Advances in Meteorology

Regional Coupled Model and Data Assimilation


Status
Published

Lead Editor

1Ocean University of China, Qingdao, China

2Earth System Research Laboratory, Boulder, USA

3Nansen Environmental and Remote Sensing Center, Bergen, Norway

4University of Maryland, College Park, USA

5Texas A&M University, College Station, USA


Regional Coupled Model and Data Assimilation

Description

Recent studies of high-resolution observations and modeling have progressively promoted the understanding of climate sciences, especially those sciences related to mesoscale and submesoscale air-sea interactions. How atmospheric and oceanic processes interact across multiple scales is a long-lasting concern that can restrict the applicability of high-resolution observations. Yet societal needs require climate studies to better resolve and evaluate regional changes/variations as well as extreme events. There are currently two important scientific questions that require further study in the climate sciences: (1) how do global and large-scale fluctuations influence the local weather and climate anomalies? and (2) how do local weather-climate perturbations feedback large-scale phenomena? To address these questions, climate modeling must simultaneously resolve higher resolutions (which might be intractable with current computers) and local mesoscale and small-scale physical processes in increasingly greater details. Coupled general circulation models (CGCMs) can assess global change due to the changes of green-house gas and natural aerosols.

High-resolution regional models can be nested into a coarse resolution CGCM, efficiently achieving dynamically downscaled and optimized utilization of computational resources. Multiple nesting levels are often used to communicate between coarse and high-resolution models, and two-way nesting algorithms at high resolution boundaries have emerged. While such a framework efficiently advances our understanding of the attribution and impact of large-scale phenomena on local conditions, it also provides an opportunity to link scientific advances with severe weather alerts at the local level. We predict that regional coupled models with well-designed boundary processing and coupled data assimilation will progressively advance climate sciences and promote local societal services.

In this special issue, we call for papers that investigate recent advances in regional coupled modeling and data assimilation, regional weather-climate analysis and prediction, parameterization of atmospheric and oceanic processes at the mesoscale and submesoscale, assessment of regional observing system, regional model error correction, and parameter optimization.

Potential topics include but are not limited to the following:

  • Regional ocean modeling and mesoscale to submesoscale oceanic physical processes
  • Regional atmosphere modeling and cloud-resolving and microphysics expression
  • Regional coupled modeling and mesoscale and small-scale air-sea interaction
  • Development, implementation, and validation of regional land and soil model
  • Regional coupling and boundary processing techniques
  • Regional coupled model predictability and local weather-climate predictions
  • Multiscale data assimilation applying to high resolution data assimilation
  • Regional coupled data assimilation with boundary effects
  • Tropical cyclone and squall forecast initialization
  • Conventional (e.g., buoys and meteorological stations data) and unconventional (e.g., satellite and radar data) assimilation within a regional coupled model
  • Regional land data assimilation and regional ecosystem data assimilation
  • Regional observing system assessment
  • Regional coupled model error studies and parameter correction

Articles

  • Special Issue
  • - Volume 2018
  • - Article ID 9434102
  • - Editorial

Regional Coupled Model and Data Assimilation

S. Zhang | Y. Xie | ... | Z. Jing
  • Special Issue
  • - Volume 2018
  • - Article ID 8912943
  • - Research Article

Assimilation of Aircraft Observations in High-Resolution Mesoscale Modeling

Brian P. Reen | Robert E. Dumais
  • Special Issue
  • - Volume 2018
  • - Article ID 7931964
  • - Research Article

Formulations for Estimating Spatial Variations of Analysis Error Variance to Improve Multiscale and Multistep Variational Data Assimilation

Qin Xu | Li Wei
  • Special Issue
  • - Volume 2017
  • - Article ID 4626585
  • - Research Article

The High Order Conservative Method for the Parameters Estimation in a PM2.5 Transport Adjoint Model

Ning Li | Yongzhi Liu | ... | Kai Fu
  • Special Issue
  • - Volume 2017
  • - Article ID 5638289
  • - Research Article

Optimal Configuration Method of Sampling Points Based on Variability of Sea Surface Temperature

Chang Liu | Yuning Lei | ... | Meizhen Zhao
  • Special Issue
  • - Volume 2017
  • - Article ID 9315601
  • - Research Article

A Potential Density Gradient Dependent Analysis Scheme for Ocean Multiscale Data Assimilation

Hongli Fu | Jinkun Yang | ... | Xiaoshuang Zhang
  • Special Issue
  • - Volume 2017
  • - Article ID 6847343
  • - Research Article

An Efficient T-S Assimilation Strategy Based on the Developed Argo-Extending Algorithm

Chaojie Zhou | Xiaohua Ding | ... | Qiang Ma
  • Special Issue
  • - Volume 2017
  • - Article ID 7314106
  • - Research Article

Evaluation of the Impact of Argo Data on Ocean Reanalysis in the Pacific Region

Xuefeng Zhang | Chaohui Sun | ... | Yuxin Zhao
  • Special Issue
  • - Volume 2017
  • - Article ID 8601296
  • - Research Article

A Quality Control Method Based on an Improved Random Forest Algorithm for Surface Air Temperature Observations

Xiaoling Ye | Xing Yang | ... | Rong Gu
Advances in Meteorology
 Journal metrics
Acceptance rate45%
Submission to final decision107 days
Acceptance to publication48 days
CiteScore1.630
Impact Factor1.577
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