NOAA/OAR report on recent JCSDA/satellite assimilation efforts 21 May 2014 Stan Benjamin - NOAA/ESRL ESRL/GSD talks Lidia Cucurull, Mariusz Pagowski, Haidao Lin AOML - Bob Atlas

Current Status - NOAA Hourly Updated Models RAP HRRR RAP - Rapid Refresh –NOAA “situational awareness” model for high-impact weather –New 18-hour forecast each hour –NOAA/NCEP operational – 1 May 2012 –RAPv2 implementation – 25 Feb 2014 –Hourly use by National Weather Service, SPC/AWC/WPC, FAA, private sector HRRR – High-Resolution Rapid Refresh -Storm/energy/aviation guidance -Real-time experimental on ESRL supercomputer -NCEP implementation planned for later

Rapid Refresh Hourly Update Cycle 1-hr fcst 1-hr fcst 1-hr fcst Time (UTC) Analysis Fields 3DVAR Obs 3DVAR Obs Back- ground Fields Partial cycle atmospheric fields – introduce GFS information 2x/day Fully cycle all land-sfc fields Hourly ObservationsRAP 2012 N. Amer Rawinsonde (T,V,RH)120 Profiler – NOAA Network (V)21 Profiler – 915 MHz (V, Tv)25 Radar – VAD (V)125 Radar reflectivity - CONUS2km Lightning (proxy reflectivity)NLDN, GLD360 Aircraft (V,T)2-15K Aircraft - WVSS (RH)0-800 Surface/METAR (T,Td,V,ps,cloud, vis, wx) Buoys/ships (V, ps) Mesonet (T, Td, V, ps)flagged GOES AMVs (V) AMSU/HIRS/MHS radiancesUsed GOES cloud-top pressure/temp13km GPS – Precipitable water~250 WindSat scatterometer2-10K

RAPv2 Data Assimilation GSI Hybrid GFS EnKF 80-member ensemble Available four times per day valid at 03z, 09z, 15z, 21z GSI Hybrid GSI HM Anx Digital Filter 18 hr fcst GSI Hybrid GSI HM Anx Digital Filter 1 hr fcst GSI HM Anx Digital Filter 18 hr fcst 13z 14z 15z 13 km RAP Cycle 1 hr fcst 80-member GFS EnKF Ensemble forecast valid at 15Z (9-hr fcst from 6Z) 18 hr fcst

Evaluation of satellite data assimilation impacts on mesoscale environment fields within the hourly cycled Rapid Refresh Haidao Lin Steve Weygandt Ming Hu Stan Benjamin Curtis Alexander Assimilation and Modeling Branch Global Systems Division NOAA Earth System Research Lab Boulder, CO Cooperative Institute for Research in the Atmosphere Colorado State University

Retrospective Experiments Set I: new sensors Extensive retro run for bias coefficients spin up Control run (CNTL) – Conventional data only 1-h cycling run, 8-day retro run (May 28 – June ) Hybrid EnKF RAP system AIRS radiance experiment CNTL + AIRS radiance data (no latency) Using 68 selected channels for RAP GOES radiance experiment CNTL + real time GOES 15 radiance data (sndrD1,sndrD2,sndrD3, sndrD4)

Impact from AIRS and GOES data (against raob hPa) Normalize Errors E N = (CNTL – EXP) CNTL T emperature May28-June hPa RMS mean GOES AIRS Relative Humidity Wind upper-air verification +1% +1.5%

Real-time % improvement from radiance DA T emperature Relative Humidity Wind hPa RMS mean +1% Radisonde verification 6 month REAL-TIME test Init Hour 11,23z 9,21z 6,18z 3,15z 0,12z 18,6z Fcst length Hrs since GFS GFS partial cycle at 09z and 21z

RAP observation denial experiments Experiments with observations denied Aircraft Profilers VAD winds RAOBs Surface (w/ METAR clouds) GPS prec water Mesonet Atmos motion vectors Radar reflectivity RAP – May-June 2011 ✔✔✔✔✔✔✔✔ RAP – May-June 2013 ✔✔✔✔✔✔ RAP – April 2014 ✔ NOAA Aircraft Data Workshop April 2014 Aviation Forecasts- Regional models 9

Summary N.Am data impact- RAP – May 2013 experiments -12z and 00z combined RAP 2013 obs impact Wind – hPa (up to FL500+) RH – hPa (to FL250) Temperature – hPa Aircraft – largest impact for wind/RH/temp – up to 20% reduction of forecast error, especially for 6h-9h forecasts Following in importance: Raob, surface, GPS-Met, AMVs airc raob sfc GPS AMV 6h F – 0h A for normalizing V – 1.8 m/s, T – 0.5K RH – 6% -20%

ModelData Assimilation RAP- ESRL (13 km) WRFv incl. physics changes Physics changes: Grell-Freitas convective scheme MYNN PBL update - Olson version RUC LSM update Thompson microphysics – v3.5.1 RRTMG radiation scheme Shallow cumulus parm w/ rad feed MODIS veg fraction/leaf area index Merge with GSI trunk Increase ensemble weight in hybrid DA 8m  2m bkg for sfc Td assim Radiance bias correction New sat assimilation (NOAA-19, METOP-B, GOES, direct readout – RARS) HRRR (3 km) WRFv incl. physics changes Physics changes: MYNN PBL update - Olson version RUC LSM update Thompson microphysics – v3.5.1 RRTMG radiation scheme MODIS veg fraction/leaf area index Numerics changes: 6 th order diffusion in flat terrain Smooth BC 3-km hybrid ens/var assimilation (was var-only in 2013) 8m  2m bkg for sfc Td assim Radar LH – 4x less intense than 2013 (2x less intense than RAP but more local) Changes with high/medium importance for overall forecast skill ESRL RAPv3/HRRR-2014 Changes (NCEP-2015)

ModelData Assimilation RAP- ESRL (13 km) WRFv incl. physics changes Physics changes: Grell-Freitas convective scheme MYNN PBL update - Olson version RUC LSM update Thompson microphysics – v3.5.1 RRTMG radiation scheme Shallow cumulus parm w/ rad feed MODIS veg fraction/leaf area index Merge with GSI trunk Increase ensemble weight in hybrid DA 8m  2m bkg for sfc Td assim Radiance bias correction New sat assimilation (NOAA-19, METOP-B, GOES, direct readout – RARS) HRRR (3 km) WRFv incl. physics changes Physics changes: MYNN PBL update - Olson version RUC LSM update Thompson microphysics – v3.5.1 RRTMG radiation scheme MODIS veg fraction/leaf area index Numerics changes: 6 th order diffusion in flat terrain Smooth BC 3-km hybrid ens/var assimilation (was var-only in 2013) 8m  2m bkg for sfc Td assim Radar LH – 4x less intense than 2013 (2x less intense than RAP but more local) Key satellite radiance changes for Haidao Lin, Ming Hu - ESRL/GSD ESRL RAPv3/HRRR-2014 Changes (NCEP-2015)

13 Verification of WRF-Chem AOD against MODIS - 550nm AOD (aerosol optical depth) verification MOZART LBC ECMWF’s IFS - LBCAnalysisAnalysis Fcst6hFcst6h Mariusz Pagowski, NOAA/ESRL/GSD

Effects of aerosols on meteorology 14 2m temp Stand. deviation of difference with/without aerosol feedback with shortwave for 3-month period in summer 2012 Mariusz Pagowski, NOAA/ESRL/GSD 2m water vapor mixing ratio

Impact of Infrared, Microwave and Radio Occultation Satellite Observations on Operational Numerical Weather Prediction Lidia Cucurull (1) and Richard A. Anthes (2) (1 ) Earth System Research Laboratory (ESRL) GSD / CIRES NOAA Office of Oceanic and Atmospheric Research (2) University Corporation for Atmospheric Research 12 th JCDSA Workshop, May 2014, College Park, MD.. 15

Impact of loss of MW and RO 16  Time period: March-April 2013  NCEP’s operational configuration  Verification done against consensus analysis (average of NCEP, ECMWF and UK Met Office analyses)  Experiments:  prctl: control, operational configuration with all the observations  prnogps: prctl without RO observations  prnoatms: prctl without ATMS observations  A potential gap in RO is a serious problem Cucurull and Anthes 2014b, in preparation

AOML’s REGIONAL TC OSSE/OSE SYSTEM Numerical Assimilation and Forecast Model: – NOAA’s Hurricane Weather Research and Forecasting (HWRF) Model Operational TC forecast model WRF-NMM dynamical core with storm-following grid nesting Options for data assimilation: – 3DVAR with NOAA Gridpoint Statistical Interpolation (GSI) Assimilation of conventional and satellite observations Satellite radiances are used only in cloud-clear conditions Grid-point-based static background errors – Hybrid 3DVAR with NOAA’s GSI-Hybrid data assimilation system Same capability for observations as GSI Applies weighting between ensemble-based and static background errors Ensemble perturbations updated by an EnKF – Ensemble Kalman Filter with NOAA/AOML/HRD Hurricane Ensemble Data Assimilation System (HEDAS) EnKF Developed in AOML as a research tool to study assimilation of TC airborne observations – H*Wind – VAM Nature run: WRF ARW embedded within ECMWF T511 Global nature run Bob Atlas, AOML/JCSDA

High Resolution Hurricane Nature Run: WRF Simulation Embedded Inside the ECMWF Nature Run 60 levels; 1km resolution; double-moment microphysics; advanced radiation schemes. RIRI ECMWF T511 Nature Run 1 km WRF-ARW Nature Run

Track forecasts from August 4 06Z for Nature (black), Control (purple), Control+WISSCR_COH (red) and Control+OAWL (green). Impact of lidar winds on HWRF Track forecasts

Relative accuracy of HWRF forecasts resulting from global or regional assimilation of OAWL Data

OAR upcoming talks  Thurs Do better aerosol forecasts improve weather forecasts? A regional modeling and assimilation studyMariusz Pagowski – OAR/ESRL  Thurs – Evaluation of satellite data assimilation impacts within the hourly cycled Rapid Refresh - Haidao Lin OAR/ESRL)  Thurs – Impact of Infrared, Microwave and Radio Occultation Satellite Observations on Operational Numerical Weather Prediction - Lidia Cucurull, OAR/ESRL 21