Abstract

1. Community Radiative Transfer Model (CRTM) WRF Nature Run with 5 km, 15 min resolution Simulated Scene at all GOES-R Channels Channel Noise Added to Simulated Scene Synthesized Channel Scene with SRF Convert to GOES-R Field of View Simultaneous Retrievals of T,Q, Winds Error Covariances for Retrieved Products Error Covariances for Background and RT Model WRF Sensitivity Run w/o GOES-R Data Impact Assessments on Storm Track, Intensity, Rainfall…. 4DDA System Produces Analysis Fields Abstract Previous Study: Case II AMSR-E Sea-Surface wind Impact a b OSSE Study GOES-R 30 min vs. 15 min Data Impacts • where • x is a vector including all possible atmospheric and surface parameters. • I is the radiance vector • B is the error covariance matrix of background • E is the observation error covariance matrix • F is the radiative transfer model error matrix GOES-R Observation System Simulation Experiment FrameworkTong Zhu (CIRA/CSU@NOAA/NESDIS) and Fuzhong Weng (NOAA/NESDIS) The Hyperspectral Environmental Suite (HES) and Advanced Baseline Imager (ABI) will be flown on the next generation of NOAA Geostationary Operational Environmental Satellite (GOES)-R platform. These two sensors will provide enhanced spatial, temporal information for atmospheric soundings and surface property retrieval. Among these products, the atmospheric temperatures, winds and humidity are extremely valuable for hurricane model simulation. In this project, we designed an Observation System Simulation Experiments (OSSE) framework to assess the impact of GOES-R measurements on hurricane prediction. AMSR-E SSW vs. GDAS 10-m Wind An OSSE study to assess the different impacts of GOES-R 30-min data vs. 15-min data was conducted. First, a 48-h nature run simulation of Hurricane Katrina was produced with the initial time at 0600 UTC 25 August 2005. The atmospheric temperature and sea surface wind fields were sampled from the nature run at two different intervals of 30-min and 15-min. 4DVAR analysis was used to assimilate the sampled data into model, and then performed two sensitivity runs. It is found that the high temporal resolution observation leads to better forecast. AMSR-E SSW GDAS 10-m Wind Hurricane Alex Aug 01, 2004 OBS: Vmax=15 m/s SSW: Vmax=16 m/s GDAS: Vmax=10 m/s Temperature retrieved from AMSUin Hurricane Isabel 2003 c d Minimum Sea-Level Pressure Maximum Wind Hurricane Danielle Aug 20, 2004 OBS: Vmax=18 m/s SSW: Vmax=20 m/s GDAS: Vmax=10 m/s Technical Approach • There are five procedures in the OSSE: • Generate a “nature” atmospheric condition based on GOES-R Proxy Dataset System. The proxy dataset is obtained from numerical model simulation results (e.g., WRF, MM5) and/or currently existed satellite observations (e.g., MODIS, SEVIRI, GOES-W/E); • Produce GOES-R sensor measurements by using JCSDA Community Radiative Transfer Model (CRTM) according to GOES-R instrument configuration; • Retrieve atmospheric temperatures and water vapor winds from GOES-R radiances; • Assimilate the synthesized observations into hurricane model with a recently developed hybrid variational scheme (HVAR); • Perform NWP model simulations to assess the impact of GOES-R data. . a 4DVAR System The Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) mesoscale forecast model version 5 (MM5) and its adjoint system are used in this study. The cost function for assimilation of temperatures and winds from AMSU and AMSR-E satellite observations can be expressed as: Vertical cross section of temperature anomalies at 06:00 UTC 09/12/2003. Left panel: west-east cross section along 22EN, and right panel: south-north cross section along 56EW for Hurricane Isabel Future Plan Our previous studies have indicated that atmospheric temperature data obtained from AMSU and AMSR-E measurements can significantly improve hurricane forecast. With the availability of GOES-R proxy data, we will conduct following two studies in the near future. 1) Study the impacts of winds retrieved from GOES-R proxy data. With the additional atmospheric wind information provided by GOES-R data, it is expected more positive impacts will be made on hurricane prediction. Impact Study with WRF Model Simulation Two 48-h simulations of Hurricane Isabel (2003) were performed. In control run there is no AMSU temperature information in hurricane initial condition. The AMSU temperature fields is incorporated in the sensitivity run. Model configuration where Tobs and Vobs are the AMSU temperature and AMSR-E SSW; T and V are the model analysis fields. H is the operator used to transform the model gridded analysis fields to observation points. The tx and ty are the satellite observation times within the 4DVAR assimilation window, and i, j represent the cloud-affected microwave radiances region where the satellite data is assimilated. R is defined as the region where satellite retrieved cloud water path (CLW)> 0.3 mm. GOES-E IR winds over high, middle and low levels at 11:45 UTC, 24 April 2006. Improved T, V fields by 4DVAR Analysis The temperature and wind fields at 250 hPa from (a) GDAS analysis, (b) HVAR scheme, and at surface level from (c) GDAS analysis, and (d) HVAR scheme for Hurricane Katrina at 0600 UTC 25 August. The 48-h Simulated tracks of Hurricane Isabel (2003) Time series of Minimum sea level pressures a Previous Study: Case I AMSU Temperature Impact 2) Test the effectiveness of two different kinds of data variational (DVAR) scheme. The first method directly assimilates GOES-R radiance with the 4DVAR scheme. The second method is so called hybrid variational scheme (HVAR), in which the atmospheric temperatures and winds are derived from a 1DVAR model, and then assimilated with the 4DVAR. Physical Based 1DVAR System Cost function: