i) Developing new techniques and novel methodologies for data assimilation (DA) and ensemble forecast (EF);
ii) Applying these techniques from global scale to convective scale modeling systems assimilating a variety of observations (radar, satellite, ground based remote sensing platforms, aircraft borne observations, UAV, in-situ, etc.) to improve numerical prediction skill;
iii) Improving the understanding of atmospheric predictability and dynamics through data assimilation and ensemble approaches from global to storm scales;
iv) Transitioning research into operations (R2O);
v) Interdisciplinary research: e.g. machine learning/AI and data assimilation, machine learning/AI and NWP forecast calibration, economic values of NWP.
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MAP Lab Research Areas
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Novel Data Assimilation Methodology
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Novel Ensemble Forecast Generation Method
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Data Assimilation for Numerical Prediction of Global Medium Range Weather
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Data Assimilation for Numerical Prediction of Hurricanes
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Data Assimilation for Numerical Prediction of CONUS Convective Scale Weather
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Data Assimilation for Numerical Prediction of Arctic Cyclone
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Data Assimilation for Sub-seasonal to Seasonal (S2S) Prediction
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Atmospheric Dynamics and Predictability
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Novel Forecast Verification Method
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Interdisciplinary: Machine Learning and Artificial Intelligence Interfaced with Data Assimilation and NWP
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O2R2O:Research to Operation Transition
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