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Presentation by Earth System Research Lab / Global Systems Division

- Bill Moninger23 March 2009

• Impact of the AMDAR observations to aviation weather forecast, public weather service, and numerical weather prediction – request of Mr. Hasegawa from JMA

• Demonstration of ESRL/GSDs real-time display of AMDAR data—used by weather services worldwide

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Bill Moninger, what I look like and where I work

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What is ESRL/GSD?

• ESRL/GSD is located in Boulder, Colorado• ESRL has about 500 employees• GSD has about 200 employees• We are in the Research branch of NOAA

– (NWS is an Operational branch of NOAA)

• We develop NWP models from global to local scales– we focus on data assimilation

– we focus on transferring our work to operations (NWS)

• We provide data to researchers and operational weather forecasters world-wide

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What we have

• ESRL/GSD operates several large supercomputers

• We gather large amounts of weather data– including experimental data such as

• WVSS-II• TAMDAR

• We are a research & development organization– with the flexibility to test new models– and new data sources

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Models we run

• Global models (will not be discussed further today)

• Mesoscale models:– The Rapid Refresh (RR)– The High Resolution Rapid Refresh (HRRR)– The Rapid Update Cycle (RUC)

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• RR:• 13-km grid• covers North

America• runs hourly

• HRRR• 3-km grid• covers NE US• soon to cover

2/3 of US• runs every 15-60

minutes

• RUC• 13-km grid• covers US• runs hourly• operational for

15+ years (in various forms)

Rapid Refresh domain

Current RUC-13 CONUS domain

HRRR domain

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RUC/RR - backbone for high-frequency aviation productsRUC/RR - backbone for high-frequency aviation products

National Convective Weather Forecast (NCWF), Icing Potential (FIP), Graphical National Convective Weather Forecast (NCWF), Icing Potential (FIP), Graphical Turbulence Guidance (GTG), and the aviation weather productsTurbulence Guidance (GTG), and the aviation weather products

1500 Z + 6-h forecast RCPF

2100 Z verification

Rapid Refresh domain – 2009

Current RUC-13 CONUS domain

AWC

Turbulence - GTG

Icing FIP

RCPF

13km resolution

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• Provide high-frequency mesoscale analyses, short-range model forecasts

• Assimilate all available observations

• Focus on aviation and surface weather:– Thunderstorms, severe weather– Icing, ceiling and visibility, turbulence– Detailed surface temperature, dewpoint, winds – Upper-level winds

• Users:– aviation/transportation– severe weather forecasting– general public forecasting

• Support from Federal Aviation Administration

Purpose for the RUC/ Rapid Refresh

“SituationalAwareness

Model”

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Operational Rapid Update CycleHourly updated short-range model run at NCEP

(aviation, severe weather, general forecast applications)

• Hybrid isentropic coordinate

• Hourly 3DVAR update cycle

• Extensive use of observations

• 13-km horizontal resolution

• Explicit 5-class microphysics

1-hrfcst

1-hrfcst

1-hrfcst

11 12 13Time (UTC)

AnalysisFields

3DVAR

Obs

3DVAR

Obs

Back-groundFields

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RUC Hourly Assimilation Cycle

Cycle hydrometeor, soil temp/moisture/snow plus atmosphere state variables

Hourly obs in 2008 RUC

Data Type ~NumberRawinsonde (12h) 80NOAA profilers 30 VAD winds 110-130 PBL – profiler/RASS ~25Aircraft (V,temp) 1400-7000 TAMDAR (V,T,RH) 0 - 800Surface/METAR 1800-Surface/METAR 1800-2000 2000 Buoy/ship 100- 200 GOES cloud winds 1000-2500 GOES cloud-top pres 10 km res GPS precip water ~300Mesonet (temp, Td) ~7000Mesonet (wind) ~4500METAR-cloud-vis-wx ~1600Radar reflectivity 1km

Observations assimilated

11 12 13 Time (UTC)

1-hrfcst

Background

Fields

Analysis

Fields

1-hrfcst

RUC 3dvar

Obs

1-hrfcst

3dvar

Obs

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Commercial aircraft observations - winds and temperature - recently – water vapor, turbulence

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Impact of AMDAR data on RUC Forecasts

• Study 1: weekend/weekday skill differences

• Study 2: AMDAR cutoff after 11 Sept 2001 terrorist attacks

• Study 3: Recent relative impact studies of AMDAR and other data sources

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Study 1: Weekend-Weekday RUC skill differences

• 20,000 fewer reports every 12 hours on weekends because package carriers (FedEx and UPS) do not fly:

• 0000-1200 UTC AMDAR volume average (2001)Weekday (Tu-Sa) 35,000 reportsWeekend (Su-Mo) 15,000 reports

• Result: a 7% increase in 3h wind forecast error at 200 hPa on weekends

Study period: January-October2001; Stan Benjamin, ESRL/GSD

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3 hr RUC Wind Forecast Errors (with respect to RAOBs) Weekend (Reduced AMDAR) minus weekday

Jan-Oct 2001

Weekend minus weekday 3h wind fcst error difference

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

850 700 500 400 300 250 200 150

Pressure level (hPa)

Win

d f

cs

t e

rro

r d

iff

(m/s

)

0.35 m/s / ~5.0 m/s= 7% better forecasts during weekdays due to more AMDAR reports

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Study 2: Effect of 11-13 Sept 2001 on RUC Skill

• No AMDAR data due to terrorist attack• 20% loss of 3h RUC wind forecast skill at 250mb

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Hourly AMDAR volume

2-15 Sept 01(starting 00z 2 Sept)

2-8 Sept 01

9-16 Sept 01

Su Mo Tu We Th Fr Sa

Su Mo Tu We Th Fr Sa

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Improvement in 3h over 12h wind forecast- September 2001

RUC 250 mbWind forecasts-Verificationagainst RAOB data

11-13 Sep

without AMDAR data,3-h forecast are no

better than 12-h

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Relative Impact Studies

• These require substantial computer time• GSD has a research supercomputer on which we

run…• …multiple retrospective runs, each with a

controlled change against a standard• to make detailed tests• Including TAMDAR evaluation, funded by the FAA

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Retrospective 10-day experiments

• We used the 2007 version of operational RUC model/assimilation software run at 20km resolution, with all observations assimilated in operational RUC except radar reflectivity

• Two periods: August 2007 and Nov-Dec 2006

• Each 10 days long (takes ~6 days to run)

• 30 experiments performed on the ’06 period

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Retrospective 10-day experiments (2)

• 13 experiments were completed for the ’07 period

• The following data types were excluded– AMDAR– TAMDAR– TAMDAR winds– TAMDAR “rejected” aircraft– Profilers– NEXRAD VAD wind profiles– GPS Integrated Precipitable Water (IPW)– Surface observations (METAR and Mesonet)

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Temperature relative impact (1)

This shows the impact of each data source shown for the US Great Lakes Region, during winter 2006, for Temperature forecasts below 6000 ft (800 mb).

AMDAR (red) has the greatest impact of all data sources investigated for 3h and 6h forecasts in this region.

Surface observations have the second greatest impact at 3h and 6h.

AMDAR has relatively little impact for 12h forecasts.

Graphs show the error increase when each observation type is

removed.

Observation types:

Red: AMDAR, including TAMDARBlue: ProfilerPink: NEXRAD VADBrown: RAOBBlue: surface (inc. Mesonets)Green: GPS-IPW

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Temperature relative impact (2)This shows relative AMDAR and TAMDAR impact for 3h Temperature forecasts valid at 0 UTC during winter 2006.

TAMDAR is responsible for about 40% of the total AMDAR impact below 6000 ft. in this region and during this period.

As a specific example, TAMDAR alone reduces 3-h temperature errors by 0.5 K at 900 mb (3000 ft.), whereas all AMDAR data (including TAMDAR) reduces temperature errors by 1.1 K at 900 mb.

More precisely: removing TAMDAR alone increases temperature errors by 0.5 K, and removing all AMDAR data increases errors by 1.1 K.

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Temperature relative impact (3)This shows the impact of each data source shown for the Great Lakes Region, during Summer 2007, for Temperature forecasts.

AMDAR (red) has the greatest impact of all data sources investigated for 3h, 6h and 12h forecasts in this region.

Surface observations have the second greatest impact.

Observation types:

Red: AMDAR, including TAMDARBlue: ProfilerPink: NEXRAD VADBrown: RAOBBlue: surface (inc. Mesonets)Green: GPS-IPW

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RH relative impact

Relative Humidity forecast impact for winter (left) and summer (right), below 6000 ft (800 mb).

AMDAR has the greatest impact of all data sources studied for 3h and 6h in the winter (left), and for 3h, 6h, and 12h in the summer (right).

TAMDAR is the only AMDAR data source that provides RH information to the RUC currently. (We do not yet ingest WVSS-II data.)

Observation types:

Red: AMDAR, including TAMDARBlue: ProfilerPink: NEXRAD VADBrown: RAOBBlue: surface (inc. Mesonets)Green: GPS-IPW

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RH relative impact

This shows relative AMDAR and TAMDAR impact for 3h Relative Humidity forecasts valid at 0 UTC during winter 2006.

In this altitude range (the lowest 6000 ft.), TAMDAR is responsible for about 60% of the total AMDAR impact for RH in this region and during this period.

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Wind impact: 3-h wind forecasts(22 - 28 April 2005)

Wind errors are reduced by 1.4 m/s at 200 mb due to the inclusion of

AMDAR data

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Direct forecaster use of AMDAR data (1)

• As a radiosonde substitute when there is none nearby (Vancouver, CAN and Houston, US)

• To accurately forecast the onset of severe storms (near airports with timely flights)

• To forecast and monitor low-level wind shear• To monitor jet stream location• To forecast downslope windstorms• To verify/correct model guidance (Montana, US)• Fire weather support• To forecast urban air quality

Many other uses detailed at http://amdar.noaa.gov

Forecasters have direct access to AMDAR data through• ESRL/GSDs web display (to be shown to you soon)

•And through NWS workstations•(This was covered by Carl Weiss earlier)

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• Mountain weather forecasts in support of rescue operations (Seattle, US)

• Improved control of aircraft spacing on descent (Ft. Worth, US)

• Improved forecast of jet-stream-induced turbulence

• Used in aircraft accident investigations (U.S. National Transportation Safety Board)

• To initialize a city-scale model used in on-shore breeze forecasting (Chicago, US)

Direct forecaster use of AMDAR data (2)

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Ongoing AMDAR observation monitoring

• We generate daily and weekly aircraft-model differences

• These are used by us (and others) to monitor aircraft data quality

• We automatically generate daily aircraft reject lists that are used in our backup and development RUC models

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Typical output from one of our evaluation web pages

This view sorted by std RH

Clicking on an ID number gives a time series for that aircraft.

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Typical output from another of our evaluation web pages

This shows aircraft - model vector wind differences.

The aircraft by the cursor has a 43 kt wind difference with the model.

Uniform differences between many aircraft and the model in a particular difference suggest model problems; otherwise, differences suggest aircraft problems.

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Distribution of AMDAR data from GSD

• Data are quality-controlled at GSD• Binary and text data are distributed via GSD’s

MADIS program– http://madis.noaa.gov/– Used by many weather service offices– Used by many research institutions– Soon to be transferred to operations

• Graphical data available over the web– http://amdar.noaa.gov/

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Demonstration of GSD’s real-time AMDAR display

• http://amdar.noaa.gov• Real-time displays are restricted• JMA has had an account since 2001

– requested by Dr. Masanori OBAYASHI– but not used recently

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http://amdar.noaa.gov/java/

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Zooming in on Japan

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Can display wind barbs

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Zooming in on Narita

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Clicking on an ascent or descent gives a sounding

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Clicking on “Get Text” gives the sounding as text

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A close look at Monday Morning’s accident

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Ascent sounding from aircraft JP9Z4Y55took off at 2142 UTC

Note strong wind direction shear in lowest levels

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Higher resolution sounding from aircraft HL7718 (Korean) took off at 2023 UTC

Note better vertical resolution lowest levels

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Zooming in on the soundingNote 49 kt wind at 1400 ft (AGL)

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This site is used by weather services and researchers world-wide

• US NWS• US FAA• Contributing US airlines• US military• State air quality forecasters• AMDAR and E-AMDAR management• Australia, Brazil, Canada, Denmark, Dubai, France,

Russia, Serbia-Montenegro, So. Africa, Spain, Switzerland, others.

• Korean Meteorological Organization has adapted our software to make their own displays…

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Summary

• AMDAR data improves NWP forecasts

• AMDAR data improves forecasts made by humans

• AMDAR quality monitoring is performed in several locations, including GSD

• GSD impact studies show AMDAR is the most important data source for many short-term, mesoscale forecasts

• AMDAR data are available from GSD’s MADIS program to approved users

• AMDAR data are available on the web to approved users at http://amdar.noaa.gov/– in plan view– as soundings

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Thank you!

William R. (Bill) Moninger

NOAA/ESRL/GSD

R/GSD1

325 Broadway

Boulder, CO 80304

303-497-6435

Bill.Moninger@noaa.gov

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‘Off-time’ assimilation

• Traditionally, a model is initialized with RAOBs at one ‘on-time’ (say, 0 UTC)

• and validated with RAOBs at the next ‘on-time’ 12 h later.• The RUC and other modern models can assimilate data at

‘off-times’…• And generate forecasts to be validated with raobs at the

next ‘on-time’• (Off-time data consist of much more than AMDAR, but we’ll

focus on AMDAR)

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0 3 6 9 12

On OnOff Off Off

Validate with Raob

Raob+

AMDAR

AMDAR AMDAR AMDAR

3-h6-h

9-h12-h

Time (UTC)

Each cycle gains the benefit of all ‘off-time’ observations.There is now enough AMDAR data to cycle every hour

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RUCWind forecast

Accuracy - Sept-Dec

2002

Verification against RAOB data over RUC domainRMS vector difference (forecast vs. obs)

RUC is able to use

recent obs to improve

forecast skill down

to 1-h projection for winds*

1 3 6 912

Analysis~ ‘truth’

* this is an important accomplishment -- need to minimize model disturbances due to imperfect data (we use “DDFI”, next slide).

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Forward integration, full physics

RUC Diabatic Digital Filter Initialization (DDFI)

-30 min -15 min Init +15 min

RUC model forecast

Backwards integration, no physics

Obtain initial fields with improved balance

Initial DFI in RUC model at NCEP - 1998 - adiabatic DFIDiabatic DFI introduced at NCEP - 2006

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Forward integration, full physics

-30 min -15 min Init +15 min

RUC model forecast

Backwards integration, no physics

Obtain initial fields with improved balance

Initial DFI in RUC model at NCEP - 1998 - adiabatic DFIDiabatic DFI introduced at NCEP - 2006

Calculate digital-filter-weighted mean of 3-d fields from each time step over DFI period

RUC Diabatic Digital Filter Initialization (DDFI)