A Possible Suite of Algorithms for the Retrieval of Water Vapor Using Saphir/Megha-Tropiques Filipe...
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A Possible Suite of Algorithms for the Retrieval of Water Vapor Using Saphir/Megha- Tropiques Filipe Aires, Laboratoire de Météorologie Dynamique, IPSL/CNRS 3 rd ISRO-CNES Joint Workshop, Ahmedabad, 17-20 October 2005 QuickTime™ and a TIFF (Uncompressed) deco are needed to see this QuickTime™ and a TIFF (Uncompressed) decom are needed to see this QuickTime™ TIFF (Uncompre are needed to
A Possible Suite of Algorithms for the Retrieval of Water Vapor Using Saphir/Megha-Tropiques Filipe Aires, Laboratoire de Météorologie Dynamique, IPSL/CNRS
A Possible Suite of Algorithms for the Retrieval of Water Vapor
Using Saphir/Megha-Tropiques Filipe Aires, Laboratoire de
Mtorologie Dynamique, IPSL/CNRS 3 rd ISRO-CNES Joint Workshop,
Ahmedabad, 17-20 October 2005
Slide 2
Goal III Develop inversion algorithm for Megha-Tropiques -
Emphasis on water vapour profile with SAPHIR - Other variables -
Information fusion for multiple instrument retrievals II Tools to
analyse the observing system - Realistic information content of
satellite observations - Impact of First Guess: forecast versus
climatological FG I Databases generation - First Guess database
-Learning database to calibrate retrieval algorithms (NN or
Bayesian)
Slide 3
Coincident level-1c Satellite Observations SaphirMadrasMSG
FLAGPrecip / Deep convection - Clear - Cloudy: (high, medium, low)
PRODTBs-Total WV -Liquid Water -TBs T cloud top Ts Climatological
Dataset ECMWF 6h forecast Pattern Recognition Temp & WV
profiles First guess Temp & WV Neural Network or Bayesian Case
Management: - Clear - thin clous (high, medium, lower) - Rainy -
Ocean / Land A priori Temp & WV Level-2a Diurnal Cycle
Ana./Interpol. Gridded Temp & WV Level-2b
Slide 4
Outline of the Presentation Preliminary Results on First Guess
and Training Databases for Megha-Tropiques Illustration with
Previous Remote Sensing Applications: - High-dimension/noisy
observations: atmospheric profiles with IASI - First guess
information: temperature and WV over land with AMSU -
Multiple-wavelength & uncertainty assessment: LST and MW
emissivities over land with SSM/I
Slide 5
DataBase Sampling PCA Clustering RTTOVS ERA40 Reanalyses: -
temperature - wv - ozone - skt Extracted Prototypes = weather
regimes Prototypes in TB space TB Observations PCA/Pattern
Recognition First Guess: - temperature - wv - ozone - skt
Slide 6
Mean atmospheric profiles Clear Thin clouds Convective clouds
Latitude: 35 Ocean / Land Multivariate: - Temperature profile -
Water vapour profile - Ozone profile (not necessary) - any ERA40
variables(SKT, TCWV,) - any auxiliary dataset (ISCCP for cloud
properties) Sampling on ERA40 / 6-hourly / 1999 Some filters: - rh
> 1% - elevation < 500m - tcc < 50% - large scale and
convective precip = 0 First Guess Dataset / Clear Case
Slide 7
Covariance and correlation matrices 25 PCA components Strong
correlation structure impor- tance of the PCA
Slide 8
PCA Base Functions TemperatureWater vapour Anomalies with
respect to mean profiles
Slide 9
Database Generator using Clustering Algorithm: K-means using
Mahalanobis distance (i.e. using PCA) statistical sampling, but
could be uniform with another sampling technique Number of
prototypes: only 10 first, to analyze the results, check that the
choices are satisfactory (variables, distance, sampling algorithm,
filters, case management, etc.) Next stage: thousands of prototypes
extracted FG and learning datasets Then, inversion algorithms:
Bayesian and NN
Slide 10
Extracted Prototypes (weather regimes) Variability mostly
conducted by water vapor Some variability from temperature, mostly
under 300 hPa
Slide 11
Extracted Prototypes Dry Atm.Wet Atm.
Slide 12
SKT and TCWV Distributions for each Prototype SKT Histograms
TCWV Histograms
Slide 13
Seasonality of Prototypes January 1999July 1999 Most frequent
weather regime in the month
Slide 14
Map of Most Frequent Prototype Clusters over OceanClusters over
Land Structures are coherent (ITCZ, ) wich implies that the
technique is pertinent and that the choices that have been made are
correct. Confidence for the databases generated
Slide 15
Prototypes Population We could emphasize extreme events or some
particular regimes
Slide 16
Clear Thin clouds Convective clouds Latitude: 35 Ocean / Land
Multivariate: - ERA40 Temperature profile - ERA40 Water vapour
profile - ERA40 Cloud type, cloud amount, LWP - ISCCP for cloud
statistics: random sampling First Guess Dataset / Partly Cloudy
Case Alternative: Use Meso-scale model simulations such as
Meso-NH
Slide 17
Database Generator: perspectives Next: - Thousands of
prototypes extracted FG & learning datasets - Then, inversion
algorithms: Bayesian and NN - Thin clouds / convective clouds
Questions: - Sampling in space of geophysical variables (i.e. what
we are interested in) or in the space of TBs (i.e. what is being
observed) - Sampling statistically (learning database) or uniformly
(FG database) - Distance for rare / particular events - Additional
variables (especially for cloudy datasets)
Slide 18
Illustration with Previous Remote Sensing Applications: -
High-dimension/noisy observations: atmospheric profiles with IASI -
First guess information: temperature and WV over land with AMSU -
Multiple-wavelength & uncertainty assessment: LST and MW
emissivities over land with SSM/I
Slide 19
IASI Instrument High-Resolution Interferometer in the Infrared
CNES / Eumetsat - Metop - in flight in 2005 Missions: operational
meteorology, climatology, atmospheric chemistry Goal: 3-D
description of the atmosphere and surface geophysical variables
Instrument characteristics: - spectral domain: 15.5 to 3.62 m (645
to 2760 cm -1 ) - spectral resolution power: 0.25 cm -1 (more than
8400 channels) - spatial resolution: 9 km pixels - instrument
noise: Gaussian Retrieval specifications: - atmospheric temperature
profile: 1K RMS, 1 km - atmospheric water vapor profile: 10%, 1-2
km - atmospheric ozone profile: 10% RMS, 2 or 3 measures - surface
temperature: 0.5K RMS Infrared Atmospheric Sounding Interferometer
METOP1 SATELLITE Collaboration: - A. Chedin - N. Scott
Slide 20
Mean IASI Spectrum and Mean IASI Noise Characteristics Aires,
Chedin, Scott, and Rossow, JAM, 2002.
Slide 21
Compression of the IASI Spectra using PCA Each spectrum X is
decomposed in a base of Eigen-Spectrum X i X = c 1.X 1 + c 2.X 2 +
+ c n.X n The number of components used n is lower than size of
observations
Slide 22
De-Noising of the IASI Spectra using PCA Each spectrum X is
decomposed in a base of Eigen-Spectrum X i X = c 1.X 1 + c 2.X 2 +
+ c n.X n + Suppressing high-order components suppresses noise
Slide 23
A Sample of the Retrieval of a Wet Situation
Slide 24
Statistics of the Retrieval for Wet Situations Aires, Rossow,
Scott, and Chedin, JGR, 2003a and 2003b.
Slide 25
Sample of the Retrieval of a Dry Situation
Slide 26
Statistics of the Retrieval for Dry Situations
Slide 27
AMSU Atmospheric Humidity and Temperature Profiles Over Land
From AMSU-A and AMSU-B Observations (1) - First Guess information -
ISCCP: cloudy / clear, skin temperature - ECMWF temperature and
humidity profiles 6 hours before the AMSU observation - AMSU
surface emissivities (2,3) at 23.8, 31.4, 50.3, 89, and 150 GHz -
Observations: Observed brightness temperatures at AMSU frequencies
NN - Temperature profile - Humidity profile + Simulated noise
Advanced Microwave Sounding Unit (1) Karbou, F., Aires, F.,
Prigent, C. Retrieval of temperature and water vapor atmospheric
profiles over Africa using AMSU microwave observations. Journal of
Geophysical Research, 110(D7), 2005. (2) Prigent, Rossow, Matthews,
Global maps of microwave land surface emissivities: Potential for
land surface characterization, Radio Science, 33, 1998. (3) Karbou,
Prigent, Eymard, and Pardo, Microwave land emissivity calculations
using AMSU-A and AMSU-B measurements, IEEE TGRS, 43(5), 2005. Over
land Collaboration: - C. Prigent - F. Karbou - L. Eymard
Slide 28
Impact of FG for Temperature Profile
Slide 29
Impact FG for Specific Humidity Profile
Slide 30
Bias Statistics / FG and NN Retrieval
Slide 31
RMS Statistics / FG and NN Retrieval
Slide 32
From the Visible to the Microwave: VIS and N-IR: NOAA / AVHRR
visible (0.58-0.68 m) and near-infrared reflectances (0.73-1.1 m)
Thermal IR: NOAA / AVHRR and geostationary (Meteosat, Goes E and W,
GMS) thermal infrared observations (~12 m) Passive microwaves: DMSP
/ SSM/I passive microwave data (between 19 and 85 GHz i.e. between
3.53 mm and 1.58 cm ) Active microwaves: ERS scatterometer (5.25
GHz i.e. 5.71 cm) Collaboration: - C. Prigent
Slide 33
Visible and Near-IR (NOAA/AVHRR) (NDVI) Thermal IR (ISCCP)
(diurnal Ts amplitude ) Passive microwave (DMSP / SSM/I) (surface
emissivities) Active microwave (ERS scatterometer) (backscattering
coefficient) Example of monthly mean products for each wavelength
range - significant processing involved - many more products
available
SURFACE SKIN TEMPERATURE Ts - Energy and water exchanges at the
land -surface boundary largely controlled by the Ts and Tair
difference - No routine measurements of Ts - Thermal infrared
provides estimates under clear sky conditions only International
Satellite Cloud Climatology Project (Rossow and Schiffer, BAMS,
1999) - Development of a method to retrieve Ts under clouds using
combined SSM/I microwave and IR satellite measurements (Aires et
al., JGR, 2001; Prigent et al., JAM, 2003) - Systematic calculation
of surface temperature from combined microwave and IR for an
all-weather time record (10 years of data soon processed)
Slide 36
SURFACE SKIN TEMPERATURE Ts Learning data base Learning phase
Operational phase Microwave and IR analysis, using a neural network
with a priori information
Slide 37
Theoretical errors for Ts calculated on data base: no bias
related to surface emissivities or cloud cover No in situ
measurements available for Ts validation: comparaison with Tair at
2m. Check for expected Ts -Tair variations with solar zenith angle,
surface humidity, cloud cover. (Prigent et al., JGR, 2003) SURFACE
SKIN TEMPERATURE Ts
Slide 38
Retrieval Results Aires, Prigent and Rossow, JGR, 2001.
Prigent, Aires and Rossow, JAM, 2003. Prigent, Aires and Rossow,
JGR, 2003.
Slide 39
Statistical analysis of the available Ts previous estimates to
infer the full Ts diurnal cycle over continents. Use of PCA
representation and iterative optimization technique (Aires,
Prigent, and Rossow, JGR, 2004) Realistic diurnal cycles are
derived, for use in land-atmosphere models SURFACE SKIN TEMPERATURE
Ts
Slide 40
Uncertainty Assessment NN: C 0 = C in + G t. H -1. G Var. Ass.:
C x = (S a -1 + K t. S y -1. K) -1 Local Gaussian approximation
around retrieval (first-order of error) Similar to variational
estimations Clean statistical estimation, no spatial information
provided Can specify all sources of uncertainty if available a
priori, otherwise, distinguish only the constant and non-constant
term Uncertainty is increased by outliers / incoherencies but for
that, needs info on interactions (i.e. correlations) provided by G
Can estimate uncertainty of very complex quantities (i.e.
Jacobians) using Monte-Carlo if needed, in high-dimensional spaces
Aires, Prigent and Rossow, JGR, 2004a, b, and c.
Slide 41
Estimation of Retrieval Uncertainties STD Error for WVSTD Error
for Ts STD Error for 19V STD Error for 19H
Slide 42
Impact of Input Incoherencies on the Retrieval Uncertainty
Slide 43
Impact of Spectral Incoherencies on the Retrieval
Uncertainty
Slide 44
Impact of Individual Perturbations on the Retrieval
Uncertainty
Slide 45
CONCLUSIONS FOR NN RETRIEVALS Global approach for remote
sensing of past and next-generation instruments: compression,
de-noising, first guess, inversion algorithm, uncertainty,
multi-wavelength, analysis of results (NN Jacobians) First guess
information is essential: can measure contribution of a priori
& obs. - provides more information - regularize the inverse
problem with additional constraint Capitalize on the nonlinear
correlation structures: - among inputs (satellite observations,
first guess) - among outputs (temperature, water vapor, surface
emissivities, etc.) - between inputs and outputs (inverse of the
RTM) Merging of satellite data very powerful: - helps separate
contributions of the various parameters - benefits from the
complementarity between observations - more robust to noise or
missing data in one type of observation Analysis tool for the
results Potential synergy with variational assimilation schemes
Need for databases of consistent / coherent, well-documented,
reliable in situ / satellite measurements
Slide 46
Coincident level-1c Satellite Observations SaphirMadrasMSG
FLAGPrecip / Deep convection - Clear - Cloudy: (high, medium, low)
PRODTBs-Total WV -Liquid Water T cloud top Ts Climatological
Dataset ECMWF 6h forecast Pattern Recognition Temp & WV
profiles First guess Temp & WV Neural Network or Bayesian Case
Management: - Clear - Partly cloudy (h, m, l) - Rainy - Ocean /
Land / viewing angle A priori Temp & WV Level-2a Diurnal Cycle
Ana./Interpol. Gridded Temp & WV Level-2b
Slide 47
CONCLUSIONS Need for RTTOVS Radiative Transfert Model for
Madras/Saphir and at the same time for METOP & NPOES Need to
have a set of radiosondes for validation purpose Our thorough
analysis of the observing system should allow us to answer to de
some important configuration/questions: - information from MSG or
not? - temperature profile from ECMWF forecasts? - what should be
done for cloudy situations - link with precipitation algorithm
(Madras)