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NASA NTRS1994

Radiation budgets in the western tropical Pacific

The usefulness of the radiances measured by operational satellites in deriving radiation budgets is demonstrated by comparing the model calculations with the Earth Radiation Budget Experiment (ERBE) fluxes. The radiation budgets in the atmosphere and at the surface in the western tropical Pacific are computed by coupling radiative transfer models to satellite retrievals of cloud and the earth surface parameters for April 1985 and April 1987. The model-computed fluxes at the top of the atmosphere agree well with the ERBE fluxes in both the solar and IR spectral regions. The difference is greater than 10 W/sq m in the outgoing longwave flux and greater than 15 W/sq m in the net downward shortwave flux. The agreement indicates that long-term earth radiation budgets in the tropical Pacific can be computed using the radiances measured by operational satellites.

Chou, Ming-Dah↗

NASA NTRS1994

Analysis of cloud top height and cloud coverage from satellites using the O2 A and B bands

Cloud height and cloud coverage detection are important for total ozone retrieval using ultraviolet and visible scattered light. Use of the O2 A and B bands, around 761 and 687 nm, by a satellite-borne instrument of moderately high spectral resolution viewing in the nadir makes it possible to detect cloud top height and related parameters, including fractional coverage. The measured values of a satellite-borne spectrometer are convolutions of the instrument slit function and the atmospheric transmittance between cloud top and satellite. Studies here determine the optical depth between a satellite orbit and the Earth or cloud top height to high accuracy using FASCODE 3. Cloud top height and a cloud coverage parameter are determined by least squares fitting to calculated radiance ratios in the oxygen bands. A grid search method is used to search the parameter space of cloud top height and the coverage parameter to minimize an appropriate sum of squares of deviations. For this search, nonlinearity of the atmospheric transmittance (i.e., leverage based on varying amounts of saturation in the absorption spectrum) is important for distinguishing between cloud top height and fractional coverage. Using the above-mentioned method, an operational cloud detection algorithm which uses minimal computation time can be implemented.

Kuze, Akihiko↗

NASA NTRS1999

A Solar Radiation Parameterization for Atmospheric Studies

The solar radiation parameterization (CLIRAD-SW) developed at the Goddard Climate and Radiation Branch for application to atmospheric models are described. It includes the absorption by water vapor, O3, O2, CO2, clouds, and aerosols and the scattering by clouds, aerosols, and gases. Depending upon the nature of absorption, different approaches are applied to different absorbers. In the ultraviolet and visible regions, the spectrum is divided into 8 bands, and single O3 absorption coefficient and Rayleigh scattering coefficient are used for each band. In the infrared, the spectrum is divided into 3 bands, and the k-distribution method is applied for water vapor absorption. The flux reduction due to O2 is derived from a simple function, while the flux reduction due to CO2 is derived from precomputed tables. Cloud single-scattering properties are parameterized, separately for liquid drops and ice, as functions of water amount and effective particle size. A maximum-random approximation is adopted for the overlapping of clouds at different heights. Fluxes are computed using the Delta-Eddington approximation.

Chou, Ming-Dah↗

NASA NTRS2022

A Deep Learning Approach to Fast Radiative Transfer

Due to the sheer volume of data, leveraging satellite instrument observations effectively in a data assimilation context for numerical weather prediction or for remote sensing requires a radiative transfer model as an observation operator that is both fast and accurate at the same time. Physics-based line-by-line radiative transfer (RT) models fulfil the requirement for accuracy, but are too slow and too costly in computational terms for operational applications. Therefore, fast methods were developed to be able to perform fast RT calculations using techniques such as spectral sampling or pre-computed look-up tables. The operational fast models currently calculate the absorption and scattering coefficients from the pre-computed regression coefficients and atmospheric state and cloud profiles. As a novel solution to this problem, this work investigates a deep learning approach to replace the regression coefficients in the fast RT models. A selection of hidden-layer neural network configurations is trained against atmospheric transmittance profile data computed by an accurate line-by-line model and their performance is evaluated and their advantages and disadvantages are discussed.

Machine learning↗

NASA NTRS1985

System enhancements of Mesoscale Analysis and Space Sensor (MASS) computer system

The interactive information processing for the mesoscale analysis and space sensor (MASS) program is reported. The development and implementation of new spaceborne remote sensing technology to observe and measure atmospheric processes is described. The space measurements and conventional observational data are processed together to gain an improved understanding of the mesoscale structure and dynamical evolution of the atmosphere relative to cloud development and precipitation processes. A Research Computer System consisting of three primary computers was developed (HP-1000F, Perkin-Elmer 3250, and Harris/6) which provides a wide range of capabilities for processing and displaying interactively large volumes of remote sensing data. The development of a MASS data base management and analysis system on the HP-1000F computer and extending these capabilities by integration with the Perkin-Elmer and Harris/6 computers using the MSFC's Apple III microcomputer workstations is described. The objectives are: to design hardware enhancements for computer integration and to provide data conversion and transfer between machines.

Hickey, J. S.↗

DOE OSTI2026

iDDS: intelligent distributed dispatch and scheduling for workflow orchestration

The intelligent distributed dispatch and scheduling (iDDS) service is a versatile workflow orchestration system designed for large-scale, distributed scientific computing. iDDS extends traditional workload and data management by integrating data-aware execution, conditional logic, and programmable workflows, enabling automation of complex and dynamic processing pipelines. Originally developed for the ATLAS experiment at the large hadron collider, iDDS has evolved into an experiment-agnostic platform that supports both template-driven workflows and a Function-as-a-Task model for Python-based orchestration. This paper presents the architecture and core components of iDDS, highlighting its scalability, modular message-driven design, and integration with systems such as PanDA and Rucio. We demonstrate its versatility through real-world use cases: fine-grained tape resource optimization for ATLAS, orchestration of large Directed Acyclic Graph (DAG) workflows for the Rubin Observatory, distributed hyperparameter optimization for machine learning applications, active learning for physics analyses, and AI-assisted detector design at the electron–ion collider. By unifying workload scheduling, data movement, and adaptive decision-making, iDDS reduces operational overhead and enables reproducible, high-throughput workflows across heterogeneous infrastructures. We conclude with current challenges and future directions, including interactive, cloud-native, and serverless workflow support.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1989

The 27-28 October 1986 FIRE IFO cirrus case study: Cirrus parameter relationships derived from satellite and lidar data

Cirrus cloud radiative and physical characteristics are determined using a combination of ground-based, aircraft, and satellite measurements taken as part of the First ISCCP Regional Experiment (FIRE) Cirrus Intensive Field Observations (IFO) during October and November 1986. Lidar backscatter data are used to define cloud base, center, and top heights and the corresponding temperatures. Coincident GOES 4 km visible (0.65 microns) and 8 km infrared window (11.5 microns) radiances are analyzed to determine cloud emittances and reflectances. Infrared optical depth is computed from the emittance results. Visible optical depth is derived from reflectance using a theoretical ice crystal scattering model and an empirical bidirectional reflectance mode. No clouds with visible optical depths greater than 5 or infrared optical depths less than 0.1 were used in the analysis. Average cloud thickness ranged from 0.5 km to 8 km for the 71 scenes. An average visible scattering efficiency of 2.1 was found for this data set. The results reveal a significant dependence of scattering efficiency on cloud temperature.

Minnis, Patrick↗

NASA NTRS1990

The 27-28 October 1986 FIRE IFO cirrus case study - Cirrus parameter relationships derived from satellite and lidar data

Minnis, Patrick↗

NASA NTRS1996

The 27-28 October 1986 FIRE IFO Cirrus Case Study: Cirrus Parameter Relationships Derived from Satellite and Lidar Data

Cirrus cloud radiative and physical characteristics are determined using a combination of ground based, aircraft, and satellite measurements taken as part of the First ISCCP Region Experiment (FIRE) cirrus intensive field observations (IFO) during October and November 1986. Lidar backscatter data are used with rawinsonde data to define cloud base, center and top heights and the corresponding temperatures. Coincident GOES-4 4-km visible (0.65 micrometer) and 8-km infrared window (11.5 micrometer) radiances are analyzed to determine cloud emittances and reflectances. Infrared optical depth is computed from the emittance results. Visible optical depth is derived from reflectance using a theoretical ice crystal scattering model and an empirical bidirectional reflectance model. No clouds with visible optical depths greater than 5 or infrared optical depths less than 0.1 were used in the analysis. Average cloud thickness ranged from 0.5 km to 8.0 km for the 71 scenes. Mean vertical beam emittances derived from cloud-center temperatures were 062 for all scenes compared to 0.33 for the case study (27-28 October) reflecting the thinner clouds observed for the latter scenes. Relationships between cloud emittance , extinction coefficients, and temperature for the case study are very similar to those derived from earlier surface-based studies. The thicker clouds seen during the other IFO days yield different results. Emittances derived using cloud-top temperature wer ratioed to those determined from cloud-center temperature. A nearly linear relationship between these ratios and cloud-center temperature holds promise for determining actual cloud-top temperature and cloud thickness from visible and infrared radiance pairs. The mean ratio of the visible scattering optical depth to the infrared absorption optical depth was 2.13 for these data. This scattering efficiency ratio shows a significant dependence on cloud temperature. Values of mean scattering efficiency as high as 2.6 suggest the presence of small ice particles at temperatures below 230 K. the parameterization of visible reflectance in terms of cloud optical depth and clear sky reflectance shows promise as a simplified method for interpreting visible satellite data reflected from cirrus clouds. Large uncertainties in the optical parameters due to cloud reflectance anisotropy and shading were found by analyzing data for various solar zenith angles and for simultaneous advanced very high resolution radiometer (AVHRR) data. Inhomogeneities in the cloud fields result in uneven cloud shading that apparently causes the occurrence of anomalously dark, cloud pixels in the GOES data. These shading effects complicate the interpretation of the satellite data. The results highlight the need for additional study or cirrus cloud scattering processes and remote sensing techniques.

Minnis, Patrick↗

NASA NTRS1990

The 27-28 October 1986 FIRE IFO Cirrus Case Study: Cirrus Parameter Relationships Derived from Satellite and Lidar Data

Cirrus cloud radiative and physical characteristics are determined using a combination of ground-based, aircraft, and satellite measurements taken as part of the FIRE Cirrus Intensive Field Observations (IFO) during October and November 1986. Lidar backscatter data are used with rawinsonde data to define cloud base, center, and top heights and the corresponding temperatures. Coincident GOES 4-km visible (0.65 micro-m) and 8-km infrared window (11.5 micro-m) radiances are analyzed to determine cloud emittances and reflectances. Infrared optical depth is computed from the emittance results. Visible optical depth is derived from reflectance using a theoretical ice crystal scattering model and an empirical bidirectional reflectance model. No clouds with visible optical depths greater than 5 or infrared optical depths less than 0.1 were used in the analysis. Average cloud thickness ranged from 0.5 km to 8.0 km for the 71 scenes. Mean vertical beam emittances derived from cloud-center temperatures were 0.62 for all scenes compared to 0.33 for the case study (27-28 October) reflecting the thinner clouds observed for the latter scenes. Relationships between cloud emittance, extinction coefficients, and temperature for the case study are very similar to those derived from earlier surface- based studies. The thicker clouds seen during the other IFO days yield different results. Emittances derived using cloud-top temperature were ratioed to those determined from cloud-center temperature. A nearly linear relationship between these ratios and cloud-center temperature holds promise for determining actual cloud-top temperatures and cloud thicknesses from visible and infrared radiance pairs. The mean ratio of the visible scattering optical depth to the infrared absorption optical depth was 2.13 for these data. This scattering efficiency ratio shows a significant dependence on cloud temperature. Values of mean scattering efficiency as high as 2.6 suggest the presence of small ice particles at temperatures below 230 K. The parameterization of visible reflectance in terms of cloud optical depth and clear-sky reflectance shows promise as a simplified method for interpreting visible satellite data reflected from cirrus clouds. Large uncertainties in the optical parameters due to cloud reflectance anisotropy and shading were found by analyzing data for various solar zenith angles and for simultaneous AVHRR data. Inhomogeneities in the cloud fields result in uneven cloud shading that apparently causes the occurrence of anomalously dark, cloudy pixels in the GOES data. These shading effects complicate the interpretation of the satellite data. The results highlight the need for additional study of cirrus cloud scattering processes and remote sensing techniques.

Minnis, Patrick↗

NASA NTRS2011

The Vertical Distribution of Thin Features Over the Arctic Analysed from CALIPSO Observations: Optically Thin Clouds - Part 1

Clouds play a crucial role in the Arctic climate system. Therefore, it is essential to accurately and reliably quantify and understand cloud properties over the Arctic. It is also important to monitor and attribute changes in Arctic clouds. Here, we exploit the capability of the CALIPSO-CALIOP instrument and provide comprehensive statistics of tropospheric thin clouds, otherwise extremely difficult to monitor from passive satellite sensors.We use 4 yr of data (June 2006.May 2010) over the circumpolar Arctic, here defined as 67-82 deg. N, and characterize probability density functions of cloud base and top heights, geometrical thickness and zonal distribution of such cloud layers, separately for water and ice phases, and discuss seasonal variability of these properties. When computed for the entire study area, probability density functions of cloud base and top heights and geometrical thickness peak at 200-400, 1000-2000 and 400-800 m, respectively, for thin water clouds, while for ice clouds they peak at 6-8, 7-9 and 400-1000 m, respectively. In general, liquid clouds were often identified below 2 km during all seasons, whereas ice clouds were sensed throughout the majority of the upper troposphere and also, but to a smaller extent, below 2 km for all seasons.

DeVasthale, Abhay↗

NASA NTRS1974

Nature of the Venus clouds as derived from their polarization

The linear polarization of sunlight reflected by Venus is analyzed by comparing observations with extensive multiple scattering computations. The analysis establishes that Venus is veiled by a cloud or haze layer of particles which have a narrow size distribution with a mean radius of about 1 micron. The refractive index of the particles is 1.44 at 0.55 micron wavelength with a small normal dispersion, the refractive index decreasing from the ultraviolet toward the infrared. The particles exist at a high level in the atmosphere, with the optical thickness unity occurring where the pressure is about 50 mb. The particle properties deduced from the polarization eliminate all but one of the cloud compositions which have been proposed for Venus. A concentrated solution of sulfuric acid provides good agreement with the polarization data.

Hansen, J. E.↗

NASA NTRS1982

Time variations of aerosols in the stratosphere following Mount St. Helens eruptions

Samples of stratospheric aerosols collected with U-2 aircraft for several months following the first three major eruptions of Mount St. Helens were analyzed for ash and liquid acid content. Ash grain sizes and compositions vary depending on collection altitude, location within the drifting cloud, and days following their injection. s computers Size distributions of ash particles vary with altitude. Generally small particles are depleted more rapidly at low altitudes (12 km) than at higher altitudes (17-18 km). Although samples collected 1 day after the first eruption of May 18, 1980, were dry, flow marks on the aircraft indicated parts of the cloud contained heavy acid concentrations. Indeed, all other samples obtained within 1 to 4 days after later eruptions (May 25 and June 12, 1980) were covered with copious amounts of liquid acid. Proportions of liquid to ash varied considerably depending on sampling location and cloud age. Because the acid-coated ash globules were large, they rapidly fell from the stratosphere until, by late June 1980, only a residue of acid droplets remained. Size distributions and concentrations of these droplets varied considerably.

Farlow, N. H.↗

NASA NTRS1979

Liquid content of the lower clouds of Venus as determined from Mariner 10 radio occultation

S- and X-band radio occulation data obtained during the Mariner 10 flyby of Venus are analyzed to determine the effects of signal attenuation due to the clouds of Venus. The signal-amplitude data are corrected for errors introduced by spacecraft motion and antenna steering during the occultation, absorption-coefficient profiles are computed, and the liquid content of the Venusian clouds is estimated. Excess attenuation effects are found and ascribed to absorption by small liquid particles, consisting of a water-sulfuric acid mixture containing 75% + or - 25% sulfuric acid, in the clouds. Three absorbing cloud layers at respective heights of 68, 60, and 48 km are suggested by the X-band absorptivity profile, but only the 68- and 48-km layers are clearly indicated by the S-band profile. The relatively slow S-band absorption at 60 km is tentatively attributed to an unknown constituent that is much more absorbing in the X-band than in the S-band. The results are compared with nephelometer measurements performed at 0.9 micron by the Venera 9 and 10 probes.

Kliore, A. J.↗

NASA NTRS1982

Satellite measurements of cloud reflectance and optical thickness

The cloud reflectance and the optical thickness have been computed with the aid of atmospheric models from the first eleven months (April 1970-February 1971) of backscattered ultraviolet (BUV) data over the Pacific Ocean. Both the cloud and the optical thickness are derived from the 380 nm channel by assuming that the entire IFOV (200 km) is filled by stratiform clouds. The analysis shows a large variability in the cloud reflectance in both the intertropic conversion zone (ITCZ) and the high latitudes. It is also found that for 90 percent of the time in the tropics, the clouds have an optical thickness of less than 10. An analysis of clouds with optical thickness between 10 and 20 shows that in the tropics the minimum frequency coincides with the dry zone at 2.5 deg S and the maximum frequency for clouds of optical thickness 10-20 is greater in summer than in winter and irrespective of the season; 50 percent of the time the clouds have an optical thickness less than 13.

Ahmad, Z.↗

NASA NTRS2019

A Fast All-Sky Radiation Model for Solar Applications with Narrowband Irradiances on Tilted Surfaces (FARMS-NIT): Part II. The Cloudy-Sky Model

The Fast All-sky Radiation Model for Solar applications with Narrowband Irradiances on Tilted surfaces (FARMSNIT) reported in Part I of this study is enhanced to include the requirements for cloudy-sky conditions. Surface radiances in 2002 narrow-wavelength bands from 0.28 to 4.0 μm are analytically computed by solving the radiative transfer equation for five independent photon paths accounting for clear-sky absorption, Rayleigh scattering, and cloud absorption and scattering. The Simple Model of the Atmospheric Radiative Transfer of Sunshine (SMARTS) is used to provide the optical thickness of the clear-sky atmosphere. Unlike Part I, which approximates the computation of aerosol scattering using the single-scattering phase function, the cloud transmittance and reflectance are efficiently retrieved from a comprehensive look-up table pre-computed by a 32-stream DIScrete Ordinates Radiative Transfer (DISORT) model for possible cloud conditions as well as solar and viewing geometries. A resolution analysis is performed to assess the optimal balance between the computational efficiency and accuracy in the development of the look-up table. Model simulations by DISORT and TMYSPEC are used to evaluate the performance of FARMS-NIT under cloudy-sky conditions. Compared to DISORT, FARMS-NIT yields 2–3% uncertainties on average, but it substantially reduces the computational time because of the independent computation of cloud properties and the implementation of the look-up table. In contrast to TMYSPEC, which uses successive steps to empirically compute plane-of-array (POA) irradiances and spectral irradiances, FARMS-NIT directly solves spectral radiances from the radiative transfer equation, which profoundly increases the accuracy in surface irradiances, especially over inclined photovoltaics (PV) panels.

Xie, Yu↗

NASA NTRS2022

Virtual Assistant for First Responders Using Natural Language Understanding and Optical Character Recognition

Commercial deep learning capabilities are available for many applications such as computer vision processing and intelligent chat bots. The Google Cloud Platform product Google Dialogflow provides lifelike conversational artificial intelligence (AI) using machine learning (ML) to generate natural conversations between computers and humans. This ML utilizes natural language understanding (NLU) to recognize a user’s intent and extracts key information into a form of entities. We have developed a user-friendly application through understanding the hazardous material database, first aid safety guidelines and observing the process of first responders who access this information in the field. We created the Trusted and Explainable Artificial Intelligence for Saving Lives (TruePAL) virtual assistant using Dialogflow1 and TensorFlow2 paired with EasyOCR.3 The chatbot supports first responders by providing voice interaction which helps limit additional steps such as browsing through multiple categories when searching for information. Using feedback from our field interviews, the voice interface has been developed to enable the first responder to focus on the immediate emergency. With less distractions, the first responder is able to engage the incident more effectively. The partial hands-free TruePAL chatbot assistant improves the accessibility to the correct guidance by an average of 1.9 seconds compared to the widely used application, NIH WISER, which requires full attention to operate. We combined this intelligent chatbot with a separate visual processing capability to produce hazardous signage analysis and generate the proper guidance for first responders. With the evolving functionality of AI tools, the use of virtual assistants in first responder technology will be an advancement, benefiting the safety of both first responders and civilians.

Chow, Edward↗

NASA NTRS1988

Tabulation of Mie scattering calculation results for microwave radiative transfer modeling

In microwave radiative transfer model simulations, the Mie calculations usually consume the majority of the computer time necessary for the calculations (70 to 86 percent for frequencies ranging from 6.6 to 183 GHz). For a large array of atmospheric profiles, the repeated calculations of the Mie codes make the radiative transfer computations not only expensive, but sometimes impossible. It is desirable, therefore, to develop a set of Mie tables to replace the Mie codes for the designated ranges of temperature and frequency in the microwave radiative transfer calculation. Results of using the Mie tables in the transfer calculations show that the total CPU time (IBM 3081) used for the modeling simulation is reduced by a factor of 7 to 16, depending on the frequency. The tables are tested by computing the upwelling radiance of 144 atmospheric profiles generated by a 3-D cloud model (Tao, 1986). Results are compared with those using Mie quantities computed from the Mie codes. The bias and root-mean-square deviation (RMSD) of the model results using the Mie tables, in general, are less than 1 K except for 37 and 90 GHz. Overall, neither the bias nor RMSD is worse than 1.7 K for any frequency and any viewing angle.

Yeh, Hwa-Young M.↗