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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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9 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2015

Global Aerosol Direct Radiative Effect From CALIOP and C3M

Aerosols are responsible for the largest uncertainties in current estimates of climate forcing. These uncertainties are due in part to the limited abilities of passive sensors to retrieve aerosols in cloudy skies. We use a dataset which merges CALIOP observations together with other A-train observations to estimate aerosol radiative effects in cloudy skies as well as in cloud-free skies. The results can be used to quantify the reduction of aerosol radiative effects in cloudy skies relative to clear skies and to reduce current uncertainties in aerosol radiative effects.

Winker, Dave↗
NASA NTRS2015

Global Aerosol Direct Radiative Effect from CALIOP and C3M

Aerosols are responsible for the largest uncertainties in current estimates of climate forcing. These uncertainties are due in part to the limited abilities of passive sensors to retrieve aerosols in cloudy skies. We use a dataset which merges CALIOP observations together with other A-train observations to estimate aerosol radiative effects in cloudy skies as well as in cloud-free skies. The results can be used to quantify the reduction of aerosol radiative effects in cloudy skies relative to clear skies and to reduce current uncertainties in aerosol radiative effects.

Winker, Dave↗
NASA NTRSDate not supplied

Parameterization of Vertical Cloud Distribution from C3M and MERRA Data Using ML Method

Clouds play a key role in regulating the hydrological cycle and the Earth's radiative energy budget. However, global climate models (GCMs) with a horizontal grid spacing on the order of 100 km have limitations in representing sub-grid cloud dynamics with spatial scales on the order of 1 km, leading to potential uncertainties in cloud radiative feedback on the global scale. In our research, we will leverage the capabilities of Deep Machine Learning (DML) methods to construct parameterizations of sub-grid volumetric cloud fraction (VCF), which is the frequency of occurrence on a grid volume accumulated in the horizontal and vertical directions. Our investigation delves into the intricate relationship between VCF obtained from the NASA CALIPSO-CloudSat-CERES-MODIS (CCCM) satellite observation data and 3-D MERRA-2 reanalysis meteorological profiling data (e.g., wind, relative humidity, temperature). Through a comprehensive one-year data training utilizing the Sequence to Sequence DML method, we have successfully disentangled the complicated cloud formation dynamics across diverse meteorological conditions through a day-to-day analysis framework. Preliminary findings reveal promising statistical agreements in geographical and vertical distributions and seasonal variations of volumetric cloud fraction between ML prediction and satellite measurements. These results underscore the aptitude of our DML model to discern underlying cloud physical processes and accurately represent sub-grid cloud formation dynamics. Additionally, we have also employed trained neural network to analyze uncertainties arising from errors in meteorological data, further enhancing the robustness of our VCF parameterization.

Shan Zeng↗
NASA NTRSDate not supplied

TPSAS-NF1676L-29044-DND

In Langley NASA, Clouds and the Earth’s Radiant Energy System (CERES) and Moderate Resolution Imaging Spectroradiometer (MODIS) are merged with Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) on the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) and CloudSat Cloud Profiling Radar (CPR). The CERES merged product (C3M) matches up to three CALIPSO footprints with each MODIS pixel along its ground track. It then assigns the nearest CloudSat footprint to each of those MODIS pixels. The cloud properties from MODIS, retrieved using the CERES algorithms, are included in C3M with the matched CALIPSO and CloudSat products along with radiances from 18 MODIS channels. The dataset is used to validate the CERES retrieved MODIS cloud properties and the computed TOA and surface flux difference using MODIS or CALIOP/CloudSAT retrieved clouds. This information is then used to tune the computed fluxes to match the CERES observed TOA flux. The capability of viewing C3M product on browsers will be available on CERES ordering tool web site in the future, https://ceres.larc.nasa.gov

David R Doelling↗
NASA NTRSDate not supplied

Uncertainties in an Observation-Based Estimate of the Global Aerosol Direct Radiative Effect

Aerosols impact Earth’s radiation budget directly through interactions with radiation and indirectly via interactions with clouds. Aerosols have significant radiative impacts on Earth’s energy budget and represent the largest uncertainty in the radiative forcing of the current climate. Although the magnitude of the aerosol direct radiative effect (DRE) is estimated to be less than that of the indirect effect, uncertainties are large. While model-based estimates of aerosol radiative forcing are subject to uncertainties in modeled aerosol properties and uncertainties in simulated cloud cover and albedo, most observation-based studies of global aerosol DRE have their own difficulties with aerosol optical properties and have been limited to cloud-free skies in most cases. Estimates of the uncertainty of the aerosol DRE are themselves uncertain and have varied widely among different published studies. In this study we use the CERES-CALIPSO-CloudSat-MODIS (C3M) product to estimate the global clear-sky and all-sky aerosol DRE. C3M merges collocated data from CERES, CALIPSO, CloudSat, and MODIS with information from reanalysis and an aerosol transport model. With CALIOP observations of aerosol below optically thin clouds and above clouds, co-located with cloud albedo from MODIS, the C3M dataset allows a detailed exploration of observational uncertainties. By perturbing the aerosol properties used and comparing radiative effects in perturbed cases with the base case we characterize uncertainties in estimated aerosol DRE due to uncertainties in the aerosol properties involved. The results also provide a basis for determining measurement requirements to improve the accuracy of DRE estimates. We will describe the approach and present results.

Dave Winker↗
NASA NTRS2019

Seasonal Variations of Arctic Low-Level Clouds and its Linkage to Sea Ice Seasonal Variations

Using CALIPSO-CloudSat-Clouds and the Earth's Radiant Energy System (CERES)-Moderate Resolution Imaging Spectrometer (MODIS) (C3M) dataset, this study documents the seasonal variation of sea ice, cloud, and related atmospheric properties in the Arctic region (70°N–82°N) during the period 2007-2010. The surface type stratification used reveals the surface type influence on the Arctic cloud liquid water path (LWP)seasonality. Four surface types are defined: i) Permanent Ocean, ii) Land, iii) Permanent Ice, and iv) Transient Sea Ice. The Transient Ice regions are further divided into sub-regions according to the onset dates of melt/freeze season. Results show that the sea ice cover has no significant direct influence on the seasonal variation of the water vapor in the lower troposphere and the low-level cloud LWP. Rather, the role played by sea ice appear indirect by modifying the surface temperature response to increased solar insolation and influencing lower tropospheric stability (LTS). The results suggest that the combined increase in atmospheric water vapor due to warming air temperatures and the weakening of the inversion strength that governs the seasonal structure of cloud LWP. Over Transient Ice regions, the results suggest that the May peak in LWP coincides with a decrease in LTS and that is insensitive to melt and freeze onset. Variations in melt and freeze onset set are found to influence probably distribution of cloud LWP where regions of earlier melt and later freeze onset how a higher likelihood of larger values.

Yueyue Yu↗
NASA NTRSDate not supplied

TPSAS-NF1676L-27246-DND

Work done after the last CERES meeting: evaluation of Ed4 SYN (with ship, buoy, and land sites data), start revising C3M, Production of Ed2.8 EBAF-surface (available through August 2016), development of the algorithm of Ed4 EBAF-surface, start evaluating GMAO products, MERRA2 and FP (Ham’s presentation), and the evaluation of the effect of multilayer cloud on surface radiation.

Seiji Kato↗
NASA NTRSDate not supplied

Ice-Over-Water Cloud Identification in an Artificial Neural Network Approach

An artificial neural network (ANN) algorithm, employing several Aqua MODIS channels, the retrieved cloud phase and total cloud visible optical depth, and temperature and humidity vertical profiles is trained to detect multilayer (ML) ice-over-water cloud systems identified by matched 2008 CloudSat and CALIPSO (CC) data. The trained MLANN was applied to 2009 MODIS data resulting in combined ML and single layer detection accuracies of 87% (89%) and 86% (89%) for snow-free (snow-covered) regions during the day and night, respectively. When corrected for the viewing-zenith-angle dependence of each parameter, the ML fraction detected is relatively invariant across the swath. Compared to the CC ML variability, the MLANN is robust seasonally and interannually, and produces similar distribution patterns over the globe, except in the polar regions. Additional research is needed to conclusively evaluate the VZA dependence and further improve the MLANN accuracy. This approach should greatly improve the monitoring of cloud vertical structure using operational passive sensors.

MODIS↗
NASA NTRSDate not supplied

Ice-Over-Water Cloud Properties in an Artificial Neural Network Approach

Clouds are a crucial component of the atmospheric energy system, particularly the radiative balance within, above, and below the troposphere. The vertical distribution of cloud mass and phase determines layer heating rates, the loss of radiation to space, and the amount of radiative heating at the surface. Thus, it is important to know how clouds are distributed both vertically and horizontally at all times of day. Satellite remote sensing is the only approach available to monitor clouds day and night around the globe. In this paper several artificial neural network (ANN) algorithms, employing several Aqua MODIS infrared channels, profiles of relative humidity and temperature from GMAO numerical weather analyses, and the retrieved total cloud visible optical depth, are trained to detect multilayer ice-over-water cloud systems and to retrieve some of their properties as identified by a year of 2008 Aqua MODIS data matched with CloudSat and CALIPSO (CC) cloud profiles. The CC lidar and radar profiles provide the vertical structure that serves as output truth for the multilayer algorithm. The neural networks were trained using one year (2008) of cloud top height data from the CC dataset, with correlation around 0.94 (0.95) and MAE as low as 0.82 (0.81) km for nonpolar regions during the day (night). Applying the trained ANN to independent year 2009 MODIS data resulted in a combined ML and single layer hit rate of 86.4% (85.1%) for nonpolar regions during the day (night). Since the ANN is trained using near-nadir MODIS pixels, infrared radiance corrections were developed as a function of view zenith angle from MODIS and applied to off-nadir pixels when processing MODIS swath data. The multilayer amount derived with the ANN is relatively invariant with increasing view zenith angle compared to the multilayer amount without the corrections.

MODIS↗