1 EMC’s Current and future GSI development John C. Derber Environmental Modeling Center NCEP/NWS/NOAA With input from: Many others

2 Project areas Advanced assimilation Use of Satellite Radiance data Cloud assimilation Doppler Radar data NSST analysis Use of GPS data Satellite winds Trace and constituent gases and aerosols Conventional observations Other Code maintenance

3 Advanced Assimilation Hybrid GSI/EnKF system (Whitaker …) –Global system – dual resolution (Kleist, Parrish, Treadon) Parallel tests for implementation in Spring 2012 begun Results very promising Presentation by Kleist this afternoon –Regional (Wu, Parrish) Using global EnKF ensembles? Initial tests promising, but not as good as global –Hurricane (Tong, HFIP) Being set up to run this hurricane season Using global EnKF ensembles 4D-Var (Rancic, GMAO) –Perturbation model to be used for all systems –Current status, being incorporated into GSI Balance constraints (Kleist, Parrish, Kim (NESDIS)) –Regional development still stalled –Inclusion of cloud and precip. parameterization underway in global. –Importance once hybrid system developed?

4 Use of Satellite Radiances Improved CRTM (vanDelst, Groff, Q. Liu (NESDIS), Han(NESDIS)) –Improved surface emissivity models/libraries –Improved surface information usage Variable characteristics over FOV Multi-levels –Improved cloud and aerosol Radiative Transfer Accuracy and efficiency –Improved characterization of instruments spectral responses for instruments

5 Use of Satellite Radiances Use of NPP data (McCarty(GMAO), Collard, Chen(NESDIS)) –Launch Oct GOES-R and SEVERI (H. Liu, Collard) SSMIS (Collard, Liang(NESDIS)) –F-16, F-17, F-18 available Improve use of current satellite data –Improved bias correction (Y. Zhu, Collard) –Improved quality control –Channel selection (Collard, Jung (U. Wisc))

6 Cloud and precip. assimilation Forward operator for cloudy radiances –Microwave (CRTM, Kim (NESDIS)) –IR (CRTM, McCarty (GMAO), Auligne (NCAR)) Analysis variable for clouds (E. Liu) –Total water variable (Met. Office) Background error covariances (McCarty (GMAO)) Observation errors (Kim) –Can introduce bias if not specified carefully –Possibly correlated

7 Doppler Radar data Use of Tail Doppler Radar for Hurricanes (Tong) Use of fixed U.S. Doppler Radar network (S. Liu, NSSL) External Doppler Radar networks (e.g., Canada) (S. Liu)

8 NSST analysis Produces analysis of the Near Sea Surface Temperature every analysis time (diurnal cycle) (X. Li) Involves inclusion of Surface warming and Surface Cooling model in ocean boundary layer Inclusion of NSST model in forecast model (diurnal cycle in forecast) Direct use of Radiance

9 NSST is a T-Profile just below the sea surface. Here, only the vertical thermal structure due to diurnal thermocline layer warming and thermal skin layer cooling is resolved Assuming the linear profiles, then, 5 parameters are enough to represent NSST: Mixed Layer Thermocline T z Deeper Ocean Diurnal Warming Profile Skin Layer Cooling Profile z z

10 Diurnal Variability of NSST at z=0 (05/17/2010 – 06/24/2010) SST:

11 New Diurnal Variability of Air temperature (05/17/2010 – 06/24/2010) New - Old New / Old New New / Old NewNew - OldNew Old

12 Validation of analysis: Histogram of O-B. 05/12/2010 – 06/24/2010 OLD (All)OLD (Used)NEW (All)NEW (Used) AVHRR_N18 Ch-4 Surface Air T Sea T OLD (Used) OLD (All)OLD (Used)OLD (All)OLD (Used)NEW (Used)OLD (All)OLD (Used)NEW (Used)OLD (All)OLD (Used)NEW (All)NEW (Used)OLD (All) OLD (Used)

13 Time series at drifting buoy locations. NEW (All)NEW (Used)OLD (All)OLD (Used) NEW - OLD Northern Mid-Latitude Atlantic, 05/12/2010 – 06/24/2010

14 Use of GPS data Operational use of Refractivity (Cucurull) Use of Bending Angle (Cucurull) –Preparation for implementation Spring 2012 –Inclusion of compressibility factors –Use of updated Refractivity coefficients Use of ground based delay (Cucurull, GSD)

15 Satellite winds Ability to read from Dump files rather than Prepbufr files. –Allows more information from data to be retained –Consistent from moving GOES IR sounding channels from PREPBUFR –Improve processing time Improve quality control of winds Add use of new satellite wind estimates (e.g., JMA water vapor winds)

16 Trace and constituent gases and aerosols Trace and constituent gases –Inclusion of climatological changes in CO2 (and eventually other gases e.g., Methane) (Yang) –Improved Ozone analysis (H. Liu, GMAO) OMI data SBUV N-19 MLS Aerosols (H-C. Huang, Z. Liu (NCAR)) –Preliminary work completed – lots more to do!

17 Conventional observations Improved quality control Specification of observation error for individual stations Bias correction Need historical data base for all conventional obs to do above. Many continual problems in locating stations, instrument types, and other meta data, etc. Field experiments

18 Other Additional analysis variables for RTMA (Wind Gusts, PBL height, visibility, etc.) – (Pondeca, Zhu) Mesonet QC - (Levine) Additional observations in BUFR and PREPBUFR (Keyser). Data mining

19 Code Maintenance Testing and evaluation of changes Preparation for implementation Optimization (coding, MPI, OPENMP) Try to maintain simplicity

20 EMC GSI development Relies heavily on collaborative development Many scientific challenges in improving system Availability of computational resources is a major problem for enhancing data assimilation system so resource neutral changes (or better) will be given priority Code maintenance extremely important and very happy with role DTC has taken on a bridge to/from operations Developments need to be coordinated across many projects and many groups