MAP's primary research interests include i) developing new techniques and novel methodologies for data assimilation and ensemble prediction; ii) applying these techniques to global scale to convective scale modeling systems to improve predictive skill; iii) using these techniques to improve the understanding of atmospheric predictability and dynamics from global to convective scales; iv) performing inter-disciplinary research, e.g., using and improving machine learning for numerical prediction; economic values of weather forecast; iv) transitioning research and development into operations (R2O).