1 Progress on Radar Data Assimilation at the NCEP Environmental Modeling Center S. Lord, G. DiMego, D. Parrish, NSSL Staff With contributions by: J. Alpert, V. K. Kumar, R. Saffle, Q. Liu NCEP: “where America’s climate, weather, and ocean services begin”

2 Overview Introductory remarks –NEXRAD observations and Data Assimilation (DA) History of NEXRAD data use in DA, including “precipitation assimilation” (Lin, Parrish) CONUS impact study (Alpert) Hurricane impact study (Liu) Summary and outlook

3 NEXRAD WSR-88D RADARS 158 operational NEXRAD Doppler radar systems deployed throughout the United States Provide warnings on dangerous weather and its location –Potentially useful for mesoscale data assimilation Data resolution of Level 2 radar radial wind –1/4 km radial resolution –1 degree of azimuth –16 vertical tilt angles –200 km range –8 minutes time resolution Wind observation processing –VAD: cartesian (u,v) wind from radial wind processing –Level 3: dealiased radial wind at 4 lowest tilts –Level 2.5: on-site processing by NCEP “superob” algorithm –Level 2.0: raw radial wind Data volume –100 Billion (10 11) potential reports/day for radar radial winds –Typically 2 Billion radial wind reports/day –0.1 Tb/day computer storage

4 NEXRAD WSR-88D RADARS A rich source of high resolution observations A rich source of high resolution observations Radial (Line of Sight) wind Radial (Line of Sight) wind Reflectivity  precipitation Reflectivity  precipitation

5 NEXRAD WSR-88D RADARS Level 2.5 Data Coverage

6 WRF 24 hour 4.5 km forecast of 1 hour accumulated precipitation valid at 00Z April 21, 2004 and corresponding radar reflectivity Radar or Model Reflectivity?

7 Five Order of Magnitude Increase in Satellite Data Over Next Ten Years Count (Millions) Daily Satellite & Radar Observation Count %of obs M obs NPOESS Era Data Volume M obs Level 2 radar data 2 B M obs

8 Integration and Testing of New Observations 1.Data Access (routine, real time)3 months 2.Formatting and establishing operational data base1 month 3.Extraction from data base1 month 4.Analysis development (I) 6-18 months 5.Preliminary evaluation2 months 6.Quality control3 months 7.Analysis development (II) 6-18 months 8.Assimilation testing and forecast evaluation1 month 9.Operational implementation6 months 10.Maintain system* 1 person “till death do us part” * Scientific improvements, monitoring and quality assurance Total Effort: person months per instrument

9 Global Data Assimilation Observations Processing Definitions –Received: The number of observations received operationally per day from providers (NESDIS, NASA, Japan, Europeans and others) and maintained by NCEP’s Central Operations. Counted observations are those which could potentially be assimilated operationally in NCEP’s data assimilation system. Observations from malfunctioning instruments are excluded. –Selected: Number of observations that is selected to be considered for use by the analysis (data numbers are reduced because the intelligent data selection identifies the best observations to use). Number excludes observations that cannot be used due to science deficiencies. –Assimilated: Number of observations that are actually used by the analysis (additional reduction occurs because of quality control procedures which remove data contaminated by clouds and those affected by surface emissivity problems, as well as other quality control decisions)

10 Global Data Assimilation Observations Processing (cont) 2002July 2005 Notes November 2005 Operations Received123 M 169.0MNov increase attributed to additional AIRS, MODIS winds, NOAA-18 and NOAA-17 SBUV data M Selected 19 M 23.6 M26.9 M Assimilated 6 M 6.7 M8.1 M

11 Overview Introductory remarks –Observations and Data Assimilation (DA) History of NEXRAD data use in DA, including “precipitation assimilation” (Parrish, Lin) CONUS impact study (Alpert) Hurricane impact study (Liu) Summary and outlook

12 VAD Winds Bill Collins, D. Parrish VAD winds reinstated 29 March 2000 –First used by RUC (June 1997) and NAM-Eta (July 1997) –Withdrawn from operations (Jan. 1999) due to problems with observation quality Error sources –Migrating birds (similar to errors in wind profilers) Southerly wind component too strong (fall) Northerly wind component too strong (spring) Characteristic altitudes and temperatures 5% of all winds –Winds of small magnitude Source unknown 8% of all winds –Outliers (large difference from model “guess”) Source unknown 7% of all winds –Random, normally distributed, errors 2x magnitude expected from engineering error analysis “Acceptably small” –Total 20% of observations have unacceptable errors Quality control programs designed to filter erroneous observations

13 ylin/pcpanl/stage2/ Generated at NCEP Hourly radar and from hourly gauge reports First generated at ~35 minutes after the top of the hour 2 nd and 3 rd at T+6h and T+18h No manual QC. Stage II Stage IV National mosaic; assembled at NCEP Input: hourly radar+gauge analyses by 12 CONUS River Forecast Centers (RFCs) Manual QC by RFCs Product available within an hour of receiving any new data “Stage II and Stage IV” Multi-sensor Precipitation Analyses Ying Lin

14 Assimilation of Precipitation Analyses 24 May 2001 – Ying Lin Motivation –Direct model precipitation contains large biases Impacts all aspects of hydrological cycle Soil moisture and surface latent heat flux particularly impacted Real-time Stage II precipitation analyses are available Assimilation technique –Precipitation nudging technique Comparison of model and observed precipitation Change model precipitation, latent heating and moisture in consistent way dependent on ratio Pmodel/Pobs Expected improvements in NAM-Eta –Short-term (0-36 h) precipitation –Cycled soil moisture and surface fluxes –2 meter temperature No negative impact on other predicted fields

15 24 May 2001 (cont) Impacts as expected –Significantly improves the model's precipitation and soil moisture fields during data assimilation (e.g. North Americal Regional Reanalysis) –Often has a significant positive impact on the first 6 hours of the model's precipitation forecast –Occasional positive impact on precipitation forecasts 24h and beyond –Modestly positive impact on forecast skill scores –Not used in snow cases due to low observational bias –No negative impact is seen on the model forecast temperature, moisture and wind fields (a) (c) (e) (f) (d) (b) OPS EDAS: TEST EDAS: 15-DAY OBS PRECIP (1-15 JUL 98) SOIL MOISTURE 15 JUL DAY PRECIP 1-15 JUL 1-HR STAGE IV PRECIP Observed Precipitation 6-h Model Forecast Without Assim. With Assim.

16 8 July 2003 NAM-Eta Upgrade Stage II and Stage IV hourly analyses merged precipitation assimilation –Analyses must arrive before data cutoff (H + 1:15) –Quality control added to merged product Assimilation of Level 3 NEXRAD 88D radial wind data –Time and space averaged data (compression) First 4 radar tilts (0.5, 1.5, 2.5, and 3.5 degrees) – the “NIDS” feed (1:4) Hourly (~1:8) Horizontal resolution of –5 km radially (1:20) –6 degrees azimuthally (1:6) Overall compression: 1:3840 –Quality control applied from VAD winds, including migrating bird contamination “These radial wind runs show little positive or negative impact in the verification statistics, so it is certainly safe to include these winds treated this way in the 3DVAR” First implementation: do no harm

17 Overview Introductory remarks –Observations and Data Assimilation (DA) History of NEXRAD data use in DA, including “precipitation assimilation” (Lin, Parrish) CONUS impact study (Alpert) Hurricane impact study (Liu) Summary and outlook

18 CONUS Impact Study with Level 2.5 Winds Why compression? –Observations contain a high degree of redundancy –Communications cannot (until recently) handle the data volume for unprocessed observations NCEP algorithm for winds processing (“Superobs”) installed on NEXRAD –Compression parameters can be modified without impacting code change management –Standard NCEP processing algorithm

19 Parameter Default Range Time Window 60 minutes [5-90 min] Cell Range Size 5 km[1-10 km ] Cell Azimuth Size 6 degrees[2-12 deg] Maximum Range 100 km[ km] Minimum Number of points required 50 [20-200] Same as Level 3 products except for additional tilts and processing algorithm Adaptable Parameters for the Level 2.5 Superob Product:

20 24-h accumulated precipitation equitable threat score (upper) and bias (lower) from Eta 32-km 60-h forecasts from 8JUN2004 – 20JUN2004 for various thresholds in inches. The solid line (+) are the radial wind super-ob level 2.5 experiment and the dash is the Eta control (▲) with NIDS level 3.0 super-obs. Level 2.5 Level 3 Impact on Precipitation Forecasts 8-20 June 2004 (2 weeks)

21 Improved RMS scores for height Level 2.5 Level 3 Height Bias Height RMS Small improvements in upper troposphere; No degradation Impact of Level 2.5 Obs on Forecast Geop. Height

22 RMS vector wind errors against RAOBS over the CONUS from Eta 32-km 60-h forecasts, 8JUN2004 – 20JUN2004 (24 forecasts). The dash line is the radial wind super-ob Level 2.5 and the solid line is the Eta control with NIDS level 3.0 super-obs. No degradation in Vector wind – slightly better near jet levels. Wind RMS Vector Error Small improvement in upper troposphere Impact of Level 2.5 Obs on Forecast Winds Level 2.5 Level 3

23 Impact of Level 2.5 Obs on Forecast Precipitation Level 2.5 Control Obs Radar Difference 24 h Forecast

24 Summary: Level 2.5 Winds Winds received operationally from every radar site (April 2003) Improved precip, height and wind scores (none from Level 3) –Data processing impacts forecast scores Subjective evaluation shows positive impact Quality control issues remain –Difficult to solve with processing at radar sites –Motivates transmission of full data set to NCEP and robust QC effort at central site

25 Overview Introductory remarks –Observations and Data Assimilation (DA) History of NEXRAD data use in DA, including “precipitation assimilation” (Lin, Parrish) CONUS impact study (Alpert) Hurricane impact study (Liu) Summary and outlook

26 Airborne Doppler Radar Data Analysis in HWRF Model Q. Liu, N. Surgi, S. Lord W.-S. Wu, D. Parrish S. Gopal and J. Waldrop (NOAA/NCEP/EMC) John Gamache ( AOML/HRD )

27 Background Initialization of hurricane vortex –GFDL model – “uncycled” system –“Spin-up” from axisymmetric model with forcing from observed parameters Surface pressure Maximum wind Radii of max. wind, hurricane and T.S. winds –Increase of observations in hurricane environment Dropsondes Satellite winds Scatterometer (QuikSCAT) Sounding radiances (AMSU, AIRS, HIRS…) Dopper radar (research) –$13 M program to add Doppler radar to GIV aircraft Use of NEXRAD data in landfall situations Hurricane is the only system uninitialized from observations at NCEP

28 Cycled Hurricane Analysis Summary Capture short-term intensity changes Account for storm motion 6 hourly cycling Use all available observations When no observations, try to correct model intensity with axisymmetric correction First time: use “bogus” vortex

29 3D-VAR Doppler Radar Data Assimilation  Data Quality Control John Gamache (HRD)  Superobs James Purser, David Parrish  x=10km,  y=10km,  z=250 m Minimum number of data: 25  NCEP Gridpoint Statistical Interpolation (GSI) analysis  Hurricane Ivan 2004 September 7  Mature storm

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36 Future Work Run more model forecast using the new analysis for weak storms Study the impact of the airborne radar data on hurricane track and intensity forecasts, particularly for weak storms Run HWRF complete cycling system during 2006 hurricane season

37 Summary and Outlook Use of NEXRAD wind data has proceeded in incremental steps over the past 9 years –Level 3  Level 2.5  Level 2 Use of reflectivity for –Precipitation analyses –Model initialization Remaining issues –Quality control –Model initialization (increasing system complexity)

38 Summary and Outlook (cont) June Implemented Level 2.5 (superobbed) data June 2006 – Hierarchical radar data ingest for WRF-NAM –Level 2.0 (full resolution radial winds) –Level 2.5 (superobbed winds) –Level 3 (“NIDS” feed) –Precip. Assimilation impacts land surface only Prototype data assimilation for hurricane initialization –Airborne Doppler radar –Coastal radar –2004 cases as prototype –2006 cases will be run as demonstration project Integrating quality control codes into NCEP North American Model (NAM) run –Visiting scientist hired (on board at EMC 30 June, 2006) Winds - expect steadily increasing impact Reflectivity - long term project requiring advanced data assimilation techniques

39 Thanks Questions?

40 Doppler Velocity Data Quality Problems 1. Noisy fields (due to small Nyquist velocity) 2. Irregular variations due to scan mode switches 3. Unsuccessful dealiasing 4. Contamination by migrating birds 5. Ground clutters due to anomalous propagation (AP) 6. Large velocities caused by moving vehicles & AP 7. Sea Clutter EMC Working with NSSL and CIMMS to address all QC issues

41 Level 2 Radar Data Assimilation Strategy NAM assimilates Level 2 data – 20 June QC codes are being ported from NSSL & CIMMS –Address all QC issues Visiting Scientist on board at NCEP (30 June) –Former NSSL scientist –Prior experience with codes –Tuning and case studies Assimilating reflectivity will be a long-term project, dependent on advanced data assimilation techniques

42 Milestone and Time Table FY06 Task 1. Complete porting existing reflectivity QC C++ code executable together with the NCEP Fortran code into single compliable executable. Shunxin Wang (QC C++ code developer) will work on this task as early as possible to meet NCEP's immediate needs. FY06 Task 2. Complete Phase 1 (by Sept, 2006) Complete initial stages of Phase 2 (Dec, 2006) Pengfei Zhang and Shunxin Wang will work together to design the NCEP/NSSL FORTRAN QC code. Li Wei working with Shunxin will combine various DA approaches towards an integrated Fortran DA for NCEP. Code sets developed during the above two phases will be ported, tested, and refined on NCEP computers by Shun Liu (and others at NCEP). FY07 and beyond …TBD

43 Flowchart of Real-time Migrating Bird Identification Raw data Calculate QC parameters Bayes identification and calculate posterior probability Night? yes Bird echoNext QC step P(  |x i ) >0.5 yes no

44 Current NSSL Radar Data QC packages Input: Fortran Data Structure Tilt-by-tilt Vr QC (bird, noisy Vr etc.) Output: Fortran Data Structure Doppler Velocity Vr QC Reflectivity Z QC Input: Level II data Hardware test pattern vol.removal Speckle filter Sun strobe filter Pixel-by-pixel 3D Z QC (clear air, bird, insect, AP, sea clutter, interference etc.) Pure clear air vol.echo removal Fortran code C++ code Dealiasing Ground Clutter Detection

45 Phase I: NSSL/NCEP Fortran QC package Input: Fortran Data Structure Dealiasing Ground Clutter Detection Tilt-by-tilt Vr QC Output: QCed Z and Vr Fortran Data Structure Reflectivity + Doppler Velocity QC Hardware test pattern removal Speckle filter Sun strobe filter Fortran code Rewrite in Fortran and integrate into Vr QC Optimize the entire package Combined QC Filter from C++ code Pure clear air vol. echo removal

46 Phase II: NSSL/NCEP Fortran QC package Input: Fortran Data Structure Tilt-by-tilt Z + Vr QC Output: QCed Z and Vr Fortran Data Structure Reflectivity + Doppler Velocity QC Fortran code Combined QC Filter a.Build test-case data base for comparing different DAs. b. Develop optimum Fortran DA code set based on comparisons with research and operational DA approaches. New Dealiasing Algorithm (DA) Ground Clutter Removal

47 Phase III: NSSL/NCEP Fortran QC package Input: Fortran Data Structure Tilt-by-tilt Z + Vr QC Output: QCed Z and Vr Fortran Data Structure Reflectivity + Doppler Velocity QC Fortran code a.Upgrade Vr QC to Z + Vr QC. b.Improve tilt-by-tilt QC based on Bayes statistics. c.Expand raw & “ground truth” data base optimize QC thresholds for radars at different regions (in terms of geographical and climatologic conditions). New Dealiasing Ground Clutter Detection Combined QC Filter

48 Strategies for Developing Unified Fortran QC package Prioritize development phases based on anticipated QC ‘skill’ and difficulties for each phase. Modularize individual components and routines (with on/off options) to facilitate CPU performance and optimization on NCEP computers. Prioritize parameters in the QC package in order to simplify or enhance the package to fit the requirement and associated resources. Develop and maintain QC archive important and/or challenging cases for comparing and testing. Includes collecting DA cases to assess different DA schemes, towards a optimum single DA code set. Monitor and capture problematic cases, expand raw & “ground truth” data base, and optimize QC thresholds for each properly-classified category (such as VCP, diurnal, seasonal, regional, etc).

49 Noisy Vr field (0022UTC)

50 Problems in Operational Dealiasing KBUF raw KBUF dealiased Level-II raw dataLevel-III NIDS

51 Review: Three-step Dealiasing for Level-II Velocities 3-Step Noise Remove (BA88 ) Select circles, Mod-VAD (u 0,v 0 ), Pre- dealiasing Horizontal averaging & variance check Calculate Vr (refined reference) Quality check (flag=0, 1 or 10) Dealiasing with continuity check Raw data Output Adopted VAD (u 0,v 0 ), Vertical check Dealiasing with Vr (skip if flag =0 or 1) Step 1 Step 2 Step 3

52 Polarimetric (KOUN) vs WSR-88D (KTLX) KOUNKTLX  HV Reflectivity Bird Storm May UTC

53 Comparison of rain and bird echoes Doppler Velocity (zoom in) Rain KPBZ Bird KTLX

54 Jung and Zapotocny JCSDA Funded by NPOESS IPO Satellite data ~ 10-15% impact