A Framework for Understanding and Modeling Mesoscale Weather Systems Using the Ensemble of Multiple Parameterizations of Physical Processes at the Land Surface and in the Atmosphere

In this paper we will develop a single integrated mesoscale weather systems modeling framework that intimately couples a land surface model and an atmospheric model with each equipped with multi-parameterization options. This framework should enable process-based ensemble weather predictions, identification of optimal combinations of process parameterization schemes, identification of critical processes controlling the coupling strength between the land surface and the atmosphere, and quantification of uncertainties in using regional meteorological models to extract high-resolution climate information for policy decision making.

We will build on the existing Weather, Research and Forecasting (WRF) atmospheric model, which already has multiple parameterization options for atmospheric processes such as convection, radiation, planetary boundary layer, and microphysics. One of our key model development efforts is to couple the WRF model with our newly developed, ensemble representation of the land surface, i.e., the Noah land surface model that was first enhanced with biophysical and hydrological realism and then equipped with multi-parameterization options (Noah-MP) for a wide spectrum of physical and ecological processes. The Noah-MP LSM is capable of generating thousands of process-based combinations of land surface parameterization schemes as opposed to the traditional approach that utilizes only a single combination. Offline Noah-MP tests show a great potential of the model in ensemble hydrological predictions.

We will perform an analysis of the sensitivity to different parameterizations over the conterminous United States using the single integrated mesoscale modeling framework described above. To prove the concept, we will present an ensemble of multi-day integrations using the model at 10-km resolution with varying physical representations for both the land surface and the atmosphere. The lateral boundary conditions are from reanalysis data. Specifically, we will focus on understanding of the interactions and feedbacks between groundwater, soil moisture, vegetation, surface energy and water fluxes, atmospheric boundary layer, convection, mesoscale circulation, and precipitation.