snowmass, 18 july 2007 impact studies with airborne doppler lidar observations: a-trec to t-parc...

Snowmass, 18 July 2007

Impact Studies with Airborne Doppler Lidar Observations: A-TReC to T-PARC

Martin Weissmann, Andreas Dörnbrack, Stephan Rahm, Oliver ReitebuchInstitut für Physik der Atmosphäre, DLR Oberpfaffenhofen, Germany

Carla CardinaliECMWF, Reading, UK


DLR lidar systems

scanning coherent 2 µm Doppler lidar:step-and-stare scan with 24 positions 24 LOS observations (~30/54 s) vertical profile of 3-D wind vector

horiz. resolution 5 - 40 kmvert. resolution 100 m

range: 0.5-12 kmrecording of every single shot (500 Hz)correction with ground return

Doppler lidar20° off nadir

dropsondes,u, v, t, rh, p

new 4 wavelength water vapour DIAL ~920-945 nm, 100 Hz, > 2 W parameter: water vapour molecule numbernadir pointing (zenith is possible)horiz. resolution: 2 - 40 kmvert. resolution: 300 - 500 mhigh spectral resolution

DIAL


4 flights in "sensitive areas" (targeting)1 flight for Greenland Tip Jet1 flight for intercomparison ASAR and lidar2 transfer flights=======================================8 flights, 1600 wind profiles, 40 000 lidar observations, 49 dropsondes

DLR participation in the Atlantic THORPEX Regional Campaign(14 - 28 November 2003)


Iceland

Ireland

ECMWF sensitivity plot and 500 hPa

http://www.sat.dundee.ac.uk/

Observations on 25 November 2003 (A-TReC)

1 scan revolution (res. ~10 km) 25 values per profile (2500 m) coverage = 36%

4 scan revolutions (res. ~40 km) 38 values per profile (~3800 m) coverage = 54%


Error lidar (u,v):RMS = 0.75-1 m/s

Statistical intercomparison of lidar and dropsondes

Weissmann et al. 2005 (JTECH)


40 km x 40 km

(Isaksen 2005)

Dropsonde(2000 times enlarged)

Radiosonde/Dropsonde:(most accurate operational wind observation)

Observational error <0.5 m/s

Assigned error: 2-3 m/s

Observational error lidar:0.75-1 m/s

Rep. error < 0.5 m/s(Frehlich and Sharman 2004)

Assigned error: 1-1.5 m/sAssigned error AMV: 2-5 m/s

Assigned errors in ECMWF data assimilation


Background departure = difference background and observation

(Std(bg-dep))2 = (Stdobs)2 + (Stdbg)2

Background departures


6 experiments 14-30 November 2003lidar, ~10 km, Std = 1 m/s lidar, ~40 km (2 averaging types), Std = 1 m/slidar, ~40 km, Std =1.5 m/s~100 dropsondes (from 10 flights)control run

thinning to grid points (40 x 40 km, 60 levels)~ 80% not used~ 3000 used measurements 5 million operational measurements used per daylidar = 0.005% additional measurements

4 un-cyled experiments to investigate targeted observations (forecast sensitivity)

Experiments with ECMWF T511 global model


Lidar u, v

Dropsonde u, v

Observation influence

0.63 0.45

Number of observations

758 388

Information content

477.5 174.6

observation influence (Cardinali et al. 2004):0 --> no influence of observations1 --> no influence of background

Observation influence (22 November 2003)

northern hemisphereAMV: 0.15Raso: 0.3


(gpd

m)

Reduction of forecast error - 48 h


(gpd

m)



area: 17 x 10-6 km2

(gpd

m)



Mean reduction over Europe, averaged over 29 forecasts (2 weeks)black: experiments with lidar, gray: experiment with 100 dropsondes

Reduction of forecast error - 500 hPa


Reduction of forecast error - 48, 72, 96 h


Reduction of u, v, z, rh, and t forecast errors

Weissmann and Cardinali 2007, QJ

Relative reduction of RH forecast error - 48, 72, 96 h

Snowmass, 18 July 2007 Why do we need more studies?

large, significant improvement in a few events, but the judgement for a future satellite mission needs to show:

the impact in different areas, flow regimes and for many cases?

a reliable quantification of the impact?

the impact in different assimilations systems?

the sensitivity to accuracy and resolution?


NOAA

DLR

20031115 at 18 Step42 20031120 at 18 Step54

20031122 at 18 Step66 20031125 at 18 Step30

Cut off low; heavy precipitation in SE-Spain

Heavy rain/strong wind in Portugaland NW-Spain

Strong winds –northwest Europe Heavy rain, Scotland and Norway

A-TReC targeting campaigns


The influence of targeted observations

-1.2

-0.8

-0.4

0

0.4

0.8

1.2

1.6

Airep Lidar Temp Drop

ATReC 200112018

Contribution to FCE Obs Rel Num

-1.2

-0.8

-0.4

0

0.4

0.8

1.2

1.6


ATReC 2003112218

Contribution to FCE ObsRel Num

-1.2

-0.8

-0.4

0

0.4

0.8

1.2

1.6


ATRec 2003112518


-1.2

-0.8

-0.4

0

0.4

0.8

1.2

1.6


ATReC 2003111518


+

+main conclusion: we need more cases

A-TReC cases were not ideal: planning needs to be improvedmaximum of sensitive area out of reach for the aircraftperiod with no high impact weather event and without low forecast error


evaluate targeted observations and determine impact in different flow regimes

larger data set is needed for sensitivity studies to determine the optimal configuration offuture satellite Doppler lidar (e.g. reduce to LOS or resolution)

investigate the value of future operational observation targeting (e.g. with satellite lidar)

test targeting strategies

derive parameters for the simulation of future satellite systems (observations in cloud gaps,correlation of backscatter and humidity, ground return over the sea etc.)

What we could do with more data


airport base: Yokota, Japan

flight time: 120 h including transfer

period: 7 weeks

costs: M$ 1.7 (80% funded)

funding agencies:DLR, NSF, JMA, ESA, FZK/IMK, Canada, EUCOS, Korea (?)

range of DLR Falcon aircraft

THORPEX Pacific Asian Regional Campaign (T-PARC), autumn 2008


Nida(2004)

Megi(2004)

Conson(2004)

Mindulle(2004)

Nanmadol(2004)

Aere(2004)

Meari(2004)

Nockten(2004)

Wu et al. (2006)


Tetsuo Nakazawa


Impact studies with DIAL water vapour data

Germany

IcelandGreenland

North America

WV

mixin

g ra

tio [m

g/kg

]

13 May 200214 May 200215 May 2002

Studies with Elias Holm, ECMWF


17 analysis with lidar data (11 down, 6 upward looking)30 000 lidar observations

ongoing:new quality controlsensitivity studies (e.g. no coupling with wind)


no correlation found between key analysis errors and lidar analysis departures or analysis diff.-neither statistical correlation nor qualitative similarity-same for B-matrix norm and total energy norm

-this confirms that results of Isaaksen et al 2005 that KAEs draw the analysis away from obs.,but now it is also shown for data sparse regions

-raises the question what KAEs describe

-results submitted to Meteorologische Zeitschrift (Koch, Ehrendorfer and Weissmann 2007)

Comparison of key analysis errors and lidar analysis departures


significant improvement of ECMWF forecasts fields (wind, temperature, and humidity)(~3% for 2-4 day forecasts over Europe)

lower assigned error than all other current wind observations --> higher observation influence

more data is needed for sensitivity studies, a more accurate quantification of the improvement, and the evaluation of observation targeting

some targeted lidar observations during COPS/E-TReC (ongoing at the moment) and THORPEX-IPY in spring 2008

what we need is a large consistent data set for statistically reliable conclusionsT-PARC, 20% funding missing, preparation needs to start soon (decision in September)evaluate the impact in different assimilation systems: ECMWF, JMA, NRL, Canada...additionally observations with TODWL on P-3?

ground campaign with the ADM demonstrator ongoing at the moment: much stronger signal than before; flight campaign possibly 2008

Summary


range=0-2.5km

range=2.5-5km

range=5-7.5km

range=7.5-12km

Correlation of backscatter and humidity

snowmass, 18 july 2007 impact studies with airborne doppler lidar observations: a-trec to t-parc...

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