Scatterometer Wind Data Processing Marcos Portabella (CSIC) Ad Stoffelen (KNMI) Maria Belmonte (KNMI) Jur Vogelzang (KNMI)

Presentación del tema: "Scatterometer Wind Data Processing Marcos Portabella (CSIC) Ad Stoffelen (KNMI) Maria Belmonte (KNMI) Jur Vogelzang (KNMI)"— Transcripción de la presentación:

1 Scatterometer Wind Data Processing Marcos Portabella (CSIC) Ad Stoffelen (KNMI) Maria Belmonte (KNMI) Jur Vogelzang (KNMI)

2 ASCAT Instrument Real aperture radar Real aperture radar C-band (5.25 GHz) C-band (5.25 GHz) VV polarization VV polarization 3 fan-beam antennas in each side 3 fan-beam antennas in each side Coverage: 2 x 550 km Coverage: 2 x 550 km Sampling: 25 & 12.5 km Sampling: 25 & 12.5 km Primary application: sea surface wind observation Primary application: sea surface wind observation ERS heritage ERS heritage

3 Geophysical Model Function CMOD-5 GMF in 3D measurement space: conical shape CMOD-5 GMF in 3D measurement space: conical shape

4 S catterometer D ata P rocessing Input (  0 values) Pre-processing Inversion Quality Control Ambiguity Removal Quality Monitoring Output wind field NWP model Stand-alone product

5 Inversion Bayesian approach: Bayesian approach: –Find closest point on 3D manifold The statistical error in finding this point is small and equivalent to a vector error of 0.5 m/s in wind The statistical error in finding this point is small and equivalent to a vector error of 0.5 m/s in wind Scatterometer vector errors are however estimated to be typically 2 m/s Scatterometer vector errors are however estimated to be typically 2 m/s Find the best WVC wind by maximizing: Find the best WVC wind by maximizing: approximated by a set of Kronecker delta functions approximated by a set of Kronecker delta functions is verified to be normal and unbiased in the wind components. The wind vector solutions by inverting the GMF will be unbiased. is verified to be normal and unbiased in the wind components. The wind vector solutions by inverting the GMF will be unbiased. wind >> measurement errors errors

6 Inversion MLE: MLE: constant; inversion valid? constant; inversion valid? ERSSeaWinds

7 Inversion c c c Cone shape more important than error information in inversion!

8 Inversion

9 Sensitivities change across the swath due to geometry variation Sensitivities change across the swath due to geometry variation Individual beam sensitivities are not complementary and/or presence of quasi-null sensitivities: it is not possible to flatten total sensitivity Individual beam sensitivities are not complementary and/or presence of quasi-null sensitivities: it is not possible to flatten total sensitivity Useful for future scatterometer concept design Useful for future scatterometer concept design SeaWinds wind direction accumulation problem can be reduced in the Ambiguity Removal step SeaWinds wind direction accumulation problem can be reduced in the Ambiguity Removal step SeaWinds

10 Ambiguity Removal (AR) Scatterometer inversion produces a set of wind (direction) solutions or ambiguities Scatterometer inversion produces a set of wind (direction) solutions or ambiguities Ambiguity removal is performed with spatial filters Ambiguity removal is performed with spatial filters

11 Azimuthal diversity MLE Wind direction (  ) Local minima Solution bands  0   180  MSS Accounting for local minima, erratic winds are produced Accounting for local minima, erratic winds are produced MSS accounts for lack of azimuthal diversity MSS accounts for lack of azimuthal diversity –A relative weight (probability) is derived for every solution –Suitable with a variational filter

12 Meteorological balance (2D-VAR) Spatial filter:  Mass conservation  Continuity equation  0 U = 0  Vertical motion < horizontal motion  Parameters:  Background error (variance)  Correlation length  Rotation vs divergence Cost function:

13 NOAA MSS @ 25 km Improved cold front Better Around rain 50 km Plots !

14 Local minimaMSS NWP model

15 Local minima MSS

16 MSS reduces nadir biases SeaWinds NoMSS SeaWinds with MSS/2DVAR

17 Conclusions In scatterometer inversion, the shape of the solution surface is more important than the measurement error information In scatterometer inversion, the shape of the solution surface is more important than the measurement error information Wind direction systematic accumulations are substantially reduced when total (wind direction) sensitivity is made uniform (i.e., constant cone curvature) Wind direction systematic accumulations are substantially reduced when total (wind direction) sensitivity is made uniform (i.e., constant cone curvature) When the latter is not possible, systematic accumulations can be reduced with sophisticated AR processing, e.g., MSS + 2D-Var When the latter is not possible, systematic accumulations can be reduced with sophisticated AR processing, e.g., MSS + 2D-Var MSS + 2D-Var is also effective in filtering noise while preserving meteorological consistency and detail, e.g., MSS + 2D-Var is also effective in filtering noise while preserving meteorological consistency and detail, e.g., –Lack of azimuthal diversity –Low winds –High resolution systems

18 Quality Control (QC) Scatterometers provide good quality sea surface winds except for: Scatterometers provide good quality sea surface winds except for: –Sea ice or land contamination –Large spatial and temporal variability (e.g., vicinity of fronts and low-pressure centres) –Rain (especially in Ku-band systems)

19 Quality Control Inversion residual value (MLE) Inversion residual value (MLE) –low = good quality wind –high = low quality wind A uniform metric is derived (Rn) A uniform metric is derived (Rn) A Rn threshold is derived to optimize A Rn threshold is derived to optimize –rejection of low quality –accept good quality SeaWindsECMWF

20 Quality Control Areas with significant Rain (large squares) effectively detected Areas with significant Rain (large squares) effectively detected Frontal and low-pressure centre areas effectively removed Frontal and low-pressure centre areas effectively removed Vast majority of spatially consitent winds are accepted (green arrows) Vast majority of spatially consitent winds are accepted (green arrows)

21 QC: KNMI vs NASA Old NASA QC was using (in addition to MLE) other parameters which are not only sensitive to rain Old NASA QC was using (in addition to MLE) other parameters which are not only sensitive to rain Old NASA QC removed substantial amount of spatially consistent winds at high wind areasQC was later modified Old NASA QC removed substantial amount of spatially consistent winds at high wind areasQC was later modified

22 Misiones futuras Dispersómetros Dispersómetros –Oceansat-2 (India) –HY-2 (China) –MetOp-B y MetOp-C (Europa) –XOVWM o SeaWinds-3 (EEUU) Radiómetros banda-L Radiómetros banda-L –SMOS, SMOSops 1-3 (Europa) –Aquarius (EEUU) SAR SAR –TerraSAR (Alemania) –Radarsat-2 (Canadá) –Sentinel-1 (Europa) –Alos-2 (Japón) ??

23 Inversion Teorema de Bayes: Teorema de Bayes: Formulación General (Gaussiana): Formulación General (Gaussiana): Maximum Likelihood Estimation: Maximum Likelihood Estimation:

24 SAR Radar de apertura sintética Radar de apertura sintética Banda C ( ~ 5 cm) entre otras (X, L) Banda C ( ~ 5 cm) entre otras (X, L) Vientos alta resolución (~ 1-5 km) Vientos alta resolución (~ 1-5 km)  = GMF(v,  )  = GMF(v,  ) Soluciones: inversión con Soluciones: inversión con –V o  auxiliar (p. ej., modelos) –Determinar  en la imagen (wind streaks) Problemas: Problemas: –Errores en parámetro auxiliar proyectados en inversión –Streaks no siempre presentes y no siempre alineados con viento

25 Formulación general (Gaussiana): Formulación general (Gaussiana): Combina SAR y modelos teniendo en cuenta respectivos errores Combina SAR y modelos teniendo en cuenta respectivos errores Problema: sistema poco sensible a dirección del viento Problema: sistema poco sensible a dirección del viento SAR (Sistemas indeterminados) SWRA

26 Imagen SARViento obtenido Modelo no detecta frente Modelo no detecta frente Viento obtenido cambia intensidad no dirección Viento obtenido cambia intensidad no dirección

27 SWRA Primer método SAR que tiene en cuenta errores Primer método SAR que tiene en cuenta errores En general, combinación estadística óptima En general, combinación estadística óptima Sensibilidad baja en dirección Sensibilidad baja en dirección –Método wind streak se puede adoptar en la función coste Con nuevos SAR de gran cobertura se abre la posibilidad de aplicaciones operacionales (zonas costeras) Con nuevos SAR de gran cobertura se abre la posibilidad de aplicaciones operacionales (zonas costeras) Este método está siendo adoptado por el Environment Canada para uso operacional Este método está siendo adoptado por el Environment Canada para uso operacional Publicación: Portabella y Stoffelen, J. Geophys. Res., 107 (C8), 2002 Publicación: Portabella y Stoffelen, J. Geophys. Res., 107 (C8), 2002

28 Modelo error geofísico Caracterización de errores medición esencial para control calidad y obtención del viento Caracterización de errores medición esencial para control calidad y obtención del viento Fuentes de ruido: Fuentes de ruido: –Instrumental –GMF –Geofísico (variabilidad sub-celda) Método empírico para modelar error geofísico Método empírico para modelar error geofísico

29 Modelo error geofísico Simulación  Combinación óptima: M = 8; = 0.55 m/s

30 Modelo error geofísico Modelo Modelo Método genérico Método genérico Se ha implementado en productos KNMI Se ha implementado en productos KNMI Publicación: Portabella y Stoffelen, IEEE Trans. Geosci. Rem. Sens., 44 (11), 2006 Publicación: Portabella y Stoffelen, IEEE Trans. Geosci. Rem. Sens., 44 (11), 2006

31 Calibración / Validación ASCAT Calibración “oceánica” basada en herencia ERS Calibración “oceánica” basada en herencia ERS –GMF CMOD5 –Garantiza continuidad Propósito Propósito –Ayudar a EUMETSAT en calibración Nivel 1 –Diseminar producto Nivel 2 Correcciones Correcciones –Visual –Wind Speed bias

32 Vientos ASCAT de gran calidad después de calibración oceánica Vientos ASCAT de gran calidad después de calibración oceánica Producto Nivel 2 KNMI “pre-operacional” hasta calibración definitiva Producto Nivel 2 KNMI “pre-operacional” hasta calibración definitiva Publicación: Verspeek, Portabella y Stoffelen, IEEE Trans. Geosci. Rem. Sens., en preparación Publicación: Verspeek, Portabella y Stoffelen, IEEE Trans. Geosci. Rem. Sens., en preparación Nivel 1b (EUMETSAT) Corte vertical para WVC #42 Corrección visual Corrección visual + wind speed bias