Diverse Planetary Boundary Layer (PBL) Algorithms within Multimodels and the Design of Unified Boundary Layer Modeling for Improved Forecasts

点击翻译按钮获取中文摘要

This research addresses improved planetary boundary layer modeling using high resolution global multi-models. As many as five different planetary boundary layer algorithms will be examined. These include 1) Florida State University's K-theory based on mixing length (local closure), 2) India's National Center for Medium Range Weather Forecasting's K-theory with mixing length where stability dependence is strongly dependent on the Bulk Richardson number, 3) a modified gradient approach to a non-local scheme that allows for fluxes down the gradient as well as for convective situations, 4) a non-local scheme based on National Center for Atmospheric Sciences' Community Climate System Model-2, where the surface fluxes are different from the above formulation and 5) a turbulent kinetic energy approach, i.e. a 1.5 order closure scheme. The study is a sequel to recently completed work that address the skills of modeling and forecasting the skills of planetary boundary layer (PBL) fluxes using single models that use single PBL schemes, a unified single model (that synthesizes all such schemes) and a multimodel superensemble that constructs bias-removed forecasts for the PBL fluxes. Benchmark observations are needed for the validation of these forecasts. These are derived from the use of reanalysis data sets that are subjected to rain rate initialization and from a vertical integral (over the troposphere) of the apparent heat sources and moisture sinks. This provides the surface fluxes of heat and moisture as a residual of the problem, since this product is more reliant on observations rather than on models it is a useful proxy for the observed fluxes. The PIs will carry out nearly 100 forecast experiments with each model, with a unified model and with the multimodel superensemble. This will provide them with measures of the errors for these fields at high resolution. This inventory of errors will be followed up with a more detailed analysis on the sources of these errors. This follows a statistical study by the PI, that demonstrates that it is possible to tag areas of model dynamics and physics that contribute to errors (including their location in three dimensions). Such an analysis for the inner details of PBL algorithms will be possible and can provide insights for future model improvements; this has not been done previously. Intellectual Merit: The research is designed as a comprehensive project with its scope encompassing many possible contributions to modeling of the atmospheric planetary boundary layer. The PIs will use a framework for addressing the PBL where finding the source of errors in PBL modeling is a central issue. The data and modeling uncertainties are covered by an ensemble/superensemble approach. The methodology is not limited to forecast modeling. It includes basic science issues on several formulations of PBL physics and ways to find their limitations. Broader Impacts: Planetary Boundary Layer fluxes impact the entire issue of cloud growth and cumulus parameterization. Those, in-turn, impact storm life cycles. This research aims not only to provide somewhat improved models for the PBL fluxes from the construction of unified single and multimodels (the superensemble) but also extends to the tagging of the areas of the PBL physics that contribute to the growth of errors. Given five such diverse local, non-local and turbulent kinetic energy based schemes, the tagging of errors (in three dimensional space) can contribute to further advancement of numerical weather prediction. Two graduate students will be educated and trained under this grant.