Applications in situational awareness high-resolution NWP -- Ideas for the Blueprint DA discussion

Transcription

1 Applications in situational awareness high-resolution NWP -- Ideas for the Blueprint DA discussion 8 March 2016 Stan Benjamin, David Dowell, Curtis Alexander NOAA/ESRL/GLOBAL SYSTEMS DIVISION Situational awareness - Blueprints for Next-Gen DA Systems 8-10 March 2016

2 DA extensions for situational awareness, retention in short-range NWP Already Cloud/hydrometeor Radar reflectivity Land-surface simple coupling Ensemble DA 13km, 3km, HRRRE Aerosols RAP-chem, HRRR-smoke Some things needed Sub-grid-scale, PDF representations Clouds, PBL, turbulence Hydrometeor/aerosol linkage min updating In-memory continuous DA, minimize I/O Full earth-system coupling Global rapid refresh 1

3 RAPv3 and HRRRv2 Initialization 13 km RAP 13z 14z 15z RAP GSI Vr assimilation Hybrid GSI Hybrid GSI Hybrid RAP reflect. Assimilation GSI HM (no Anx spin-up) (DFI) GSI HM Anx GSI HM Anx Digital Filter 18 hr fcst Digital Filter 18 hr fcst Digital Filter 18 hr fcst 3 km HRRR 3-km Interp 1 HRRR hr pre-fcst reflect. Assimilation (pre-fcst) (No DFI) Refl Obs HRRR GSI Vr Hybrid assim GSI HM Anx 15 hr fcst

4 RAP/HRRR: Hourly-Updating Weather Forecast Models 13-km Rapid Refresh (RAP) to 21h (May 2016) Initial & Lateral Boundary Conditions 3-km High-Resolution Rapid Refresh (HRRR) to 18h (May 2016) Initial & Lateral Boundary Conditions 750-m HRRR nest Wind Forecast Improvement Project Experiment (ongoing) 3-km High-Resolution Time Lagged Ensemble (HRRR-TLE) Expanded RAP to match NAM for SREF (May 2016) 3-km High- Resolution Rapid Refresh Alaska Testing (HRRR-AK) w/mrms radar data (Spring 2016) Prototype 3-km storm-scale HRRR ensemble (HRRRE) (Spring 2016) 3

5 RAPv3/HRRRv2 Observation Data Assimilation Changes New in RAPv3/HRRRv2 Radial Velocity (RAPv3) Lightning (RAPv3) Mesonet (RAPv3/HRRRv2) RARS Radiances (RAPv3) 2016 Hourly Observation Type Variables Observed Observation Count Rawinsonde Temperature, humidity, wind, pressure 125 Profiler NOAA/ 915 MHz Wind, virtual temperature 2 / Radar VAD Wind 125 Radar Radial velocity 125 radars Radar reflectivity CONUS 3-d refl Latent htg, rain,snow,graupel ~1,500,000 Lightning NLDN / GOES-R Flash density Proxy reflectivity 10K Aircraft Wind, temperature 3,000-25,000 Aircraft - WVSS Humidity Surface/METAR Temperature, moisture, wind, press, clouds/ceiling, visibility, current weather Surface/Mesonet Temperature, moisture, wind ~5K-12K Buoys/ships Wind, pressure GOES AMVs Wind 10K AMSU/HIRS/MHS (RARS) Radiances (direct readout) 250K/200K/200KK GOES Radiances (fire location/intensity-smoke) ~3,000K GOES-R Lightning, cloud-top cooling >3,000K GOES cloud-top press/temp Brightness temp ~380K GPS Precipitable water Humidity 300 METOP-B AScat Winds ~30,000 4

6 RUC/RAP/HRRR: Improving Forecast Skill Crossover in forecast skill between Nowcasting/Extrapolation vs Numerical Weather Prediction Forecast Skill Less Skill More Skill -- Extrapolation -- Persistence HRRR 3-km Radar Data Assimilation RUC 13-km Radar Data Assimilation Pre-Radar Data Assimilation Improving forecast skill and halving crossover period every ~3-4 years Forecast Length (Hours) 5

7 RAP/HRRR: Improving Forecast Skill Critical Success Index X 100 Less Skill More Skill HRRR Forecast Skill for Reflectivity (30 dbz) reflectivity heating rate: low-cost assimilation, significant forecast improvement radar data assimilation in RAP and HRRR no radar data assimilation Forecast Length (Hours) 6

8 RAP/HRRR Development History Less Skill More Skill HRRR precipitation location skill improves by 50% over past 5 years Underforecast Overforecast HRRR precipitation bias reduced by 60% over past 5 years 7

9 RUC/RAP: Diabatic Digital Filter Assimilation Digital filter-based assimilation initializes ongoing / developing convection / precipitation regions Use for following obs types: Radar reflectivity Lightning Satellite cloud-top cooling rate -20 min -10 min Initial +10 min + 20 min Backwards integration, no physics Forward integration,full physics with obs-based latent heating Initial fields with improved balance, storm-scale circulation RUC / RAP model forecast 8

10 RUC/RAP: Diabatic Digital Filter Assimilation Latent Heating Promotes Mesoscale Circulations in Regions of Precipitation Observed Reflectivity U wind component difference (radar no radar) Low-Level Upper-Level Quick. Baseline for ens DA including HRRRE 1400 UTC 22 Oct 2008 Z = 3 km Enhanced Convergence Enhanced Divergence 9

11 RAP/HRRR Cloud and Precip Hydrometeor Analysis Observations Map to cloud field Merge cloud field Quick. Baseline for ens DA including HRRRE No cloud Cloud Unknown Update hydrometeors based on the cloud field 10

12 Rapid Refresh GSI Options Surface Obs Special treatments for surface observations Quick. Baseline for ens DA including HRRRE

13 GSI namelists from GSD Namelist explanation Default value RAP value dfi_radar_latent_heat_time_period DFI forward integration window in minutes metar_impact_radius METAR cloud obs impact radius in grid number metar_impact_radius_lowcloud l_gsd_terrain_match_surftobs l_sfcobserror_ramp_t l_sfcobserror_ramp_q l_pbl_pseudo_surfobst l_pbl_pseudo_surfobsq l_pbl_pseudo_surfobsuv pblh_ration METAR low cloud observation impact radius in grid number if.true., GSD terrain match for surface temperature observation namelist logical for adjusting surface temperature observation error namelist logical for adjusting surface moisture observation error if.true. produce pseudo-obs in PBL layer based on surface obs T if.true. produce pseudo-obs in PBL layer based on surface obs Q if.true. produce pseudo-obs in PBL layer based on surface obs UV percent of the PBL height within which to add pseudo-obs 4 4.true..true..true..true..false

14 GSI namelists from GSD Namelist explanation Default value RAP value dfi_radar_latent_heat_time_period DFI forward integration window in minutes metar_impact_radius METAR cloud obs impact radius in grid number metar_impact_radius_lowcloud l_gsd_terrain_match_surftobs l_sfcobserror_ramp_t l_sfcobserror_ramp_q l_pbl_pseudo_surfobst l_pbl_pseudo_surfobsq l_pbl_pseudo_surfobsuv pblh_ration METAR low cloud observation impact radius in grid number if.true., GSD terrain match for surface temperature observation namelist logical for adjusting surface temperature observation error namelist logical for adjusting surface moisture observation error if.true. produce pseudo-obs in PBL layer based on surface obs T if.true. produce pseudo-obs in PBL layer based on surface obs Q if.true. produce pseudo-obs in PBL layer based on surface obs UV percent of the PBL height within which to add pseudo-obs 4 4.true..true..true..true..false

15 Improved forward model for 2m surface obs when available, improved information matching l_use_2mq4b if.true. use 2m Q/T as part of background to calculate surface Q observation innovation.true. Z >>8m, k=1 level for NAM, GFS Z=8m, k=1 level for RAP (σ=0.998) Z=2m, shelter height for temp/dewpoint obs Z=0m, atmos/sfc interface

16 Purposes for RUA Rapidly Updated Analysis high-frequency environmental nowcasts 1. Pure situational awareness use all observations as precisely as possible. (Use high-res fcst as background, e.g., HRRR ) 2. Initial conditions for extrapolation model (e.g., AutoNowcaster, CIWS) 3. Initial conditions for hydrodynamic model (may require multivariate equilibrium not needed for #1 or #2). Note: #3 is approaching #1 and #2 but not there yet.

17 Vision: Unification for 3-d nowcasting under RUA Diagnose all other fields from 3-d best estimate of atmosphere/earth-system Cloud cover PBL height 80m winds RUA should include Water in all forms Atmosphere: water vapor, hydrometeor types (mixing ratio, number concentration, bins, etc.) Land-surface field soil, vegetation, snow cover Contribution to QPE 3-d aerosol/smoke/chemistry

18 Current nowcast components Observations Remotely sensed Radar MRMS national/international composite Ancillary radar CASA, etc. PBL profiler Satellite GOES, polar-orbiter radiance, cloud, scat Camera road cams, all-sky Ceilometer, visibility - cloud In situ surface, aircraft, raob, tower) Modern data assimilation merger GSI initializing 3km HRRR one start

19 RAP/HRRR variables updated in data assimilation

20 DA for Land Surface Model (LSM) for HRRR/RAP 9 soil layers, 2 snow layers Surface observations are used to update the LSM through the data assimilation step. For example, the soil temperature is decreased and soil moisture is increased where the model is too warm and too dry compared to the surface observations. Soil Temperature Example Soil Adjustments 20 UTC 03 June 2013 Soil Moisture Cooling Warming Moistening Drying

21 Snow-cover updating HRRRv2 full land-sfc/snow cycling Wyoming Nebraska Denver Colorado HRRRv2-exp ESRL HRRRv1-oper NCEP NOHRSC Snow water equivalent 06z 20 May 2015 inches

22 HRRRv2 Real-Time Evaluation: Precipitation 2013 Warm Season (June-August) HRRR 0-6 hr precipitation forecast Difference against Stage IV Dry Moist 21

23 HRRRv2 Real-Time Evaluation: Precipitation 2015 Warm Season (June-August) HRRR 0-6 hr precipitation forecast Difference against Stage IV Reduction in high precipitation bias Dry Moist 22

24 HRRR-chem assimilation of WFABBA HRRR-chem-3km PM25- sfc 11h fcst valid 17z 24 Feb

25 RAP-chem assimilation of PM2.5 data Rapid Refresh with Chemistry Real time forecasts predicting weather and air quality based on WRF-Chem, GSI (Mariusz Pagowski) WRF-Chem coupled with chemistry via RACM chemical mechanism including MOSAIC/VBS for prediction of secondary organic aerosols (SOA). MEGAN biogenic emissions, NEI and RETRO/EDGAR anthropogenic emissions, Chemical deposition, Convective and turbulent chemical transport, Photolysis, Advective chemical transport performed simultaneously with meteorology ("online"), Lateral chemical boundary conditions obtained from RAQMS model real time forecasts. 24

26 HRRRE (HREF) Prototype 3- km stormscale HRRR ensemble domain 2016 Single core (ARW) Ensemble DA Stochastic physics Assimilation Forecast members 9 members 1 hr forecast hr forecast 21 cycles / day 4+ fcsts / day 21z Prev Day Start 00z, 12z, 15z, 18z Beginning development of formal 3-km data assimilation and forecast ensemble More accurate storm-details from ensemble data assimilation 25

27 Observations HRRRE (HREF) 12 hr Forecasts Valid 00 UTC 8 Mar 2016 Six Member Ensemble 26

28 Challenges of storm-scale DA number of radar and cloud observations highly variable in space/time: fair weather only a few observations, convective storms many observations methods needed to deal with non-gaussian ensemble distributions and nonlinear observation operators: variable transformations and/or advanced DA methods examples of non-gaussian distributions: (1) bimodal distributions (some ens members have conv storm, some don t), (2) raindrop number concentration example of nonlinear observation operator: reflectivity (proportional to log q if assimilated observation is in dbz) 27

29 Future for situational awareness DA Requirement - 5-min DA w/ radar and satellite data while maintaining quick speed keeping all data (grids esp.) in memory all the time is essential. Design toward an 80-mem 1km ensemble updating every 5 min, even global [Note: some evidence that if we do a good job computing the obs operators (ensemble priors) at the exact observation time, 10-min assimilation windows will be good enough for storm-scale DA] Coupled land-surface/chem/atmos DA - incl. sub-grid repr (e.g., clouds) on-demand capability for very high resolution applications: DA and NWP grids generated where there are risks of severe weather, heavy rain / snow, fire, etc.; grid discarded when risk is gone, perhaps after only a few hours Use of JEDI-like, community next-gen DA 28