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AMDAR (aircraft) and Radar Data Assimilation

Ming Hu1,2, Stan Benjamin2, William Moninger1,2, Curtis Alexander1,2, Stephen Weygandt2, David Dowell2 , Eric James1,2

1CIRES, University of Colorado at Boulder, CO, USA, 2 NOAA/ESRL/GSD/AMB, Boulder, CO, USA

Scientific Conferences of WMO–RA III Meeting

18 September 2014 - Asuncion, Paraguay

Thanks to Shun Liu and Jacob Carley from NCEP for contributions in radar data assimilation and process

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What is Data Assimilation• Numerical Weather Prediction (NWP) is an initial-condition

problem– Given an estimate of the present state of the atmosphere (initial

conditions), and appropriate surface and lateral boundary conditions, the model simulates (forecasts) the atmospheric evolution

– The more accurate the estimate of the initial conditions, the better the quality of the forecasts (“accurate” doesn’t mean to fit to the observations very close)

• Data Assimilation: The process of combining observations and short-range forecasts to obtain an initial condition for NWP

• The purpose of data assimilation is to determine as accurately as possible the state of the atmospheric flow by using all available information for NWP

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Data Assimilation: Variational Method (VAR)

• J is called the cost function of the analysis (penalty function) – Jb is the background term

– Jo is the observation term

• The dimension of the model state is n and the dimension of the observation vector is p:

– xt true model state (dimension n)

– xb background model state (dimension n)

– xa analysis model state (dimension n)

– y vector of observations (dimension p) – H observation operator (from dimension n to p)

– B covariance matrix of the background errors (xb – xt) (dimension n n)

– R covariance matrix of observation errors (y – H[xt]) (dimension p p)

J(x) = (x-x b)TB - 1(x-x b)+(y-H[x])TR - 1(y-H[x])= J b + J o

In this talk, we will use variational method to explain AMDAR and Radar data assimilation but most of steps are same for Ensemble based data analysis method

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VAR: Background Term

• Background (forecast field): xb

• Analysis: x– Start from x=x b

• Analysis increment: x-xb

• Background error covariance: B– Variance: the background quality – Correlation

• Horizontal and vertical relation between 2 analysis point• Balance: relation between two analysis variables

J (x ) = (x-x b ) T B - 1 (x-x b )+(y-H[x ] ) T R - 1 (y-H[x ] )

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VAR: Observation Term

• Observation: y• Observation operator: H[x]

– Conventional observation: • T, wind, moisture, Ps• 3D interpolation

– Non-conventional observations:• Radiance, Radar, GPSRO, …• Complex function

• Observation innovation: y-H[x]• Observation error variance: R

– Assumption: No correlation between two observations

J (x ) = (x-x b ) T B - 1 (x-x b )+ (y-H[x] ) T R - 1 (y-H[x] )

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VAR: Steps of using observations

Step 1: Understand observations Y• Name• Variables observed• Geographic and time distribution • Space and time resolution• Observation errors• Quality Control and bias correction• Acceptable format (BUFR,…)• …

J (x ) = (x-x b ) T B - 1 (x-x b )+(y-H[x] ) T R - 1 (y-H[x] )

Step 2: Build observation operator:link analysis variables x to observations H[x]• Conventional observations:

– AMDAR observations– 3D interpolation

• Non conventional observations: Complex function– Radar Reflectivity = f(qr, qs, qh)– Radar radial wind=f(u, v, w)

Step 3: Define observation error variance R

1 2 3 1 2Background term are the same for all observations

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AMDAR data assimilation• Understand AMDAR (commercial aircraft) Data

– What is AMDAR– What is observed– Data coverage and resolution– Observation errors– Quality control and bias correction

• Use AMDAR data in data assimilation• The impact of the AMDAR data

– Regional NWP system (Rapid Refresh for US/NCEP)– Global NWP system (ECMWF)

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Understand AMDAR Data• AMDAR: Aircraft Meteorological Data Relay

• AMDAR is the automated measurement and transmission of meteorological data from an aircraft platform

• The AMDAR observing system is now recognized by WMO as a critical component of the WMO Global Observing System(GOS)

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What is observed in AMDAR• Vertical profiles are derived as the aircraft is on ascent or descent • en-routed data are derived at cruise altitudes of around 35,000 feet (10,500 meters).• The meteorological parameters can be measured or derived:

1. Air temperature (static air temperature) 2. Wind speed and direction3. Pressure altitude (barometric pressure)4. Turbulence (Eddy Dissipation Rate or Derived Equivalent Vertical Gust)

• Additional non-meteorological parameters include:1. Latitude position 2. Longitude3. Time4. Icing indication (accreting or not accreting)5. Departure and destination airport6. Aircraft roll angle7. Flight number

• A water vapor (humidity) measurement can also be derived– Water vapor sensor: The Water Vapor Sensing System 2nd Generation (WVSS-II)– Operationally in the USA and Europe

Temperature , Wind, Pressure

Location, time, flight number

Humidity

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List of AMDAR Participating Airlines

• As of July 2013, more than 400,000 observations per day world wide• 2863 aircraft are currently reporting world-wide, not including

Aeromexico data (coming soon) since 1-Aug-2014

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24h AMDAR Data Coverage

Typically, every 5-10 minutes of regular real-time reports of meteorological variables whilst en-route at cruise level

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AMDAR Vertical profiles

• High resolution vertical profiles: High-reso profiles are reported every 300 feet in low level and every 1000 feet in middle and upper

• Number vary a lot by airport:• Busy airport during the day : every 15 min or less• Many: couple profiles per day

Details in http://amdar.noaa.gov/new_soundings/2014_08_31.html

LAS VegasAscent Sounding

Montreal Ascent Sounding (No moisture)

DenverDescent Sounding

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AMDAR data coverage over US

18 – 21 afternoon 21 – 00 early night

00 – 03 night 03 – 06 early morning 06 – 09 morning

09 – 12 day 12 – 15 day

15 – 18 day

Time in plots is US mountain time.

Big various during day and night

Obs

Num

ber

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AMDAR data coverage over S. America

20-23 23-02 02-05 05-08

08-11 11-14 14-17

17-20

Time in plots is local time in Asuncion, Paraguay

Most of observations are during the night time

During the day time

Late afternoon to early night night Early morning

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Data Quality• The expected uncertainties for basic AMDAR data

parameters (based on AMDAR Reference Manual):Variable UncertaintyTemperature +/- 1.0 CWind Vector +/- 2-3 m/sPressure Altitude +/- 4 hPa

AMDAR observation error used by GSI for US NAM and RAP applications

• Data analysis uses tuned observation errors

Pressure (hPa) Temperature (C) Wind (m/s) moisture (RH %)

700 0.87349 2.7272 16.605

500 0.74080 2.6219 17.791

200 0.93292 2.3213 19.748

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AMDAR data quality control• GSD AMDAR Rejection list:

– Week-long statistics– Used by us to generate daily

aircraft reject lists for RAP and 3km HRRR (GSD/NOAA regional models)

– Used actively by NWS and other centers

Statistics of aircraft obs against Rapid Refresh 1-h forecasts:

bias_T > 2 (°C), std_T > 2 (°C),bias_S > 2 (m/s), std_S > 5 (m/s),bias_DIR > 7°, std_DIR > 30°,std_W > 5(m/s), rms_W > 7(m/s),bias_RH > 10%, std_RH > 20%,

;tail errors FSL MDCRS N bs_T Std_T bs_S std_S bs_D std_D std_W rms_W bs_RH std_RH (failures)

00000400 T W R 0 -------- 0 0.0 0.0 0.0 0.0 0 0 0.0 0.0 0.0 0.0 UND_Piper ( resrch_T_W_R )00000450 T W R 0 -------- 0 0.0 0.0 0.0 0.0 0 0 0.0 0.0 0.0 0.0 UND_Piper ( resrch_T_W_R )EU8001 - W - 10081 10081 409 -0.5 0.9 0.5 6.1 -1 15 7.4 9.1 0.0 0.0 Unknown ( std_S std_W rms_W )N194AA - W - 2777 2777 483 0.2 0.7 0.4 1.6 -7 14 1.2 2.3 0.0 0.0 Unknown ( bias_DIR )N203WN T - - 11060 11060 3085 2.6 1.2 0.2 2.4 1 8 1.8 3.5 -7.6 13.5 Unknown ( bias_T )N220WN T - - 11002 11002 3268 2.7 1.3 0.3 2.2 1 11 1.8 3.4 -9.2 16.7 Unknown ( bias_T )N402WN - - R 11149 11149 2853 -0.3 0.8 0.1 2.3 1 10 1.8 3.4 6.5 29.1 Unknown ( std_RH )N407WN - - R 11087 11087 2459 -0.4 1.3 1.0 2.6 -1 10 2.2 4.0 0.4 21.6 Unknown ( std_RH )N421LV T - - 10973 10973 2948 3.5 1.2 0.3 2.0 -1 7 1.4 2.9 0.0 0.0 Unknown ( bias_T )

Example of GSD AMDAR rejection list:

Aircraft tail number

Which observed variables to reject

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GSD Aircraft Rejection ListVector wind difference between

AMDAR observations and RR 1h forecasts.

Shows some obvious bad aircraft winds near Chicago.(Used by NWS to help identify bad aircraft data.)

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Bias of AMDAR obs and bias correction• Comparing with RAOBs, AMDAR temperature observations

are biased:• At flight levels, AMDAR have a warm bias. (BUT RAOBs have a cold bias at

the same level (ref 1). So exactly how to balance these two biases to move the model temperature toward the truth is a difficult --and somewhat philosophical--question)

• Bias can be dependent to fly phase (ref 2):– Descent: a strong cool bias– Ascent: a slight warm bias

• Bias correction (BC) to the AMDAR observations– ECMWF: variational aircraft temperature BC– NCEP: testing similar method as ECMWF with GSI– NASA/GMAO: bias calculated after analysis and var method

1.Bomin Sun, Anthony Reale, Steven Schroeder, Dian J. Seidel and Bradley Ballish (2013) Toward improved corrections for radiation-induced biases in radiosonde temperature observations. JGR 118, 4231-4243.2.Barry Schwartz and Stanley G. Benjamin, 1995: A Comparison of Temperature and Wind Measurements from ACARS- Equipped Aircraft and Rawinsondes. Wea. Forecasting, 10, 528–544.

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Use AMDAR data in analysis• Observation operator for AMDAR data:

– Conventional observations: T, Q, U, V– 3D interpolation from grid analysis field to

observation location– Same as other conventional observation, such as

sounding and surface observations• Better to define a data type for AMDAR, for

example, in NCEP PrepBUFR:

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Use AMDAR data in analysis• Convert data to the values and format analysis

system accepts. Use GSI as an example: 1. Convert AMDAR data into temperature, U and V

component of wind observation, specific humidity2. Encode the observations into PrepBUFR file with all

other conventional observations.• Let analysis system run … • After analysis, must check the analysis impact:

– How many observations are used in the analysis– The distribution of the analysis increment over

analysis domain in one analysis– Data impact from OSE, …

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AMDAR data impact: RAP• The Rapid Refresh (RAP) is a US operational hourly

updated regional numerical weather prediction system for aviation and severe weather forecasting.

Configuration:• 13 km horizontal North American grid• Twice daily partial cycles from GFS

atmospheric fields• Hourly continue cycled land-surface fieldsModel:• WRF-ARW dynamic coreData Assimilation:• GSI 3D-VAR/GFS-ensemble hybrid data

assimilation• GSI non-variational cloud/precipitation

hydrometeor (HM) analysis • Diabatic Digital Filter Initialization (DDFI)

using hourly radar reflectivity observation

RAP version 1 operational implementation: 01 May 2012RAP version 2 operational implementation: 24 February 2014

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Observations used in RAPHourly Observations (2012)

Rawinsonde (T,V,RH)

Profiler – NOAA Network (V)

Profiler – 915 MHz (V, Tv)

Radar – VAD (V)

Radar reflectivity - CONUS

Lightning (proxy reflectivity)

Aircraft (V,T)

Aircraft - WVSS (RH)

Surface/METAR (T,Td,V,ps,cloud, vis, wx)

Buoys/ships (V, ps)

Mesonet (T, Td, V, ps)

GOES AMVs (V)

AMSU/HIRS/MHS radiances

GOES cloud-top pressure/temp

GPS – Precipitable water

WindSat scatterometer

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Impact of AMDAR moisture in RAP

RH – national – 1000-400 hPa

#1 obs type = aircraftDistant #2 tie – Surface, GPS, raobs

RAP 2013

Valid 00z - daytime

Valid 12z - nighttime

12z and 00z combined

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Impact of AMDAR Temp in RAP

Valid 00z - daytime

Valid 12z - nighttime

12z and 00z combined

national - 1000-100 hPa flight levels - 300-100 hPa

#1 = Aircraft#2 = RAOBs, surfaceAircraft more impact at 3h, more at flight levels

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Impact of AMDAR wind in RAP

Wind - national – 300-100#1 = Aircraft#2 = RAOBs, Some impact from sfc, GPS-Met, AMVs

Valid 00z - daytime

Valid 12z - nighttime

12z and 00z combined

RAP Domain US CONUS region

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AMDAR data impact (ECMWF)

From The Benefits of AMDAR to Meteorology and Aviation (WIGOS Technical Report 2014-1, Version 1, Jan 2014) Available one line: https://docs.google.com/file/d/0BwdvoC9AeWjUbG1MRlAyU0dhZEk/edit

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Radar data assimilation• Understand Radar Data

– What is weather Radar – What is observed– Data coverage and resolution from single radar– Observation errors– Radar data Quality Control

• Radar observation operator• Assimilate radar reflectivity data with DDFI

and the data impact in RAP

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What is weather radar• Weather radar, also called weather surveillance

radar (WSR) and Doppler weather radar, is a type of radar used to locate precipitation, calculate its motion, and estimate it type (rain, snow, hail etc.)

• Modern weather radars are mostly pulse-Doppler radar, capable of detecting the motion of rain droplets (radial velocity) in addition to the intensity of the precipitation (reflectivity)

• Both types of the data can be analyzed to determine the structure of storms and their potential to cause the severe weather

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Radar Coordinate and ResolutionRadar coordinate:The radar is located at the origin of the coordinate system; the Earth’s surface lies in the x-y plane:Azimuth angle Θ ; Elevation angle α; Range R.

WSR88D data resolution:• Beam width: 1 degree• Scan levels (tilt): 14 scans • Maximum range: 230 km • Gate resolution:

• 250 m for Radial wind• 1 km for reflectivity

• Products update every 6 minutes

α

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Understand 3D radar coverage

3 dimensional radar coverage from ground levelMaury Markowitz - Own work

The approximate height and width of the radar beam with distance from the radar site.

Values in red represent the different elevation angles in this VCP

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US Radar Coverage

One of the only networks of fairly comprehensive, storm-scale data

reflectivity

Radial velocity Courtesy of Jacob Carley from NCEP

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Radar Data Quality Control at NCEP• To meet the high standard required by data

assimilation, it is necessary to develop simple and efficient QC technique for operational applications.

• Radar data quality control is a necessary and initial step for operational applications of radar data.

• Develop statistically reliable QC techniques for automated detection of QC problems in operational environments

• Among various of radar data quality problems, radar measured velocities can be very different (≥10 m/s) from the air velocities in the presence of migrating birds.

Courtesy of Shun Liu from NCEP

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Radar data QC at NCEP

Courtesy of Shun Liu; more details see Shun Liu’s talk at GSI tutorial 2010: http://www.dtcenter.org/com-GSI/users/docs/presentations_2010.phpOr Shun Liu, et al: WRS-88D Radar Data Processing at NCEP. Submitted to Wea. Forecasting.

Scatter plots of radial wind

Before QC

after QC

Composite reflectivity Before QC

after QC

composite dual-pol variable CC

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Radial Velocity Operator

• No term for the vertical velocity (w) in GSI• Suggest only radial velocity observations

from lower elevations should be considered– Avoid contamination of the horizontal

wind field, especially due to hydrometeor sedimentation

Vr(θ, α)=u cosα cosθ + v cosα sinθ +[w sinα]

Elevation angle α 90° - azimuth angle θ

Wind components from background

Courtesy of Shun Liu and Jacob Carley from NCEP

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Reflectivity Operator (example)

T <= 0 °C (dry snow)

T > 0 °C (wet snow)

rain hail

snow

From model forecast rain, snow, hail mixing ratio: qr, qh, qs

ToDavid C. Dowell, Louis J. Wicker, and Chris Snyder, 2011: Ensemble Kalman Filter Assimilation of Radar Observations of the 8 May 2003 Oklahoma City Supercell: Influences of Reflectivity Observations on Storm-Scale Analyses. Mon. Wea. Rev., 139, 272–294.

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Example of Radial Velocity Data analysis with GSI

Full wind vectors Wind analysis Increment

Low row:Analysis using a 1/4th of the default decorrelation length

Upper row:Analysis using default decorrelation length

Radar reflectivity at 0900 UTC on 23 May 2005

Need to modify Background Error Matrix to improve Rv Courtesy of Shun Liu from NCEP

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Reflectivity data assimilation in RAP/HRRR

Radar reflectivity data are assimilated through Diabatic digital filter initialization (DDFI)

Forward integration,full physics with radar-based latent heating

-20 min -10 min Initial +10 min + 20 min

Backwards integration, no physics

Initial fields with improved balance, storm-scale circulation

+ RUC/RAP Convection suppression

RUC / RAP/ HRRR model forecast

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Impact of DDFI with reflectivity Low-level

Convergence

K=4 U-comp. diff (radar - norad)

K=17 U-comp. diff

(radar - norad)

NSSL radar reflectivity

(dBZ)

14z 22 Oct 2008Z = 3 km

Upper-levelDivergence

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Eastern US, Reflectivity > 25 dBZ11-21 August 2011

• 3km HRRR forecasts improve upon RAP 13km forecasts, especially at coarser scales much better upscaled skill

• Radar DDFI adds skill at both 13km and 3km

CSI 13 km CSI 40 km

HRRR no radarRAP no radar

HRRR radarRAP radar

HRRR no radarRAP no radar

HRRR radarRAP radar

Radar Reflectivity Verification

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HRRR Radar Reflectivity Assimilation

3-km Interp

Refl Obs

1 hr pre-fcst Obs

GSI HM Anx

GSI 3D-VAR

HM

Obs

15 hr fcst

13z 14z 15z

3 kmHRRRRun

15 hr fcst 3-km Interp

15 hr fcst 3-km Interp

15 hr fcst

3-km Interp

HRRR 2013 introduction of

3-km data assimilation (DA)On 10 April 2013

Latency reduced 45 min to 1-2 hrs

DigitalFilter

DigitalFilter

DigitalFilter

DigitalFilter

20

09

-12

20

13

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HRRR 2013 Pre-forecast HourTemperature Tendency (i.e. Latent Heating) = f(Observed Reflectivity)LH specified from reflectivity observations applied in four 15-min periodsNO digital filtering at 3-kmReflectivity observations used to specify latent heating in previous 15-min period as follows:• Positive heating rate where obs reflectivity ≥ 35 dBZ over depth ≥ 200 mb (avoids bright banding)• Zero heating rate where obs reflectivity ≤ 0 dBZ• Model microphysics heating rate preserved elsewhere

LH = Latent Heating Rate (K/s)p = PressureLv = Latent heat of vaporizationLf = Latent heat of fusionRd = Dry gas constantcp = Specific heat of dry air at constant pf[Ze] = Reflectivity factor converted to rain/snow condensatet = Time period of condensate formation (300s i.e. 5 min)

-45

-30

-15

0

-60 -45 -30 -15 0Model Pre-Forecast Time (min)

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HRRR 3-km Reflectivity Assimilation

Radar Obs05z 18 May 2013

3-km radar DANO 3-km radar

DA

05z + 45min

45 min fcst

0-hr fcst0-hr fcst

45 min fcst

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HRRR Retrospective Statistical Verification

Optimal Bias = 1.0

With 3-km DAWithout 3-km

DA

With 3-km DAWithout 3-km

DA

Statistical Retrospective Comparison30 May - 04 June 2012 (55 matched runs)

3-km grid ≥ 35 dBZ Eastern US

Improved 0-2 hr convection

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Real-time HRRR case study

HRRR 13 UTC run 8 hr forecast valid 21 UTC

HRRR CompositeReflectivity

ObservedReflectivity

Moore, OK 20 May 201320 – 21 UTC Tornadic Supercell