Una Mirada a la Teledetección Satelital y la Asimilación de Datos a Través de los Dispersómetros

Transcription

1 Una Mirada a la Teledetección Satelital y la Asimilación de Datos a Través de los Dispersómetros Marcos Portabella Hot Topics in Meteorology, Noviembre, 2005.

2 Contents 1. Introduction 2. Scatterometers 3. Satellite retrievals 4. Data assimilation 5. Future satellite mission planning 6. European organizations involved in meteorological Remote Sensing

3 1. Introduction Satellite instrument types Active: Radars (microwave), Lidars (UV) Passive: Radiometers, sounders (all freqs./channels) Satellite orbit types Compromise between coverage & resolution Satellite versus conventional (ground stations, ships, airplanes, buoys, radiosondes, etc.) Better coverage of the oceans and data sparse areas Less sensitive to extreme weather Area means: small repr. errors (for DA)

4 ( km) ( km) -Most Earth observation satellites are LEO: high resolution good coverage -e.g., Meteosat, GOES, MSG: coarser resolution continuous coverage Also: Non-polar low orbit ( km), e.g., TRMM (rain & cloud liquid content) Mid orbit (about 10,000 km), e.g., GPS (water vapor and temp. profiles) Molniya orbit (high excentricity): Russian Earth Observation satellites

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7 2. Scatterometers Active microwave instrument (radar) Measures surface roughness cm-scale surface roughness well correlates with local 10-m wind Resolution: km (and finer)

8 SeaWinds rotating dish antennae Ku-band (2 cm) Dual polarization ERS Scatt fixed fan-beam antennae C-band (5 cm) VV polarization

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10 3. Satellite retrievals Forward problem Wind model Relation between measurements and geophysical variables, i.e., the Geophysical Model Function (GMF). The (empirical) GMF is usually derived using collocations with buoy and/or NWP data (regression). Scatterometer wind GMF: o σ = [ + B cos( φ) + B cos(2 ] Z B ) φ Sea Ice model

11 σ o = Inversion problem [ + B cos( ) + B cos(2 ] Z B 0 1 φ φ 1 2 ) Two unknowns: wind speed (B coefficients) wind direction (φ) Main problems: non-linearity errors number of independent observations over same scene (e.g., different azimuth angles, incidence angles, frequencies or channels)

12 Inversion problem

13 Inversion problem Several inversion approaches: - Bayesian, exact algebraic solutions, relaxation, least square estimation, truncated eigenvalue expansions, etc (Rodgers, 2000) Bayes theorem: General approach (for underdetermined problems) Assumptions: measurement and background errors uncorrelated and Gaussian. B: background error covariance matrix R: observation error covariance matrix H n : observation operator (GMF) ) ( ) ( ) ( t t o o t x x y y x P P P )] ( ) ( 2 1 )} ( { )} ( { 2 1 exp[ ) ( 1 1 b b o o x x B x x x y R x y x T n T n a H H P Observation term Background term

14 Maximum Likelihood Estimation (MLE) [for (over)determined problem] 1 Pa ( x) exp[ { y 2 o H n ( x)} T R 1 { y o H n ( x)}] y 0 MLE = 1 N N i= 1 ( o o σ ) mi σ si ( o kp σ ) si 2 Scatterometer GMF Quasi-linear in wind speed Highly non-linear in wind direction (leading to solution ambiguities) 1 Pa ( x) exp[ { y 2 o H T ( x)} R n ( x x 2 1 { y o H 1 T ) 1 b B ( x x b n )] MLE ( x)} Solution bands Local minima Wind direction (φ)

15 Standard procedure Ambiguities: cost function minima

16 Selected Wind Field Spatial filter: Median, Variational analysis

17 Multiple Solution Scheme (MSS) Ambiguities: cost function bands A wide range of probable solutions exists in nadir (of 144 solutions per observation cell) Locally, 100-km product is pretty Unique (P threshold is 10-7 )

18 Selected Wind Field Spatial filter: Variational analysis (2D-Var) 100-km MSS 1. Full use of solution probability info 2. Meteorological balance in Ambiguity Removal (2D-VAR

19 50 km Plots! Improved cold front Better Around rain

20 Quality Control MLE-based QC MLEhigh probability of wind MLElow probability of wind (sea ice, land, rain, confused sea state, etc.) y 0

21 Motivation for data assimilation Example: low-pressure centre misplacement and front less pronounced

22 4. Data assimilation Stoffelen, A., Scatterometry, PhD thesis, ISBN , 1998.

23 Generalized inverse problem for data assimilation: J( x) = ( y x a o H[ x]) T R 1 ( y o H[ x]) + ( x x b ) T B 1 ( x x = x + K y H[ x ]) K: gain or weight matrix b ( o b b ) Main assumptions: linearity, errors unbiased and Gaussian, observation errors uncorrelated Statistical objective: minimum variance (least-squares estimation) Characterization of errors is crucial H is a collection of interpolation operators from grid points to observation points conversions from model variables to observed parameters (GMFs) H is linear or linearized. For scatterometers, assimilation is performed in wind (state) domain, where R is Gaussian Model state has 10 7 dimensions (grid points x levels x variables) and R has 10 5 dimensions underdetemined problem

24 B looks like cov( e1, e2) var( e2) cov( e, e ) Cross-correlations in off-diagonal terms cov( e1, e3) cov( e, ) 2 e3 var( e ) 3 B is positive and can be diagonalizedj(x) becomes quadratic One observation impacts other grid points and variables var( e1 ) B = cov( e1, e2) cov( e1, e3) 2 3 Information spreading (Underdetermination overcome), smoothing (data-dense areas), and meteorological balance Boutier and Courtier, ECMWF (1999)

25 How to characterize errors? R: instrument errors, geophysical errors, representativeness errors (uncorrelated) B: background errors (correlated) Thinning Observational method: Hollingsworth and Lonnberg (1986) c ( i, j) = ( y H x )( y H x ) i i b j j b T

26 Data Assimilation schemes J( x) = ( y H[ x]) OI: Analitical solution x a = x b K too big; solved locally (boxes) o T R 1 + K( yo H[ xb]) ; ( y J T T K= BH ( HBH + R) o 1 H[ x]) + ( x x = 0 b ) T B 1 ( x x b ) 3D-Var: J 0 ; look for minimum using gradient information Solved globally Holm, ECMWF (2003)

27 4D-Var: Use observations at appropriate time Analysis consistent with past and future observations Kalman filter: Eqs. solved at each time step B evolves in time (errors decrease where more obs; increase because of model errors Holm, ECMWF (2003)

28 DA scheme & scatterometer impact on NWP output Improved forecasts of extreme wind events Isaksen & Stoffelen, 2000

29 ECMWF forecast improvements over the last 20 years Forecast improves by 1 day per decade SH improvements mainly due to satellite data

30 Largest impact from satellite data in NH! Largest improvement step when satellite data was first assimilated (1979) DA systems are improving too

31 5. Future satellite mission planning OSE - Observing System Experiment Assess added value of current obs. Systems OSSE - Observing System Simulation Experiment Assess added value of future systems True atmosphere: independent NWP model All observing systems have to be simulated SOSE Sensitivity Observing System Experiment New method to assess the added value of future observing systems in real extreme events Only new observing system has to be simulated

32 Sensitivity Observing System Experiment (SOSE ) Combined assimilation of existing and synthetic observations 1. Realistic 2. Provides a good short-term (2-day) forecast 3. Not in conflict with existing observations choice requirements Pseudo-truth = analysis + perturbation Perturbation candidate: sensitivity structures SIMULATED TRUE ATMOSPHERE Simulation of the new observing system Combined assimilation of synthetic and existing observations in operational NWP environment Impact on 2-day forecast Requires simulation of only the new observing system WM-RW presentation; KNMI, 7 November 2005 Gert-Jan Marseille, KNMI (2005)

33 Forecast failure 2-day ECMWF forecast 120 m error in Z500 WM-RW presentation; KNMI, 7 November 2005 Gert-Jan Marseille, KNMI (2005)

34 Improved forecast 2-day improved forecast error halved WM-RW presentation; KNMI, 7 November 2005 Gert-Jan Marseille, KNMI (2005)

35 Aeolus: Lidar Principle λ vlos = f 2 v HLOS = vlos /sin( ϕ) v = u sin( ψ) v cos( ψ) HLOS with Doppler shifted wavelength Aerosols/molecules moving with the wind WM-RW presentation; KNMI, 7 November 2005 Gert-Jan Marseille, KNMI (2005)

36 ADM follow-on candidates Dual perspective Tandem-Aeolus information content single LOS vs. dual-perspective WM-RW presentation; KNMI, 7 November 2005 Gert-Jan Marseille, KNMI (2005)

37 Case study: 28/1/ Aeolus WM-RW presentation; KNMI, 7 November 2005 Gert-Jan Marseille, KNMI (2005)

38 Case study: 28/1/ Dual-Perspective WM-RW presentation; KNMI, 7 November 2005 Gert-Jan Marseille, KNMI (2005)

39 DWL analysis impact 500 hpa wind Tandem DualPersp. Dual-perspective better than single LOS (Aeolus) Tandem-Aeolus scenario recovers large scale structures WM-RW presentation; KNMI, 7 November 2005 Gert-Jan Marseille, KNMI (2005)

40 6. European organizations involved in meteorological Remote Sensing European Space Agency (ESA) Supports experimental and non-operational missions ITT - Invitation To Tender (mission concept: involves industry and academy) AO - Announcement of opportunities (scientific research on instruments) Trainee programs: Spanish Trainee, Young Graduate Trainee Research Fellowships: post docs European Meteorological Satellite (EUMETSAT) Supports (mainly) operational missions ITT & AO programs similar to ESA but for operational missions Research Fellowships (Graduates & post docs) Satellite Application Facilities (Visiting & Associated Scientists) European Centre for Medium-Range Weather Forecast (ECMWF) Operational data assimilation best specialists (staff vacancies) Links with ESA & EUMETSAT (via Research fellowships) Links with National Meteorological Centres & Universities (use of facilities) Work opportunities through Met. Centres, e.g., DWL PhD position at KNMI!

41 EUMETSAT Satellite Application Facilities (SAF) Aim: Research & Development on satellite data products Current SAFs (initial operations) Support to Nowcasting and Very Short Range Forecasting (Host: INM) Ocean and Sea Ice (Host: Meteo France) Climate Monitoring (Host: DWD ; KNMI is among partners) Numerical Weather Prediction (Host: UK Met ; KNMI is among partners) Land Surface Analysis (Host: IM ; KNMI is among partners) SAFs under development Ozone Monitoring (Host: FMI ; KNMI is among partners) GRAS Meteorology (Host: DMI; IEEC is among partners) Support of Operational Hydrology and Water Management (Host: UGM) Internal positions: project staff (R&D), Associated Scientist (Research project) External positions: Visiting Scientist (Research project)

42 ERS N 14 W 12 W W 46 N L 44 N 44 N NH radiosonde observations: ECMWF model underestimates veering, backing of winds A. Hollingsworth 1994 Especially lack of veering Mostly warm advection Lack cross-isobar flow at surface Also for MetOffice, ERA40 A. Brown et al 2005 Cold 42 N N Visible by scatterometer data, e.g., this ERS-2 case 1010 Warm advection 40 N 40 N 14 W 12 W 10 W Hans Hersbach, ECMWF (2005) WISE 2004, Reading Slide 42

43 QuikSCAT vs model wind dir Stratify w.r.t. Northerly, Southerly wind direction. (Dec 2000 Feb 2001) Large effect warm advection Small effect cold advection Similar results for NCEP WISE 2004, Reading Slide 43 Hans Hersbach, ECMWF (2005)