1 / 20

Development of GEMS Cloud Data Processing Algorithm

Development of GEMS Cloud Data Processing Algorithm. Yong-Sang Choi 1 , Bo-Ram Kim 1 , Heeje Cho 2 , Myong -Hwan Ahn 1 (Former COMS PI), and Jhoon Kim 3 (GEMS PI) 1 Ewha Womans University, Seoul 2 Seoul National University, Seoul 3 Yonsei University, Seoul.

laurel-mckee
Download Presentation

Development of GEMS Cloud Data Processing Algorithm

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. Development of GEMS Cloud Data Processing Algorithm Yong-Sang Choi1, Bo-Ram Kim1, Heeje Cho2, Myong-Hwan Ahn1(Former COMS PI), and Jhoon Kim3(GEMS PI) 1Ewha Womans University, Seoul2Seoul National University, Seoul3Yonsei University, Seoul

  2. Clouds significantly affect gas/aerosol retrievals! • Cloud contamination causeserrors in air mass factor, and errors in gas/aerosol retrievals.

  3. Comparison of UV cloud products

  4. O2-O2 absorption band (Acarreta, Haan, and Stammes 2004 JGR) 8 7 6 5 4 3 2 1 0 Collision induced absorption 223 253 283 Absorption cross-section of the O2-O2collision complex near 477 nm, based on measurements by Newnhamand Ballard [1998] at 283 K (red curve) and 223 K (blue curve). The curve for 253 K (green) was obtained by interpolation. (adopted from OMI ATBD) 460 a 465 a 470 a 475 a 480 485 a 490

  5. GEMS cloud algorithm will provide CH and CF by using O2-O2 absorption. • Main cloud products: • Cloud height (zc) • Effective cloud fraction (cf) • Main bands: • O2-O2 absorption band (460−490 nm)

  6. GEMS cloud algorithm will provide CH and CF by using O2-O2absorption. ISSUE: How to build DOAS and LUT • Main cloud products: • Cloud height (zc) • Effective cloud fraction (cf) • Main bands: • O2-O2 absorption band (460−490 nm)

  7. DOAS-calculated O2-O2 absorption factors (Rc and Ns) are compared with LUT, to get cloud products. • Calculation of Rcand Ns Rc • LUT variables (7D or 8D) Ns Temperature profile?

  8. DOAS-calculated O2-O2 absorption factors (Rc and Ns) are compared with LUT, to get cloud products. • Calculation of Rcand Ns ISSUE: How to effectively extract cloud fraction and cloud height from LUT fitting? Rc Ns • LUT variables (7D or 8D) Temperature profile?

  9. Sequence of cloud height (zc) and cloud fraction (cf) retrievals Cloud fraction DB OBS C zc Cloud height for Cf (Albedo = 0.8) DB

  10. Sequence of cloud height (zc) and cloud fraction (cf) retrievals zc Cloud fraction DB OBS C zc Cloud height for Cf (Albedo = 0.8) DB

  11. Generation of GEMS synthetic cloud-radiation data TOA UV Radiance (Reflectance) Retrieval Algorithm Forward RT simulation Cloud Information 3D NWP model

  12. Data and models • Cloud-to-radiance conversion • SCIATRAN (ver 3.1) • Cloud properties • WRF model simulation*of • Case: Typhoon Muifa (03UTC, August 6, 2011) • WRF output •  Geometry • Liquid & ice water contents • “Centroid” cloud height *by Prof. S.-Y. Hong’s team in Yonsei Univ.

  13. Assumptions • Standard atmosphere • McLinden climatology • Surface albedo • based on WRF’s land category • Cloud water droplet size = 10 μm • Cloud ice particle size = 50 μm of ‘fractal’ shape

  14. A test for cfretrieval Rmeas Synthetic data Rclear clear sky simulation Rcloud overcast simulation assuming Lambertian reflector (Ag = 0.8)

  15. A test for cfretrieval Rmeas Synthetic data Rclear clear sky simulation Rcloud overcast simulation assuming Lambertian reflector (Ag = 0.8)

  16. Relation between effective cloud fraction and cloud optical thickness in synthetic data • 1% of cloud pixels exceed cf value of 1. • These clouds are optically thick (τc≥ 25), having albedo over 0.8.

  17. Error analysis with synthetic data Artificial errors were given to cloud height, and then the sensitivity to errors in effective cloud fraction were tested with our synthetic data. Results show that the sensitivity is fairly small, meaning that cloud height limitedly affects the retrieval accuracy of effective cloud fraction.

  18. Sequence of cloud height (zc) and cloud fraction (cf) retrievals zc How about this? Cloud fraction DB OBS C zc Cloud height for Cf (Albedo = 0.8) DB Probably Fine

  19. Error analysis with synthetic data Artificial errors were given to cloud height, and then the sensitivity to errors in effective cloud fraction were tested with our synthetic data. Results show that the sensitivity is fairly small, meaning that cloud height limitedly affects the retrieval accuracy of effective cloud fraction.

  20. Future study topics and call for In-depth discussion issues in Cloud/aerosol breakout session in GEMS International Science Workshop (October 2013, Korea) • Cloud/aerosol effects on various gas retrievals • Accuracy of cloud/aerosol products • Validation plans for cloud/aerosol products • Synthetic cloud-radiation simulators • Comparison of algorithms using different bands: O2-A, O2-B, O2-O2, Raman scattering • Etc. Yong-Sang Choi (ysc@ewha.ac.kr)

More Related