1 / 17

Radar/lidar retrievals in ice clouds: Blind tests, radiative fluxes and a unified approach

This study presents blind tests and a unified approach for radar and lidar retrievals in ice clouds. The algorithms accurately retrieve extinction, but IWC and effective radius retrievals are sensitive to mass-size relation. The study also includes simulations of instrumental limitations faced by EarthCARE and assesses the accuracy of retrievals in terms of radiative fluxes and heating rates.

eholliday
Télécharger la présentation

Radar/lidar retrievals in ice clouds: Blind tests, radiative fluxes and a unified approach

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. Radar/lidar retrievals in ice clouds:Blind tests, radiative fluxesand a unified approach Robin Hogan, Malcolm Brooks, Anthony Illingworth University of Reading, UK David Donovan Claire Tinel KNMI, The Netherlands CETP, France

  2. Blind Tests • At the last CloudNet meeting I reported a “Blind Test” of the Donovan and Tinel algorithms: • I used aircraft spectra to simulate radar/lidar profiles • Dave Donovan and Claire Tinel ran their algorithms to derive profiles of extinction, IWC and effective radius • What we found: • Both algorithms retrieved extinction very accurately, even for 1-way optical depths of up to 7 (lidar reduced by 10-6) • Stable to uncertain lidar extinction-to-backscatter ratio • However, re and IWC retrievals sensitive to mass-size relation

  3. In this talk… • Second Blind Test simulates instrumental limitations that will be faced by EarthCARE for: • 10-km dwell (1.4 seconds), 400-km altitude, 355-nm lidar • Instrument noise, finite sensitivity, multiple scattering • Ultimate test: are these retrievals accurate enough to constrain the radiation budget? • Perform radiation calculations on true & measured profiles • Assess errors in terms of fluxes and heating rates • What are the most sensitive of the retrieved parameters? • Radar/lidar combination better than radar alone? • Also test Z-IWC, Z-, Z-re relationships (from EUCREX) • Could the best aspects of the two algorithms be blended into one?

  4. Second Blind Test: more realistic • Ice size distributions from EUCREX aircraft data • Correction for 2D-C undercounting of small ice crystals • Radar • Simulate 100-m oversampling of 400-m Gaussian pulse • Noise added based on signal-to-noise and number of pulses • Lidar • Add molecular scattering appropriate for 355 nm • Instrument noise: photon counting - Poisson statistics • True lidar sensitivity: incomplete penetration of cloud • Multiple scattering: Eloranta model with 20-m footprint • Extinction-to-backscatter unknown but constant with height • Night-time operation, negligible dark current

  5. Radar Instrument noise • Five new profiles from EUCREX dataset • This is what they would look like without instrument noise or multiple scattering • Note strong lidar attenuation Lidar

  6. Radar Instrument noise • Five new profiles… • With instrument noise & multiple scattering • Radar virtually unchanged except finite sensitivity • Lidar noise noticeable • Lidar multiple scattering increases return • Note: “radar-only” relationships derived using same EUCREX dataset so not independent! Lidar

  7. Good case: lidar sees full profile • Extinction and effective radius reasonable when use same habit and include multiple scattering Extinction coefficient Effective radius Donovan: includes multiple scattering Difference between Mitchell and Francis et al. mass-size Tinel: no multiple scattering

  8. Good case: radiation calculations • OLR and albedo good for both radar/lidar and radar-only (but radar-only not independent) Longwave up Mass-size relationship: Error~10 Wm-2 Underestimate radiative effect if multiple scattering neglected Shortwave up

  9. Difference between Mitchell and Francis et al. mass-size relation Wild retrieval where lidar runs out of signal Tinel: no multiple scattering Typical case: radar/lidar retrievals • No retrieval in lower part of cloud • Important to include multiple-scattering in retrieval Extinction coefficient Effective radius Donovan: good retrieval at cloud top

  10. Underestimate radiative effect if multiple scattering neglected Albedo too low (80 W m-2): lower part of cloud is important but mass-size less so (10 W m-2) Typical case: radiative fluxes • At top-of-atmosphere, lower part of cloud important for shortwave but not for longwave OLR excellent:lower part not important Longwave up Shortwave up

  11. Typical profile: Heating rates • Heating profile would be reasonable if full profile was retrieved • What do we do when the lidar runs out of signal? Erroneous 80 K/day heating No cloud observed so no heating by cloud here

  12. Radar/lidar only Blended Scale radar-only retrieval to match here Radar only Possible solution: blend profiles • Where lidar runs out of steam, scale radar-only retrieval for seamless join • Better result than pure radar/lidar or radar only Lidar becomes unreliable here Extinction coefficient Shortwave up

  13. Sensitivity of radiation to retrievals • Longwave: Easy! (for plane-parallel clouds…) • Sensitive to extinction coefficient - good news: this can be retrieved accurately independent of assumption of crystal type • Insensitive to effective radius, habit or extinction/backscatter • OLR insensitive to lower half of cloud undetected by lidar • “Blending” usually gets in-cloud fluxes to better than 5 W m-2 • Shortwave: More difficult • Most sensitive to extinction coefficient • Need full cloud profile: blending enables TOA shortwave to be retrieved to ~10 W m-2, in-cloud fluxes less accurate • Some sensitivity to habit and therefore effective radius • Slight sensitivity to extinction/backscatter ratio • Best way to blend radar/lidar & radar-only profiles?

  14. Differences between algorithms • The two algorithms are the same in their use of radar as a constraint for the lidar inversion, but • Donovan algorithm minimizes Re’ variation in lowest gates • Tinel algorithm minimizes No* variation in whole profile • Multiple scattering currently being added to Tinel algorithm • What are Re’ and No*??? • Basically Re’ ~ (Z/)1/4 • and No* ~ (/Zb)1/(1-b) where b is around 0.42 (from aircraft) • In minimizing variations in these parameters, powers don’t make much difference so • Donovan algorithm minimizes variation in Z/ • Tinel algorithm minimizes variation in Z0.42/ • Now write minimalist version of these algorithms

  15. b

  16. Unified algorithm? • Minimize variation in which parameter? • Seems more physical that concentration parameter No* is constant? • Better approach: use cost function that penalizes variations in No*, Re’ and extinction  (recall wild  retrieval earlier) • Minimize variations in furthest gates or in whole profile? • Retrieval much more likely to go wild in furthest gates, so here the constraint should be strongest • Multiple scattering? • Can add Eloranta parameterization in the iterative loop of any algorithm

More Related