Motivation

Predicting precipitation accurately is one of the biggest challenges in weather forecasting, resulting primarily from inability of Numerical Weather Prediction (NWP) models to represent well the physical processes of precipitation. These processes  occur on small spatial scales that are not explicitly resolved in the models. A  parameterized representation is used instead which is inevitably characterized with uncertainty. The uncertainty results from inabiity to represent well the full range of natural variability with a prescribed set of parameters and from incomplete understanding of the processes.  For these reasons the microphysics parameterization uncertainty is the leading source of error for the precipitation prediction.

Research Question

The ensemble  prediction approach has been introduced recently in NWP practice to reduce the errors due to the uncertainties within various physical parameterizations in the model, including the microphysics. The ensemble prediction is based on assumption that statistical mean of an ensemble of  forecasts using different realizations of the parameterization would be more consistent with the verifying observations than each individual forecast because random errors would average out.  The success of the ensemble forecasting depends on optimality of representation of the uncertainty in the leading sources of errors when forming the ensemble. The primary research question addressed in this project is : How to objectively estimate the microphysics parameterization uncertainty to improve the prediction of precipitation?

Research Approach

In this project a novel method for optimally representing the microphysics parameterization uncertainty using observations is investigated. The method is based on objective estimation of a joint distribution of possible values of parameters within the parameterization, using precipitation-sensitive observations. The objective estimation is performed by means of data assimilation numerical techniques.  The project included three consecutive studies using a simplified NWP model of evolution of an idealized convective system. The microphysics parameterization in the model is representative of the parameterizations used in the NWP practice.

Major Findings

In the first study a fully nonlinear and computationally expensive, data assimilation technique was used to accurately solve the parameter estimation problem for 10 important physical parameters within the parameterization using radar reflectivity observations. The uncertainty of these parameters results from assuming them to be uniform across variety of  natural conditions, and from inaccurate knowledge of  the values.

The estimates are expressed in terms of multi-parameter probability distribution of  joint values of the parameters.  Ten physical parameters were considered  for two distinct precipitation regimes.  For both regimes the computed probability distribution indicates complex nonlinear relationships between the different parameters and with the observations.  The uncertainty was also found to vary with the precipitation regime. Although revealing about the properties of the parameterization, such uncertainty cannot be well represented in the NWP practice.  Further analysis was performed  to reformulate the representation of the uncertanty in terms of variations of  non-dimensional multiplicative coefficients that were assigned to different individual parameterized processes. 

The ne...

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