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Progress on Radar Data Assimilation at the NCEP Environmental Modeling Center

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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 ... – PowerPoint PPT presentation

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Title: Progress on Radar Data Assimilation at the NCEP Environmental Modeling Center


1
Progress on Radar Data Assimilationat 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 Americas 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 (1011) 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
  • Radial (Line of Sight) wind
  • Reflectivity ? precipitation

5
NEXRAD WSR-88D RADARSLevel 2.5 Data Coverage
6
Radar or Model Reflectivity?
WRF 24 hour 4.5 km forecast of 1 hour accumulated
precipitation valid at 00Z April 21, 2004 and
corresponding radar reflectivity
7
Five Order of Magnitude Increase in Satellite
Data Over Next Ten Years
NPOESS Era Data Volume
Daily Satellite Radar Observation Count
2005 210 M obs
2003-4 125 M obs
Level 2 radar data 2 B
2002 100 M obs
Count (Millions)
2000
1990
2010
2010-10of obs
8
Integration and Testing of New Observations
  1. Data Access (routine, real time) 3 months
  2. Formatting and establishing operational data
    base 1 month
  3. Extraction from data base 1 month
  4. Analysis development (I) 6-18
    months
  5. Preliminary evaluation 2 months
  6. Quality control 3 months
  7. Analysis development (II) 6-18
    months
  8. Assimilation testing and forecast evaluation 1
    month
  9. Operational implementation 6 months
  10. Maintain system 1 person till death do
    us part

Total Effort 29-53 person months per instrument
Scientific improvements, monitoring and quality
assurance
9
Global Data AssimilationObservations Processing
  • Definitions
  • Received The number of observations received
    operationally per day from providers (NESDIS,
    NASA, Japan, Europeans and others) and maintained
    by NCEPs Central Operations. Counted
    observations are those which could potentially be
    assimilated operationally in NCEPs 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 AssimilationObservations Processing
(cont)
2002 July 2005 Notes November 2005 Operations
Received 123 M 169.0M Nov. 2005 increase attributed to additional AIRS, MODIS winds, NOAA-18 and NOAA-17 SBUV data 236.1 M
Selected 19 M 23.6 M 26.9 M
Assimilated 6 M 6.7 M 8.1 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 WindsBill 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

13
Stage II and Stage IVMulti-sensor
Precipitation AnalysesYing Lin
Stage II
  • Generated at NCEP
  • Hourly radar and from hourly gauge
  • reports
  • First generated at 35 minutes
  • after the top of the hour
  • 2nd and 3rd at T6h and T18h
  • No manual QC.

Stage IV
  • National mosaic assembled
  • at NCEP
  • Input hourly radargauge analyses by
    12 CONUS River Forecast Centers (RFCs)
  • Manual QC by RFCs
  • Product available within an hour
  • of receiving any new data
  • http//www.emc.ncep.noaa.gov/mmb/
  • ylin/pcpanl/stage2/

14
Assimilation of Precipitation Analyses24 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

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
    115)
  • 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 (14)
  • Hourly (18)
  • Horizontal resolution of
  • 5 km radially (120)
  • 6 degrees azimuthally (16)
  • Overall compression 13840
  • 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
Adaptable Parameters for the Level 2.5 Superob
Product
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 60-230 km Minimum Number of points
required 50 20-200 Same as Level 3
products except for additional tilts and
processing algorithm
20
Impact on Precipitation Forecasts
8-20 June 2004 (2 weeks)
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.
21
Impact of Level 2.5 Obs on Forecast Geop. Height
Improved RMS scores for height
Height RMS
Height Bias
Small improvements in upper troposphere No
degradation
22
Impact of Level 2.5 Obs on Forecast Winds
No degradation in Vector wind slightly better
near jet levels.
Wind RMS Vector Error
Small improvement in upper troposphere
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.  
23
Impact of Level 2.5 Obs on Forecast Precipitation
24 h Forecast
Control
Obs Radar
Level 2.5
Difference
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 RadarData 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 AnalysisSummary
  • 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
  • Dx10km, Dy10km, Dz250 m
  • Minimum number of data 25
  • NCEP Gridpoint Statistical Interpolation (GSI)
    analysis
  • Hurricane Ivan 2004 September 7
  • Mature storm

30
Guess Field
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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 2005 - 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
ThanksQuestions?
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
Night?
no
yes
Bayes identification and calculate posterior
probability
P(B xi) gt0.5
no
yes
Bird echo
Next QC step
44
Current NSSL Radar Data QC packages
Doppler Velocity Vr QC
Reflectivity Z QC
Input Level II data
Input Fortran Data Structure
Pure clear air vol.echo removal
Ground Clutter Detection
Hardware test pattern vol.removal
Dealiasing
Speckle filter
Tilt-by-tilt Vr QC (bird, noisy Vr etc.)
Sun strobe filter
Pixel-by-pixel 3D Z QC (clear air, bird, insect,
AP, sea clutter, interference etc.)
Output Fortran Data Structure
Fortran code
C code
45
Phase I NSSL/NCEP Fortran QC package
Reflectivity Doppler Velocity QC
Combined QC Filter from C code
Input Fortran Data Structure
Pure clear air vol. echo removal
Hardware test pattern removal
Ground Clutter Detection
Speckle filter
Dealiasing
Sun strobe filter
Rewrite in Fortran and integrate into Vr QC
Tilt-by-tilt Vr QC
Output QCed Z and Vr Fortran Data Structure
Optimize the entire package
Fortran code
46
Phase II NSSL/NCEP Fortran QC package
Reflectivity Doppler Velocity QC
Input Fortran Data Structure
Combined QC Filter
New Dealiasing Algorithm (DA)
Ground Clutter Removal
  • 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.

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

New Dealiasing
Tilt-by-tilt Z Vr QC
Output QCed Z and Vr Fortran Data Structure
Fortran code
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
Level-II raw data
Level-III NIDS
KBUF
KBUF
raw
dealiased
51
Review Three-step Dealiasing for Level-II
Velocities
Raw data
3-Step Noise Remove (BA88 )
Step 1
Select circles, Mod-VAD (u0,v0), Pre-dealiasing
VAD (u0,v0), Vertical check
Horizontal averaging variance check
Step 2
Calculate Vr (refined reference)
Quality check (flag0, 1 or 10)
Dealiasing with Vr (skip if flag 0 or 1)
Step 3
Dealiasing with continuity check
Output
Adopted
52
Polarimetric (KOUN) vs WSR-88D (KTLX)
KOUN
KTLX
Bird
Storm
rHV
Reflectivity
May 24 2003 0852UTC
53
Comparison of rain and bird echoesDoppler
Velocity (zoom in)
KPBZ
KTLX
Rain
Bird
54
Jung and Zapotocny JCSDA Funded by NPOESS IPO
Satellite data 10-15 impact
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