NOAA ESRL GSD Earth Modeling Branch Boulder, CO USA 6 th Conf on Weather/Climate/New Energy Economy January 2015 HRRR/RAP assimilation/model improvements.

Slides:



Advertisements
Similar presentations
Adaptation of the Gridpoint Statistical Interpolation (GSI) for hourly cycled application within the Rapid Refresh Ming Hu 1,2, Stan Benjamin 1, Steve.
Advertisements

Jared H. Bowden Saravanan Arunachalam
Modifications to the MYNN PBL and Surface Layer Scheme for WRF-ARW Joseph Olson1,2 John M. Brown1 1NOAA-ESRL/GSD/AMB 2Cooperative Institute for Research.
Improved high-impact weather HRRR/RAP forecast accuracy from assimilation of satellite-based cloud, lightning, convection, AMVs, fire, and radiance data.
Rapid Refresh and RTMA. RUC: AKA-Rapid Refresh A major issue is how to assimilate and use the rapidly increasing array of off-time or continuous observations.
The 2014 Warn-on-Forecast and High-Impact Weather Workshop
ESRL – Some Recommendations for Mesoscale Ensemble Forecasts Consolidate all NCEP regional storm-scale model runs perhaps under HRRRE (or other) banner.
Transitioning unique NASA data and research technologies to the NWS 1 Evaluation of WRF Using High-Resolution Soil Initial Conditions from the NASA Land.
1 NGGPS Dynamic Core Requirements Workshop NCEP Future Global Model Requirements and Discussion Mark Iredell, Global Modeling and EMC August 4, 2014.
Consortium Meeting June 3, Thanks Mike! Hit Rates.
Global Forecast System (GFS) Model Previous called the Aviation (AVN) and Medium Range Forecast (MRF) models. Global model and 64 levels Relatively primitive.
Jamie Wolff Jeff Beck, Laurie Carson, Michelle Harrold, Tracy Hertneky 15 April 2015 Assessment of two microphysics schemes in the NOAA Environmental Modeling.
NOAA ESRL GSD Assimilation and Modeling Branch WoF / Hi-Impact Wx Workshop 1 April 2014 – Norman, OK From RAPv3/HRRR-2014 to the NARRE/HRRRE era Stan Benjamin.
Weather Model Development for Aviation Stan Benjamin and Steve Weygandt: Assimilation and Modeling Branch, Chief/Deputy NOAA Earth System Research Laboratory,
Earth Science Division National Aeronautics and Space Administration 18 January 2007 Paper 5A.4: Slide 1 American Meteorological Society 21 st Conference.
Verification Summit AMB verification: rapid feedback to guide model development decisions Patrick Hofmann, Bill Moninger, Steve Weygandt, Curtis Alexander,
Use of satellite observations in wind and solar forecasting and in offshore wind resource assessments Melinda Marquis Renewable Energy Program Manager.
Assimilation of AIRS Radiance Data within the Rapid Refresh Rapid Refresh domain Haidao Lin Steve Weygandt Ming Hu Stan Benjamin Patrick Hofmann Curtis.
COSMO General Meeting, Offenbach, 7 – 11 Sept Dependance of bias on initial time of forecasts 1 WG1 Overview
NCEP Production Suite Review: Land-Hydrology at NCEP
Development of an EnKF/Hybrid Data Assimilation System for Mesoscale Application with the Rapid Refresh Ming Hu 1,2, Yujie Pan 3, Kefeng Zhu 3, Xuguang.
The NOAA Rapid Update Cycle (RUC) 1-h assimilation cycle WWRP Symposium -- Nowcasting & Very Short Range Forecasting – 8 Sept 2005 – Toulouse, France Stan.
Météo-France / CNRM – T. Bergot 1) Introduction 2) The methodology of the inter-comparison 3) Phase 1 : cases study Inter-comparison of numerical models.
Modification of GFS Land Surface Model Parameters to Mitigate the Near- Surface Cold and Wet Bias in the Midwest CONUS: Analysis of Parallel Test Results.
Experiences with 0-36 h Explicit Convective Forecasting with the WRF-ARW Model Morris Weisman (Wei Wang, Chris Davis) NCAR/MMM WSN05 September 8, 2005.
Potential Benefits of Multiple-Doppler Radar Data to Quantitative Precipitation Forecasting: Assimilation of Simulated Data Using WRF-3DVAR System Soichiro.
Seasonal Modeling (NOAA) Jian-Wen Bao Sara Michelson Jim Wilczak Curtis Fleming Emily Piencziak.
WMO/WWRP Workshop Use of NWP for Nowcasting 25 October 2011 Evaluation of the 3-km High Resolution Rapid Refresh (HRRR) as Nowcast Guidance NOAA/ESRL/GSD.
Storm-Scale Modeling with HRRR toward Warn-On-Forecast
Transitioning unique NASA data and research technologies to the NWS 1 Evaluation of WRF Using High-Resolution Soil Initial Conditions from the NASA Land.
WRF Problems: Some Solutions, Some Mysteries Cliff Mass and David Ovens University of Washington.
Development and Testing of a Regional GSI-Based EnKF-Hybrid System for the Rapid Refresh Configuration Yujie Pan 1, Kefeng Zhu 1, Ming Xue 1,2, Xuguang.
2006(-07)TAMDAR aircraft impact experiments for RUC humidity, temperature and wind forecasts Stan Benjamin, Bill Moninger, Tracy Lorraine Smith, Brian.
Evaluation of impact of satellite radiance data within the hybrid variational/EnKF Rapid Refresh data assimilation system Haidao Lin Steve Weygandt Ming.
Edward Mansell National Severe Storms Laboratory Donald MacGorman and Conrad Ziegler National Severe Storms Laboratory, Norman, OK Funding sources in the.
Evaluation of radiance data assimilation impact on Rapid Refresh forecast skill for retrospective and real-time experiments Haidao Lin Steve Weygandt Stan.
GSI applications within the Rapid Refresh and High Resolution Rapid Refresh 17 th IOAS-AOLS Conference 93 rd AMS Annual Meeting 9 January 2013 Patrick.
Modeling and Evaluation of Antarctic Boundary Layer
NOAA/OAR report on recent JCSDA/satellite assimilation efforts 21 May 2014 Stan Benjamin - NOAA/ESRL ESRL/GSD talks Lidia Cucurull, Mariusz Pagowski, Haidao.
Module 17 MM5: Climate Simulation BREAK. Regional Climate Simulation for the Pan-Arctic using MM5 William J. Gutowski, Jr., Helin Wei, Charles Vörösmarty,
1 RUC Land Surface Model implementation in WRF Tanya Smirnova, WRFLSM Workshop, 18 June 2003.
NCEP Production Suite Review
16th Annual WRF Users’ Workshop
Recent and Future Advancements in Convective-Scale Storm Prediction with the High- Resolution Rapid Refresh (HRRR) Forecast System NOAA/ESRL/GSD/AMB Curtis.
Consortium Meeting Feb 07, Our Audience, Hits: January 2013.
Assimilation of AIRS SFOV Profiles in the Rapid Refresh Rapid Refresh domain Haidao Lin Ming Hu Steve Weygandt Stan Benjamin Assimilation and Modeling.
Rapid Update Cycle-RUC. RUC A major issue is how to assimilate and use the rapidly increasing array of offtime or continuous observations (not a 00.
Lightning data assimilation in the Rapid Refresh and evaluation of lightning diagnostics from HRRR runs Steve Weygandt, Ming Hu, Curtis Alexander, Stan.
1 Xiaoyan Jiang, Guo-Yue Niu and Zong-Liang Yang The Jackson School of Geosciences The University of Texas at Austin 03/20/2007 Feedback between the atmosphere,
Land-Surface evolution forced by predicted precipitation corrected by high-frequency radar/satellite assimilation – the RUC Coupled Data Assimilation System.
2004 Developments in Aviation Forecast Guidance from the RUC Stan Benjamin Steve Weygandt NOAA / Forecast Systems Lab NY Courtesy:
Global vs mesoscale ATOVS assimilation at the Met Office Global Large obs error (4 K) NESDIS 1B radiances NOAA-15 & 16 HIRS and AMSU thinned to 154 km.
WRF-based rapid updating cycling system of BMB(BJ-RUC) and its performance during the Olympic Games 2008 Min Chen, Shui-yong Fan, Jiqin Zhong Institute.
WARN ON FORECAST AND HIGH IMPACT WEATHER WORKSHOP 09 February 2012 Use of Rapid Updating Meso‐ and Storm‐scale Data Assimilation to Improve Forecasts of.
HRRR Primary FAA-CoSPA NCEPESRL/GSD/AMB RAP Dev1 RAPv2 Primary HRRR Dev1 RAPv1 NCO RAP Dev2 RAP Retro HRRR Retro Retrospective Real-Time HRRR (and RAP)
RUC Convective Probability Forecasts using Ensembles and Hourly Assimilation Steve Weygandt Stan Benjamin Forecast Systems Laboratory NOAA.
GLFE Real-time TAMDAR Impact Experiments with the 20km RUC Stan Benjamin,Tracy Lorraine Smith, Bill Moninger, Brian Jamison NOAA Forecast Systems Laboratory.
NOAA Capabilities Relevant to DOE Solar Forecasting FOA (DE-FOA ) Webinar May 30, 2012.
Rapid Update Cycle-RUC
Tadashi Fujita (NPD JMA)
High-Resolution Rapid Refresh (HRRR): WRF Enhancements and Challenges
Update on the Northwest Regional Modeling System 2013
Aircraft weather observations: Impacts for regional NWP models
Aircraft-based Observations:
Rapid Update Cycle-RUC Rapid Refresh-RR High Resolution Rapid Refresh-HRRR RTMA.
New Developments in Aviation Forecast Guidance from the RUC
2018 EnKF Workshop Development and Testing of a High-Resolution Rapid Refresh Ensemble (HRRRE) David Dowell, Trevor Alcott, Curtis Alexander, Jeff Beck,
Cliff Mass and David Ovens University of Washington
Consortium Meeting June 6, 2019
Case Study: Evaluation of PBL Depth in an Erroneous HRRR forecast for CI Keenan Eure.
Presentation transcript:

NOAA ESRL GSD Earth Modeling Branch Boulder, CO USA 6 th Conf on Weather/Climate/New Energy Economy January 2015 HRRR/RAP assimilation/model improvements for wind/solar energy guidance Stan Benjamin Joseph Olson Eric James Jaymes Kenyon Curtis Alexander John Brown Tanya Smirnova Ming Hu New wind/solar observations enabling model improvements (tower, SURFRAD) Major improvements in wind/solar (cloud) guidance in 2015 from HRRRv2/RAPv3 Rapid Refresh version 3 Scheduled NCEP implementation Jun2015 ESRL RAPv3 update occurred 1 Jan 2015 High-Resolution Rapid Refresh V2 Scheduled NCEP implementation Jun2015 ESRL HRRRv2 update occurred 1 Jan

Rapid Refresh and HRRR NOAA hourly updated models (situational awareness for energy, aviation, severe weather, etc.) AMS-2015 – 6 th EnergyJanuary 2015 HRRR/RAP upgrades for energy 2 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 –June 2015 Initial -- NCEP implement 30 Sept 2014 Version 2 – GSD Planned NCEP –June 2015

3 HRRR (and RAP) Future MilestonesHRRR Milestones Wind Forecast Errors in RAP and HRRR - A problem for wind energy / PBL/ surface forecasts WFIP – first extensive evaluation of HRRR/RAP forecasts w/ tower wind obs. Substantial improvement from RAP over RUC, but… Remaining wind errors evident WFIP Northern Study Area (NSA) Results from Wilczak et al, BAMS, 2015 Model forecast errors limit usefulness of renewable energy

4 HRRR (and RAP) Future MilestonesHRRR Milestones Eric James Energy Tues 1145am Cloud Deficiency in RAP and HRRR - A problem for solar energy / PBL/ convection forecasts

4-h forecast of composite reflectivity: valid 00Z 18 Jun 2014 HRRR (and RAP) Future MilestonesHRRR Milestones Convective Evolution 17 June UTC 18 June 2014 Real-Time Forecast Temperature Pseudo-Obs Enabled Observations Coleridge, NE Control run develops too much high-based convection that grows upscale Data assimilation change improves timing and evolution of convection 17 th ARAM5 January 2015HRRR Implementation 5 AMS-2015 – 6 th EnergyJanuary 2015 HRRR/RAP upgrades for energy 5

ModelData Assimilation RAP (13 km) WRF-ARWv3.6+ incl. physics changes Physics changes: Grell-Freitas convective scheme Thompson MP -- Aerosol-aware MYNN PBL -- cloud/non-loc mixing RUC LSM -- MODIS seasonal LAI -- Reduced wilting point Shallow cu parm w/rad feedback RRTMG radiation scheme Direct and diffuse GHI components Merge with GSI trunk Radial velocity assimilation Mesonet assimilation RARS data assimilation Radiance bias correction Pseudo-PBL obs for temperature Improved 2m Temp/Dewpoint Background Estimate Low-reflectivity precip building HRRR (3 km) WRF-ARWv3.6+ incl. physics changes Physics changes: Thompson MP - Aerosol-aware MYNN PBL -- cloud/non-loc mixing RUC LSM -- MODIS seasonal LAI -- Reduced wilting point Shallow cu parm w/rad feedback RRTMG radiation scheme Direct and diffuse GHI components Numerics changes: 6 th order diffusion in flat terrain Merge with GSI trunk 3-km hybrid assimilation Hydrostatic rebalance after analysis Radial velocity assimilation Mesonet assimilation Pseudo-PBL obs for temperature Improved 2m Temp/Dewpoint Background Estimate Low-reflectivity precip building Full cloud/precip hydrometeor assim RAPv3/HRRRv2 Changes ( ) Strong cloud /solar / near-sfc wind forecast impact

HRRR (and RAP) Future MilestonesHRRR Milestones  Grell−Freitas “scale-aware” shallow cumulus (subgrid-scale) Accounts for nonlocal mixing of heat & water vapor from condensed (non-precipitating) subgrid plumes “Scale-aware” mass flux scheme Produces subgrid cloud water & ice  coupled to radiation  MYNN boundary-layer cloud fraction (subgrid-scale) Function of local and PBL-mean RH, surface heat flux, resolution Activates in absence of resolved / parameterized cloud fraction in column Diagnoses subgrid cloud water & ice (specifically for radiation coupling)  RUC-LSM  Reduced wilting points  Maintain cropland at wilting point  Aerosol-aware Thompson cloud microphysics More smaller clouds over land Physics updates to reduce RAP/HRRR biases 7

First implemented with RAPv2 / HRRR 2013 – better convection, wind forecasts Improved mixing length formulations to flexibly change behavior across stability spectrum Improved surface layer scheme (customize to PBL ) Further enhancements for RAP v3 / HRRR v2 -- cloud mixing, coupling to different shallow cumulus schemes RAP / HRRR use of MYNN PBL scheme UTC Wind speed bias (m/s) RAPv1 RAPv2 RAPv3 NightAfternoon Morning Evening NightAfternoon Morning Evening RAPv1 RMS wind vector error (m/s) RAPv2 RAPv UTC MYNN-modifiedMYJ TKE (color bars) 8

Example: RAP cold-start tests without/with aerosol-aware cloud microphysics. More small-scale cloud with more CCN over land. NCEP RAPv3/HRRRv Changes Use of forecast aerosol fields to have prognostic cloud- condensation nuclei (CCN). 9

Thinner soil layer in energy / moisture budgets Potential for increased near-surface diurnal cycle Reduced warm bias at night, cold bias in day Updates to RUC Land Surface Model Increased roughness Z 0 for forests, cropland, urban New formulation to compute effective roughness length Z 0eff in the grid box (exponential) ======================= Version 3 (partial list) Seasonal variations of Z 0 for MODIS cropland category Seasonal variations of LAI based on the current vegetation fraction and variability of this parameter for different vegetation types OLD ~ 8 m 0 (cm) NEW ~ 8 m 0 (cm) RAP improvements Increased number of levels in soil domain – 9 levels Version th ARAM - AviationJanuary 2015Physics RAP / HRRR 10 AMS-2015 – 6 th EnergyJanuary 2015 HRRR/RAP upgrades for energy 10

ComponentItems GSI Data Assimilation Canopy water cycling Temp pseudo-innovations thru model boundary layer More consistent use of surface temp/dewpoint data GFO Convective Parameterization Shallow cumulus radiation attenuation Improved retention of stratification atop mixed layer Thompson Microphysics Aerosol awareness for resolved cloud production Attenuation of shortwave radiation MYNN Boundary Layer Mixing length parameter changed Thermal roughness in surface layer changed Coupling boundary layer clouds to radiation RUC Land Surface Model Reduced wilting point for more transpiration Keep soil moisture in croplands above wilting point HRRR (and RAP) Future MilestonesHRRR Milestones HRRR Model Warm/Dry Bias Mitigation Total Resource Change: Approximately ~6% increase in runtime for aerosols 17 th ARAM5 January 2015HRRR Implementation 11 AMS-2015 – 6 th EnergyJanuary 2015 HRRR/RAP upgrades for energy 11

8 Aug.2014 Reduced wilting point 13 Aug Change for cropland soil moisture 15 Aug Q2m diagnostic for data assim Testing RAPv3 LSM Changes NCEP Production Suite Review2-3 December 2014Rapid Refresh / HRRR 12 NCEP RAPv2 GSD test RAPv3 Temperature bias (K) Dewpoint bias (k) Warm Cold Moist Dry

NCEP OPER RAPv2 GSD RAPv3 pre-NCEP Warm Cold Temperature bias (K) Dewpoint bias (k) Temperature RMS (K) Dewpoint RMS (K) Moist Dry Overall: Rapid Refresh 12h Surface verification 13 East US July 2014

NCEP OPER RAPv2 GSD RAPv3 pre-NCEP Warm Cold Temperature bias (K) Dewpoint bias (k) Temperature RMS (K) Dewpoint RMS (K) Moist Dry Overall: Rapid Refresh 12h fcst -Surface diurnal verification 14 East US July 2014 RAPv3-RAPv2 Evening Night Morning Afternoon Evening Night Morning Afternoon

NCEP OPER RAPv2 GSD RAPv3 pre-NCEP too high too low 10m Wind bias (m/s) 10m wind RMS vector error (m/s) Overall: Rapid Refresh 12h Surface verification 15 East US July th ARAM - AviationJanuary 2015Physics RAP / HRRR 15 AMS-2015 – 6 th EnergyJanuary 2015 HRRR/RAP upgrades for energy 15

NCEP OPER RAPv2 GSD RAPv3 pre-NCEP too high too low DIURNAL Variation 10m Wind bias (m/s) DIURNAL Variation 10m wind RMS vector error (m/s) Overall: Rapid Refresh 12h fcst – 10m wind diurnal verification 16 East US July th EnergyJanuary 2015Physics RAP / HRRR 16 AMS-2015 – 6 th EnergyJanuary 2015 HRRR/RAP upgrades for energy 16 NightAfternoon Morning Evening NightAfternoon Morning Evening

17 Rapid Refresh 12h Upper-air verification NCEP OPER RAPv2 GSD RAPv3 pre-NCEP Temperature RMS (K) Relative Humidity RMS (%) Wind RMS vector (m/s) CONUS Vs. raobs July 2014 RAPv3 better RAPv3 better RAPv3 better

Results: SW-down Verification Observations (1 hr averages, all 14 SURFRAD stations) RAPv3-preliminary - RAP-Dev2 (13 km) 12 hr fcst RAPv3 - RAP-Dev3 (13 km) 12 hr fcst RAPv2 - RAP-Oper (13 km) 12 hr fcst Oper: does not include subgrid clouds or LSM mods (WRFv3.5.1) Dev2: has improved subgrid clouds and sh. cu scheme (WRFv3.5.1) Dev3: Dev2 enhancements + LSM wilting point mod (WRFv3.6) 18 Much better SW radiation agreement with SURFRAD obs with RAPv3-final than RAPv2

HRRR (and RAP) Future MilestonesHRRR Milestones 3000 ft 1000 ft 500 ft 29 Jan 30 Jan31 Jan Control Prototype Diagnostic (w/ subgrid stratus) Ceiling – new cloud-fraction diagnostic for RAP/HRRR Results: Probability of Detection (12-h forecasts, CONUS)

HRRR (and RAP) Future MilestonesHRRR Milestones Example: 30 January 2013 Control (12-h forecast valid 2000 UTC) Reflectivity Mosaic (2000 UTC) GOES Visible (3-h loop ending 2000 UTC) Source: UCAR kft (AGL)

HRRR (and RAP) Future MilestonesHRRR Milestones Example: 30 January 2013 selected ceiling reports versus 12-h ceiling forecasts (valid 2000 UTC) Control Prototype Cloud Fraction-Based Diagnostic kft (AGL) KDIK: OVC090 KRAP: BKN028 KUNU: OVC007 CYWG: OVC th ARAM - AviationJanuary 2015Physics RAP / HRRR 21

Winds – Substantial RAPv3 improvement for both upper-air and near-surface, for all seasons. Cloud – Improved SW-down radiation from physics changes and upcoming cloud-fraction diagnostic. Summary: RAPv3/HRRRv2 vs. RAPv2/HRRRv1 for wind / solar energy guidance RAPv3/HRRRv2 changes Substantial for some parameterizations (MYNN PBL, RUC LSM, Thompson cloud) Data assimilation improvements also drive RAPv3/HRRRv2 (ens, radar, sfc) Major impact on wind and solar forecast accuracy PBL, cloud, LSM, radiation work in concert New observations (tower, SURFRAD/solar, including proprietary data) critical for evaluation and assimilation. NCEP OPER RAPv2 GSD RAPv3 pre-NCEP

ESRL – experimental version RAPv3 – GSD testing in 2014 Final RAP-primary impl - 1/1/2015 – Will initialize 2014 ESRL-HRRR(v2) – Improved PBL, LSM, cu-parm, DA – WRFv3.6.1 w/ Thompson/NCAR aerosol-aware microphysics HRRRv2 – GSD testing in 2014 – Final HRRR-primary impl - 1/1/2015 – Initialized by 2014 RAP (v3) – Improved radar assimilation, hybrid assimilation, PBL/cloud physics RAPv4 – GSD testing in 2015 – Hourly RAP ensemble data assimilation HRRRv3 – GSD testing in 2015 – Target: Improved 3km physics + improved data assimilation. NWS-NCEP - operational – Planned dates Implement June 2015 Implement 2016 HRRR (and RAP) Future MilestonesHRRR Milestones RAP/HRRR Implementation Map 23

Evolution of hourly updated NOAA modeling Feb 2014 –Rapid Refresh v2 – oper at NCEP Sep 2014 – HRRR v1 – oper at NCEP 2015 – RAPv3 and HRRRv2 – est. June impl. Model – improved physics – MYNN PBL / LSM/snow, aerosol-aware cloud μphysics numerics improvements, updated WRF Assimilation – radar assim for light precip, lightning, 2m T/Td forward model, rad wind  Improved surface/lower trop forecasts, convective environment, clouds North American Rapid Refresh Ensemble NMMB, ARW cores Hourly updating with GSI-hybrid EnKF (4DEnVar?) Initially 6 members, 3 each core, physics diversity (stochastic only or with RAP/NAM/NCAR physics suites) Hourly forecasts to 24-h NMMB +ARW members to 84-h 4x/day Rapid Refresh  NARRE 2016 – RAPv4/HRRRv – Ensemble Rapid Refresh/NAM – NARRE (w/ hybrid 4d-ens/var DA) 2019? – Ensemble HRRR – HRRRE – ( ultimately with hourly ~3km ensemble DA) HRRR Future NCEP 24