The effects of aerosols on weather and climate are the largest uncertainty in predicting anthropogenic impact on the current weather and climate models. In this project, technology on GPU (graphics processing units) and Asynchronous I/O is used to empower cloud-resolving models (CRMs) and consequently significantly improve the modeling capability of the aerosol effects on weather prediction and climate change studies.

The diurnal cycle is a fundamental mode of atmospheric variability and has a major impact on weather and climate prediction. In addition, it provides a robust test of physical processes represented in atmospheric models that are used for studying the water and energy cycles. Most climate models simulate precipitation too early over both land and ocean.

Microphysical (cloud) processes developed at NASA Goddard were implemented into a next generation of weather forecast model (e.g. WRF).

Modern multi-sensor satellite observations provide a more complete view of cloud, precipitation, and aerosols processes over globe; meanwhile, it is becoming a challenge for remote sensing and modeling communities to harness these observations.

Clouds and precipitation are highly coupled with land surface on the timescales of days to months, which challenges current weather and climate prediction models. High-resolution cloud models, coupled with land surface models, can address this process explicitly.

It is known that General Circulation Models (GCMs) have insufficient resolution to accurately simulate hurricane near-eye structure and intensity. Their physics packages (e.g., cumulus parameterizations) are also known limiting factors in simulating hurricanes.

The Goddard Cumulus Ensemble (GCE) model, a cloud resolving model (CRM), has been developed and improved at NASA Goddard Space Flight Center over the past two decades.

Welcome to the Mesoscale Dynamics & Modeling Group. Our mission is to conduct research to understand the physics and dynamics of atmospheric processes through the use of computer-based simulations and various observations, including satellite remote sensing, aircraft and surface-based in-situ observations.

The NASA  Multi-scale Modeling Framework (MMF) is based on the coupling of the 2-dimensional Goddard Cumulus Ensemble Model (2DGCE) and the Goddard Earth Observing System (GEOS) GCM.

The WRF model (Michalakes et al., 2001) is a next-generation mesoscale atmospheric/chemistry model and data assimilation system model developed at National Center for Atmospheric Research (NCAR) and grass-roots collaboration with several institution and universities. 

The POLArimetric Radar Retrieval and Instrument Simulator (POLARRIS) is state-of-art multi polarimetric radar simulator and algorithm emulator,built upon the multi-instrumental simulator package, the Goddard Satellite Data Simulator Unit (G-SDSU) (Matsui 2013, Matsui et al. 2014).

The proposed modeling effort will further study cloud and precipitation processes over many scales of motion, ranging from cloud microphysical processes up to the large-scale circulations that organize the growth and decay of precipitation systems.

The WRF ARW version 3.1.1 was coupled with the Spectra-Bin Microphysics (SBM) part of the HUCM [Khain et al., 2011], and called the WRF-SBM. Cloud hydrometeors are categorized into one-water and six-ice classes, i.e., water droplets, ice crystals (plate, column, dendrite), snow aggregates, graupel, and hail.