1. © The Aerospace Corporation 2014 Observation Impact on WRF Model Forecast Accuracy over Southwest Asia Michael D. McAtee Environmental Satellite Systems Division (ESSD) User Applications and Integration (UA&I) The Aerospace Corporation ESSD/UA&I May 2014 Approved for Public Release – Distribution Unlimited

2. 2 michael.mcatee@aero.org ESSD/UA&I Overview Observation Impact Assessment Tool History and Description System Configuration for Southwest Asia Impact Results –Period Averages for “conventional” and satellite data –Special focus SSMIS, CrIS, ATMS, GPS RO Summary and Future Work

3. 3 michael.mcatee@aero.org ESSD/UA&I Impact Tool History and Description Based on results presented in a JCSDA newsletter, Air Force Weather Agency (AFWA) personnel were encouraged to pursue the development of an observation impact assessment tool based on techniques developed at the Naval Research Laboratory (NRL) AFWA tasked the National Center for Atmospheric Research (NCAR) with developing such a tool that could be used with its operational forecast model and data assimilation system (WRF and WRF DA) leveraging previous work that had been done to develop 4DVAR Aerospace working with NCAR, AFWA and its contractors installed the system in AFWA’s development environment Forecast Sensitivity to Observations

4. 4 michael.mcatee@aero.org ESSD/UA&I Impact Tool History and Description The tool, which is called Forecast Sensitivity to Observations (FSO), has the ability, using adjoint methods, to quantitatively estimate the impact that assimilating observations has on short-range WRF model forecast accuracy An FSO capability was subsequently developed by NCAR for AFWA that works with the Gridpoint Statistical Interpolation (GSI) system The FSO system used in this study consists of the GSI with Lanczos minimization, WRF, and the adjoint to WRF Forecast Sensitivity to Observations

5. 5 michael.mcatee@aero.org ESSD/UA&I The Case for FSO? Meets a need to know not only the impact of observations but also their relative value A single run of FSO can provide the relative value of all observations assimilated without the need for multiple and computationally expensive with- and without- model runs FSO can provide the critical information needed to intelligently select which channels from space based remote sensors will be assimilated FSO can be used to monitor the health of an NWP center’s data assimilation system as well as the health of the observations it uses FSO can determine which individual observations improved or degraded the forecast Forecast Sensitivity to Observations

6. 6 michael.mcatee@aero.org ESSD/UA&I FSO Configuration AFWA SW Asia Domain (T4) WRF (v3.5), GSI (v3.2 with Lanczos minimization) –45 km grid spacing –57 levels, model top 10 mb –Limited data assimilation (DA) cycle –DA Cycle times: 00,06,12,18 UTC 12 hour forecast length Dry energy forecast error metric Impact computed for sub region Study period 1-18 March 2014 for 00 and 12 UTC cycles Forecast Error Sensitivity wrt Potential Temperature ( o K) F=

7. 7 michael.mcatee@aero.org ESSD/UA&I FSO Configuration Aircraft reports(AIRCFT) Radiosondes (Sound) Atmospheric motion vectors from geostationary satellites (SATWND) Surface observations (METAR, Synop) Observations Assimilated AIRCFT Sound SATWND Low Level SATWND Upper Level Synop METAR

8. 8 michael.mcatee@aero.org ESSD/UA&I SFCSHP FSO Configuration Ships and buoys (SFCSHP) GPS bending angle (COSMIC) AMSUA (Aqua,N15,N18,N19) MHS (N18,N19) AIRS (Aqua) IASI (MetOp-A) Observations Assimilated GPS MHS N19AMSUA N19 AIRSIASI

9. 9 michael.mcatee@aero.org ESSD/UA&I FSO Configuration HIRS/4 (N19, MetOp-A) SSMIS*(F17, F18) ATMS* (NPP) CrIS* (NPP) * Not Operationally Assimilated at the time of the study Observations Assimilated HIRS ATMSCrIS SSMIS F17

10. 10 michael.mcatee@aero.org ESSD/UA&I FSO Configuration Variational Bias Correction

11. 11 michael.mcatee@aero.org ESSD/UA&I Observation Impact Results Larger Impact Period Average Impact and Total Count by Observation Parameter Radiance observations have the greatest total impact largely based on the strength of their numbers and coverage The impact of all non-radiance observation types is ~25% of the total impact

12. 12 michael.mcatee@aero.org ESSD/UA&I Observation Impact Results Period Average Impact per Observation GPS largest impact per observation Surface Pressure also has a very large impact per observation Radiance data has a relatively small impact per observation

13. 13 michael.mcatee@aero.org ESSD/UA&I Observation Impact Results Period Average Radiance Data Impact by Sensor ATMS NPP CrIS NPP AIRS Aqua SSMIS F18 SSMIS F17 AMSUA Aqua AMSUA N19 AMSUA N18 AMSUA N15 MHS N19 MHS N18 HIRS4 N19 Larger Impact All radiance data reduces the 12- hour forecast error AMSUA data from N18 and N19 have the largest and second largest impact respectively followed by CrIS, AIRS, AMSUA from N15, and SSMIS from F17 ATMS NPP CrIS NPP AIRS Aqua SSMIS F18 SSMIS F17 AMSUA Aqua AMSUA N19 AMSUA N18 AMSUA N15 MHS N19 MHS N18 HIRS4 N19

14. 14 michael.mcatee@aero.org ESSD/UA&I Observation Impact Results SSMIS Data thinned to reduced the effect of correlated observation error Channels whose weighting functions peak close to surface or above model top not used Some channels “blacklisted” Good forecast error reduction achieved by assimilating temperature channel data Moisture channel data impact is small but beneficial

15. 15 michael.mcatee@aero.org ESSD/UA&I Observation Impact Results ATMS Data thinned to reduced the effect of correlated observation error Channels whose weighting functions peak close to surface or above model top not used Good forecast error reduction achieved by assimilating temperature channel data Moisture channel data impact is very small * * Figure from NOAA/STAR

16. 16 michael.mcatee@aero.org ESSD/UA&I Observation Impact Results CrIS 399 channel subset of the data received by AFWA 84 channels assimilated Data thinned Largest Impact with subgroup of channel numbers less than 80

17. 17 michael.mcatee@aero.org ESSD/UA&I Observation Impact Results Non-Radiance Measurements Radiosonde data (SOUND) has the largest impact of the non- radiance observation types Surface observations over land (ADPSFC) have the next largest impact Aircraft (AIRCFT) data has the third largest impact followed closely by the combined impact from the COSMIC satellites Impact of winds derived by tracking features with geostationary satellites (SATWND) is relatively small COSMIC (1-6)

18. 18 michael.mcatee@aero.org ESSD/UA&I Observation Impact Results GPS Bending Angle Significant overall impact Most of impact is in the upper troposphere and low to mid stratosphere

19. 19 michael.mcatee@aero.org ESSD/UA&I Summary and Future Work The GSI version of the observation impact assessment tool Forecast Sensitivity to Observations was used to determine the relative impact of various types of weather observations on 12-hour WRF model forecast accuracy over South West Asia Radiance data accounted for nearly 75% of the total impact achieved through assimilating all observations The assimilation of ATMS, CrIS, and SSMIS data was successful and acted to reduce the forecast error Apply the FSO tool to more domains which are higher resolution ~15 KM