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

2 ESSD/UA&I Overview Observation Impact Assessment Tool History and Description System Configuration for Southwest Asia Impact Results –Select day –Period Averages Summary and Future Work

3 ESSD/UA&I Impact Tool History and Description Based on results presented in a JCSDA newsletter, AFWA16 th WS 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 ESSD/UA&I Impact Tool History and Description The tool, which is called Forecast Sensitivity to Observations (FSO),has the ability, using adjoint techniques, to quantitatively estimate the impact that assimilating observations has on short-range WRF model forecast accuracy The FSO system used in this study consists of WRF DA, WRF, the adjoint to WRF, and the adjoint to WRF DA An FSO capability has been developed to work with the Gridpoint Statistical Interpolation (GSI) system Forecast Sensitivity to Observations

5 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 ESSD/UA&I So How Does FSO Work? Non-Linear (NL) forecast models can be linearized (with simplifications) The resulting Tangent-Linear (TL) model represents the linear evolution of small perturbations The mathematical transpose of the TL code is called the Adjoint (ADJ) and it transports sensitivities back in time The ADJ of the data assimilation system is needed to compute the sensitivity to observations. It can be computed with various methods but the one used in FSO is the Lanczos minimization (Fisher 1997, Tremolet 2008) Forecast Sensitivity to Observations

7 ESSD/UA&I Implementation in WRF Observation (y) WRF-VAR Data Assimilation WRF-ARW Forecast Model Forecast (x f ) Derive Forecast Accuracy Background (x b ) Analysis (x a ) Adjoint of WRF-ARW Forecast TL Model (WRF+) Observation Sensitivity (  F/  y) Background Sensitivity (  F/  x b ) Sensitivity at the Initial Time (  F/  x 0 ) Observation Impact (  F/  y) Adjoint of WRF-VAR Data Assimilation Obs Error Sensitivity (  F/  ob ) Gradient of F (  F/  x f ) Define Forecast Accuracy Forecast Accuracy (F) Bias Correction Sensitivity (  F/  k ) Figure from NCAR which was adapted from Liang Xu at NRL

8 ESSD/UA&I FSO Configuration AFWA SW Asia Domain (T4) WRF, WRF DA Ver –45 km grid spacing –57 levels, model top 10 mb –Limited data assimilation (DA) cycle –DA Cycle times: 00,06,12,18 UTC 24 hour forecast length Dry energy forecast error metric Impact computed for sub region Study period 1-29 January 2012 for 00 and 12 UTC cycles Forecast Error Sensitivity wrt Potential Temperature ( o K) F=

9 ESSD/UA&I FSO Configuration Aircraft Reports(AIREPS) Radiosondes (Sound) Feature Track Winds (GeoAMV) SSMIS Retrievals (SSMIS_RV) of Total Precipitable Water (TPW) and Ocean Surface Wind Speeds(OSSW) Surface observations (METAR, Synop) GPS Refractivities (GPSRF) Ships and Buoys SSMIS brightness temperatures for select temperature and humidity “sounding” channels (SSMIS)* AIREPSSound GeoAMV METAR SSMIS_RV Synop Observations Assimilated * Not operationally assimilated by AFWA at the time the study was conducted

10 ESSD/UA&I FSO Configuration: SSMIS Brightness Temperatures Data Selection and Source Data obtained from FNMOC via NRL Monterey in BUFR Data processed through NRL developed Universal Pre-Processor (UPP) by FNMOC Resource limitations prevented assimilation of data from more than one satellite at a time F17 chosen to be assimilated because of superior domain coverage for 00 and 12Z cycles

11 ESSD/UA&I FSO Configuration: SSMIS Brightness Temperatures Quality Control Data thinned to reduced the effect of correlated observation error Channels whose weighting functions peaked close to surface or above model top not used Data near coastlines not used Extensive QC….8 separate tests –Data over mixed sfc type not used –Data over precip areas not used –Data over heavy cloud cover not used –Two ob minus background gross checks Suspected bad channels “blacklisted”

12 ESSD/UA&I FSO Configuration: SSMIS Brightness Temperatures Variational Bias Correction

13 ESSD/UA&I FSO Configuration: SSMIS Brightness Temperatures Variational Bias Correction

14 ESSD/UA&I Forecast Error Sensitivities at the Initial and Forecast Times (  F/  x 0 ) U (  F/  x f ) U Initial Time: 28 Jan 2012 at 12 ZForecast Time: 29 Jan 2012 at 12 Z ( o K) (m s -1 )

15 ESSD/UA&I Single Day Impact Results: 28 Jan 2012 at 12 Z Number of obs assimilated indicated at the end of the bar Feature track winds (GeoAMV) largest number of observations and largest impact SSMIS TPW and OSWS retrievals (SSMIS_RV) have a large impact Surface observations (Synop and METAR) have large impact Larger Impact Observation Impact on 24-Hour Forecast (Base Time: ) Sound Synop GeoAMV AIREP GPSRF METAR Ships SSMIS_RV Buoy SSMIS % of Error Reduction Attributable to Given Observation Type

16 ESSD/UA&I Individual Observation Impact, 28 Jan Z Radiosondes –Wind, Temps, Humidity –Most observations are outside of area of interest but still reduce the forecast error –Humidity obs (Q) positively impact “dry” energy forecast V TQ U green to blue shaded dot indicates forecast error reduction yellow to red shaded dot indicates forecast error increase

17 ESSD/UA&I T Observation Impact, 28 Jan Z Aircraft Reports (AIREPs) –Multiple levels –Wind and temperature –Automated and manual –Some tracks show pattern of positive to negative impact over the track (valid time of ob?) U V green to blue shaded dot indicates forecast error reduction yellow to red shaded dot indicates forecast error increase

18 ESSD/UA&I Observation Impact, 28 Jan Z Feature Track Winds (GeoAMVs) –Multiple levels –Number of observations varies greatly day to day V U green to blue shaded dot indicates forecast error reduction yellow to red shaded dot indicates forecast error increase

19 ESSD/UA&I P (all) Observation Impact, 28 Jan Z METARs –Wind, Temps, Pressure, Humidity –Nearly 50% of observations are not used mostly in complex terrain –Humidity obs (Q) positively impact “dry” energy forecast P (used) T(all) T(used) U(all) U(used) V(all) V(used) Q(all) black dot indicates the ob not used green to blue shaded dot indicates forecast error reduction yellow to red shaded dot indicates forecast error increase

20 ESSD/UA&I Observation Impact, 28 Jan Z GPS Refractivities (GPSRF) –Refractivity –Multiple levels –Generally only 1- 3 observations in the domain –Generally the highest impact per ob Ship –Mostly temp & winds –A few press & humidity –Can include buoys Buoy –Same as ships –From buoy data center GPSRF Ship T Buoy T green to blue shaded dot indicates forecast error reduction yellow to red shaded dot indicates forecast error increase

21 ESSD/UA&I Monthly Average Observation Impact: January 2012 Larger Impact Average Observation Impact on 24-Hour Forecast (1-29 January 2012) Sound Synop GeoAMV AIREP GPSRF METAR Ships SSMIS_RV Buoy SSMIS % of Error Reduction Attributable to Given Observation Type Sound Synop GeoAMV AIREP GPSRF METAR Ships SSMIS_RV Buoy SSMIS Combined affect of SSMI/S (retrievals of ocean surface wind speed, total precipitable water, and brightness temperatures for temperature and humidity channels) is nearly 15% of the total error reduction achieved through the assimilation of all observations

22 ESSD/UA&I Summary The observation impact assessment tool Forecast Sensitivity to Observations was used to determine the relative impact of various types of weather observations on 24-hour WRF model forecast accuracy over portions of South West Asia The system was modified so SSMIS brightness temperatures were assimilated and their impact assessed On average, the assimilation of SSMIS brightness temperatures, total precipitable water, and ocean surface wind speeds accounted for nearly 15% of the error reduction achieved through the assimilation of all observations

23 ESSD/UA&I Future Work Gridpoint Statistical Interpolation (GSI) System AFWA recently transitioned from WRF DA to GSI AFWA had NCAR modify FSO to work with the GSI Validation efforts underway New capabilities –observation impact by level and by channel –Capacity to determine impact for multiple satellite sensors Additional Domains