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TAMDAR and its impact on RUC Forecasts

Bill Moninger, Stan Benjamin, Brian Jamison, Tracy Lorraine Smith, Ed

Szoke, Tom SchlatterNOAA / ESRL / GSD

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AMDAR and TAMDAR

• “AMDAR” (Automated Meteorological Data and Recording) – are automatically sent from commercial aircraft, mostly large jets

• “TAMDAR” (Tropospheric AMDAR) – automatic reports from (currently) ~50 turboprops flying regionally in the US Midwest

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World-wide AMDAR coverage, Tuesday 5 June 2007

more than 250,000 observations in 24 h

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Over CONUS, all altitudes

more than 172,000 observations in 24 h

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Coverage is limited to major hubs below 20 Kft, (without TAMDAR)

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Below 20 Kft, with TAMDAR – better regional coverage in the Midwest

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TAMDAR Variables

• TAMDAR measures temperature and winds aloft, as does the rest of the AMDAR fleet

• In addition, TAMDAR measures– water vapor– turbulence (not discussed)– icing (not discussed)

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Parallel real-time RUC cycles

• “dev2” – includes TAMDAR and all other data typically assimilated by the RUC

• “dev” – lacks only TAMDAR data

• Both cycles use NAM boundary conditions

• Both run at 20-km, but are otherwise similar to the operational 13-km runs

• Background fields are set equal every 48 h

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Verification

• dev and dev2 forecasts are verified against RAOBs

• Usually at 00 UTC only (when TAMDAR are flying)

• Usually we look at 3-h forecasts

• We consider 3 regions

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Great Lakes Region includes 13 RAOBsEastern US Region includes 38 RAOBs

National Region is the RUC domain (CONUS and adjacent)

Results today are from the Great Lakes region, where most TAMDAR aircraft fly

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RAOB Verification

• RUC and RAOB soundings are interpolated to 10 mb levels

• Comparisons of T, RH, W are made at every level from the surface upward

• No RAOB data are automatically eliminated

• A few RAOBs eliminated by hand– (about a dozen in 18 months)

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Temperature RMS error time series, 3-h forecasts, Great Lakes Region, surface to 500 mb

TAMDAR impact up to 0.2 K

Red: dev RMS error

Blue: dev2 RMS error

Black: difference

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Temperature RMS error profile, 3-h forecasts, Great Lakes Region

TAMDAR impact 0.4K at 900 mb

For 4 months in early 2007

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Maximum expected impact• Because of RAOB instrument and

representativeness errors, we expect the forecast error to never be less than the analysis error

• The analysis RMS T error is ~ 0.5 K• TAMDAR reduces the 3-h forecast error from 1.6

K to 1.2 K, or 0.4 K• The maximum reduction would be from 1.6 K to

0.5 K, or 1.1 K• Thus TAMDAR impact represents about 35% of

the maximum expected 3-h T forecast error reduction at 900 mb.

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Wind error time series, 3-h forecasts, Great Lakes Region, surface to 500 mb

TAMDAR impact small but consistently positive. Limited by

low quality aircraft heading information.

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Wind RMS error profile, 3-h forecasts, Great Lakes Region

TAMDAR impact:0.25 m/s at 700 mb

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Maximum expected impact

• Analysis fit to RAOBs is ~2.2 m/s

• Thus, TAMDAR impact on 3-h wind forecasts represents a 15% reduction in 3-h wind forecast error at 700 mb

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RH error time series, 3-h forecasts, Great Lakes Region, surface to 500 mb

TAMDAR impact up to 2 %RH

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RH RMS error profile, 3-h forecasts, Great Lakes Region

TAMDAR impact

~2 %RH below 550 mb

(slightly negative at 200 mb! – of little importance)

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RH RMS error profile, no TAMDAR (red), TAMDAR (blue), and Analysis (black), Great Lakes Region

For 3-h RH forecasts, TAMDAR reduces the RMS error by 15% to 25% of the way to the analysis fit below 550 mb, thus represents

a 15-25% reduction in 3-h RH forecast error

in this region

RMS error for the dev2 analysis

RMS errors for the dev

and dev2 3-h forecasts

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Retrospective Runs

• All RUC data were saved for a 10-day period– 12 UTC 26 November to 12 UTC 5 December 2006– Includes a potent winter storm early, and more

moderate weather later

• We have rerun the RUC over this period with a variety of– assimilation parameters– TAMDAR data variations

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RH Assimilation parameters

• High temporal- and spatial-resolution upper-air RH data have not been available in the past

• What is the appropriate RH observation error (like a weight) to be used for TAMDAR?

• We’ve tested several errors for impact.• Too small an error => overfitting• Too large an error => reduced impact

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An example of overfitting. (This plot uses verification data from mandatory levels only.)

Between 13 Sept – 1 Dec 2005, RH error was set to 1%. Note negative

TAMDAR impact.

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RH error tuning

• We have tried the following RH errors

• 1%, 3%, 12%, 18%, 25%

• 1% yields overfitting and negative TAMDAR impact on 3-h RH forecasts

• 12% yields best 3-h RH forecast impact

• Higher RH errors cause a gradual dropoff in TAMDAR impact

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Increased TAMDAR impact on 3-h RH forecasts with 12% RH error*

New processing causes TAMDAR to reduce 3-h RH forecast errors (RMS) by

5 %RH at 500 mb.

Under old processing (3% RH error). maximum reduction in 3-h RH forecast error was

3.5 %RH

* some other changes as well, as detailed in the preprint

Each curve shows the amount that TAMDAR reduces the RMS

error.

New processing reduces the error more

=> more TAMDAR impact.

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A look back and ahead (1)

• TAMDAR has improved the skill of RUC T, W, and RH forecasts

• TAMDAR data from the Mesaba fleet is likely to become an operational data set (purchased by the NWS) in the near future

• The data will be restricted to the U. S. Government, but will be used in publicly-available operational models

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A look ahead (2)

• AirDat will install TAMDAR on additional fleets over the next several months

• Covering Alaska and the Western US• These fleets include some jet aircraft

– higher altitudes, speeds (implications??)– better heading => reduced wind errors

• GSD will evaluate the quality and impact of these data (with FAA funding)

• (Unfortunately, these new data will not be available beyond GSD, per AirDat)

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TAMDAR in Alaska: 1 Pen Air aircraft out of 8 planned

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Anchorage TAMDAR sdg, compared with RAOB 1.5 h later(agreement is pretty good, even for wind which AirDat does not yet vouch for

due to some suspect magnetic declination data)

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We expect to report at the AMS Annual Meeting next January on initial results

from these new fleets and regions

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Indirect RH impact

• Can TAMDAR temp. and wind. forecasts alone improve RH forecasts?– by providing better RH advection forecasts

• We performed a retro run using no TAMDAR RH data

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Answer: no TAMDAR impact on 3-h RH forecasts when TAMDAR RH data are excluded

Red: all TAMDAR (and other) data

Blue: no TAMDAR RH data

Black: no TAMDAR data at all

Removing TAMDAR RH data degrades forecast skill at 3-h as much as removing all TAMDAR data.

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Some TAMDAR impact on 9-h RH forecasts when TAMDAR RH data are excluded

Red: all TAMDAR (and other) data

Blue: no TAMDAR RH data

Black: no TAMDAR data at all

TAMDAR W and T data alone reduce 9-h RH forecast errors at 450-500 mb about half as much as the whole TAMDAR dataset does.

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Decreased vertical resolution decreases TAMDAR impact on 3-h T forecasts by

~30% at 750 mb

~10% at 900 mb

Effect of vertical resolution on TAMDAR 3-h Temperature forecast impact

Each curve shows the amount that TAMDAR reduces the RMS

error.

Low-res reduces the error less => less TAMDAR impact.

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Vertical Resolution

• Higher resolution data can improve forecasts, but increases communication costs

• We tested the impact of vertical resolution on forecast skill

• Full resolution:– every 10 mb in lowest 200 mb– every 25 mb above that

• Reduced resolution:– every 10 mb in the lowest 100 mb– every 50 mb above that– yielded about 50% less TAMDAR data

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Results

• Decreased vertical resolution degrades 3-h T forecast skill by 10% to 30%

• Moreover, the maximum possible vertical resolution has provided critical information during some potentially-severe weather events.