Uncertainty in Radar Retrievals, Model Parameterizations, Assimilated Data and In-Stu Observations: Implications for the Predictability of Weather Workshop

(Oct. 30-Nov. 2, 2018)

Session 1 – Uncertainties in Representation of Model Processes

Uncertainties in Representation of Microphysics – Hugh Morrison

Uncertainties in Model Initial Conditions – Aaron Johnson

Uncertainties in Representation of Model Processes – Ming Xue

Use of Stochastic Modeling to Determine Predictability – Judith Berner

Session 2 – Uncertainties in Measurements

Uncertainties in Ground-based Radar Observations – Guifu Zhang

Uncertainties in Products Derived from Radar – Alexander Ryzhkov

Uncertainties in In-Situ Observations of Cloud Microphysics – Greg McFarquhar

Uncertainties in Quantitative Precipitation Estimation – Jian Zhang

Satellite Data Assimilation and Microphysics – Thomas Jones

Session 3 – Variational, Ensemble and Hybrid Data Assimilation

Data Assimilation of Satellite Observations – Jason Otkin

Uncertainties in Data Assimilation of Observations – Xuguang Wang

NCEP Modeling and Data Assimilation Plans – Jacob Carley

Stochastic Approaches in the High Resolution Rapid Refresh Ensemble – Isidora Jankov

Session 4 – Uncertainties Associated with Predictability Limitations

Numerical Simulations and Observational Analysis Related to Predictability – Chris Davis

Predictability and Data Assimilation – Fuqing Zhang

Ensemble Systems and Weather Prediction Applications – Glen Romine

Atmospheric Predictability – Dale Durran

Summary Documents

Data Assimilation and Variational Analysis

Measurements and Retrievals

Representation of Model Processes


Discussion Group 1

Discussion Group 2

Discussion Group 3

Discussion Group 4