Abstract

1. a b Abstract Centralized Proxy Data Concept Proxy Data II – Model Simulated GOES-R Proxy Data Management SystemFuzhong Weng (NOAA/NESDIS), Tong Zhu (CIRA), Allen Huang (CIMSS), Daniel Zhou (NASA/LaRC), Manajit Sengupta (CIRA), Ben Ruston (NRL), Mitch Goldberg (NOAA/NESDIS), and Jim Yoe (NOAA/NESDIS) In addition to full disk simulation of GOES-R bands, GOES-R proxy data system will also provide mesoscale simulation sections for various locations and under different weather conditions, such as hurricanes, convections, front systems, high-level clouds over ocean, land, coast areas. For the development of operational-certified GOES-R product algorithms and processing systems, the GOES-R Algorithm Working Group (AWG) program requests a high quality of proxy data for algorithm developments, testing and assessments. The requested proxy data will be initially integrated from the AWG funded projects to the NOAA cooperative institutes and other government laboratories, processed and managed through a high performance data management system operated at the Office of Research and Applications. The central tasks in the proxy data management system will be the delivery of simulation and observation-based GOES- R levele1B data, the development of visualization tools for various formats of proxy data, and the design of a GOES-R Observing System Simulation Experiment (OSSE) framework for demonstrating the potential impacts of GOES-R data on NWP forecasts. Full Disk Simulation of GOES-R ABI Bands using JCSDA CRTM model. The simulated GOES-R Radiances are verified against Meteosat Second Generation (MSG) observations. The figures show (a) SEVIRI MSG WV7.3 µm Brightness Temperature (Tb) at 12:45 UTC 03/03/2004, (b) CRTM model simulated Tb at the same time. Front systems Hurricanes a b Convections Coast ocean Proxy Data I – Observation Based a Vision PCRTM retrieved time series of vertical profiles of T, RH, H2O from NAST-I measurements on 09/09/2004 (by Xu Liu). Comparison of MSG observed Tb at WV7.3 µm with CRTM simulated Tb at ABI 7.3 µm water vapor band. Total 15387 MSG observation points are selected under clear sky condition ( based on MSG VIS 0.6 µm). High-level clouds GOES-R AWG application teams will be empowered with a high-quality of GOES-R proxy data sets matched with in-situ measurements for algorithm assessment, developments and validation. GOES-R Proxy Data Users are: a b Major Milestones • The major milestones will be: • Year 1: Integration of GOES-R proxy data and visualization tools  • Year 2: Observing System Simulation Experiment (OSSE) Framework                     • Year 3: Documentation of GOES-R proxy data and Level 1B user guide • The deliverables (outcomes) will be: • 2006: • MODIS data matched with TSI, AERONET, CLOUDNET • Several months of simultaneous hourly obs from GOES-E&W imager matched with SURFRAD, hourly SEVIRI data (Jan, Apr, Jul, Oct) for land team • GOES RSO, SRSO imagery • Full disk-15 min SEVIRI including dust storms, GOES-11 IHOP imagery • MODIS 1.38 micron, ABI 10.4 micron from MAS • Scanning-HIS radiance data, co-located temp/water vapor and O3 profiles from INTEX experiment) for AQAC Team • 1 month GOES and SEVIRI data (full res), simulated ABI • MODIS/SeaWiFS global L1 data matched with ozone, wind speed and pressure, SeaBASS for ocean color • 2007: • GOES-R OSSE 3dvar and 4dvar with covariance matrix • GOES-R Impacts assessment report • 2008: • ABI and HES level 1b user manual • Algorithm theoretical base documents for proxy data Comparison of observation from Ch 4 (10.7 µm) of GOES-12 with Band 13 (10.35 µm) of GOES-R ABI: 10/02/2002 Hurricane Lili (by Manajit Sengupta) MODIS Lvele 1B data at 36 spectral bands with 1km resolution: Band 30 (9.58 ~ 9.88 µm) ↔ ABI 9.7 µm (total ozone, turbulence and winds) Band 33 (13.2 ~ 13.5 µm) ↔ ABI 13.3 µm (air temp, cloud heights and amounts) Survey of Data Needed • A survey from AWG for most needed proxy data was conducted. This is the discovery from the survey: • MODIS and AIRS should be matched with sondes and ground truths, • The data scattered among individual research groups are integrated with some levels of quality control, • Land surface emissivity products imbedded in sounding retrieval system should be analyzed with known error properties, • NAST-I data and retrievals with a high resolution should be convolved to GOES-R spectral and spatial resolutions, • Simulated GOES-R ABI imagery has larger discrepancies compared to ABI-like observations and should be improved with better radiative transfer and cloud model physics, • The observation system simulation experiments with MM5 4dvar/WRF should be pursued to assimilate both radiances and satellite retrievals of T,Q, Wind. Technical Approach • Our technical approach is: • Keep close communications with AWG Application Teams on new proxy data requirements • Work on the priority data set (currently, MODIS and AIRS) requested by AT • Keep pace with new satellite technology and timeliness integration of new sensor data into GOES-R proxy data system • Produce well documented metadata on GOES-R proxy data • Evolve the proxy data system for GOES-R cal/val • Build the state-of-the art infrastructure for GOES-R data assimilation a b GOES, MODIS IR emissivity data can be used to simulate ABI IR bands over land. (by Ben Ruston)