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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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NASA NTRSDate not supplied

Surface Radiation Budget (SRB) Release 2.5 Longwave cloud props 3 hourly Data in Native Format (SRB_REL2.5_LW_CLDPROPS_3HRLY)

The Surface Radiation Budget (SRB) data set contain 47 surface and cloud properties including cloud fraction, cloud visible optical depth, cloud top temperature, cloud top pressure, cloud base pressure, and cloud water and ice content for each of 5 cloud types and in total. In addition to these cloud properties, the parameters also include surface pressure, total column ozone, day/night flag, and precipitable water. All perameters were measured at three hourly intervals for each day for the entire globe between 07/01/1983 and 06/30/2005 and serve as inputs to the Global Energy and Water-Cycle Experiment (GEWEX) longwave algorithm (GLW). [Location=GLOBAL] [Temporal_Coverage: Start_Date=1998-01-01; Stop_Date=2005-06-30] [Spatial_Coverage: Southernmost_Latitude=-90; Northernmost_Latitude=90; Westernmost_Longitude=-180; Easternmost_Longitude=180] [Data_Resolution: Latitude_Resolution=1 degree; Longitude_Resolution=Ranges from 1 degree (tropics and subtropics) to 120 degrees (the poles).; Temporal_Resolution=3 hourly; Temporal_Resolution_Range=3 hourly].

CLOUD TOP TEMPERATURE↗

NASA NTRS2019

Advances in Neural Network Detection and Retrieval of Multilayer Clouds for CERES Using Multispectral Satellite Data

An artificial neural network (ANN) algorithm, employing several Aqua MODIS infrared channels, the retrieved total cloud visible optical depth, and vertical humidity profiles is trained to detect multilayer (ML) ice-over-water cloud systems as identified by matched CloudSat and CALIPSO (CC) data. The multilayer ANN, or MLANN, algorithm is also trained to retrieve the optical depth and the top and base heights of the upper-layer ice clouds in ML systems. The trained MLANN was applied to independent MODIS data resulting in a combined ML and single layer hit rate of 80% (77%) for nonpolar regions during the day (night). The results are more accurate than currently available methods and the previous version of the MLANN. Upper-layer cloud top and base heights are accurate to ±1.2 km and ±1.6 km, respectively, while the uncertainty in optical depth is ±0.457 and ±0.556 during day and night, respectively. Areas of further improvement and development are identified and will be addressed in future versions of the MLANN.

CERES↗

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↗