A Possible Suite of Algorithms for the Retrieval of Water Vapor Using Saphir/Megha-Tropiques Filipe...
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- Slide 1
- 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
- Slide 34
- NOAA + GEOST. (VIS & IR) ISCCP Algorithm Neural Net.
Retrieval Kohonen Clustering SnowWetlandsVegetation Soil Moisture
Ts Diurnal Cycle Ampl. Spline / PCA Interpolation Neural Net.
Retrieval Cloudy Clear ERS (MW active) AVHRR (VIS &NIR) SSM/I
(MW passive) Land surf. Reflectances Backscatering Coefficients MW
model Pre- Processing Pathfinder Calibrated Satellite Observations
Land Surf. Emissivities Surf. Skin Temperature NCEP Reanalysis
- Slide 35
- 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)