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Regional Coupled Model and Data Assimilation

Regional Coupled Model and Data Assimilation Hindawi Advances in Meteorology Volume 2018, Article ID 9434102, 2 pages https://doi.org/10.1155/2018/9434102 Editorial 1,2 3 4 1,2 5 6 S. Zhang , Y. Xie, F. Counillon , X. Ma, P. Yu, and Z. Jing Key Laboratory of Physical Oceanography, Ministry of Education, Ocean University of China, Qingdao, China Qingdao National Laboratory of Marine Science and Technology (QNLM), Qingdao, China Earth System Research Laboratory, National Ocean and Atmosphere Administration, Department of Commerce, Boulder, CO, USA Nansen Environmental and Remote Sensing Center and Bjerknes Centre for Climate Research, Bergen, Norway CICS/ESSIC, University of Maryland, College Park, MD, USA Department of Oceanography, Texas A&M University, College Station, TX, USA Correspondence should be addressed to S. Zhang; szhang@ouc.edu.cn Received 29 March 2018; Accepted 29 March 2018; Published 8 May 2018 Copyright © 2018 S. Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. To further understand the mechanisms of local weather In this special issue, we collect 8 papers that cover the and climate anomalies and predict them skillfully, high- regional observing system sampling and impact, quality con- resolution coupled modeling and data assimilation is nec- trol of observations in regional details, assimilation of non- essary. On the one hand, regional coupled model and data conventional observations, improved assimilation method, assimilation is an important means to understand the details and advanced assimilation strategy as well as parameter of local mesoscale and submesoscale air-sea interactions estimation for local regional phenomena. and how atmospheric and oceanic processes interact across How to configure the sampling points is an interesting multiple scales. On the other hand, societal needs require topic in the regional observing system design. The paper titled weather-climate studies to better resolve, evaluate, and pre- “Optimal Configuration Method of Sampling Points Based dict regional changes/variations as well as extreme events. on Variability of Sea Surface Temperature” uses the 𝐾-means We need to detect how large-scale uc fl tuations influence the clustering algorithm to optimize the sampling strategy based local weather and climate anomalies and how local weather- on historical SST observations. eTh gained results provide a climate perturbations feedback to large-scale background. new method for configuring sampling points of ocean in situ To address these questions, weather and climate modeling observation with limited resources. In the paper titled “Eval- must simultaneously resolve higher resolutions and local uation of the Impact of Argo Data on Ocean Reanalysis in the mesoscale and small-scale physical processes in increasingly Pacific Region,” the authors use the framework of Observing greater details. Given the limitation of computing power, System Simulation Experiments (OSSEs) to evaluate the the dynamically downscaling framework that nests high- impact of Argo data on ocean reanalysis in the Pacific region. resolution regional coupled models into a coarse-resolution They found that the Argo temperature and T-S relationship global earth system model is an efficient way in such studies. play an important role in the analysis of this region while While such a framework efficiently advances our understand- the direct adjustment from the Argo salinity observations ing of the attribution and impact of large-scale phenomena is relatively trivial in the northwest Pacicfi if the Argo on local conditions, it also provides an opportunity to link temperature data are used during the reanalysis. eTh quality scientific advances with severe weather alerts at the local level. control of observations is an important aspect of regional We expect that regional coupled models with well-designed modeling and data assimilation. In the paper titled “A Quality boundary processing and coupled data assimilation can pro- Control Method Based on an Improved Random Forest gressively advance climate sciences and promote local societal Algorithm for Surface Air Temperature Observations,” the services. authors combine the articfi ial sfi h swarm algorithm and the 2 Advances in Meteorology random forest regression function to apply quality control to surface temperature at multiple stations. eTh ir work presents apromising method onsurfaceairtemperatureobservations for regional modeling and assimilation studies. eTh assimilation of nonconventional observations is a very important aspect in regional weather and climate stud- ies. Aircraft-based data are a promising source of above- surfaceobservationstobeassimilated intomesoscalemodels. eTh paper titled “Assimilation of Aircraft Observations in High-Resolution Mesoscale Modeling” discusses the results of assimilating TAMDAR (the Tropospheric Airborne Mete- orological Data Reporting) observations. eTh impact of TAMDAR observations in 1 km WRF (Weather Research and Forecasting) model is evaluated. They found that the regional forecast can be improved signicfi antly with such aircraftobservationsalthoughtheyonlyuseasimplenudging scheme. The assimilation method is a key point for the regional coupledmodelanddataassimilation. Inthepapertitled “An Efficient T-S Assimilation Strategy Based on the Developed Argo-Extending Algorithm,” authors present a new strategy to use the sea surface temperature (SST) to reconstruct T- S profiles and improve assimilation quality when in situ T-S profiles are insufficient. eTh paper titled “A Potential Density Gradient Dependent Analysis Scheme for Ocean Multiscale Data Assimilation” addresses the issue of how to maintain oceanic mixing along potential density surface in ocean data assimilation (ODA) and proposes a new ODA strategy. Their resultsshowthatthenewschemesignicfi antlyimproves the model forecast skills with more dynamically consistent information in initial conditions. In the paper titled “For- mulations for Estimating Spatial Variations of Analysis Error Varianceto ImproveMultiscaleandMultistepVariational Data Assimilation,” the authors discuss the issue of construct- ing multiscale error covariance in variational data assimi- lation. eir Th method successively improves the variational assimilation quality, being very promising, although it is only demonstrated in idealized experiments. The special issue also includes one paper discussing the problem of air pollution transport. In the paper titled “The High Order Conservative Method for the Parameters Esti- mation in a PM Transport Adjoint Model,” authors apply 2.5 Piecewise Parabolic Method (PPM), a high order and conser- vative interpolation, to the parameter estimation in a PM 2.5 transport adjoint model. In this piece of interesting work, numerical experiments are taken to show the accuracy of PPMinspace andits abilitytoincreasethe well-posedness of the inverse problem. In real case simulation experiments, the results are in good agreement with the observations from the 21th APEC Summit. S. Zhang Y. Xie F. Counillon X. Ma P. Yu Z. Jing International Journal of The Scientific Advances in Advances in Geophysics Chemistry Scientica World Journal Public Health Hindawi Hindawi Hindawi Hindawi Publishing Corporation Hindawi Hindawi www.hindawi.com Volume 2018 www.hindawi.com Volume 2018 www.hindawi.com Volume 2018 http://www www.hindawi.com .hindawi.com V Volume 2018 olume 2013 www.hindawi.com Volume 2018 Journal of Environmental and Public Health Advances in Meteorology Hindawi Hindawi www.hindawi.com Volume 2018 www.hindawi.com Volume 2018 Submit your manuscripts at www.hindawi.com Applied & Environmental Journal of Soil Science Geological Research Hindawi Volume 2018 Hindawi www.hindawi.com www.hindawi.com Volume 2018 International Journal of International Journal of Agronomy Ecology International Journal of Advances in International Journal of Forestry Research Microbiology Agriculture Hindawi Hindawi Hindawi Hindawi Hindawi www.hindawi.com Volume 2018 www.hindawi.com Volume 2018 www.hindawi.com Volume 2018 www.hindawi.com Volume 2018 www.hindawi.com Volume 2018 International Journal of Journal of Journal of International Journal of Biodiversity Archaea Analytical Chemistry Chemistry Marine Biology Hindawi Hindawi Hindawi Hindawi Hindawi www.hindawi.com Volume 2018 www.hindawi.com Volume 2018 www.hindawi.com Volume 2018 www.hindawi.com Volume 2018 www.hindawi.com Volume 2018 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Advances in Meteorology Hindawi Publishing Corporation

Regional Coupled Model and Data Assimilation

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Publisher
Hindawi Publishing Corporation
Copyright
Copyright © 2018 S. Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
ISSN
1687-9309
eISSN
1687-9317
DOI
10.1155/2018/9434102
Publisher site
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Abstract

Hindawi Advances in Meteorology Volume 2018, Article ID 9434102, 2 pages https://doi.org/10.1155/2018/9434102 Editorial 1,2 3 4 1,2 5 6 S. Zhang , Y. Xie, F. Counillon , X. Ma, P. Yu, and Z. Jing Key Laboratory of Physical Oceanography, Ministry of Education, Ocean University of China, Qingdao, China Qingdao National Laboratory of Marine Science and Technology (QNLM), Qingdao, China Earth System Research Laboratory, National Ocean and Atmosphere Administration, Department of Commerce, Boulder, CO, USA Nansen Environmental and Remote Sensing Center and Bjerknes Centre for Climate Research, Bergen, Norway CICS/ESSIC, University of Maryland, College Park, MD, USA Department of Oceanography, Texas A&M University, College Station, TX, USA Correspondence should be addressed to S. Zhang; szhang@ouc.edu.cn Received 29 March 2018; Accepted 29 March 2018; Published 8 May 2018 Copyright © 2018 S. Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. To further understand the mechanisms of local weather In this special issue, we collect 8 papers that cover the and climate anomalies and predict them skillfully, high- regional observing system sampling and impact, quality con- resolution coupled modeling and data assimilation is nec- trol of observations in regional details, assimilation of non- essary. On the one hand, regional coupled model and data conventional observations, improved assimilation method, assimilation is an important means to understand the details and advanced assimilation strategy as well as parameter of local mesoscale and submesoscale air-sea interactions estimation for local regional phenomena. and how atmospheric and oceanic processes interact across How to configure the sampling points is an interesting multiple scales. On the other hand, societal needs require topic in the regional observing system design. The paper titled weather-climate studies to better resolve, evaluate, and pre- “Optimal Configuration Method of Sampling Points Based dict regional changes/variations as well as extreme events. on Variability of Sea Surface Temperature” uses the 𝐾-means We need to detect how large-scale uc fl tuations influence the clustering algorithm to optimize the sampling strategy based local weather and climate anomalies and how local weather- on historical SST observations. eTh gained results provide a climate perturbations feedback to large-scale background. new method for configuring sampling points of ocean in situ To address these questions, weather and climate modeling observation with limited resources. In the paper titled “Eval- must simultaneously resolve higher resolutions and local uation of the Impact of Argo Data on Ocean Reanalysis in the mesoscale and small-scale physical processes in increasingly Pacific Region,” the authors use the framework of Observing greater details. Given the limitation of computing power, System Simulation Experiments (OSSEs) to evaluate the the dynamically downscaling framework that nests high- impact of Argo data on ocean reanalysis in the Pacific region. resolution regional coupled models into a coarse-resolution They found that the Argo temperature and T-S relationship global earth system model is an efficient way in such studies. play an important role in the analysis of this region while While such a framework efficiently advances our understand- the direct adjustment from the Argo salinity observations ing of the attribution and impact of large-scale phenomena is relatively trivial in the northwest Pacicfi if the Argo on local conditions, it also provides an opportunity to link temperature data are used during the reanalysis. eTh quality scientific advances with severe weather alerts at the local level. control of observations is an important aspect of regional We expect that regional coupled models with well-designed modeling and data assimilation. In the paper titled “A Quality boundary processing and coupled data assimilation can pro- Control Method Based on an Improved Random Forest gressively advance climate sciences and promote local societal Algorithm for Surface Air Temperature Observations,” the services. authors combine the articfi ial sfi h swarm algorithm and the 2 Advances in Meteorology random forest regression function to apply quality control to surface temperature at multiple stations. eTh ir work presents apromising method onsurfaceairtemperatureobservations for regional modeling and assimilation studies. eTh assimilation of nonconventional observations is a very important aspect in regional weather and climate stud- ies. Aircraft-based data are a promising source of above- surfaceobservationstobeassimilated intomesoscalemodels. eTh paper titled “Assimilation of Aircraft Observations in High-Resolution Mesoscale Modeling” discusses the results of assimilating TAMDAR (the Tropospheric Airborne Mete- orological Data Reporting) observations. eTh impact of TAMDAR observations in 1 km WRF (Weather Research and Forecasting) model is evaluated. They found that the regional forecast can be improved signicfi antly with such aircraftobservationsalthoughtheyonlyuseasimplenudging scheme. The assimilation method is a key point for the regional coupledmodelanddataassimilation. Inthepapertitled “An Efficient T-S Assimilation Strategy Based on the Developed Argo-Extending Algorithm,” authors present a new strategy to use the sea surface temperature (SST) to reconstruct T- S profiles and improve assimilation quality when in situ T-S profiles are insufficient. eTh paper titled “A Potential Density Gradient Dependent Analysis Scheme for Ocean Multiscale Data Assimilation” addresses the issue of how to maintain oceanic mixing along potential density surface in ocean data assimilation (ODA) and proposes a new ODA strategy. Their resultsshowthatthenewschemesignicfi antlyimproves the model forecast skills with more dynamically consistent information in initial conditions. In the paper titled “For- mulations for Estimating Spatial Variations of Analysis Error Varianceto ImproveMultiscaleandMultistepVariational Data Assimilation,” the authors discuss the issue of construct- ing multiscale error covariance in variational data assimi- lation. eir Th method successively improves the variational assimilation quality, being very promising, although it is only demonstrated in idealized experiments. The special issue also includes one paper discussing the problem of air pollution transport. In the paper titled “The High Order Conservative Method for the Parameters Esti- mation in a PM Transport Adjoint Model,” authors apply 2.5 Piecewise Parabolic Method (PPM), a high order and conser- vative interpolation, to the parameter estimation in a PM 2.5 transport adjoint model. In this piece of interesting work, numerical experiments are taken to show the accuracy of PPMinspace andits abilitytoincreasethe well-posedness of the inverse problem. In real case simulation experiments, the results are in good agreement with the observations from the 21th APEC Summit. S. Zhang Y. Xie F. Counillon X. Ma P. Yu Z. Jing International Journal of The Scientific Advances in Advances in Geophysics Chemistry Scientica World Journal Public Health Hindawi Hindawi Hindawi Hindawi Publishing Corporation Hindawi Hindawi www.hindawi.com Volume 2018 www.hindawi.com Volume 2018 www.hindawi.com Volume 2018 http://www www.hindawi.com .hindawi.com V Volume 2018 olume 2013 www.hindawi.com Volume 2018 Journal of Environmental and Public Health Advances in Meteorology Hindawi Hindawi www.hindawi.com Volume 2018 www.hindawi.com Volume 2018 Submit your manuscripts at www.hindawi.com Applied & Environmental Journal of Soil Science Geological Research Hindawi Volume 2018 Hindawi www.hindawi.com www.hindawi.com Volume 2018 International Journal of International Journal of Agronomy Ecology International Journal of Advances in International Journal of Forestry Research Microbiology Agriculture Hindawi Hindawi Hindawi Hindawi Hindawi www.hindawi.com Volume 2018 www.hindawi.com Volume 2018 www.hindawi.com Volume 2018 www.hindawi.com Volume 2018 www.hindawi.com Volume 2018 International Journal of Journal of Journal of International Journal of Biodiversity Archaea Analytical Chemistry Chemistry Marine Biology Hindawi Hindawi Hindawi Hindawi Hindawi www.hindawi.com Volume 2018 www.hindawi.com Volume 2018 www.hindawi.com Volume 2018 www.hindawi.com Volume 2018 www.hindawi.com Volume 2018

Journal

Advances in Meteorology – Hindawi Publishing Corporation

Published: May 8, 2018

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