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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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At least 451 records · Page 25
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NASA NTRS2023

Tropical Three-dimensional Cloud Climatology during El Niño Events with CALIPSO Level-3 Cloud Products

The El Niño–Southern Oscillation (ENSO) is a large-scale climatic phenomenon that originates in the tropical Pacific Ocean but affects global climate patterns. Its warm phase El Niño occurs irregularly every two to seven years and peaks in winter. A previous case study showed opposite cloud anomalies at high, middle and low altitude regions during a weak 2006/2007 El Niño and a moderate 2009/2010 El Niño, suggesting a non-linear response of tropical clouds to El Niño sea surface temperature (SST) forcing. To further understand the cloud response to SST, we examine the three-dimensional (3-D) cloud anomalies during all recent five El Niño events since 2006 using level-3 (L3) cloud products from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) mission. During its 17+ years operation, CALIPSO delivered near-global measurements of cloud and aerosol profiles at unprecedented high resolution and sensitivity. In this work, we provide a comprehensive picture of cloud anomalies during El Niño, with a particular focus on thin cirrus clouds at high altitudes, which are challenging for passive sensor detection algorithms. Specifically, the vertical cloud anomalies are calculated with the L3 3-D Cloud Occurrence Product, the horizontal cloud anomalies are from the L3 Global Energy and Water Exchanges (GEWEX) product, and the ice cloud extinction profiles and ice water content are obtained from the L3 Ice Cloud product. These findings will improve current knowledge of ENSO from a new perspective.

El Nino↗
NASA NTRSDate not supplied

Creating A Consistent Historical NASA POWER Solar Radiation Dataset to Support Renewable Energy, Building Energy Efficiency and Agro-Climatology Decisions

Prediction of Worldwide Energy Resources (POWER) project provides irradiance dataset to support renewable energy, building energy efficiency and agricultural needs. These datasets are derived from Global Energy and Water Cycle Experiment Surface Radiation Budget (GEWEX SRB) and Clouds and the Earth’s Radiant Energy System (CERES SYN1Deg). A systematic bias has been reported between these two datasets for the years with overlapping observations. For obtaining a consistent climate data record spanning the entire time record of observations, it is crucial to understand and remove the bias in the irradiance dataset. Inconsistency in solar radiation data can lead to inaccurate conclusions about solar energy potential and obscure real trends in solar radiation patterns that would impact energy availability assessments. In this study, we adapt quantile mapping approach to remove the systematic bias and to improve reliability of shortwave and longwave irradiance data. We present a validation of the bias corrected data against ground truth. For each 1° latitude and 1° longitude grid box across the globe, we match the CDFs of the reference dataset (CERES SYN1Deg) to that of the SRB dataset, thereby, adjusting the irradiance values to match the empirical distribution of two different measurements. The performance of quantile mapping is evaluated by using the metrics such as Mean Absolute Deviation (MAD). The results indicate that the quantile mapping significantly improves the accuracy and reliability of solar irradiance dataset especially for the weather conditions associated with high cloud cover and extreme irradiance values. The initial range of MAD for the studied sites for daily data was 4 to 19 Wm-2. After correction these reduced to 3 to 7 Wm-2. The findings from this study have important implications for solar energy system design, agricultural planning, and climate modeling community. Reducing the inconsistency and biases in solar irradiance dataset can enable better planning and operation of solar energy systems, leading to increased efficiency and cost-effectiveness. Additionally, this work also contributes to the statistical post-processing techniques in the renewable energy domain and highlights the potential of historical and near-real-time NASA POWER dataset as a valuable resource for solar energy research and applications.

POWER↗
NASA NTRS2023

Tropical Three-dimensional Cloud Climatology during El Niño Events with CALIPSO Level-3 Cloud Products

A previous case study showed opposite cloud anomalies at high, middle and low altitude regions during a weak 2006/2007 El Niño and a moderate 2009/2010 El Niño. In this work, we use CALIPSO Level 3 (L3) Cloud Products to provide a comprehensive picture of cloud anomalies during all recent five El Niño events since 2006, with a particular focus on thin cirrus clouds at high altitudes, which are challenging for passive sensor detection algorithms. Specifically, the vertical cloud anomalies are calculated with the L3 Cloud Occurrence Product, the horizontal cloud anomalies are from the L3 GEWEX product, and the ice cloud extinction profiles and ice water content are obtained from the L3 Ice Cloud product. The cloud anomalies are further analyzed with the magnitude and pattern of sea surface temperature (SST) anomalies. These findings will improve current knowledge of ENSO from a new perspective.

El Nino↗
NASA NTRSDate not supplied

Towards a Marine Stratus Climatology on Drizzle Occurrence from CALIPSO

Marine stratus are a predominant feature of our planet with the annual mean coverage exceeding 20%. They strongly reflect sunlight, yet exert only a modest effect on outgoing infrared radiation, providing a significant net cooling to the Earth’s radiative balance. Their formation is coupled to boundary layer circulations that are driven, in part, by cloud top radiative cooling and evaporative cooling from precipitation in downdrafts. Understanding how these cloud systems evolve as the climate changes is a key question that requires additional information on their lifecycle and microphysical properties to accurately represent their behavior in global circulation models. From a large-scale perspective, insight into the microphysical properties of marine stratus at cloud top can be realized through estimates of the effective radius (Re) of the droplet size distributions derived from MODIS observations. Estimates on the occurrence of rain/drizzle are available from CloudSat. Together these observations indicate that precipitation frequently occurs in clouds with higher cloud top Re. This relationship is consistent with the well documented shift in cloud top droplet size distributions towards fewer, yet larger droplets prior the onset of precipitation. Here we report on a new and complementary set observations from the CALIPSO mission. The approach derives an extinction-to-backscatter ratio (Sc, also known as the cloud lidar ratio) using an established relationship that depends on observations of the lidar attenuated backscatter and volume depolarization ratio within the cloud. Because Sc is strongly and inversely related to Re, a change in the derived Sc from higher to lower values corresponds to a change in the droplet size distribution as seen by MODIS. This change in the lidar signals at cloud top clearly identifies clouds that are capable of precipitation. The presentation provides a brief overview of the approach for deriving Sc and compares CALIOP-derived Sc with observations from other techniques. CALIOP classifications of drizzling clouds, based on the retrieved values Sc, are compared to independent, collocated assessments of drizzle occurrence reported in the standard CloudSat data products. Regional and seasonal comparisons highlight the strengths and weaknesses of the two sensors. A machine learning approach that combines information from both CALIOP and CloudSat showcases possible improvements in the global identification of scenes likely to contain rain-bearing clouds.

CALIPSO↗
NASA NTRSDate not supplied

A Long-Term GOES Satellite Overshooting Cloud Top and Anvil Cloud Climatology Over South America

The modern-era GOES satellite series began in 1994 with the GOES-8 satellite, and was augmented in 2018 with higher spatial resolution and more frequent imaging when GOES-16 became operational. GOES imagery has provided forecasters and researchers new perspectives into cloud top patterns associated with severe convection, and the ability to better forecast convection in regions without adequate ground-based weather radar coverage. While much attention has been given to convection over North America, convection over South America can be equally, if not more, intense and frequent. Recent studies have demonstrated that overshooting cloud tops (OT) and surrounding anvil clouds can be detected within infrared satellite imagery. Relative storm updraft intensity metrics such as the tropopause-relative infrared brightness temperature, the prominence of an OT relative to its surrounding anvil, and cloud top height can also be derived using automated methods combined with reanalysis data. These automated OT detection and intensity estimation methods have recently been applied to all GOES images collected over South America, in combination with the MERRA-2 reanalysis, from 1995 to 2022 at NASA Langley within a project supported by the NASA Applied Sciences Disasters program. Innovative aggregation methods have merged these products into daily, monthly, annual, and multi-annual composites, with hourly time bins, at ~4 km pixel spacing to enable researchers a new opportunity to study South American convective processes throughout the diurnal cycle. These products have recently become publicly available from NASA. This presentation will overview this new dataset, and novel insights into South American convection depicted by the data.

Kristopher Bedka↗
NASA NTRSDate not supplied

Towards A Multi-Decadal Climatology of Lightning From Space

Since 1995, NASA has launched three different low-Earth orbit (LEO) missions to observe global lightning from space: the Optical Transient Detector (OTD), the Tropical Rainfall Measuring Mission (TRMM) Lightning Imaging Sensor (LIS), and the International Space Station (ISS) LIS. ISS LIS completed its mission in November 2023, and is currently in Phase F, producing final data.

Timothy J Lang↗