Aircraft-based Observations: Impact on weather forecast model performance

Aircraft-based Observations: Impact on weather forecast model performance Stephen S. Weygandt Eric James, Stan Benjamin, Bill Moninger, Brian Jamison, Geoff Manikin* NOAA Earth System Research Laboratory *NOAA National Centers for Environmental Prediction Friends and Partners of Aviation Weather NBAA Convention, Oct 10-11, 2017

ABO impact HRRR (and on HRRR weather RAP) Milestones Future models: Milestones Key points #1: Aircraft data most important observation type over North America for 3-12h (situational awareness) forecast accuracy (winds, temperature, Rel. Hum.) #2: Increased aircraft data has improved US (and global) forecast skill (2011-2015) #3: Geographical and temporal gaps in aircraft data provide opportunity for improved forecast accuracy through improved aircraft participation Ascent / descent 0-15 kft

Rapid Refresh and HRRR NOAA hourly updated models 13-km Rapid Refresh (RAP) 3-km High Resolution Rapid Refresh (HRRR) RAP Version 3 NCEP implement Aug 2016 Version 4 GSD (including HRRR-AK) NCEP Spring 2018 Hourly updating maximize use of ALL observations

Rapid Refresh HRRR (and hourly HRRR RAP) cycling Milestones Future Milestones improves guidance Use latest observations EACH HOUR to obtain freshest, most accurate snapshot of atmospheric state 1-hr fcst Background Fields 3DVAR Obs 1-hr fcst Analysis Fields 3DVAR Obs 1-hr fcst Get more accurate short-range weather forecasts for decision making WIND forecast errors 1h 3h 6h 12h Wind forecast improvement from hourly updating for 1-h vs. 6-h forecast 11 12 13 Time (UTC) 1 Jan 10 Oct 2017 vector wind RMS error for CONUS rawinsondes for different forecast lengths

Observations HRRR (and HRRR assimilated: RAP) Milestones Future Milestones RAP and HRRR Hourly Observation Type Variables Observed Observation Count Rawinsonde Temperature, Humidity, Wind, Pressure 120 / 12h Aircraft Wind, Temperature 2,000-15,000 / hr Aircraft WVSS, Tamdar (3 Aug ) Humidity 0 800 / hr Surface/METAR Temperature, Moisture, Wind, Pressure, Clouds, Visibility, Weather 2200-2500 Surface/Mesonet Temperature, Moisture, Wind ~5K-12K Buoys/ships Wind, Pressure 200-400 Profiler 915 MHz Wind, Virtual Temperature 20-30 Radar VAD Wind 125 Radar Radial Velocity 125 radars Radar reflectivity CONUS 3-d refl Rain, Snow, Graupel 1,500,000 Lightning (proxy reflectivity) NLDN GOES AMVs Wind 2000-4000 Polar Orbiter Satellite Radiances very large Geostationary Satellite Radiances large GOES cloud-top press/temp Cloud Top Height 100,000 GPS Precipitable water Humidity 260 WindSat Scatterometer Winds 2,000 10,000

Variables measured by each observation type Observation Types Temperature Wind Rel. Hum. Pres. / Height cloud/ saturation/ Hydrometeor Radiance Extent Raob Y Y Y Y --- --- 3D, 2x/day Surface Y Y Y Y Y --- 2D Aircraft Y Y Y Radar Reflectivity Radar Vr, VAD (~15%) --- --- (icing pireps not used) --- 3D --- --- --- --- Y --- 3D (in precip) --- Y --- --- --- --- 3D, Column GPS-met --- --- Y --- --- --- Column GOES (cloud, AMV) Satellite Radiance IN SITU OBS RADAR OBS --- --- Y --- Y --- 3D --- --- --- --- --- Y* 3D SATELLITE OBS *Satellite radiance: a complex function of temperature and humidity profiles Radar radial velocity: single wind component; AMV winds: height assignment issues

Regional Observation Impact studies with RAP - GSD Observation gaps are major source in limiting forecast accuracy, even over US RAP observation impact study 3 seasons, 8 (9) observation types Rawinsonde Aircraft obs Radar reflectivity VAD winds Surface obs GPS-Met AMV (winds) GOES clouds (Satellite Radiance) Aircraft observations most important observation type -- Ascent/descent and en route obs both important -- Water vapor observations (about 1/7 total) improve RH forecast accuracy Radar Coverage Sample aircraft obs coverage Raob Coverage

Aircraft observations -- most important data source for weather prediction skill Rawinsonde Aircraft Impact on WIND in 1000-100 hpa layer AC <350 mb AC >350 mb AC Temp+RH Reflectivity VAD Surface GPS AMV GOES 3/6/9/12 hr impact for each obs type Forecast degradation for withholding each obs type Raob Withhold: A ALL Rawinsonde B ALL Aircraft C Aircraft above 350 hpa D Aircraft below 350 hpa E Aircraft temp/humidity F ALL Profiler G ALL Radar Reflectivity H ALL VAD winds I ALL surface obs J ALL GPS-Met PW K ALL AMVs winds L GOES (winds/clouds) Aircraft obs most important for wind accuracy at all forecast lengths Significant impact also from rawinsonde, surface observations, GOES observations (likely from clearing of spurious convection)

Most Observation HRRR (and HRRR RAP) impact: Milestones Future Aircraft, Milestones raobs, GOES Rawinsonde Aircraft Radar reflect. VADs GPS 25% Error reduction (6-h) GOES Sat. radiance Surface AMVs Rawinsonde Aircraft Radar reflect. VADs GPS GOES Sat. radiance 25% Error reduction (6-h) Surface AMVs Relative humidity impact (%) (1000-400 mb) Rawinsonde Aircraft Profiler Radar Reflectivity VAD winds GPS-Met PW GOES RARS data Radiance data Surface obs AMVs Rawinsonde Wind impact (m/s) Spring retrospective Aircraft Radar reflect. VADs GPS 25% Error reduction (6-h) GOES (1000-100 mb) Sat. radiance Surface AMVs Temperature impact (k) (1000-100 mb)

Aircraft observation also important for global model forecast skill (satellite data dominates) Aircraft Raob/balloon Aircraft Cloud-drift vecs Surface obs Surface obs Global model obs impact: John Eyre UK Met Office - Impact on 24h global forecast error (FSOI) Contributions to the total observation impact on a moist 24-hour forecast-error energynorm, surface-150 hpa. Sat obs

Global aircraft observation importance will increase as global models start hourly cycling Global aircraft observation density

RUC skill HRRR improved (and HRRR RAP) Milestones from Future more Milestones aircraft obs RUC model frozen, skill improvement solely from more aircraft obs Monthly aircraft obs counts RUC RAP 6-h upper-level Wind RMS error 2011 2015 2011 Slide 12 2015 2008 2010 2012 2014 2016 Date http://www.wmo.int/pages/prog/www/gos/abo/data/statistics/aircraft_obs_cmc_mthly_ave_daily_reports_by_program.jpg

FAA aircraft obs study (GSD, AvMet) GOALS: Quantify gaps in airborne observations (spatial, temporal, parameter, etc,) Identify most cost effective ways to obtain airborne data AMDAR obs density CONUS (obs/km**2) per 24h period 2017 MWR article by James and Benjamin Study sponsored by FAA Tammy Farrar project sponsor

AMDAR obs densitry -- time of day -- CONUS Morning 12z 18z Evening 00z 06z Afternoon 18z 00z Overnight 06z 12z

AMDAR obs by elevation full day CONUS All levels 15-30 kft 30-45 kft Ascent / descent 0-15 kft

Regional Observation Impact studies with RAP - GSD Significant gap to achieve profiles every 300 km/3h -- achieving frequent aircraft profiles out of regional airports estimated to significantly improve forecast accuracy Airborne observation study sponsored by FAA (GSD, AvMet) 2017 article by James and Benjamin, MWR What are the coverage expansion priorities For improved model skill? Slide 16 0-15 kft Ascent / descent Data for an entire day

Improved mesoscale fields will lead better small-scale forecasts Previous results: Increased RAP skill leads to improved HRRR skill for thunderstorms and other weather hazards Improvements will be extended by ongoing storm-scale ensemble data assimilation for HRRR and future stormscale ensemble forecast system Improved short-term skill from HRRR ensemble HRRR ensemble skill HRRR current skill Slide 17 Wiring chart for HRRR Ensemble