Presentation on theme: "Numerical Weather Prediction (Met DA) The Analysis of Satellite Data"— Presentation transcript:
1Numerical Weather Prediction (Met DA) The Analysis of Satellite Data (lecture 1:Basic Concepts)Tony McNallyECMWF
2INTRODUCTION…is satellite data important for NWP ?…what does a satellite instrument measure…different types of satellite instrument2. ATMOSPHERIC TEMPERATURE SOUNDING…weighting functions…definition of the forward and inverse problem3. RETRIEVAL (inverse) ALGORITHMS…solutions to reduced (simplified) problems…statistical regression methods…forecast background (1DVAR) methods4. DIRECT RADIANCE ASSIMILATION…the use of pre-processed (corrected) radiance observations…the use of raw radiance observations5. SUMMARY
3Key elements of the NWP system The forecast model time evolves fields of geophysical parameters (e.g. T/Q/U/V/Ps/O3) following the laws of thermodynamics and chemistryThe initial conditions used to start the forecast model are provided by the analysisThe analysis is generated from observations relating to the geophysical parameters combined with a priori background information (usually a short-range forecast from the previous analysis).This combination process is known as data assimilation
4intermittently correct The Data Assimilation ProcessObservationsintermittently correctthe time evolution(or trajectory) of theforecast model
5IS SATELLITE DATA IMPORTANT FOR NWP ? A 4 month sample of ECMWF forecasts with different observations deliberately withdrawnThese Observing System Experiments (OSEs), aimed at measuring the impact of different types of observation,…now routinely confirm that satellite data are the single most important component of the global observingnetwork for NWP.Forecast skillN. HemisphereForecast skillS. Hemisphere
6Some of the main Satellite instruments currently used at ECMWF On NOAA / NASA / EUMETSAT polar orbiting spacecraftHigh resolution IR Sounder (HIRS), Advanced Microwave Sounding Unit (AMSU), Atmospheric IR Sounder (AIRS), Infrared Atmospheric Sounding Interferometer (IASI), Advanced Microwave Scanning Radiometer (AMSR), TRMM (TMI)On DMSP polar orbiting spacecraftSpecial Sensor Microwave Imager (SSMI,SSMI/S)Geostationary spacecraftMETEOSAT , GOES , GMS / MTSATScatterometer spacecraftERS / Quickscat / ASCATGPS spacecraftMETOP-GRAS / COSMICCovered in other lectures
8What do satellite instruments measure? They DO NOT measure TEMPERATUREThey DO NOT measure HUMIDITY or OZONEThey DO NOT measure WINDSatellite instruments simply measure the radiance L that reaches the top of the atmosphere at given frequency v . The measured radiance is related to geophysical atmospheric variables (T,Q,O3, clouds etc…) by the radiative transfer equationmeasured by the satelliteOur description of the atmosphereSurfacereflection/scatteringSurfaceemissionCloud/raincontribution++++ ...Planck source term* dependingon temperature of the atmosphereAbsorption in theatmosphereOther contributions to themeasured radiances
9The Radiative Transfer (RT) equation measured by the satelliteOur description of the atmosphereSurfacereflection/scatteringSurfaceemissionCloud/raincontribution++++ ...
10The Radiative Transfer (RT) equation “Forward problem”Surfacereflection/scatteringSurfaceemissionCloud/raincontribution+++“Inverse problem”(Details of the RT equation are covered more in other lectures…Bell + Matricardi).
11Measuring radiances in different frequencies (channels) By deliberately selecting radiation at different frequencies or CHANNELSsatellite instruments can provide information on specific geophysicalvariables from different regions of the atmosphere.In general, the frequencies / channels used within NWPmay be categorized as one of 3 different types …atmospheric sounding channels (passive instruments)surface sensing channels (passive instruments)surface sensing channels (active instruments)Note:In practice (and often despite their name!) real satellite instruments havechannels which are a combination of atmospheric sounding and surfacesensing channels
121. ATMOSPHERIC SOUNDING CHANNELS Atmospheric sounding channels with no contribution from the surface
131. ATMOSPHERIC SOUNDING CHANNELS These channels are located in parts of the infra-red and microwave spectrum for which the main contribution to the measured radiance is from the atmosphere and can be written:Where B=Planck functiont = transmittanceT(z) is the temperaturez is a height coordinateThat is they try to avoid frequencies for which surface radiation and cloud contributions are important. They are primarily used to obtain information about atmospheric temperature and humidity (or other constituents that influence the transmittance e.g. CO2).AMSUA-channel 5 (53GHz)HIRS-channel 12 (6.7micron)
142. SURFACE SENSING CHANNELS (PASSIVE) Surface sensing channels with no contribution from the atmosphere
152. SURFACE SENSING CHANNELS (PASSIVE) These are located in window regions of the infra-red and microwave spectrum at frequencies where there is very little interaction with the atmosphere and the primary contribution to the measured radiance is:Where Tsurf is the surface skintemperature and E the surfaceemissivityB[v,Tsurf ](u,v) (i.e. surface emission)These are primarily used to obtain information on the surface temperature and quantities that influence the surface emissivity such as wind (ocean) and vegetation (land). They can also be used to obtain information on clouds/rain and cloud movements (to provide wind information)SSM/I channel 7 (89GHz)HIRS channel 8 (11microns)
163. SURFACE SENSING CHANNELS (ACTIVE) These (e.g. scatterometers) actively illuminate the surface in window parts of the spectrum such thatsurface scattering [ (u,v) ]These primarily provide information on ocean winds (via the relationship with sea-surface emissivity ) without the strong surface temperature ambiguity .Quick-scat
18ATMOSPHERIC TEMPERATURE SOUNDING If radiation is selected in a sounding channel for whichand we define a function K(z) =When the primary absorber is a well mixed gas (e.g. oxygen or CO2) with known concentration it can be seen that the measured radiance is essentially a weighted average of the atmospheric temperature profile, orThe function K(z) that defines this vertical average is known as aWEIGHTING FUNCTION
19IDEAL WEIGHTING FUNCTIONS If the weighting function was adelta-function, this would mean thatthe measured radiance is sensitiveto the temperature at a single levelin the atmosphere.zK(z)If the weighting function was abox-car function, this would meanthat the measured radiance wasonly sensitive to the temperaturebetween two discrete atmosphericlevelszK(z)
20REAL ATMOSPHERIC WEIGHTING FUNCTIONS High in the atmosphere verylittle radiation is emitted, butmost will reach the top of theatmospherezAt some level there is anoptimal balance between theamount of radiation emittedand the amount reaching thetop of the atmosphereK(z)A lot of radiation is emitted from thedense lower atmosphere, but verylittle survives to the top of theatmosphere due to absorption.K(z)
21REAL WEIGHTING FUNCTIONS continued... The altitude at which the peak of the weighting function occurs depends on the strength of absorption for a given channelChannels in parts of the spectrum where the absorption is strong (e.g. near the centre of CO2 or O2 lines ) peak high in the atmosphereChannels in parts of the spectrum where the absorption is weak (e.g. in the wings of CO2 O2 lines) peak low in the atmosphereAMSUABy selecting a number of channels with varying absorption strengths we sample the atmospheric temperature at different altitudes
22MORE REAL WEIGHTING FUNCTIONS ... HIRS19 channelsAMSUA15 channelsAIRS2378IASI8461
23How do we extract atmospheric information (e. g How do we extract atmospheric information (e.g. temperature) from satellite radiances?…retrievals….
24EXTRACTING ATMOSPHERIC TEMPERATURE FROM RADIANCE MEASUREMENTS If we know the entire atmospheric temperature profile T(z) then we can compute (uniquely) the radiances a sounding instrument would measure using the radiative transfer equation. This is sometimes known as the forward problemIn order to extract or retrieve the atmospheric temperature profile from a set of measured radiances we must solve what is known as the inverse problemUnfortunately as the weighting functions are generally broad and we have a finite number of channels, the inverse problem is formally ill-posed because an infinite number of different temperature profiles could give the same measured radiances !!!See paper by Rodgers 1976 Retrieval of atmospheric temperature and composition from remote measurements of thermal radiation. Rev. Geophys.Space. Phys. 14,
25SATELLITE RETRIEVAL ALGORITHMS for NWP The linear data assimilation schemes used in the past at ECMWF such as Optimal Interpolation (OI) were unable to assimilate radiance observations directly (as they were nonlinearly related to the analysis variables) and the radiances had to be explicitly converted to temperature products before the analysis.This conversion was achieved using a variety of retrieval algorithms that differed in the way they used prior informationAll retrieval schemes use some (either explicit of implicit) form of prior information to supplement the information of the measured radiances and solve the inverse problem !Three different types of retrieval have been used in NWP:Solutions to reduced inverse problemsRegression / Neural Net (statistical) methodsForecast background (1DVAR) methods
261. Solutions to reduced inverse problems We acknowledge that there is a limited amount of information in the measured radiances and re-formulate the ill-posed inverse problem in terms of a reduced number of unknown variables that can be better estimated by the data e.g. Deep mean layer temperatures, Total Column Water / Ozone or EOF’s (eigenfunctions)Unfortunately it is difficult to objectively quantify the error in these quantities (which is very important to use the retrieval in NWP) due to the sometimes subjective choice of reduced representation.2. Regression and Library search methodsUsing a sample of temperature profiles matched (collocated) with a sample of radiance observations/simulations, a statistical relationship is derived that predicts e.g atmospheric temperature from the measured radiance. e.g. NESDIS operational retrievals or the 3I approachThese tend to be limited by the statistical characteristics of the training sample / profile library and will not produce physically important features if they are statistically rare in the training sample. Furthermore, their assimilation can destroy sharp physical features in the analysis!3. Forecast Background or 1D-Var MethodsThese use an explicit background or first-guess profile from a short range forecast and performoptimal adjustments using the measured radiances. The adjustments minimize a cost function
271D-Var RETRIEVALS AND THE COST FUNCTION It can be shown that maximum likelihood approach to solving the inverse problem (which is a particular case of the generalized analysis problem covered in previous lectures replacing T(z) with a vector x and L with y) requires the minimization of a cost function J which is a combination of 2 distinct terms.1D state or profileRadiance vectorRT equationFit of the solution to the background estimate of the atmospheric state weighted inversely by the background error covariance B (note this is not the Planck function!)Fit of the solution to the measured radiances (y) weighted inversely by the measurement error covariance R (observation error + error in observation operator H)The solution obtained is optimal in that it fits the prior (or background)information and and measured radiances respecting the uncertainty in both.
28Sa = B - 1D-Var RETRIEVALS continued … HB One simple linear form of the 1D-Var solution obtained by minimization of the cost function is given by the expression:correction termHere we see that the retrieved profile (Xa) is equal to the background profile (Xb) plus a correction term applied. Furthermore we can quantify the error covariance Sa of the 1D-Var retrieval which is needed for subsequent assimilation:Sa = B -HBimprovement termThe retrieval being an improvement over the background information (assuming all parameters are correctly specified).
291D-Var RETRIEVALS continued … The magnitude of the improvement over the background clearly depends on a number of parameters, but one crucial factor is the number of channels and shape of the weighting functions implied by the radiative transfer operator HHIRS 19 channelsIASI 8461 channels
30CHARACTERISTICS OF 1D-Var RETRIEVALS These have a number of advantages that make them more suitable for NWP assimilation than other retrieval methodsThe prior information (short-range forecast) is very accurate (more than statistical climatology) which improves retrieval accuracy.The prior information contains information about physically important features such as fronts, inversions and the tropopause.The error covariance of the prior information and resulting retrieval is better known (crucial for the subsequent assimilation process).The 1DVAR is a very clean 1D analogue of the full 3D/4D assimilation process using consistent auxiliary information (B,R,H,Xb)BUT the error characteristics of the 1DVAR retrieval may still be very complicated due to its correlation with the forecast background which will be used again in the main analysis…Direct radiance assimilation…?
31… But do we still really need to do explicit retrievals for NWP ?
32“Observation operator” H = radiative transfer equation DIRECT ASSIMILATION OF RADIANCE DATAVariational analysis methods such as 3DVAR and 4DVAR allow the direct assimilation of radiance observations (without the need for and explicit retrieval step).This is because such methods do NOT require a linear relationship between the observed quantity and the analysis variables)The retrieval is essentially incorporated within the main analysis by finding the 3D or 4D state of the atmosphere that minimizes“Observation operator”H = radiative transfer equationVector of allobserved data3/4D atmosphericstate vectorIn direct radiance assimilation the forecast background still provides the prior information to supplement the radiances, but it is not used twice (as would be the case if 1D-Var retrievals were assimilated ).
33DIRECT ASSIMILATION OF RADIANCE DATA By the direct assimilation of radiances we avoid the problem of assimilating retrievals with complicated error structures.BUTThere are still a number of significant problems that must be handledspecifying the covariance (B) of background errorsspecifying the covariance (R) of radiance errorremoving biases and ambiguities in the radiances / RT modelSome of these issues are simplified by the direct assimilation of raw (unprocessed) radiance observations
34DIRECT ASSIMILATION OF RAW RADIANCE DATA Further to the move away from retrievals to radiance data, many NWP centres are starting to assimilate raw radiances (level-1b/1c)Avoid complicated errors (random and systematic) introduced by (unnecessary) pre-processing such as cloud clearing, angle (limb) adjustment and surface corrections.Avoid having to change (retune) our assimilation system when the data provider changes the pre-processingFaster access to data from new platforms (e.g. AMSU data from NOAA-16 assimilated 6 weeks after launch)Allows consistent treatment of historical data for re-analysis projects (ERA-40) and other climate studies
35A QUICK REVIEW OF KEY CONCEPTS Satellite instruments measure radiance (not T,Q or wind)Sounding radiances are broad vertical averages of the temperature profile (defined by the weighting functions)The retrieval of atmospheric temperature from the radiances is ill- posed and all retrieval algorithms use some sort of prior informationThere has been an evolution in NWP away from the use of retrievals to the direct assimilation of radiances with simpler error characteristicsTo further simplify the assimilation process, NWP centres have moved towards the direct assimilation of raw radiances
36Topics covered in Lecture 2: 1. BACKGROUND ERROR STRUCTURESWhy are they important ?How do we estimate them ?2. AMBIGUITY BETWEEN VARIABLESTemperature and humiditySurface and the atmosphereClouds / precipitation and the atmosphere3. SYSTEMATIC ERRORS4. WIND ADJUSTMENTS FROM RADIANCESDirect and indirect wind adjustments