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Presentation by Earth System Research Lab / Global Systems Division - Bill Moninger 23 March 2009 Impact of the AMDAR observations to aviation weather forecast, public weather service, and numerical weather prediction request of Mr. Hasegawa from JMA

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presentation by earth system research lab global systems division bill moninger 23 march 2009
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
what is esrl gsd
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
what we have
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
models we run
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)
slide6

Rapid Refresh domain

Current RUC-13 CONUS domain

HRRR domain

  • 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)
slide7

RUC/RR - backbone for high-frequency aviation products

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

Rapid Refresh domain – 2009

13km resolution

RCPF

1500 Z + 6-h forecast RCPF

Current RUC-13 CONUS domain

Turbulence - GTG

AWC

2100 Z verification

Icing FIP

purpose for the ruc rapid refresh
Purpose for the RUC/ Rapid Refresh
  • 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

“Situational

Awareness

Model”

slide9

Operational Rapid Update Cycle

Hourly 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-hr

fcst

1-hr

fcst

1-hr

fcst

Back-

ground

Fields

Analysis

Fields

3DVAR

3DVAR

Obs

Obs

Time

(UTC)

11 12 13

slide10

1-hr

fcst

1-hr

fcst

1-hr

fcst

Background

Fields

Analysis

Fields

RUC 3dvar

3dvar

Obs

Obs

Time

(UTC)

11 12 13

Observations assimilated

Cycle hydrometeor, soil temp/moisture/snow

plus atmosphere state variables

Hourly obs in 2008 RUC

Data Type ~Number

Rawinsonde (12h) 80

NOAA profilers 30

VAD winds 110-130

PBL – profiler/RASS ~25

Aircraft (V,temp) 1400-7000 TAMDAR (V,T,RH) 0 - 800

Surface/METAR 1800-2000

Buoy/ship 100- 200

GOES cloud winds 1000-2500

GOES cloud-top pres 10 km res

GPS precip water ~300

Mesonet (temp, Td) ~7000

Mesonet (wind) ~4500

METAR-cloud-vis-wx ~1600

Radar reflectivity 1km

RUC Hourly Assimilation Cycle

slide11

Commercial aircraft observations

- winds and temperature

- recently – water vapor, turbulence

impact of amdar data on ruc forecasts
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
study 1 weekend weekday ruc skill differences
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 reports

Weekend (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

slide14
3 hr RUC Wind Forecast Errors (with respect to RAOBs) Weekend (Reduced AMDAR) minus weekday Jan-Oct 2001

0.35 m/s / ~5.0 m/s

= 7% better forecasts during weekdays due to more AMDAR reports

study 2 effect of 11 13 sept 2001 on ruc skill
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
hourly amdar volume 2 15 sept 01 starting 00z 2 sept
Hourly AMDAR volume2-15 Sept 01(starting 00z 2 Sept)

2-8 Sept 01

Su Mo Tu We Th Fr Sa

9-16 Sept 01

Su Mo Tu We Th Fr Sa

improvement in 3h over 12h wind forecast september 2001
Improvement in 3h over 12h wind forecast- September 2001
  • RUC 250 mb
  • Wind forecasts
  • Verification
  • against RAOB data

without AMDAR data,

3-h forecast are no

better than 12-h

11-13 Sep

relative impact studies
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

retrospective 10 day experiments
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

retrospective 10 day experiments 2
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)

temperature relative impact 1
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.

Observation types:

Red: AMDAR, including TAMDAR

Blue: Profiler

Pink: NEXRAD VAD

Brown: RAOB

Blue: surface (inc. Mesonets)

Green: GPS-IPW

Graphs show the error increase when each observation type is removed.

slide22

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.

slide23

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 TAMDAR

Blue: Profiler

Pink: NEXRAD VAD

Brown: RAOB

Blue: surface (inc. Mesonets)

Green: GPS-IPW

slide24

RH relative impact

Observation types:

Red: AMDAR, including TAMDAR

Blue: Profiler

Pink: NEXRAD VAD

Brown: RAOB

Blue: surface (inc. Mesonets)

Green: GPS-IPW

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.)

slide25

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.

slide26

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

direct forecaster use of amdar data 1
Direct forecaster use of AMDAR data (1)
  • 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)
  • 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

direct forecaster use of amdar data 2
Direct forecaster use of AMDAR data (2)
  • 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)
ongoing amdar observation monitoring
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

slide30

This view sorted by std RH

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

Typical output from one of our evaluation web pages

slide31

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.

distribution of amdar data from gsd
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/
demonstration of gsd s real time amdar display
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
slide41
Ascent sounding from aircraft JP9Z4Y55took off at 2142 UTCNote strong wind direction shear in lowest levels
slide42
Higher resolution sounding from aircraft HL7718 (Korean) took off at 2023 UTCNote better vertical resolution lowest levels
this site is used by weather services and researchers world wide
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…
summary
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
thank you
Thank you!

William R. (Bill) Moninger

NOAA/ESRL/GSD

R/GSD1

325 Broadway

Boulder, CO 80304

303-497-6435

Bill.Moninger@noaa.gov

off time assimilation
‘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)
slide50

Each cycle gains the benefit of all ‘off-time’ observations.

There is now enough AMDAR data to cycle every hour

On

Off

Off

On

Off

Validate withRaob

AMDAR

AMDAR

AMDAR

Raob

+

AMDAR

3-h

6-h

9-h

12-h

9

6

12

0

3

Time (UTC)

slide51

RUC

Wind forecast

Accuracy -

Sept-Dec

2002

6

9

1

3

12

Analysis

~ ‘truth’

RUC is able to use recent obs to improve forecast skill down to 1-h projection for winds*

Verification against RAOB data over RUC domain

RMS vector difference (forecast vs. obs)

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

ruc diabatic digital filter initialization ddfi
RUC Diabatic Digital Filter Initialization (DDFI)

Initial DFI in RUC model at NCEP - 1998 - adiabatic DFI

Diabatic DFI introduced at NCEP - 2006

-30 min -15 min Init +15 min

Backwards integration, no physics

Forward integration,

full physics

Obtain initial fields with improved balance

RUC model forecast

slide53

RUC Diabatic Digital Filter Initialization (DDFI)

Initial DFI in RUC model at NCEP - 1998 - adiabatic DFI

Diabatic DFI introduced at NCEP - 2006

-30 min -15 min Init +15 min

Backwards integration, no physics

Forward integration,

full physics

Obtain initial fields with improved balance

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

RUC model forecast