1. Challenges in Using GOES Data Within Operational Numerical Models Dr. Louis W. Uccellini Director National Centers for Environmental Prediction 6 th GOES Users’ Conference Madison, WI November 3, 2009

2. 2 Background: Historical Perspective in the use of LEO/GEO Data for Forecast Application Current Use of GOES Data in NCEP Models Recent Results from Model Assessment (“Drop Out” Team Report) Future Trends with GOES-R (and beyond) Summary Overview

3. 3 Background: Historical Perspective in the use of LEO/GEO Data for Forecast Application

4. 4 GEO Principally used by forecast community: became the “eyes” for the forecaster –Timely access a key Sheets, R.C., 1990. The National Hurricane Center – Past, Present, and Future. Wea. Forecasting, 5: 185-232. “The greatest single advancement in observing tools … was unquestionably the advent of the geosynchronous meteorological satellite. If there was a choice of only one observing tool for use in meeting the responsibilities of the NHC, the author would clearly choose the geosynchronous satellite with its present day associated accessing, processing, and displaying systems …”

5. 5 LEO Higher spatial resolution The backbone of the Global Observing System for numerical models Was not readily available to the forecaster in “real time”

6. 6 Today Distinction is becoming blurred –LEO data is more readily available in “real time” for forecaster applications –GEO is used quantitatively in numerical models (winds, land surface, SST) NOAA-18 Image of CA fires GOES Sounder-Derived Precipitable Water

7. 7 QuikSCAT Hurricane Force Extratropical Cyclone kts Geostationary IR Intense, non-tropical cyclones with hurricane force winds Feb 09, 2007, North Atlantic

8. Hurricane Force Extra-tropical Cyclones Detection and Warning Trend using QuikSCAT 2000-2009 8 Hurricane Force Warning Initiated Dec 2000 Detection increased with: -Forecaster familiarity -Data availability -Improved resolution -Improved algorithm QuikSCAT Launch Jun 99 Hurricane Force Wind Warning Initiated Dec 00 25 km QuikSCAT Available in N-AWIPS Oct 01 12.5 km QuikSCAT available May 04 Improved wind algorithm and rain flag Oct 06 Totals A-289 P-269 558

9. 9 The SST (top) and OHC (bottom) in the pre-storm environment for Hurricane Katrina. The storm intensity and positions from the NHC best track are indicated by the circles. Mainelli, et. al., 2008, Wea. Forecasting, 23, 3-16 OHC: Ocean Heat Content derived from oceanic T and salinity climatology, SST analysis, and radar altimetry sea height anomaly (SHA) fields created from a blend of Jason-1 data and the Geosat Follow-On (GFO)

10. 10 Current Use of GOES Data in NCEP Models

11. 11 Satellite Data used in Operational NWP at NCEP Working through the Joint Center for Satellite Data Assimilation HIRS sounder radiances AMSU-A sounder radiances AMSU-B sounder radiances GOES sounder radiances GOES, Meteosat, GMS winds GOES precipitation rate SSM/I precipitation rates SSM/I ocean surface wind speeds AVHRR SST AVHRR vegetation fraction AVHRR surface type SBUV/2 ozone profile and total ozone IASI (Feb ’09) TRMM precipitation rates ERS-2 ocean surface wind vectors Windsat Quikscat ocean surface wind vectors Altimeter sea level observations (ocean data assimilation) AIRS MODIS Winds COSMIC ~34 instruments Operational InstrumentsResearch Instruments Multi-satellite snow cover Multi-satellite sea ice Mix of Instruments

12. 12 GFS/NAM –Satellite wind –Sounder data => Moisture RUC –Derived product imagery => precipitable water Model Use of GOES Data

13. 13 Use is rather limited Why? –RUC Perspective – relative accuracy of Derived Product Images –GFS – limited domain precludes wider use Current Use of GOES in NCEP Models

14. 14 Recent Results from Model Assessments

15. 15 Air Quality WRF NMM/ARW Workstation WRF WRF: ARW, NMM ETA, RSM GFS, Canadian Global Model Satellites 99.9% Regional NAM WRF NMM North American Ensemble Forecast System Hurricane GFDL HWRF Global Forecast System Dispersion ARL/HYSPLIT For eca st Severe Weather Rapid Update for Aviation Climate CFS 1.7B Obs/Day Short-Range Ensemble Forecast NOAA Model Production Suite MOM3 NOAH Land Surface Model Coupled Global Data Assimilation Oceans RTOFS/HYCOM WaveWatch III NAM/CMAQ

16. 16 “Dropout” Cases February 2009 Northern Hemisphere Southern Hemisphere

17. 17 “Dropout” Cases March 2009 Northern Hemisphere Southern Hemisphere

18. What is a “Dropout”? The criteria that a 5-day 500 mb Anomaly Correlation (AC) height score must meet in order to be considered a dropout: At least one of the following criteria must be met (for NH and SH) –a) ECMWF minus GFS ≥ 15 AC points –b) GFS AC ≤ 0.70 –c) ECMWF AC ≤ 0.75 Dropouts are not only a GFS problem; here the CAN, FNMOC, & UK meet the above criteria

19. 19 The Dropout Team Not necessarily a “model” issue Appears to be related to Observations, data Quality Control (QC), and analysis issues Very complicated; each case seems to have a unique combination of reasons Focus: Observational Data Data assimilation/Analysis Model NCEP: Brad Ballish, DaNa Carlis, Jordan Alpert, V. Krishna Kumar, Joe Carr, Yangrong Ling JCSDA: Rolf Langland, NRL, Charles Skupniewicz and James Vermeulen, FNMOC

20. 20 Some “Findings” Still Very Preliminary Not necessarily a model issue; remedial action includes using ECMWF analysis in GFS with very positive results within existing model system Data focus on conventional satellite, and aircraft observations Could be bias issues –Warm bias in aircraft data –Bias, altitude assignment and QC issues with satellite winds –Potential impact compounded by over sampling (aircraft and satellite) Could have analysis issue with respect to how the observation biases are handled, especially in the tropics and the SH –Size of analysis window (2.5 vs 6 vs 12 hr) could be an important issue –Bias can influence the background guess causing deviations from truth that are perpetuated by the cycling Specific data sets appear to contribute: Sat Winds, Aircraft Studied conventional satellite, and aircraft observations, as well as non-conventional satellite radiance observation types

21. 21 Future Trends with GOES-R (and beyond)

22. The Advanced Baseline Imager ABI CurrentCurrent ImagerSounder Spectral Coverage 16 bands 5 bands19 bands Spatial resolution 0.64  m Visible 0.5 km Approx. 1 km10 km Other Visible/near-IR1.0 km n/a Bands (>2  m)2 km Approx. 4 km Spatial coverage Full disk4-12 per hour Every 3 hoursn/a CONUS 12 per hour ~4 per hour1 per hour MesoscaleEvery 30 sec n/an/a

23. WGNE 25, 2-6 November 2009 23 4-D Var Status at NCEP/EMC NCEP has partnered with GMAO to build a prototype 4-D Var system based on the NCEP GSI system –GMAO has adopted GSI for their analysis system –Weekly working meetings between GMAO and NCEP –Combined code management GSI 4-D Var code infrastructure developed by GMAO –Upgrade to GSI delivered spring 2009 –Code merger with NCEP’s latest GSI is completed Methodology follows Met Office strategy –Uses perturbation model –Can be used for GFS, NAM and hurricane systems Initial perturbation and tangent linear models developed and working –Adjoint models being developed –Operating 4D-Var prototype anticipated within next 2 months

24. WGNE 25, 2-6 November 2009 24 4-D Var Status at NCEP/EMC (cont) Phase 1 development –FY10: Prototype 4D-VAR system testing at T190 (~70km) –Q2FY11: Pre-operational global testing –FY11: Application to hurricanes and diagnostic studies Q3 FY11: Initial implementation (dependent on operational computing resources) Phase 2 development –FY10-14: Collaborate with additional partners to enhance hybrid 4D- VAR system Expected partners: ESRL, CIRA and U. Maryland Hybrid uses ensemble-based information to improve representation of flow-dependent background errors

25. 25 The combined use of GOES/POES continues to provide foundation for NWS warnings and forecasts Current use of GOES data in models is limited –QC Issues –Coverage –Cannot take full advantage of improved temporal resolution Future use of GOES-R ABI –Expanded coverage and more rapid updates of full disc are critical advancements Parallel effort in 4DVAR is also a key aspect for wider utilization of GOES data –Supported through the GOES Project Office –Involves GSFC/GMAO partnership Summary

26. 26 Appendix

27. 27