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  1. Status of combining ground-based with satellite-based measurements in the atmospheric state retrieval • Kerstin Ebell, Emiliano Orlandi, Anja Hünerbein, • Ulrich Löhnert, Susanne Crewell

  2. Outline • accomplished tasks since last meeting • first results of a full ground-/satellite-based retrieval in clear sky (synthetic data) • ideas for next paper Looking forward to discussions

  3. Accomplished tasks • paper accepted: • implementation of RTTOV as forward operator in the IPT (AH, KE):short visit to TROPOS on 11-13 March 2013 • framework for a full combined retrieval using synthetic observations from clear-sky radiosondes

  4. Full retrieval – Settings • settings as for „offline“ calculations presented in paper: • instrument noise as measurement uncertainty • forward model uncertainties due to trace gas concentrations and surface emissivity • radiosonde climatology as prior information in addition: • random noise applied to synthetic observations • for ground-based MWRused usual IPT radiativetransfer operator (notPAMTRA) to speed upthe calculations

  5. Full retrieval – Computational time • case study for one selected profile clear-sky profile in Lindenberg • close to mean atmospheric conditions with IWV of 14.4 kg m-2

  6. Full retrieval – Convergence • robust convergence behavior for all retrieval except for AERI • retrievals converge most often after 3rd iteration

  7. Full retrieval – Retrieved profiles

  8. Full retrieval – Retrieval errors

  9. Full retrieval – HATPRO-SEVIRI Temperature (K) abs. humidity (gm-3)

  10. Full retrieval – HATPRO-IASI Temperature (K) abs. humidity (gm-3)

  11. Full retrieval – AERI Temperature (K) abs. humidity (gm-3)

  12. Full retrieval – True and estimated errors Temperature (K) • true error not necessarily decreases • estimated uncertainty does  more realistic values +AMSU-A/MHS +SEVIRI +IASI

  13. Full retrieval – True and estimated errors Temperature (K) • benefit of ground-based MWR observations clearly visible AMSU-A/MHS +HATPRO SEVIRI +HATPRO IASI +HATPRO

  14. Full retrieval – True and estimated errors abs. humidity error (gm-3) • improvement in humidity profile less obvious +AMSU-A/MHS +SEVIRI +IASI

  15. Full retrieval – True and estimated errors rel. abs. humidity error (%) • improvement in humidity profile less obvious +AMSU-A/MHS +SEVIRI +IASI

  16. Full retrieval – True and estimated errors abs. humidity (gm-3) • benefit of ground-based MWR observations also in absolute humidity profile clearly visible below 600 hPa AMSU-A/MHS +HATPRO SEVIRI +HATPRO IASI +HATPRO

  17. Full retrieval – DOF in T • same numbers as in paper  results can be confirmed

  18. Full retrieval – DOF in q • same numbers as in paper  results can be confirmed

  19. Full retrieval – Conclusion • good results for HATPRO + satellites under idealized conditions  results of „offline“ calculations as presented in paper confirmed • AERI forward model (LBLRTM) far too slow to use for operational application • problem of non-convergence for AERI retrieval not solved yet  so, how would the retrieval behave under real conditions? need to move on and use real observations @JOYCE  reduced observational vector: focus on HATPRO + (cloud radar) + SEVIRI

  20. Ideas for next paper Working title: „Atmospheric profiling combining ground-based with satellite-based observations at the Jülich Observatory for Cloud Evolution JOYCE“ • Introduction • Observations: HATPRO TBs, radar Z, SEVIRI IR TBs • Retrieval framework: 1DVAR, forward models, Sa, Se • Case studies: clear sky, liquid stratus, ice cloud • Discussion and outlook

  21. Observations • baseline observations: • HATPRO TBs • cloud radar Z (retrieval will be performed on Cloudnet time grid = 30s) • SEVIRI IR TBs: • every 5 min from rapid scans (instantaneous measurement) • correct time to be determined for Jülich site; measurement will be included at the nearest Cloudnet time  temporal representativeness error assumed to be 0 • spatial representativeness error estimated from Seviri 3x5 (?) pixel variability: assumption this variability well represents the inner-pixel variability • measurements will be parallax-corrected • use selected channels in the retrieval, not all; TB differences, too! (as used in the SEVIRI retrievals)

  22. Retrieval framework optimal estimation (1DVar) with forward models: • PAMTRA: feature to simulate Z not implemented and tested in the IPT so far • RTTOV: already used for clear sky cases, not for clouds yet... • no direct input of liquid effective radius! only possible to choose from following cloud types: stratus continental, stratus maritime, cumulus continental clean, cumulus continental polluted, cumulus maritime, cirrus  needs to be adapted • reff_ice as direct input parameter (use Jenny‘s reff here as prior?) • choice of two ice crystal shapes • atmospheric state vector: T, q, LWC, IWC, reff_liq, reff_ice • need to take into account the forward model uncertainties due model settings/paramters  sensitivity studiess • check for consistent assumptions in forward models

  23. Retrieval framework Sa: error covariance matrix of prior information • T, q Essen monthly/seasonal radiosonde climatology • LWC from climatology (based on Lindenberg Cloudnet data + Frisch approach) • no prior information for IWC Se: meas. and forward model error covariance matrix • instrument noise • spatial representativity error of SEVIRI measurements  case dependent • forward model uncertainties: emissivity, trace gases, parameter/distribution assumptions,...  suite of sensitvity studies: either before IPT and include uncertainties in Se or different IPT realizations with different assumptions: how strong do the results differ? („ensemble IPT simulations“)

  24. Case studies • 3 cases: • clear sky, stratus liquid cloud, ice cloud • during HOPE (1 April- 31 May 2013): • at least 2 radiosonde profiles per day • two MWRs at JOYCE: if TOPHAT data is missing, SUNHAT can be used (only zenith) • characterization of these cases (history of airmass, Anja‘s trajectory analysis, results of satellite retrievals (JS, AH?), could be used to constrain forward model/prior information) • IPT results, sensitivity studies • evaluation: comparison to radiosoundings, tower data, GPS, radiative closure at surface?

  25. Potential cases SUNHAT 0-24 ✔ my favorites

  26. Clear sky: 2.4. morning clear-sky, some light cirrus clouds in the afternoon (2/8), no rain

  27. Clear sky: 7.4. clear-sky, only a few cirrus clouds during noon

  28. Clear sky: 22.4. clear-sky day with only few cirrus clouds in the morning and afternoon, weak southerly winds

  29. Clear sky: 4.5., IOP 11 ✔ only very few high clouds in the morning, afterwards perfect clear-sky conditions, weak westerly winds

  30. Liquid water cloud: 21.4. ✔ clear-sky in the early morning, rapidly increasing cloudiness with weak wind rotating from NW to W, overcast from 06 UTC on

  31. Liquid water cloud: 23.4. mostly overcast, light rain between 11-14 UTC, weak SW-wind

  32. Liquid water cloud: 30.5. strong cloudiness with only few clearings (7/8), at late afternoon short clearance at about 17 UTC, afterwards again overcast situation, wind from south

  33. Ice cloud: 27.4. ✔ most of the time completely overcast with rain in the morning, significant lower temperatures than the day before, weak northerly winds

  34. Ice cloud: 30.4. broken cloudiness during morning and evening, in between overcast cloudiness, weak easterly winds, no rain

  35. Ice cloud: 21.5. in the morning only few high cirrus clouds, rapidly increasing cumulus clouds, at early afternoon overcast with beginning rain, strong rain in the late evening, wind turns from east to west during the day

  36. Ice above liquid cloud: 18.5., IOP13 ✔ SUNHAT mostly overcast until late afternoon, some light rain in the morning, clearing up at late afternoon (15 UTC), low cumulus clouds during daytime, clear-sky conditions in the evening, low windspeed

  37. Next steps • Identify case studies • set up of operational IPT for JOYCE: • run and test standard version of IPT for these cases ( LWC, T, q) • implement and test new LWC prior information • implement and test PAMTRA including Z forward model • implement IWC retrieval (Z-IWC-T method) • implement RTTOV in standard IPT version (incl. clouds) might also use synthetic profiles (see Emililano‘s slides) first to test the new framework

  38. Next steps • SEVIRI: • determine surface emissivity for JOYCE site and uncertainty • determine spatial and temporal representativeness error: • use Cloudnet data and simple cloud algorithm + COSMO to simulate SEVIRI obs with a high temporal resolution  check temporal variability (representative for variability within the SEVIRI pixel • check variability of m x n SEVIRI pixels • airmass analysis • application of satellite retrievals for these cases

  39. Thank you!Ideas, suggestions, questions...?