SSH detrending further reduces the errors.

2012 ◽  
Vol 27 (1) ◽  
pp. 124-140 ◽  
Author(s):  
Bin Liu ◽  
Lian Xie

Abstract Accurately forecasting a tropical cyclone’s (TC) track and intensity remains one of the top priorities in weather forecasting. A dynamical downscaling approach based on the scale-selective data assimilation (SSDA) method is applied to demonstrate its effectiveness in TC track and intensity forecasting. The SSDA approach retains the merits of global models in representing large-scale environmental flows and regional models in describing small-scale characteristics. The regional model is driven from the model domain interior by assimilating large-scale flows from global models, as well as from the model lateral boundaries by the conventional sponge zone relaxation. By using Hurricane Felix (2007) as a demonstration case, it is shown that, by assimilating large-scale flows from the Global Forecast System (GFS) forecasts into the regional model, the SSDA experiments perform better than both the original GFS forecasts and the control experiments, in which the regional model is only driven by lateral boundary conditions. The overall mean track forecast error for the SSDA experiments is reduced by over 40% relative to the control experiments, and by about 30% relative to the GFS forecasts, respectively. In terms of TC intensity, benefiting from higher grid resolution that better represents regional and small-scale processes, both the control and SSDA runs outperform the GFS forecasts. The SSDA runs show approximately 14% less overall mean intensity forecast error than do the control runs. It should be noted that, for the Felix case, the advantage of SSDA becomes more evident for forecasts with a lead time longer than 48 h.


2015 ◽  
Vol 2 (2) ◽  
pp. 513-536 ◽  
Author(s):  
I. Grooms ◽  
Y. Lee

Abstract. Superparameterization (SP) is a multiscale computational approach wherein a large scale atmosphere or ocean model is coupled to an array of simulations of small scale dynamics on periodic domains embedded into the computational grid of the large scale model. SP has been successfully developed in global atmosphere and climate models, and is a promising approach for new applications. The authors develop a 3D-Var variational data assimilation framework for use with SP; the relatively low cost and simplicity of 3D-Var in comparison with ensemble approaches makes it a natural fit for relatively expensive multiscale SP models. To demonstrate the assimilation framework in a simple model, the authors develop a new system of ordinary differential equations similar to the two-scale Lorenz-'96 model. The system has one set of variables denoted {Yi}, with large and small scale parts, and the SP approximation to the system is straightforward. With the new assimilation framework the SP model approximates the large scale dynamics of the true system accurately.


2021 ◽  
Author(s):  
Helen E. Phillips ◽  
Amit Tandon ◽  
Ryo Furue ◽  
Raleigh Hood ◽  
Caroline Ummenhofer ◽  
...  

Abstract. Over the past decade, our understanding of the Indian Ocean has advanced through concerted efforts toward measuring the ocean circulation and its water properties, detecting changes in water masses, and linking physical processes to ecologically important variables. New circulation pathways and mechanisms have been discovered, which control atmospheric and oceanic mean state and variability. This review brings together new understanding of the ocean-atmosphere system in the Indian Ocean since the last comprehensive review, describing the Indian Ocean circulation patterns, air-sea interactions and climate variability. The second International Indian Ocean Expedition (IIOE-2) and related efforts have motivated the application of new technologies to deliver higher-resolution observations and models of Indian Ocean processes. As a result we are discovering the importance of small scale processes in setting the large-scale gradients and circulation, interactions between physical and biogeochemical processes, interactions between boundary currents and the interior, and between the surface and the deep ocean. In the last decade we have seen rapid warming of the Indian Ocean overlaid with extremes in the form of marine heatwaves. These events have motivated studies that have delivered new insight into the variability in ocean heat content and exchanges in the Indian Ocean, and climate variability on interannual to decadal timescales.This synthesis paper reviews the advances in these areas in the last decade.


2009 ◽  
Vol 22 (15) ◽  
pp. 4066-4082 ◽  
Author(s):  
Andrew Mc C. Hogg ◽  
William K. Dewar ◽  
Pavel Berloff ◽  
Sergey Kravtsov ◽  
David K. Hutchinson

Abstract Small-scale variation in wind stress due to ocean–atmosphere interaction within the atmospheric boundary layer alters the temporal and spatial scale of Ekman pumping driving the double-gyre circulation of the ocean. A high-resolution quasigeostrophic (QG) ocean model, coupled to a dynamic atmospheric mixed layer, is used to demonstrate that, despite the small spatial scale of the Ekman-pumping anomalies, this phenomenon significantly modifies the large-scale ocean circulation. The primary effect is to decrease the strength of the nonlinear component of the gyre circulation by approximately 30%–40%. This result is due to the highest transient Ekman-pumping anomalies destabilizing the flow in a dynamically sensitive region close to the western boundary current separation. The instability of the jet produces a flux of potential vorticity between the two gyres that acts to weaken both gyres.


Ocean Science ◽  
2021 ◽  
Vol 17 (6) ◽  
pp. 1677-1751
Author(s):  
Helen E. Phillips ◽  
Amit Tandon ◽  
Ryo Furue ◽  
Raleigh Hood ◽  
Caroline C. Ummenhofer ◽  
...  

Abstract. Over the past decade, our understanding of the Indian Ocean has advanced through concerted efforts toward measuring the ocean circulation and air–sea exchanges, detecting changes in water masses, and linking physical processes to ecologically important variables. New circulation pathways and mechanisms have been discovered that control atmospheric and oceanic mean state and variability. This review brings together new understanding of the ocean–atmosphere system in the Indian Ocean since the last comprehensive review, describing the Indian Ocean circulation patterns, air–sea interactions, and climate variability. Coordinated international focus on the Indian Ocean has motivated the application of new technologies to deliver higher-resolution observations and models of Indian Ocean processes. As a result we are discovering the importance of small-scale processes in setting the large-scale gradients and circulation, interactions between physical and biogeochemical processes, interactions between boundary currents and the interior, and interactions between the surface and the deep ocean. A newly discovered regional climate mode in the southeast Indian Ocean, the Ningaloo Niño, has instigated more regional air–sea coupling and marine heatwave research in the global oceans. In the last decade, we have seen rapid warming of the Indian Ocean overlaid with extremes in the form of marine heatwaves. These events have motivated studies that have delivered new insight into the variability in ocean heat content and exchanges in the Indian Ocean and have highlighted the critical role of the Indian Ocean as a clearing house for anthropogenic heat. This synthesis paper reviews the advances in these areas in the last decade.


Author(s):  
Florian Le Guillou ◽  
Sammy Metref ◽  
Emmanuel Cosme ◽  
Julien Le Sommer ◽  
Clément Ubelmann ◽  
...  

AbstractDuring the past 25 years, altimetric observations of the ocean surface from space have been mapped to provide two dimensional sea surface height (SSH) fields which are crucial for scientific research and operational applications. The SSH fields can be reconstructed from conventional altimetric data using temporal and spatial interpolation. For instance, the standardDUACS products are created with an optimal interpolation method which is effective for both low temporal and low spatial resolution. However, the upcoming next-generation SWOT mission will provide very high spatial resolution but with low temporal resolution.The present paper makes the case that this temporal-spatial discrepancy induces the need for new advanced mapping techniques involving information on the ocean dynamics. An algorithm is introduced, dubbed the BFN-QG, that uses a simple data assimilation method, the back-and-forth nudging, to interpolate altimetric data while respecting quasigeostrophic dynamics. The BFN-QG is tested in an observing system simulation experiments and compared to the DUACS products. The experiments consider as reference the high-resolution numerical model simulation NATL60 from which are produced realistic data: four conventional altimetric nadirs and SWOT data. In a combined nadirs and SWOT scenario, the BFN-QG substantially improves the mapping by reducing the root-mean-square errors and increasing the spectral effective resolution by 40km. Also, the BFN-QG method can be adapted to combine large-scale corrections from nadirs data and small-scale corrections from SWOT data so as to reduce the impact of SWOT correlated noises and still provide accurate SSH maps.


2013 ◽  
Vol 31 (2) ◽  
pp. 207
Author(s):  
Jose Antonio Moreira Lima

This issue presents a set of papers related to the development of ocean forecasting models with data assimilation skills for the South Atlantic Ocean, more specifically for the Metarea V maritime region whose western border is delimited by the Brazilian shelf. This work has been done with the collaboration of many Brazilian researchers under the Oceanographic Modeling and Observation Network (REMO) research group. The evolution from an early stage of running ocean models with mean climatological forcings aiming at the study of specific oceanographic processes to the present stage of running operational ocean forecast models with synoptic forcings and data assimilation had a strong contribution from researchers with a meteorological background, who brought their expertise on numerical weather forecasting.The papers present distinct topics associated with an ocean forecasting system, such as a detailed description of network design and implementation of the ocean circulation models, a proposed approach of nesting distinct models starting from a large scale Atlantic Ocean grid to regional high-resolution local grids, data assimilation methods, synoptic sea surface fields obtained from remote sensing, surface heat fluxes, and planning observational measurement programs for assimilation and model evaluation.We hope that these papers contribute towards developing this specific area of operational oceanic forecasting within the Brazilian scientific and ocean technology communities. We still have a steady way to follow in order to consolidate and improve the propo-sed initiatives, but the first steps were already given and sound results are now available. In the near future, we foresee continuous improvement of oceanic models and data assimilation methods as well as collaboration with interested researchers from Brazilian and foreign institutions. Jose Antonio Moreira LimaInvited Editor  Este volume apresenta um conjunto de artigos relacionados com o tema previsão oceânica de curto prazo para o Oceano Atlântico Sul, mais especificamente para a região marítima Metarea V, através de modelos numéricos de circulação com assimilação de dados observacionais. Este trabalho está sendo desenvolvido a partir da cooperação de diversos pesquisadores brasileiros colaboradores da Rede de Modelagem e Observação Oceanográfica (REMO). No estudo dos processos oceanográficos, a evolução do estágio de rodar modelos oceânicos utilizando forçantes climatológicas médias para o estágio atual de rodar modelos operacionais com forçantes sinóticas e assimilação de dados teve uma forte contribuição de pesquisadores oriundos da área de meteorologia, que trouxeram seu conhecimento aplicado dos modelos de previsão do tempo.Os artigos abordam diversos tópicos associados com um sistema de previsão oceânica, tais como uma descrição detalhada do projeto e implementação dos modelos de circulação oceânica; aninhamento escalonado de modelos com escalas distintas, a partir de malha computacional do Oceano Atlântico, para malhas regionais com alta resolução espacial; métodos de assimilação de campos e dados observados; campos sinóticos da superfície do mar através sensoriamento remoto; fluxos de calor de superfície; e planejamento de observações para assimilação e avaliação dos modelos.Desejamos que estes artigos contribuam para desenvolvimento desta área específica de previsão oceânica operacional junto às comunidades científica e de tecnologia oceânica brasileira. Temos ainda um extenso caminho pela frente para consolidar e aperfeiçoar as iniciativas propostas, mas os primeiros passos foram dados e bons resultados já estão disponíveis. Para o futuro, vislumbramos aprimoramento contínuo dos modelos oceânicos e métodos de assimilação de dados, assim como a colaboração com pesquisadores interessados de instituições brasileiras ou estrangeiras. Jose Antonio Moreira LimaEditor Convidado 


2015 ◽  
Vol 22 (5) ◽  
pp. 601-611 ◽  
Author(s):  
I. Grooms ◽  
Y. Lee

Abstract. Superparameterization (SP) is a multiscale computational approach wherein a large scale atmosphere or ocean model is coupled to an array of simulations of small scale dynamics on periodic domains embedded into the computational grid of the large scale model. SP has been successfully developed in global atmosphere and climate models, and is a promising approach for new applications, but there is currently no practical data assimilation framework that can be used with these models. The authors develop a 3D-Var variational data assimilation framework for use with SP; the relatively low cost and simplicity of 3D-Var in comparison with ensemble approaches makes it a natural fit for relatively expensive multiscale SP models. To demonstrate the assimilation framework in a simple model, the authors develop a new system of ordinary differential equations similar to the two-scale Lorenz-'96 model. The system has one set of variables denoted {Yi}, with large and small scale parts, and the SP approximation to the system is straightforward. With the new assimilation framework the SP model approximates the large scale dynamics of the true system accurately.


Ocean Science ◽  
2019 ◽  
Vol 15 (2) ◽  
pp. 443-457 ◽  
Author(s):  
Ann-Sophie Tissier ◽  
Jean-Michel Brankart ◽  
Charles-Emmanuel Testut ◽  
Giovanni Ruggiero ◽  
Emmanuel Cosme ◽  
...  

Abstract. Ocean data assimilation systems encompass a wide range of scales that are difficult to control simultaneously using partial observation networks. All scales are not observable by all observation systems, which is not easily taken into account in current ocean operational systems. The main reason for this difficulty is that the error covariance matrices are usually assumed to be local (e.g. using a localisation algorithm in ensemble data assimilation systems), so that the large-scale patterns are removed from the error statistics. To better exploit the observational information available for all scales in the assimilation systems of the Copernicus Marine Environment Monitoring Service, we investigate a new method to introduce scale separation in the assimilation scheme. The method is based on a spectral transformation of the assimilation problem and consists in carrying out the analysis with spectral localisation for the large scales and spatial localisation for the residual scales. The target is to improve the observational update of the large-scale components of the signal by an explicit observational constraint applied directly on the large scales and to restrict the use of spatial localisation to the small-scale components of the signal. To evaluate our method, twin experiments are carried out with synthetic altimetry observations (simulating the Jason tracks), assimilated in a 1/4∘ model configuration of the North Atlantic and the Nordic Seas. Results show that the transformation to the spectral domain and the spectral localisation provides consistent ensemble estimates of the state of the system (in the spectral domain or after backward transformation to the spatial domain). Combined with spatial localisation for the residual scales, the new scheme is able to provide a reliable ensemble update for all scales, with improved accuracy for the large scale; and the performance of the system can be checked explicitly and separately for all scales in the assimilation system.


2020 ◽  
Vol 148 (12) ◽  
pp. 4783-4798
Author(s):  
James Wilson ◽  
Dan Megenhardt ◽  
James Pinto

AbstractThis paper examines nowcasts of precipitation from the High-Resolution Rapid Refresh (HRRRv2) model from the summer of 2017 along the Colorado Front Range. It was found that model nowcasts (2 h or less) of precipitation amount were less skillful than extrapolation of the KFTG WSR-88-D data at a spatial scale of 120 km. It was also found that local-scale (mesoscale) influences on rainfall intensity and amount have a much greater impact on rainfall intensity than large-scale (synoptic) influences. Thus, large-scale trends are not useful for modifying extrapolation nowcasts on the local scale. Errors in the HRRR nowcasts are attributed to an inability of the model and data assimilation to resolve convergence along outflow boundaries and other terrain-influenced mesogamma-scale flows that contribute to storm formation and evolution. While the HRRRv2 1-h nowcasts were strongly correlated with observed precipitation events, the nowcast precipitation amounts were in error by more than a factor of 2 about 50% of the time, with half of the cases being overestimates and half being underestimates. A large fraction of the HRRRv2 overestimates were associated with stratiform rain events. It is speculated that this was a result of misinterpretation of the radar bright band as more intense precipitation aloft by the data assimilation scheme. A large fraction of the HRRRv2 underestimates occurred when the data assimilation and model were unable to fully resolve the low-level convergence along small-scale, narrow boundaries that led to new storm initiation and/or storm growth.


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