The oceanic forecast has become increasingly important in the 21st century. Forecasting products based on numerical ocean models have been used in a wide variety of applications, ranging from guiding maritime transportation, planning recreational activities, supporting hazards, emergency responses, and monitoring the health of ecological systems in coastal oceans and so on. Although the past two decades have witnessed great advances in ocean observing technology, data assimilation methodology, and development of real-time forecasting systems, there is still a gap in the forecast accuracy between what the present state-of-the-art numerical ocean models can reach and that required by government policymakers and stakeholders. Therefore, it is highly needed to continue to invest efforts in improving the accuracy of numerical ocean prediction.
Data assimilation is the method that integrates observations from satellites and a range of in-situ observing platforms into numerical models and thus is central to the improvement of numerical ocean prediction. Therefore, the focus of this special issue will be placed on topics regarding methodologies, applications and assessments of oceanic data assimilation and real-time forecasting systems, with the objective of providing the community a review of the updated progresses relevant to the topics that are helpful to improve marine environment forecast skills. We welcome original and novel manuscripts related to any of the following research topics:
1. Developments of data assimilation and parameter estimation theories and methodologies
2. Assimilation of observations from satellites, radars, and other new observing platforms
3. Assessment of real-time oceanic numerical prediction systems
4. Development and validation of high-resolution oceanic reanalysis datasets
5. Predictability of numerical ocean models with data assimilation
6. Data assimilation for coupled models, including ocean-atmosphere models, ocean-biogeochemical models, and others.
7. Observing System Simulation Experiments (OSSEs).
8. Application of AI in ocean modeling, data assimilation and real-time forecasting
The oceanic forecast has become increasingly important in the 21st century. Forecasting products based on numerical ocean models have been used in a wide variety of applications, ranging from guiding maritime transportation, planning recreational activities, supporting hazards, emergency responses, and monitoring the health of ecological systems in coastal oceans and so on. Although the past two decades have witnessed great advances in ocean observing technology, data assimilation methodology, and development of real-time forecasting systems, there is still a gap in the forecast accuracy between what the present state-of-the-art numerical ocean models can reach and that required by government policymakers and stakeholders. Therefore, it is highly needed to continue to invest efforts in improving the accuracy of numerical ocean prediction.
Data assimilation is the method that integrates observations from satellites and a range of in-situ observing platforms into numerical models and thus is central to the improvement of numerical ocean prediction. Therefore, the focus of this special issue will be placed on topics regarding methodologies, applications and assessments of oceanic data assimilation and real-time forecasting systems, with the objective of providing the community a review of the updated progresses relevant to the topics that are helpful to improve marine environment forecast skills. We welcome original and novel manuscripts related to any of the following research topics:
1. Developments of data assimilation and parameter estimation theories and methodologies
2. Assimilation of observations from satellites, radars, and other new observing platforms
3. Assessment of real-time oceanic numerical prediction systems
4. Development and validation of high-resolution oceanic reanalysis datasets
5. Predictability of numerical ocean models with data assimilation
6. Data assimilation for coupled models, including ocean-atmosphere models, ocean-biogeochemical models, and others.
7. Observing System Simulation Experiments (OSSEs).
8. Application of AI in ocean modeling, data assimilation and real-time forecasting
