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”

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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

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NEXRAD WSR-88D RADARS

A rich source of high resolution observationsA rich source of high resolution observations Radial (Line of Sight) windRadial (Line of Sight) wind Reflectivity Reflectivity precipitation precipitation

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NEXRAD WSR-88D RADARSLevel 2.5 Data Coverage

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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?

7Five Order of Magnitude Increase in Satellite Data Over Next Ten YearsFive Order of Magnitude Increase in Satellite Data Over Next Ten YearsFive Order of Magnitude Increase in Satellite Data Over Next Ten YearsFive Order of Magnitude Increase in Satellite Data Over Next Ten Years

Count

(Mill

ions)

Daily Satellite & Radar Observation Count

20001990 2010 2010-10%of obs

2002 100 M obs

NPOESS Era Data Volume

2003-4 125 M obs

Level 2 radar data 2 B

2005 210 M obs

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Integration and Testing of New Observations

1. Data Access (routine, real time) 3 months2. Formatting and establishing operational data base 1 month3. Extraction from data base 1 month4. Analysis development (I) 6-18 months5. Preliminary evaluation 2 months6. Quality control 3 months7. Analysis development (II) 6-18 months8. Assimilation testing and forecast evaluation 1 month9. Operational implementation 6 months10. Maintain system* 1 person “till death do us part”

* Scientific improvements, monitoring and quality assurance

Total Effort: 29-53 person months per instrument

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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 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)

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Global Data AssimilationObservations Processing (cont)

2002 July2005

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

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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• Quality control programs designed to filter erroneous observations

13http://www.emc.ncep.noaa.gov/mmb/

ylin/pcpanl/stage2/

• 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 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

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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

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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: OPS EDAS:

TEST EDAS: TEST EDAS:

15-DAY OBS PRECIP (1-15 JUL 98)SOIL MOISTURE

SOIL MOISTURE15 JUL 98

15 JUL 9815-DAY PRECIP

15-DAY PRECIP

1-15 JUL

1-15 JUL1-HR STAGE IV PRECIP

Observed Precipitation6-h Model Forecast

Without Assim.With Assim.

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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

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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

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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

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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

Adaptable Parameters for the Level 2.5 Superob Product:

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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)

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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

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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

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Impact of Level 2.5 Obs on Forecast Precipitation

Level 2.5

ControlObsRadar

Difference

24 hForecast

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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)

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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

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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

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3D-VAR Doppler Radar Data Assimilation

Data Quality Control John Gamache (HRD)

SuperobsJames 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 7Mature storm

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Guess Field

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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

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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)

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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

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ThanksQuestions?

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Doppler Velocity Data Quality Problems

1. Noisy fields (due to small Nyquist velocity)2. Irregular variations due to scan mode switches3. 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

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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

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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

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Flowchart of Real-time Migrating Bird Identification

Raw data

Calculate QC parameters

Bayes identification and calculate posterior probability

Night?

yes

Bird echo Next QC step

P(|xi) >0.5

yes

no

no

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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

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Phase I: NSSL/NCEP Fortran QC package

Input:Fortran Data Structure

Dealiasing

Ground Clutter Detection

Tilt-by-tilt Vr QC

Output: QCed Z and VrFortran 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

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Phase II: NSSL/NCEP Fortran QC package

Input:Fortran Data Structure

Tilt-by-tilt Z + Vr QC

Output: QCed Z and VrFortran 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

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Phase III: NSSL/NCEP Fortran QC package

Input:Fortran Data Structure

Tilt-by-tilt Z + Vr QC

Output: QCed Z and VrFortran 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

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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).

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Noisy Vr field (0022UTC)

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Problems in Operational Dealiasing

KBUF

raw

KBUF

dealiased

Level-II raw data Level-III NIDS

51

Review: Three-step Dealiasing for Level-II Velocities

3-Step Noise Remove (BA88 )

Select circles, Mod-VAD (u0,v0), 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 (u0,v0), Vertical check

Dealiasing with Vr (skip if flag =0 or 1)

Step 1

Step 2

Step 3

52

Polarimetric (KOUN) vs WSR-88D (KTLX)

KOUN KTLX

HV Reflectivity

Bird

Storm

May 24 2003 0852UTC

53

Comparison of rain and bird echoesDoppler Velocity (zoom in)

Rain

KPBZ

Bird

KTLX

54

Jung and Zapotocny

JCSDAFunded by

NPOESS IPO

Satellite data ~ 10-15% impact

Impact of Removing AMSU, HIRS, GOES Wind, Quikscat Surface Wind Data on Hurricane Track Forecasts in the Atlantic Basin - 2003 (34 cases)

-20.0

-15.0

-10.0

-5.0

0.0

5.0

10.0

15.0

12 24 36 48 72 96 120

Forecast Hour

% Im

prov

emen

t NOAMSU

NOHIRS

NOGOESW

NOQuikscat

Impact of Removing AMSU, HIRS, GOES Wind, Quikscat Surface Wind Data on Hurricane Track Forecasts in the East Pacific Basin - 2003 (24 cases)

-60.0

-50.0

-40.0

-30.0

-20.0

-10.0

0.0

10.0

20.0

30.0

12 24 36 48 72

Forecast Hour

% Im

pro

vem

ent

NOAMSU

NOHIRS

NOGOESW

NOQuikscat