P1.9 APPLICATIONS OF SYNTHETIC GOES-R OBSERVATIONS FOR MESOSCALE WEATHER ANALYSIS AND FORECASTING Jack Dostalek*, Lewis Grasso, Manajit Sengupta Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, Colorado Mark DeMaria NOAA/NESDIS, Fort Collins, Colorado 1. INTRODUCTION One of the most important * advantages of Geostationary Operational Environmental Satellite-R (GOES-R) will be the high time resolution of the observations. It is unlikely that any satellite will be able to provide this type of data on a regular basis prior to the launch of GOES-R. For this reason, synthetic observations are being generated from a numerical cloud model (Colorado State Univer- sity/Regional Atmospheric Modeling System: CSU/RAMS) in combination with an observational op- erator that contains radiative transfer algorithms. The emphasis of this work is on product development for mesoscale weather forecasting. Some of the cases that are being simulated by RAMS: •Oklahoma severe weather outbreak of 8-9 May 2003 • Lake effect snow event of 12-13 February 2003 • Hurricane Lili from 30 September-3 October 2002 • Hurricane Isabel of 11-13 September 2003 • Western fog event of 12 January 2004 2. RAMS OVERVIEW The numerical cloud model used for this study was RAMS version 4.3 (Pielke et al. 1992). The following features of RAMS were used to simulate the mesoscale weather events: •The model was run non-hydrostatic and compressible (Tripoli and Cotton 1982). •Momentum was advanced using a leapfrog scheme while scalars were advanced using a forward scheme. Both methods used second order advection. •Vertical and horizontal turbulence coefficients were parameterized using the Smagorinsky (1963) deforma- tion based eddy viscosity with stability modifications (Lilly 1962). • The following hydrometeor species were included in the simulation: Cloud droplets, rain droplets, aggre- gates, graupel, hail, snow, and pristine ice. Both graupel and hail are mixed phased; that is, liquid water may exist on the surface of each particle. Snow and pristine ice are each divided into five habit categories: columns, hexagonal, dendrites, needles, and rosetta. The mass mixing ratio and number concentration were prognosed using a two-moment bulk microphysical scheme (Meyers et al. 1997) for all hydrometeor types except cloud droplets. The cloud droplet mass mixing ratio was * Corresponding author address: Jack Dostalek, 1375 Campus Delivery; Fort Collins, CO 80523-1375. email: dostalek@cira.colostate.edu. predicted using a one-moment scheme. (Work is ongo- ing to include cloud droplets into the two-moment scheme.) For all species, the mean diameter was diag- nosed. •Other prognostic variables were the three components of velocity, perturbation Exner function, total water, and ice-liquid potential temperature, (Tripoli and Cotton 1981). •RAMS uses the Arakawa fully staggered C grid (Ara- kawa and Lamb 1981). •Perturbation Exner function tendencies, used to update the momentum variables, were computed using a time split scheme similar to Klemp and Wilhelmson (1978). •Lateral boundaries used the Klemp-Wilhelmson condi- tion; that is, the normal velocity component specified at the lateral boundary is effectively advected from the interior. •A wall with friction layers was specified at the top boundary. • The Land Ecosystem Atmospheric Feedback model, version 2 (Walko et al. 2000) was employed. 3. OBSERVATIONAL OPERATOR OVERVIEW The observational operator used for computing bright- ness temperatures was developed at the Cooperative Institute for Research in the Atmosphere (Greenwald et al. 2002). It consists of three main components: radia- tive transfer models, hydrometeor optical (or single- scatter) property models, and a gas extinction model. The specific components are: • The radiative transfer model at infrared wavelengths uses the Delta-Eddington 2-stream method (Deeter and Evans 1998). • The r adiative transfer model at solar wavelengths uses the spherical harmonic discrete ordinate method (Evans 1998). •Cloud optical properties at all wavelengths are based on anomalous diffraction theory (Mitchell 2000; Mitchell 2002; Greenwald et al. 2002) applied to both liquid and ice particles. • The g as extinction at all wavelengths is based on the OPTRAN radiative transfer model (McMillin et al. 1995). 4. SYNTHETIC/REAL GOES COMPARISONS As a first test of the RAMS/observational operator mod- eling approach, the severe weather and lake effect snow cases were simulated. Synthetic GOES 10.7 µ m imagery was created and compared with real GOES