AMB Verification and Quality Control monitoring Efforts involving RAOB, Profiler, Mesonets, Aircraft Bill Moninger, Xue Wei, Susan Sahm, Brian Jamison.

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

A Brief Guide to MDL's SREF Winter Guidance (SWinG) Version 1.0 January 2013.
An Regional Enemble Kalman Filter Data Assimilation System Employing GSI Observation Processing and Initial Tests for Rapid Refresh Forecast Configurations.
“Where America’s Climate, Weather and Ocean Services Begin” NCEP CONDUIT UPDATE Brent A Gordon NCEP Central Operations January 31, 2006.
Operational Forecasting and Sensitivity-Based Data Assimilation Tools Dr. Brian Ancell Texas Tech Atmospheric Sciences.
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
NATS 101 Lecture 3 Climate and Weather. Climate and Weather “Climate is what you expect. Weather is what you get.” -Robert A. Heinlein.
Rapid Update Cycle Model William Sachman and Steven Earle ESC452 - Spring 2006.
Real-time WRF EnKF 36km outer domain/4km nested domain 36km outer domain/4km nested domain D1 (36km) D2 (4km)
Brian Ancell, Cliff Mass, Gregory J. Hakim University of Washington
Transitioning unique NASA data and research technologies to the NWS 1 Evaluation of WRF Using High-Resolution Soil Initial Conditions from the NASA Land.
Atmospheric Modeling at RENCI Brian J. Etherton. Atmospheric Modeling at RENCI Focus of RENCI for C- STAR project is to provide modeling support/development.
Global Forecast System (GFS) Model Previous called the Aviation (AVN) and Medium Range Forecast (MRF) models. Global model and 64 levels Relatively primitive.
ATOC 6700 Weather Forecasting Discussion. Student Introductions Tell us: – Your name – Your year in ATOC graduate program – The topic of your research.
Transitioning CMAQ for NWS Operational AQ Forecasting Jeff McQueen*, Pius Lee*, Marina Tsildulko*, G. DiMego*, B. Katz* R. Mathur,T. Otte, J. Pleim, J.
Office of Research and Development National Exposure Research Laboratory, Atmospheric Modeling Division, Applied Modeling Research Branch October 8, 2008.
“1995 Sunrise Fire – Long Island” Using an Ensemble Kalman Filter to Explore Model Performance on Northeast U.S. Fire Weather Days Michael Erickson and.
Incorporation of TAMDAR into Real-time Local Modeling Tom Hultquist Science & Operations Officer NOAA/National Weather Service Marquette, MI.
Assimilation of GOES Hourly and Meteosat winds in the NCEP Global Forecast System (GFS) Assimilation of GOES Hourly and Meteosat winds in the NCEP Global.
MADIS to LITTLE_R Converter MADIS observation types MADIS to LITTE_R converter Future work.
1 Aircraft Data: Geographic Distribution, Acquisition, Quality Control, and Availability Work at NOAA/ESRL/GSD and elsewhere.
1 AMDAR Quality Assurance Bradley Ballish NOAA/NWS/NCEP/NCO/PMB SSMC2/Silver Spring 23 March, 2009.
Verification Summit AMB verification: rapid feedback to guide model development decisions Patrick Hofmann, Bill Moninger, Steve Weygandt, Curtis Alexander,
Assimilation of AIRS Radiance Data within the Rapid Refresh Rapid Refresh domain Haidao Lin Steve Weygandt Ming Hu Stan Benjamin Patrick Hofmann Curtis.
Operational Forecasting of Turbulence in Radial Bands around Mesoscale Convective Systems (MCS’s) 06 August 2013 Midwest US Melissa Thomas, Lead & Training.
VERIFICATION OF NDFD GRIDDED FORECASTS IN THE WESTERN UNITED STATES John Horel 1, David Myrick 1, Bradley Colman 2, Mark Jackson 3 1 NOAA Cooperative Institute.
GLFE Status Meeting April 11-12, Presentation topics Deployment status Data quality control Data distribution NCEP meeting AirDat display work Icing.
Verification for the High Impact Weather Prediction Project (HIWPP) and other ESRL/GSD verification work Steve Weygandt, Bonny Strong Bill Moninger, Jeff.
Course Evaluation Closes June 8th.
1 Presentation by Earth System Research Lab / Global Systems Division - Bill Moninger 23 March 2009 Impact of the AMDAR observations to aviation weather.
1 Results from Winter Storm Reconnaissance Program 2008 Yucheng SongIMSG/EMC/NCEP Zoltan TothEMC/NCEP/NWS Sharan MajumdarUniv. of Miami Mark ShirleyNCO/NCEP/NWS.
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.
MADIS Airlines for America Briefing Meteorological Assimilated Data Ingest System (MADIS) FPAW Briefing Steve Pritchett NWS Aircraft Based Observations.
1 Gridded Localized Aviation MOS Program (LAMP) Guidance for Aviation Forecasting Judy E. Ghirardelli and Bob Glahn National Weather Service Meteorological.
Transitioning unique NASA data and research technologies to the NWS AIRS Profile Assimilation - Case Study results Shih-Hung Chou, Brad Zavodsky Gary Jedlovec,
Evaluation of radiance data assimilation impact on Rapid Refresh forecast skill for retrospective and real-time experiments Haidao Lin Steve Weygandt Stan.
1 Developing Assimilation Techniques For Atmospheric Motion Vectors Derived via a New Nested Tracking Algorithm Derived for the GOES-R Advanced Baseline.
The LAMP/HRRR MELD FOR AVIATION FORECASTING Bob Glahn, Judy Ghirardelli, Jung-Sun Im, Adam Schnapp, Gordana Rancic, and Chenjie Huang Meteorological Development.
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.
Boundary layer depth verification system at NCEP M. Tsidulko, C. M. Tassone, J. McQueen, G. DiMego, and M. Ek 15th International Symposium for the Advancement.
Wind Gust Analysis in RTMA Yanqiu Zhu, Geoff DiMego, John Derber, Manuel Pondeca, Geoff Manikin, Russ Treadon, Dave Parrish, Jim Purser Environmental Modeling.
Page 1© Crown copyright 2005 Met Office Verification -status Clive Wilson, Presented by Mike Bush at EWGLAM Meeting October 8- 11, 2007.
1 TEMPERATURE EFFECT OF MUON COMPONENT AND PRACTICAL QUESTIONS OF ITS ACCOUNT IN REAL TIME Berkova 1,2 M., Belov 1 A., Eroshenko 1 E., Yanke 1 V. 1 Institute.
NCAR April 1 st 2003 Mesoscale and Microscale Meteorology Data Assimilation in AMPS Dale Barker S. Rizvi, and M. Duda MMM Division, NCAR
MSG cloud mask initialisation in hydrostatic and non-hydrostatic NWP models Sibbo van der Veen KNMI De Bilt, The Netherlands EMS conference, September.
1 RUC Land Surface Model implementation in WRF Tanya Smirnova, WRFLSM Workshop, 18 June 2003.
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.
Comparison of LM Verification against Multi Level Aircraft Measurements (MLAs) with LM Verification against Temps Ulrich Pflüger, Deutscher Wetterdienst.
Gridded WAFS Icing Verification System Matt Strahan WAFC Washintgon.
Towards Assimilation of GOES Hourly winds in the NCEP Global Forecast System (GFS) Xiujuan Su, Jaime Daniels, John Derber, Yangrong Lin, Andy Bailey, Wayne.
Deutscher Wetterdienst FE VERSUS 2 Priority Project Meeting Langen Use of Feedback Files for Verification at DWD Ulrich Pflüger Deutscher.
The National Weather Service Goes Geospatial – Serving Weather Data on the Web Ken Waters Regional Scientist National Weather Service Pacific Region HQ.
1 Recent AMDAR (MDCRS/ACARS) Activities at GSD New AMDAR-RUC database that helps evaluate AMDAR data quality Optimization study that suggests data can.
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)
Station lists and bias corrections Jemma Davie, Colin Parrett, Richard Renshaw, Peter Jermey © Crown Copyright 2012 Source: Met Office© Crown copyright.
GLFE Real-time TAMDAR Impact Experiments with the 20km RUC Stan Benjamin,Tracy Lorraine Smith, Bill Moninger, Brian Jamison NOAA Forecast Systems Laboratory.
Rapid Update Cycle-RUC
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.
Lidia Cucurull, NCEP/JCSDA
Real-time WRF EnKF 36km outer domain/4km nested domain D1 (36km)
Impact of aircraft data in the MSC forecast systems
Cliff Mass and David Ovens University of Washington
Status of the Regional OSSE for Space-Based LIDAR Winds – Feb01
Presentation transcript:

AMB Verification and Quality Control monitoring Efforts involving RAOB, Profiler, Mesonets, Aircraft Bill Moninger, Xue Wei, Susan Sahm, Brian Jamison 1

Observations we verify against RAOBs at 0 and 12 UTC – Temperature, Relative Humidity, Wind, Height METARs -- every hour – Temperature, Dewpoint, Wind – Ceiling – Visibility Wind profilers – every hour – Wind Results available within a few hours of model run time Results for individual stations and for pre-defined regions 2

Models in our verification database (upper air, compared with RAOBs): NCEP operational models in blue Regional: Op20 (Operational 13 on 20km grid) Bak13 (Bakup RUC on 13km grid) isoBak13 (isobaric Bak13) Bak20 (Backup on 20km grid) Dev13 (development RUC) Dev1320 (Dev13 on 20km grid) RR1h (1h cycling RR) isoRR1h (isobaric vsn of above) RR1h_dev (dev. version of RR1h) RR1h_dev130 (130 grid) RRnc (non-cycling RR) RRncRLL (rotated lat-lon) HRRR HRRR_dev isoDev1320 (dev 13 iso on 20km grid) NAM (North America 32 km) Global: GFS FIM FIM_prs (from isobaric files) FIMX FIMY FIM9TACC (GSI initialization) F9EMTACC (EnKF initallization) F0EMTACC (10km grid) Real-Time: Retrospective: 177 (so far) retrospective model runs Testing different model configurations, assimilation methods, combinations of data 3

Web interface Time-series and Vertical profiles of ob-forecast differences For pre-defined regions (regional to global) and individual stations Hourly to 60-day averages routinely available Summary statistics and per-observation differences Products are experimental; designed for the specific needs of our group 4

Upper air verification Bias (forecast minus ob) RMS (forecast minus ob) Of Temperature, Relative Humidity, Wind, Height 5

6 Time Series

Time-series of RAOB data against Operational and Backup RUC models For entire RUC region Results every 12 h (0 and 12 UTC) Averaged from 1000 – 100 mb (No time averaging here) Difference between the two models RMS of temperature difference with respect to RAOBs in the RUC region 7

Time-series of RAOB data against Operational and Backup RUC models For entire RUC region Results every 12 h (0 and 12 UTC) Averaged from 1000 – 100 mb (No time averaging here) Some apparent bad RAOBs on Aug 21 st 24 th and 27 th effect both models equally, as indicated by the difference curve Difference between the two models RMS of temperature difference with respect to RAOBs 8

9 Vertical Profiles

Profile of RAOB data against Operational and Backup RUC models For entire RUC region Results every 12 h (0 and 12 UTC) Averaged for the month of August

We can look at the individual soundings these statistics are based on. Here’s RAOB from Jackson, MS… 11

Overlaid with HRRR 1h forecast for the same location Includes hydrometeors (Cloud, Rain, Snow, Ice, Graupel) 12

Time-series of Wind Profiler data against HRRR and RR models For entire NPN profiler network Note results are available for every hour (instead of every 12h with RAOBs) 13

This list sorted by RMS of vector (forecast – ob) wind difference Surface verification Week-long statistics of mesonet sites against Rapid Refresh 1-h forecasts Used to generate ‘use lists’ of good surface sites, used at GSD and NCEP. 14

Mesonet corrections We plan to use our long-term observation-model statistics to experimentally correct surface observations where possible We generate ob-model wind biases – For each model wind octant – For most sites, biases are relatively constant month-by- month – So we can potentially correct for these biases The test will be if these “corrected” winds improve model forecasts We plan a similar effort for correcting Temperature and Dewpoint 15

Aircraft – Model differences This shows vector wind difference between AMDAR obs and RR 1h forecasts. Shows some obvious bad aircraft winds near Chicago. (Used by NWS to help identify bad aircraft data.) 16

Week-long statistics of aircraft obs against Rapid Refresh 1-h forecasts Used by us to generate daily aircraft reject lists for RR, HRRR and backup RUC Used actively by NWS and AirDat Aircraft weekly statistics 17

Verification infrastructure MySQL database – MyISAM tables – good for our ‘data warehousing’ application – Currently about 300 G in size C, Perl, Java, Python used to populate the database via cron on jet and other computers Java web pages for data display Perl and Python cgi scripts provide data from the database to web pages 18

Thank You Questions? 19