TPSAS-NF1676L-30093-DND
Presentation updates the CERES Science team on the status of the FLASHFlux working group progress toward v4A. There is also a description of new sorts of ways the data are being used.
Paul Stackhouse↗
Engineering topics
Takmeng Wong
Publications and source records attributed to Takmeng Wong.
TPSAS-NF1676L-26925-DND
Observationally-based atmospheric energy balance is analyzed using Clouds and the Earth's Radiant Energy System (CERES)-derived TOA and surface irradiance, Global Precipitation Climatology Project (GPCP)-derived precipitation, dry static and kinetic energy tendency and divergence estimated from ERA-Interim, and surface sensible heat flux from SeaFlux. The residual tends to be negative over tropics and positive over mid-latitudes. A negative residual implies that precipitation rate is too small, divergence is too large, or radiative cooling is too large. The residual of atmospheric energy is spatially and temporally correlated with cloud objects to identify cloud types associated with the residual. Spatially, shallow cumulus, cirrostratus, and deep convective cloud object occurrence are positively correlated with the absolute value of the residual. The temporal correlation coefficient between the number of deep convective cloud objects and individual energy components, net atmospheric irradiance, precipitation rate, and the sum of dry static and kinetic energy divergence and their tendency over western Pacific are, respectively, 0.84, 0.95, and 0.93. However, when all energy components are added, the atmospheric energy residual over tropical Pacific temporally correlates well with the number of shallow cumulus cloud objects over tropical Pacific. Because shallow cumulus does not alter enough atmospheric energy compared to the residual, these suggest 1) if retrieval errors associated with deep convective clouds are causing the column integrated atmospheric energy residual, the errors vary among individual deep convective clouds, 2) it is possible that the residual is associated with processes in which shallow cumulus clouds affect deep convective clouds and hence atmospheric energy budget over tropical western Pacific, and 3) it is also possible that cumulus congestus present cirrostratus that missed in massive sensor retrieval contribute the residual. Cloud objects used in the study that leads to the above conclusions are derived from Moderate Resolution Imaging Spectroradiometer (MODIS). In this investigation, we use Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) and CloudSat data to evaluate and verify above findings. Specifically, we focus on 3) above and analyze frequency of occurrence of cumulus congestus present under cirrostratus and correlation to the energy balance residual.
Seiji Kato↗
TPSAS-NF1676L-35172-DND
The wide field of view instrument on Earth Radiation Budget Experiment (ERBE) Earth Radiation Budget Satellite (ERBS) took measurements from November 1984 through September 1999. It provides top-of-atmosphere (TOA) reflected shortwave and emitted longwave irradiance at a 10 degree by 10 degree spatial resolution. We have recently re-processed ERBS wide field of view data with algorithms similar to those used in the CERES process (Shrestha et al. 2019). We also calibrated ERBS shortwave and longwave irradiance with CERES-derived irradiances. Combining ERBS and CERES data, we analyze the time series of global mean net TOA irradiance from 1985. The change of TOA net radiation over 30 years derived from ERBS and CERES data is larger than the change derived by Allan et al. (2014). The difference in the net irradiance change from two data sets is, however, within the uncertainty of ocean heating rates derived from ocean temperature measurements in 80?s. We also investigate the spatial distribution of changes in reflected shortwave irradiances derived from ERBS and CERES periods. The spatial distribution of reflected shortwave irradiance changes over the tropics and sub-tropical regions derived from our data set is qualitatively consistent with the result of Norris et al. (2016).
Antonio Viudez-Mora↗
TPSAS-NF1676L-30579-DND
The motivation for this study is to develop a Machine learning methodology for the determination of CERES clear-sky scenes and the estimation of clear-sky TOA fluxes from CERES-TOA radiance measurements without using any imager information.
Bijoy Vengasseril Thampi↗
TPSAS-NF1676L-27237-DND
Objective - It is observed that 5.6% of all CERES Terra/Aqua data contains missing cloud cover information or insufficient imager data for a reliable scene identification (some times it can reach up to 50% of data for a specific scene type) - The unavailability of imager data lead to gaps in global radiation budget dataset. - In this study, our objective is to develop a Machine learning methodology for the improved determination of CERES scene type and subsequent clear-sky TOA flux estimation using standalone CERES TOA radiance measurements (without any MODIS/Imager data).
CERES↗
Convective Aggregation and Indices Examined from CERES Cloud Object Data
Explore the source record for details and available documents.
Kuan‐Man Xu↗