Evaluation of Ice Cloud Properties from Passive Satellite Retrievals using CALIPSO and CloudSat Data
Abstract
Knowledge of ice cloud optical, micro- and macro-physical properties is important for a wide range of weather and climate applications but challenging to accurately diagnose. Passive satellite imager data provide the most optimal horizontal and temporal resolution of clouds across the globe, but little direct information on vertical structure. Active sensors, on the other hand, provide high vertical resolution but limited spatial and temporal coverage. Assumptions regarding the ice crystal size distribution, including their shapes and habits are needed in passive and active remote sensor retrieval schemes since they cannot be inferred directly. This paper compares optical depth, ice water path and cloud top height derived during daytime from GOES-16, Himawari-8 and MODIS to retrievals derived from CloudSat and CALIPSO (CC) data. The passive sensor retrievals are derived assuming two different scattering models. One is based on a single-habit model (SHM) of hexagonal ice columns with roughened surfaces which is used in the current Edition 4 MODIS cloud algorithm for the NASA Clouds and the Earth's Radiant Energy System (CERES) project. The other is based on a new two-habit model (THM, the ice cloud consists of an ensemble of hexagonal columns and 20-element aggregates with specific habit fractions at each particle size bin) being evaluated for the next edition of CERES cloud and radiation products. The asymmetry factor at 0.65 μm for the THM is 0.04 lower than that for the SHM. This will result in lower optical depths overall when retrieved using the THM, as well as higher cloud top heights for optically thin clouds, both of which are desired outcomes based on previous validation studies. In this study, we use several CC data products to assess the relative level of agreement in the passive sensor retrievals of optical depth, cloud height and ice water path when the SHM and THM are employed over a wide range of ice cloud conditions. We perform the evaluation using MODIS data, which is limited to nadir only matchups with CC data. We also evaluate the retrievals from Himawari-8 and GOES-16 to establish an initial assessment on potential view angle dependencies. It is anticipated that this study will contribute to an improved understanding of passive sensor ice cloud retrieval uncertainties and help guide future improvements.
- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2018
- Bibcode:
- 2018AGUFM.A23B..07S
- Keywords:
-
- 0319 Cloud optics;
- ATMOSPHERIC COMPOSITION AND STRUCTUREDE: 0320 Cloud physics and chemistry;
- ATMOSPHERIC COMPOSITION AND STRUCTUREDE: 3310 Clouds and cloud feedbacks;
- ATMOSPHERIC PROCESSESDE: 3360 Remote sensing;
- ATMOSPHERIC PROCESSES