Guidance for Targeted Observations during N-AMMA

and data impact results: 3 studies

1. Sharanya Majumdar (RSMAS/U.Miami)2. Jason Dunion & Sim Aberson (NOAA/AOML/HRD)

3. Rolf Langland (NRL Monterey)Acknowledgments: NASA N-AMMA, NOAA THORPEX, NOAA JHT, ONR

TCSP/N-AMMA Workshop, Baltimore, May 15-17 2007

1. Targeted Wind Observations

In order to improve a 2-day forecast of 700 hPa winds offshore of west Africa, in which locations should we collect supplementary observations?

ti totv

EnsembleInitialization time Observing time Verification time

t

2-3 days 2 days

Targeted observing method: Ensemble Transform Kalman Filter (ETKF)

• Theory: Bishop et al. (MWR 2001)• Application to winter storm reconnaissance:

Majumdar et al. (MWR 2002, QJRMS 2002)• Comparison with other methods for tropical

cyclones: Majumdar et al. (MWR 2006)• Need to account for:

– Observation variables, operators, error (co)variance– Data assimilation scheme– Forward propagation of error variance between

observation and verification times

Daily GuidancePredictions made 48 hours prior to observing time.

145-member NCEP, ECMWF and CMC ensembles

Guidance was available daily by 1400 UTC onhttp://orca.rsmas.miami.edu/~majumdar/amma/

EXAMPLE OF GUIDANCE: AEW that developed into Helene, 9/11/06-9/15/06

AA

AA

AA

Target areas: local uncertainty in wind field

(1) Easterly Wave(2) Anticyclone to north(3) African Easterly Jet region behind waveA

Future Work1. Qualitative understanding of ETKF guidance.

2. Identify average ‘sensitive regions’ over season.

3. Evaluate ability of ETKF to predict (in)sensitive areas for

assimilation of observations.

a. Larger ensemble => better ETKF (use THORPEX TIGGE?)

b. Sensitivity to variables? (winds, T, mixing ratio?)

4. Data-impact experiments.

2. Importance of Relative Humidity

Saharan Air Layer Experiment (SALEX) NOAA G-IV Mission 050807n

SAL 3SAL 2

SAL 1

AEW 1

Irene

Polar 1

1616

2121

Satellite (SSMI/AMSR-E) imagery of TPW may or may not capture dry air over the ocean that affects hurricane intensity.Dry air is sometimes concentrated in a narrow mid-low level jet that is not visible in TPW. Compare 2 SALEX dropwindsondes …

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GPS Sonde

Jordan

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Wind Speed (kt)

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21 RH 21 WIND

Thin SAL between 600-700 hPa. Strong narrow dry jet missed by satellite.

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S16 RH 16 WIND

Broad SAL between 500-900 hPa. Consistent with satellite.

Numerical model may fail to capture local RH structure and dry air intrusion into

hurricane.

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GFS

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Drop #17

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Drop #22

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Potential for Targeted Humidity Observations: Helene 2006

Location of highest ensemble spread (uncertainty) is in area of high RH gradient. These locations are also where satellite and in-situ observations disagree with each other and with models.

LEFT: Influence of relative humidity (RH) data from dropwindsondes on NCEP GFS Track forecast. AVHM=with RH. T254=no RH. BEST=best track.

Assimilation of RH dissipates Irene!

ONE DAY LATER: RH maintains Irene!

ABOVE: Assimilation of RH data in the dropwindsondes acted to dry the lower troposphere to the north-east of Irene.

Summary• SALEX data identified a narrow jet of dry air, not found in satellite

data. Jet may modify hurricane intensity.• Investigate further using N-AMMA data.• Thermodynamic data altered NWP forecasts of hurricane track in a

few cases.• Interaction between dry air and hurricane may be better predicted by

assimilating RH in high-resolution hurricane models (e.g. HWRF), leading to improved forecasts of intensity change.

• Targeted observing strategies identify locations of high RH gradient in which RH observations ought be assimilated.

• Can generalize dry air intrusion beyond SAL: mid-latitude troughs; dry air advected in inflow (e.g. Hurricane Rita)

NRL-Monterey: Marine Meteorology Division

What is the impact of observation data in this region ?

1-30 September 2006: observations assimilated at 00UTC in NOGAPS-NAVDAS (3d-Var) – data received in real-time

3. AMMA Data Impact on NOGAPS

NRL-Monterey: Marine Meteorology Division

Methodology:

• The impact of each observation can be quantified in terms of how much it reduces (or increases) the short range forecast error on the new analysis trajectory vs. the old background trajectory.

• The forecast error metric is the 24hr forecast error in the global domain, energy-weighted to combine temperature, moisture, and winds from the surface to near tropopause level: forecast error units = J kg-1

• Tools required: adjoint versions of the global forecast model and the data assimilation procedure

Refs: Langland and Baker (Tellus 2004), Langland (MWR 2005)

NRL-Monterey: Marine Meteorology Division

RESULTS: (negative numbers = error reduction = good!)

Observation Type Impact (J kg-1) # of Obs

Radiosondes -1.9192 49,353

Land-surface -0.1360 6,089

Ship-surface -0.0218 1,607

Geo-sat winds -1.5486104,874

Aircraft -0.5107 31,246

Scatterometer winds -0.0192 390

AMSU-A radiances -0.4740 68,697

SSM/I precip. water -0.2107 31,372

Total Obs -4.8402293,628

Results for observations assimilated at 00UTC 1-30 Sept 2006

in the region 0°N-30°N, 30°W-30°E – using data received in real-time

NRL-Monterey: Marine Meteorology Division

Ranking of 8* most-valuable central African radiosonde stations – in terms of 24hr forecast error metric – NOGAPS-NAVDASStation Lat Lon Impact (Jkg-1) #profiles Station Name

64700 12.13N 15.03E -0.4202 24 Ndjamena, Chad

61291 12.53N 7.95W -0.2783 28 Bamako, Mali

65344 6.35N 2.38E -0.2063 26 Cotonou, Benin

60018 28.32N 16.38W -0.1984 26 Tenerife, Canaries

61024 16.97N 7.98E -0.1803 28 Agadez, Niger

60680 22.80N 5.43E -0.1779 28 Tamanrasset, Algeria

61052 13.48N 2.17E -0.1188 28 Niamey, Niger

61641 14.73N 17.5W -0.0978 27 Dakar, Senegal

Results for observations assimilated at 00UTC 1-30 Sept 2006 in the region 0°N-30°N, 30°W-30°E - using data received in real-time

* The combined impact of all 20 radiosonde stations in the selected region = -1.9192 J kg-1, and the impact of all in-situ and satellite observation data = -4.8402 J kg-1