1 / 38

Elisa Carboni 1 , Roy Grainger 1 , Joanne Walker 1 , Anu Dudhia 1 , Richard Siddans 2

A new scheme for sulfur dioxide retrieval from IASI measurements: application to recent volcanic eruptions. . Elisa Carboni 1 , Roy Grainger 1 , Joanne Walker 1 , Anu Dudhia 1 , Richard Siddans 2. (1) University of Oxford, Oxford, United Kingdom.

tadhg
Download Presentation

Elisa Carboni 1 , Roy Grainger 1 , Joanne Walker 1 , Anu Dudhia 1 , Richard Siddans 2

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. A new scheme for sulfur dioxide retrieval from IASI measurements: application to recent volcanic eruptions. Elisa Carboni1, Roy Grainger1, Joanne Walker1, Anu Dudhia1, Richard Siddans2 (1) University of Oxford, Oxford, United Kingdom. (2) Rutherford Appleton Laboratory, Didcot, United Kingdom.

  2. Outline IASI measurements introduction: IR spectra - SO2 absorption band SO2 retrieval: Optimal Estimation and covariance matrix Fast forward model Sensitivity to ash and cloud Preliminary results: Evolutions of Eyjafjallajökull eruption (April-May 2010) Grimsvötn eruption (May 2011) Puyehue-Cordón Caulle eruption (June 2011) Summary and further work

  3. Thermal infrared spectra ash SO2 absorption bands Brightness Temperature [k] 1 3 Wavenumber [cm-1]

  4. troposphere stratosphere troposphere BTD (std.atm. - plume) stratosphere

  5. - IASI is sensitive to both the amount of SO2 and the altitude of the plume - amount and altitude have different spectral signature => we attempt to retrieve both - Note that getting the altitude correct is important not just for itself, but also in order to get the correct amount of SO2, since the signal depends strongly on altitude. - often near real time algorithm make assumption about height of the plume, and this can lead to large error in the retrieved amount this applies to both UV and IR methods

  6. OPTIMAL ESTIMATION APPROACH y is the measurement vector, x the state vector, F forward model,Symeasurement error vector physics, radiative transfer Usually: y would contain the IASI measured radiances Sy would represent noise on those measurements x would contain all atmospheric + surface parameters that affect these radiances and are imperfectly known - in this case including H2O,T, cloud and many other minor species, as well as SO2. However: - we're not interested in most of these potential state variables - SO2 is very rarely present in significant amounts in the IASI spectra (except during volcanic eruptions and degassing regions) - H2O,T are well predicted by Met data - Other gases have features which are spectrally uncorrelated with SO2 we can greatly simplify the retrieval problem by including in the Sy covariance matrix all the differences between the IASI observed radiances and radiances predicted by a forward model, based on ECMWF met data. and... Sy(i,j) = [(ymi-ysi)-(ymi-ysi)][(ymj-ysj)-(ymj-ysj)]

  7. We compute the error covariance Sy using similar conditions but when we know there is no SO2 in the atmosphere. This will give a measurement covariance which allows deviations due to cloud and trace-gases but still enable the SO2 signature to be detected. For Eyja eruption we consider the data, in the same geographic region, of the same month the year before (eg. April or May 2009) We also use a global covariance matrix, considering all the data of 4 days (1 every season) of 2009 4 day - 14 orbit a day-> 5x106 pixels Global April May

  8. Fast forward model State vector: - Total column amount of So2 - Altitude H - Thickness s - Surface temperature Ts s + ECMWF profile (temperature, h2o, p, z) H Ts fast radiative transfer (RTTOV + SO2 RAL coefficients) FM IASI measurements IASI simulated spectra OE retrieval best estimate of stare vector: SO2 amount, plume altitude, Ts

  9. Ash and cloud effects? To test if the retrieval is sensitive to ash/cloud etc... IASI forward model can include aerosol and cloud. Obtained with refractive index measured by Daniel Peters. Ash Re=2 [mm], h=3 [km] Water Cloud Re=10 [mm], h=3 [km] Dust Re=2 [mm], h=3 [km]

  10. Sensitivity of retrieval to presence of ash and cloud Retrieval using simulations with ASH at different optical depth and altitude SO2 underestimate for thick ash high cost !! SO2 signal ‘covered’ from cloud within or above the plume OK for cloud below the plume!!! grrr

  11. Eyjafjallajökull eruption 14 April - 17 May 2010 IASI - SO2 [DU]

  12. IASI total mass of SO2 Total mass of So2 in the atmospheric plume obtained assuming an average area of 25 x 25 km for any IASI pixel The error bar is in the worse scenario of correlated errors: obtained as sum of all the pixels errors (overestimate in case of independent error)) SO2 plume mass [Tg] From (Thomas and Prata, ACP 2011)

  13. IASI IASI

  14. IASI - CALIPSO ~ 3h difference

  15. Grimsvötn eruption 21 - 26 May 2011

  16. Puyehue-Cordón Caulle eruption 5 - 13 June 2011

  17. Minimum error estimate surface contrast = skin temperature - temperature of the first atm. layer std. atm. profiles assumption: we know the altitude of the plume. considering 60 overpass a month => error reduced of 1/sqrt(60) 1 DU = 0.0285 g/m2 SO2 monthly errors [km] [g/m2]

  18. Ecuador monthly mean - degassing The monthly mean SO2 column amount for January 2010. The black triangle indicates the location of the Tungurahua volcano. Error on monthly mean

  19. Summary and further work New scheme for IASI developed to retrieve height and amount of SO2 Uses our new detection scheme applied to pixels for the full retrieval Initial results seem to compare reasonably to other observations. Though sometimes see much larger amounts then uv methods Thick ash can affect the retrieval, recognizable from cost >2 Cloud at the same altitude or above the plume mask the So2 signal Retrieving the ash and cloud (optical depth, altitude and effective radius) properties is possible and subject of parallel work at AOPP & RAL. Next steps: - SO2 comparison with ground and others satellite measurements - Altitude compare with Models and more comparison with CALIPSO - Combined retrieval IASI + G0ME-2 THANK YOU!

  20. From (Thomas and Prata, ACP 2011) IASI 11:30 SO2 [DU] H [hPa]

  21. Error analysis SO2, H, s, Ts linear error on the ‘true’ state obtained as: Xa=[ 0.5, 400, 100, 290] DXa=[100, 1000, 1, 20] ‘true state’ NO thermal contrast +10o thermal contrast

  22. Error analysis ‘true state’ NO thermal contrast retrieved state +10o thermal contrast retrieved state

  23. OPTIMAL ESTIMATION APPROACH y is the measurement vector, x the state vector, F forward model,Symeasurement error vector physics, radiative transfer Usually: y would contain the IASI measured radiances Sy would represent noise on those measurements x would contain all atmospheric + surface parameters that affect these radiances and are imperfectly known - in this case including H2O,T, cloud and many other minor species, as well as SO2. However: - we're not interested in most of these potential state variables - SO2 is very rarely present in significant amounts in the IASI spectra (except during volcanic eruptions and degassing regions) - H2O,T are well predicted by Met data - Other gases have features which are spectrally uncorrelated with SO2 we can greatly simplify the retrieval problem by including in the Sy covariance matrix all the differences between the IASI observed radiances and radiances predicted by a forward model, based on ECMWF met data. and... Sy(i,j) = [(ymi-ysi)-(ymi-ysi)][(ymj-ysj)-(ymj-ysj)]

  24. Sensitivity of retrieval to presence of ash

  25. Sensitivity of retrieval to presence of clouds

  26. METeorological OPerational satellite programme (MetOp)‏ European polar-orbiting meteorological satellite. Operational in May 2007 First of tree polar satellite system (EPS) that will cover 14 years Equator crossing time 9:30 local time Advanced Very High Resolution Radiometer (AVHRR/3) NOAA 6-channel visible/IR (0.6-12 µm) imager, 2000 km swath, 1 x 1 km resolution. Global imagery of clouds, ocean and land. 35 kg, 622/39.9 kbit/s (high/low rate), 27 W. http://www.esa.int/ Global Ozone Monitoring Experiment (GOME-2)ESA/EUMETSAT Scanning spectrometer, 250-790 nm, resolution 0.2-0.4 nm, 960 km or 1920 km swath, resolution 80 x 40 km. Global coverage can be achieved within one day. High-resolution Infra-Red Sounder (HIRS/4)NOAA 20-channel optical/IR filter-wheel radiometer, 2000 km swath, IFOV 17.4 km (nadir). 35 kg, 2.9 kbit/s, 21 W. Replaced on MetOp-C by IASI. Infrared Atmospheric Sounding Interferometer (IASI)CNES/EUMETSAT Fourier-transform spectrometer, 3.62-15.5 µm in three bands. Four IFOVs of 20 km at nadir in a square 50 x 50 km, step-scanned across track (30 steps), synchronised with AMSU-A. 2000 km swath. Resolution 0.35 cm-1. Radiometric accuracy 0.25-0.58K. Global coverage will be achieved in 12 hours.

  27. Eyjafjallajökull eruption - MIRS stereo height vs IASI 7 May 10 5 0 16 May 10 5 0

  28. Sensitivity of retrieval to presence of ash

  29. Sensitivity of retrieval to presence of clouds

  30. Eyjafjallajökull eruption - SO2 amount IASI vs OMI

  31. Night time AIRS SO2 retrieval at ~03:00 UT

  32. OE detection theory [Rodger 2000] The optimal estimate of x taking into account total measurement error may be computed as: Sytot is computed considering an appropriate ensemble of N measured spectra to construct an estimate of total measurement error variance-covariance Syobs [ Walker, Dudhia, Carboni, Atmos. Meas. Tech. Discuss., 2010 ]

  33. Reference spectra - IASI spectra Brightness Temperature [k] Reference spectra - IASI spectra Brightness Temperature [k]

  34. SO2 detection Morning of 15th April 2010 as observed using OMI, GOME-2 and SCIAMACHY near-real-time total column SO2 retrievals as implemented in the Support to Aviation Control Service (SACS Web, 2010). v1 SO2 fractional enhancement v3 SO2 fractional enhancement 15 April - morning

  35. RETRIEVAL METHOD OPTIMAL ESTIMATION [Rodgers 2000] Initial state estimate: x0 A priori: xa Run forward model: f(xi) Compare to J = [y - f(xi)]Se-1[y - f(xi)] + measurements (y): [xi - xa]Sa-1[xi - xa] Update state: xi → xi+1(Levenburg-Marquardt) Stop when: J is small , or when i is large. NB Optimal estimation method provides quality control and error estimate

  36. Total mass of SO2 Total mass of So2 in the atmospheric plume obtained assuming an average area of 25 x 25 km for any IASI pixel The error bar is in the worse scenario of correlated errors: obtained as sum of all the pixels errors (overestimate in case of independent error) SO2 plume mass [Tg] Julian day 20 30 10 20 April May

More Related