IASI CH4 Operational Retrieval Feasibility -

Optimal Estimation Method

Study Overview + SummaryRichard Siddans, Jane Hurley

Rutherford Appleton Laboratory

Consultants: Spectroscopy: Anu Dudhia, Peter Bernath

GOSAT data: Hartmut Boesch

Final Presentation, Eumetsat, 4 February 2015

Overview

• The overall objective of this study is to specify an optimal estimation (OE)-based retrieval algorithm for methane from IASI measurements

• Scheme should be sufficiently accurate to provide an independent verification of the IASI L2 PPF output (which is expected to be based on a faster statistical method).

• Through this study, a scheme which had been initially developed through the UK National Centre for Earth Observation, was extended, comprehensively documented, validated and delivered to Eumetsat.

• The scheme provides global height-resolved distributions of methane from Metop/IASI observations in the 8 micron range.

Study Tasks

RAL IASI CH4 retrieval scheme

Retrieval Technique: Optimal estimation

Radiative Transfer RTTOV (with customised coefficients to improve accuracy and vary HDO)

Spectral range: 1240-1290 cm-1 (cf. Razavi et al, ACP 2009)

Retrieval species: CH4

N2OH2OHDO scaling factorSurface temperature

Measurement errors: Noise derived from spectral fits(dependent on scene radiance)

A priori + covariance matrix

Fixed a priori profileCovariance: MACC global covariance or 10% (whichever larger) with 6km correlation length

Background profiles Temperature, and a priori surface temperature, H2O from ECMWF

Line strengths scaled by 1.04

1232-1290 cm-1 Better transmission to ground

N2O retrieval replaced by (i) Fixing N2O based on ECMWF PV

+ zonal climatology(ii) Retrieve effective cloud-height

and fraction

Also scan dependent systematic residuals derived (accommodate systematic errors in RT or IASI)

Stratosphere: TOMCAT zonal mean (no time dependence)Troposphere: Fixed value

Current scheme

Measurement vector• 1232.25 - 1290 cm-1 (some gaps)

– Based on Razavi et al assessment of trade-off between information • Measurement errors combine diagnosed IASI noise

and RTTOV errors

Averaging kernels (mid-lat; no surface/air temperature contrast)

GOSAT

IASI column averaged mixing ratio

GEOSCHEM

Monthly averages of daytime data for July 2009 (GOSAT day-time)

Column averaged vmr comparison

AK for column average as fn latitude

Modelling N2O• Tropospheric N2O is even more well-mixed than CH4, with much more

consistent (easier to model) growth-rate of 0.75 ppb/yr (0.23% per year). – Variations are < 0.5% with latitude, seasonally etc

• So far we assumed N2O has a fixed tropospheric mixing ratio of 322 ppbv, and model the (significant) variation in the stratosphere using ECMWF PV:

1. ECMWF Vorticity convert to PV on potential temperature levels

2. Equivalent latitude derived

3. ACE-FTS N2O zonal, seasonal climatology interpolated in equivalent latitude / pot. Temp.

4. Resulting profiles interpolated back to original ECMWF grid

New for study: N2O profile scaled by

f = 1 + 0.0023*(year-2009)

• Existing retrievals corrected by applying this factor to CH4

• Better to scale N2O used in retrieval• Only TCCON co-locations re-run so far

Systematic residual patterns• Early retrievals exhibit systematic residuals + scan dependent biases.• These currently mitigated by

– Scaling CH4 input to FM by 1.04 (like line-strength change)– Fitting scale factor for two residual patterns (mean nadir residual and

across-track variation of residual)

Task 2: Update spectroscopic database …

• LBLRTM v12.2 instead of RFM, using AER v3.2 line parameter database rtweb.aer.com/lblrtm_frame.hmtl

• CH4 and CO2 line-mixing, and fixed HDO/H2O ratio

Conclusion: changes in retrieved CH4 small compared to existing bias correction (need for bias correction not explained)

Current FM:

• RTTOV using RFM-derived coefficients• HITRAN 2008

Updated FM:

• RTTOV using LBLRTM-derived coefficients• HITRAN 2012

Task 3: Applicability to Metop-A, -B, -C

• Only code changes relate to file handling (different input / output file names/paths to reflect platform)

• E.g. All the following assumed the same for Met-A vs B– Noise model– ISRF– Systematic residuals– PCs used to compress/filter L1 (if relevant)

• Main variables needed to cope with time evolution– Analyses (from T, water vapour prior)– Emissivity databases– N2O trend (CH4 prior constraint & need not vary)

Performance of Retrieval on Metop-A vs. Metop-B

Means for August 2013

Every second day

1 in 4 pixels

Retrieved RSF0 (Metop A)

• Only instrument related change detected due to Metop A processor upgrade on 16 May 2013, leading to ~8ppb change in xCH4

Task 4: Validation

• TCCON L2 Comparisons– TCCON, GOSAT

• MLO Case Study– Mauna Loa in-situ – monthly and interannual dependencies

• Pixel/Scan Dependence Metop-A/-B– Closest pixels– Inter-pixel and inter-scan angle dependence

• Cloud Contamination– RAL flag, AVHRR, IASI L2

• Updates since PM2– N2O trend included in retrieval

– TCCON + MLO data processed based on L1-PCs

TCCON/GOSAT L2 Comparisons

TCCON sites

N2O scaled inretrieval

Scatter: N2O scaled in retrieval

MLO Case Study

Comparison to in situ measurements at Mauna Loa Observatory

• High correlation between total column average and MLO• Bias explained by stratospheric contribution to column

average; corrected in derived lower tropospheric sub column but this estimate is more noisy than total.

• MLO N2O time series agrees with assumed N2O

Updated GEOSCHEM comparisons for 4 years (new data from Univ. Edinburgh)

Pixel/Scan Dependence Metop-A/-B

Metop-A vs. Metop-B – scan angle dependence

CH4 concentrations Other retrieved parameters

Day/night, ocean/land separated

Cloud Contamination

The effect of cloud contamination on the retrieved concentrations of CH4 assessed using • the cloud fraction/height retrieved within the RAL retrieval • the cloud fractions from AVHRR/3• the cloud fraction/height from IASI L2 v6 products.

• Note that CH4 scheme does not run for “obvious” cloud• No scenes processed where obs-sim(ECMWF) @ 12

microns < -5K• Also quality control removes scenes where internal

(RAL) cloud fraction > 0.1

• Data from August 2013 averaged as a function of each cloud parameter…

Cloud ContaminationNumber of retrievals as fn of L2 fraction/height

Cloud ContaminationAverage CH4 as function of L2 fraction/height

Weak dependence on residual cloud in almost all scenes.

Task 6: Use of L1 Principal Components

Comparison of Cost (MetB August 2013) Standard retrieval (Day)

L1 PCs (Day)

Difference

• PCs can be used without significantly degrading the CH4 retrieval.

• Should actually improve precision but this not seen in practise

• Cannot yet exploit reduction in measurement errors, as other FM error (surface related) is limiting fit

• Within this study the RAL IASI CH4 retrieval scheme (original developed via the UK NCEO) has been demonstrated, documented, extended and delivered to Eumetsat

• By fitting IASI measured spectra to an RMS in brightness temperature of order 0.1K, the scheme delivers 2 pieces of information on the profiles and CH4 column average mixing ratios with a precision of 10-30 ppbv;

• Validation against TCCON and Mauna LOA indicates agreement performance in line with this precision estimate

• The scheme has been demonstrated to function equally well on Metop A and B data; The scheme can also function equally well with L1 PCs and full L1 spectra.

• Scheme jointly retrieves effective cloud parameters by exploiting the presence of N2O spectral features in the fit window; the N2O profile itself is accurately defined using a combination of ECMWF PV and TOMCAT model climatology. – The study has confirmed this approach to be very effective at accounting for

residual cloud in IASI scenes. • Without correction, the scheme generates methane mixing rations that are biased

by about 4% with some scan angle dependence. This bias (which is empirically corrected) cannot be explained by the updates to spectroscopy checked here.

• The scheme is based on RTTOV and so is relatively fast (capable of processing in NRT with modest computational resource)

Summary

• RTTOV coefficients should be updated based on latest spectroscopy and line-mixing models– Could enable wider spectral range to be exploited.– Study aslo found some issues with existing coefficients RTTOV leading to

spurious structures in averaging kernels when represented on grid finer than retrieval levels – these should be addressed.

• The scheme relies on very good knowledge of temperature, particularly near to the surface; emissivity modelling is also very important over arid regions– Currently relies on ECMWF + Wisconsin databases for these– Errors in ECMWF analyses, inc interpolation to the IASI observation time /

location, make a significant contribution to overall methane retrieval errors. – The current IASI L2 (version 6) temperature and humidity profiles have been

found to be extremely promising in the parallel RAL / Eumetsat study to investigate the potential for AMSU+MHS observations to improve the operational IASI OEM temperature and humidity retrievals. – Study also implemented spectral emissivity retrieval into Eumetsat scheme– Potential for IASI T+humidity+emissivity to improve methane retrievals

should be assessed.

Further Work

Addition of the CH4 3.7 micron band:• Reflected solar component gives near surface sensitivity.• As in TIR, nearby N2O features could give effective cloud correction• Region has relatively low SNR, but PCs could help (see Atkinson et al AMT,2010)

Further work beyond study