Data Assimilation & Prediction

The Maximum Likelihood Filter with state space localization

Zupanski M, 2021: The Maximum Likelihood Filter with state space localization. Mon. Wea. Rev., 149 (10), 3505-3524.

The Maximum Likelihood Ensemble Filter for Computational Flame and Fluid Dynamics

Wang Y, Guzik S, Zupanski M, Gao X, 2021: The Maximum Likelihood Ensemble Filter for Computational Flame and Fluid Dynamics. IMA J. Appl. Math., 86, 631-661.

Detection of Non-Gaussian Behavior Using Machine Learning Techniques: A Case Study on the Lorenz 63 Model

Quantification of Optimal Choices of Parameters in Lognormal Variational Data Assimilation and Their Chaotic Behavior

All-Sky Radiance Assimilation of ATMS in HWRF: A Demonstration Study.

Wu, T.-C., M. Zupanski, L. D. Grasso, C. D. Kummerow, and S-.A. Boukabara

 

 

Improvements to cloud top brightness temperatures computed from the CRTM at 3.9 μm

Quantification of Optimal Choices of Parameters in Lognormal Variational Data Assimilation and Their Chaotic Behavior

Fletcher S., A. Kliewer, and A. S. Jones, 2018: Quantification of Optimal Choices of Parameters in Lognormal Variational Data Assimilation and Their Chaotic Behavior, DOI: 10.1007/s11004-018-9765-7

Uncertainty in solid precipitation and snow depth prediction for Siberia using the Noah and Noah-MP land surface models

A case study involving single observation experiments performed over snowy Siberia using a coupled atmosphere-land modelling system

Impacts of assimilating vertical velocity, latent heating, or hydrometeor water contents retrieved from a single reflectivity data set