Xiujuan Su 1 , John Derber 2 , Jaime Daniel 3 ,Andrew Collard 1

1. Assimilation of GOES hourly shortwave and visible AMVs in GSI Xiujuan Su 1, John Derber2, Jaime Daniel3 ,Andrew Collard1 1: IMSG, 2: EMC/NWS/NOAA, 3.NESDIS Introduction NESDIS has been producing GOES hourly atmospheric motion vectors (AMVs) since 2010 , the product is in operation now. NCEP has made efforts to make use of the product in the forecast and data assimilation system. GOES hourly IR and water vapor cloud top AMVs are assimilated in the next 2014 spring implementation package. In this poster, we will present the efforts to assimilate GOES hourly short wave and visible AMVs into our forecast and data assimilation system. Results Summary and future plans The forecast impact results from parallel experiments with different quality schemes show that the experiment named prvisb has best forecast impacts. Adding GOES hourly short wave and visible AMVs has neutral impacts on northern hemisphere and slight positive impact over southern hemisphere, the more impacts occur at lower atmosphere (1000 to 700mb), which corresponds with assimilated data locate. The impact on precipitation forecast is mixed slight degradation at first 36 hour, and slight positive at 36 to 60 hours and 60 to 84 hour forecast. The data has negative impacts over tropical wind at 850mb and neutral at 200mb. Carefully examining the horizontal wind vectors distribution from both types of data found that there are some issues related the data quality. For examples: The examples show that there are systematic differences between observation and background for some areas, the areas can occur any time and any geographic regions. What causes the systematic differences between observation and background and which is right are unknown. NESDIS is going to produce GOES hourly AMVs with new GOES-R algorithm to replace current GOES AMV products. Therefore future efforts will focus on the new products. The wind vector differences between observations and background for the new product will be examined to see whether similar patterns of vector difference between observations and backgrounds occur and study the reason behind these patterns. The impacts of data on forecast-standard forecast score ▬ Verification against its own analysis (averaged over January 20 to March 8, 2013) (— Control run, other colors: Test runs) Data and preparing for assimilation GOES hourly short wave AMVs are derived from short wave IR channels at night and visible AMVs are from visible channel during day time. Almost all of data for these two products are below 700mb and data counts vary from cycle to cycle, the typical counts are about 55000 during night cycles for short wave AMVs and 35000 for day cycles for visible AMVs. The products are never used in GSI duo to quality issues. Therefore a lot of efforts focus on examining quality of data, defining quality control schemes and observation errors used in GSI. The quality control parameters: quality indicator (QI) and expected error (EE) are from the data producer. The quality control scheme was determined after studying the relationship between observation minus background statistics and QI and EE. Observation errors used in GSI for these to types of data were defined after examining observation minus background statistics and comparing these statistics with ones from other AMVs which are already assimilated in GSI. The Parallel experiments were conducted on global hybrid global data assimilation and forecast system with current operational T254L64 forecast model and 20130923 GSI trunk version. Control run includes GOES hourly IR and water vapor cloud top AMVs. The period covers from January 20 to March 08,2013. Three quality control schemes were tested. The three quality control schemes are: Prvisa: QI(without forecast) >=90, EE/wspeed<0.55 Prvisab: QI (without forecast) >=90.0, WW/ speed <0.55 No land data for northern hemisphere (lat >20) Prvisc: QI (without forecast) >-90, EE/Wspeed <0.4 Observation error*1.5 for latitude >-20 degree PpppP ▬ Verification against its own analysis (averaged over January 20 to March 8, 2013) 2. The impacts on precipitation in USA ▬Verification against rain gauge data in USA (— Control run, —other colors Test runs) 22,,222