1. NOAA/OAR report on recent JCSDA/satellite assimilation efforts 13 May 2015 Stan Benjamin - NOAA/ESRL Contributions from - ESRL/GSD – Haidao Lin (poster – today) Lidia Cucurull (talk – Thurs) Mariusz Pagowski -ESRL/PSD – Jeff Whitaker -NSSL – Thomas Jones (talk – Thurs) Global cloud assimilation / increment - GSI with GOES/MT cloud composite Terra Ladwig, Ming Hu - GSD

2. Improved high-impact weather HRRR/RAP forecast accuracy from assimilation of satellite-based cloud, lightning, convection, AMVs, fire, and radiance data JCSDA Science Symposium – 13 May 2015 Stan Benjamin, Haidao Lin, Steve Weygandt, Curtis Alexander, Ming Hu NOAA/ESRL/GSD, Boulder, CO Background HRRR (3km)/ RAP (13km) are the hourly updated models for NOAA. Focus – situational awareness, decision making for severe weather, transportation, energy, all-season high-impact weather. HRRR/RAP are only NOAA models that currently assimilate satellite- based cloud and radar reflectivity data. Contributions 2015 HRRR/RAP additions at NCEP: lightning, radial wind, mesonet HRRR/RAP assimilation at ESRL – 2014-2015: Cloud-top cooling from GOES Fire data - ABBA, MODIS/VIIRS -HRRR-chem-smoke, RAPchem Direct readout radiance data from JPSS Initial testing of global cloud analysis (in progress) Results from RAP OSEs (obs sensitivity experiments) from radiances (RAP-based biased correction) and observations – Haidao Lin Refinement of static B, localization for short-range fcst – Ming Hu Rapid Refresh reflectivity - 0h valid 02z 26 Jan 2012 With lightning assim No lightning assim

3. One-month retro % improvement from radiance DA within RAP Radisonde verification Init Hour 11,23z 9,21z 6,18z 3,15z 0,12z 18,6z Fcst length 1 3 6 9 12 18 Hrs since GFS 2 0 9 6 3 9 GFS partial cycle at 09z and 21z RARS included (v3) Regular feed (v2) Normalize Errors E N = (CNTL – EXP) CNTL Temperature 1-hr 3-hr 6-hr 9-hr 12-hr 18-hr Relative Humidity 1-hr 3-hr 6-hr 9-hr 12-hr 18-hr Wind 1-hr 3-hr 6-hr 9-hr 12-hr 18-hr Half bar = 2*standard error 95% significant threshold 100-1000 hPa RMS mean OAR/ESRL/GSD – Haidao Lin

4. Hybrid 4DEnVar testing at ESRL/PSD HIWPP funded collaboration with NCEP/EMC to implement hybrid 4DEnVar in Q1FY16. PSD contributions: –Added 4D-IAU capability to GFS. –modifications/updates to EnKF code (developed at PSD) to support 4DEnVar and 4D-IAU. –Updates to stochastic physics in GFS (developed at PSD) to reduce resolution sensitivity. –Tuning of weight given to static B, localization length scales, and stochastic physics parameters. OAR/ESRL/PSD – Jeff Whitaker and team

5. Chem data assimilation testing at ESRL/GSD OAR/ESRL/GSD – Mariusz Pagowski

6. GOSA (Global Observing Systems Assessment) Group Recently formed – 1 October 2014 Maximize and optimize the uses of current and future global observations to improve numerical weather prediction forecast skill in NOAA’s models. This includes algorithm development, management, and science support Quantitatively evaluate the complementarity of different observing systems through OSSEs and OSEs to help NOAA management prioritize mission designs in a cost-effective way Why is GOSA so important? OSSEs save taxpayer $$ as they allow to analyze tradeoffs in the design and configuration of proposed observing systems (e.g. coverage, resolution, accuracy and data redundancy) 6 ESRL/GSD – Lidia Cucurull

7. NSSL Experimental Warn-on-Forecast System for ensembles (NEWS-e) Motivation: Providing accurate, short range (0-2 h) probabilistic forecasts of severe convective storms is a key component of the Warn-on-Forecast project Model-based probabilistic forecasts can aid in severe weather warning guidance and potentially lead to significantly improved lead times –Move from observation based warnings to a mix of observations and forecasts NEWS-e is an experimental high resolution (3 km) ensemble data assimilation system designed to test this concept –This system assimilates a combination of conventional, radar, and satellite observations onto a 3 km grid with a cycling frequency of 15 min. http://www.nssl.noaa.gov/projects/wof/news-e/ OAR/NSSL – Thomas Jones

8. Rapid Refresh and HRRR NOAA hourly updated models (situational awareness for energy, aviation, severe weather, etc.) JCSDA ScienceMay 2015 HRRR/RAP sat assim – high-impact wx 8 13km Rapid Refresh (RAP) (mesoscale) 3km High Resolution Rapid Refresh (HRRR) (storm-scale) RAP HRRR Version 2 -- NCEP implement 25 Feb 2014 Version 3 – GSD Planned NCEP – Aug 2015 Initial -- NCEP implement 30 Sept 2014 Version 2 – GSD Planned NCEP – Aug 2015

9. Data assimilation for RAP and HRRR 9 RAP Data Assimilation cycle Observations Hourly cycling model HRRR EnKF- hybrid, Radar and Cloud anx

10. HRRR 10-hr fcst made at 10 AM for 8 PM 27 April 2014 Observed radar HRRR forecast rotation track ✖ ✖ HRRR (and RAP) Future MilestonesHRRR Milestones HRRR Supercell Forecast Arkansas Actual tornado path Tornadic thunderstorm ✖ Fatalities HRRR

11. Hourly Observation TypeVariables ObservedObservation Count RawinsondeTemperature, Humidity, Wind, Pressure120 Profiler – 915 MHzWind, Virtual Temperature20-30 Radar – VADWind125 RadarRadial Velocity125 radars Radar reflectivity – CONUS3-d refl  Rain, Snow, Graupel1,500,000 Lightning(proxy reflectivity) GOEGOES-R, ground Aircraft Wind, Temperature2,000 -15,000 Aircraft - WVSSHumidity0 - 800 Surface/METAR Temperature, Moisture, Wind, Pressure, Clouds, Visibility, Weather 2200 - 2500 Surface/MesonetTemperature, Moisture, Wind~5K-12K Buoys/shipsWind, Pressure200 - 400 GOES AMVsWind2000 - 4000 AMSU/HIRS/MHS (RARS) Radiances1K-10K GOESRadianceslarge GOES cloud-top press/tempCloud Top Height100,000 GPS – Precipitable waterHumidity260 WindSat ScatterometerWinds2,000 – 10,000 RAPv3: Observations used (Sat-based) 11 HRRRv2 – all except radiances into 3km GSI assimilation

12. Hourly Observation TypeVariables ObservedObservation Count RawinsondeTemperature, Humidity, Wind, Pressure120 Profiler – 915 MHzWind, Virtual Temperature20-30 Radar – VADWind125 RadarRadial Velocity125 radars Radar reflectivity – CONUS3-d refl  Rain, Snow, Graupel1,500,000 Lightning(proxy reflectivity)NLDN Aircraft Wind, Temperature2,000 -15,000 Aircraft - WVSSHumidity0 - 800 Surface/METAR Temperature, Moisture, Wind, Pressure, Clouds, Visibility, Weather 2200 - 2500 Surface/MesonetTemperature, Moisture, Wind~5K-12K Buoys/shipsWind, Pressure200 - 400 GOES AMVsWind2000 - 4000 AMSU/HIRS/MHS (RARS) Radiances1K-10K GOESRadianceslarge GOES cloud-top press/tempCloud Top Height100,000 GPS – Precipitable waterHumidity260 WindSat ScatterometerWinds2,000 – 10,000 RAPv3: Observations used (new 2015) 12 HRRRv2 – all except radiances into 3km GSI assimilation

13. Outline HRRR (3km)/ RAP (13km) are the hourly updated models for NOAA. Focus – situational awareness, decision making for severe weather, transportation, energy, all-season high- impact weather. HRRR/RAP are only NOAA models that currently assimilate satellite-based cloud and radar reflectivity data. 2015 HRRR/RAP additions at NCEP: lightning, radial wind, mesonet HRRR/RAP assimilation at ESRL – 2014-2015: Cloud-top cooling from GOES Fire data from ABBA, MODIS for HRRR-chem-smoke, RAPchem Direct readout radiance data from JPSS Initial testing of global cloud analysis (in progress) Results from RAP OSEs (obs sensitivity experiments) from radiances and observations (including AMVs)

14.  Implement the enhanced variational bias correction scheme (developed by EMC/NCEP) with cycling  Remove some high-peaking channels to fit the model top of RAP, removes O 3 channels  Include the direct readout (Regional ATOVS Retransmission Services(RARS)) data  Include new sensors/data  GOES sounding data from GOES-15  AMSUA/MHS from NOAA-19 and METOP-B ； Radiance DA updates for RAPv3 (Aug-2015 NCEP implementation)

15. Radiance channels selected for RAPv3 (2015) AMSU-A (remove high-peaking channels) NOAA-15: channels 1-10, 15; NOAA-18: channels 1-8, 10,15; NOAA-19: channels 1-7, 9-10, 15; METOP-A: channels 1-6, 8-10,15 (removed channel 8 on 26 Sep. 2014 per NCEP note) ; METOP-B: channels 1-10, 15; HIRS4 (remove high-peaking and O 3 channels) METOP-A: channels: 4-8, 10-15; MHS NOAA-18/19, METOP-A/B : channels 1-5; GOES (remove high-peaking and ozone channels) GOES-15 (sndrD1,sndrD2,sndrD3,sndrD4): channels 3- 8, 10-15. RARS - NOAA-15/18/19, METOP-A/B

16. Daily averaged percent (%) AMSU-A channel 3 from NOAA-18 +/- 1.5 h time window Averaged over one-month period (20130501-20130531) Regular Feed (RAPv2) RARS Feed (RAPv3) IDEAL -- No latency/cutoff (100%) 5.8% 41% 100% Percent against ideal conditions (using gdas data sets)

17. Hourly averaged observation number and hourly averaged observation % against GDAS hourly averaged number per channel hourly averaged observation percent against GDAS 0%-26% 10%-64% AMSU-A channel 3 from NOAA-18 Regular (RAPv2) RARS (RAPv3) GDAS

18. Retrospective Experiments (RAPv3) Control run (CNTL) – (conventional data only) 1-h cycling run, one-month retro run (May 01 – May 31 2013) RAP Hybrid EnKF system RAP radiance regular feed (limited availability) CNTL + RAP radiance regular feed data (amsua/mhs/hirs4/goes) Including RAPv3 radiance updates except including RARS data RARS data included (improved availability) CNTL+RAP radiance regular feed data + RARS data (RARS data for amsua/mhs) Including all RAPv3 radiance updates

19. One-month retro % improvement from radiance DA Radisonde verification Init Hour 11,23z 9,21z 6,18z 3,15z 0,12z 18,6z Fcst length 1 3 6 9 12 18 Hrs since GFS 2 0 9 6 3 9 GFS partial cycle at 09z and 21z RARS included Regular feed Normalize Errors E N = (CNTL – EXP) CNTL Temperature 1-hr 3-hr 6-hr 9-hr 12-hr 18-hr Relative Humidity 1-hr 3-hr 6-hr 9-hr 12-hr 18-hr Wind 1-hr 3-hr 6-hr 9-hr 12-hr 18-hr Half bar = 2*standard error 95% significant threshold 100-1000 hPa RMS mean

20. One-month retro 6h fcst improvement from radiance Radisonde verification Half bar = 2*standard error 95% significant threshold 100-1000 hPa RMS mean Temperature Relative Humidity Wind RARS included- control run

21. 21 HRRR-chem – assimilation of WFABBA HRRR-chem- 3km PM25- surface 11h fcst valid 17z 24 Feb 2015

22. Example: RAP cold-start tests without/with aerosol-aware cloud microphysics. More small-scale cloud with more CCN over land. NCEP RAPv3/HRRRv2-2015 Changes Use of forecast aerosol fields to have prognostic cloud- condensation nuclei (CCN). 22

23. Cloud-top cooling (CTC) assimilation NOAA/ESRL/GSD and UAH Tracy Lorraine Smith, Steve Weygandt// John Mecikalski RAP experiment: Analysis For 00h 25dBz 19z 19 June 2014. Improved CSI, POD, FAR with CTC RAP1h Forecast valid 20z 19 June 2014 CSI, POD same with CTC. Cloud-top cooling data from U.Alabama-Huntsville. Assimilation of larger cooling than -3K/15min. Next step: CTC assimilation at 3km in HRRR.

24. Example: Northern Hemisphere NASA LaRC (GOES-East, GOES- West, MT-10, MT-2) data coverage via a plot of cloud top pressure Example increment for cloud analysis applied to GFS (T126 resolution) Positive values indicate where cloud water was removed from the background by the cloud analysis. Extension of GSI-cloud/hydrometeor non-var DA from previous RAP/HRRR application to GFS/global - demonstration 11 May 2015 2200 UTC Ming Hu, Terra Ladwig OAR/ESRL/GSD Under Sandy Supplemental funding from NWS/NCEP in collaboration with NCAR (Tom Auligné et al)

25. OAR/related upcoming contributions  Wed – 1630 (poster) – Haidao Lin – ESRL/GSD - Evaluation of sat data assimilation impacts within the hourly cycled Rapid Refresh – Jason Otkin (U.Wisc/NESDIS) – GOES-based verification/guidance system using HRRR  Thurs – 1350 – Pius Lee – ARL – Aerosol assimilation for AQ forecasting – 1510 – Thomas Jones – NSSL – Experimental ensemble DA for future Warn-on-Forecast – 1610 – Lidia Cucurull – OSE/OSSE activities at GSD  Fri – 0900 – Sandy MacDonald - ESRL – Invited keynote 25