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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 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS1992

Foundations for statistical-physical precipitation retrieval from passive microwave satellite measurements. I - Brightness-temperature properties of a time-dependent cloud-radiation model

The relationship between emerging microwave brightness temperatures (T(B)s) and vertically distributed mixtures of liquid and frozen hydrometeors was investigated, using a cloud-radiation model, in order to establish the framework for a hybrid statistical-physical rainfall retrieval algorithm. Although strong relationships were found between the T(B) values and various rain parameters, these correlations are misleading in that the T(B)s are largely controlled by fluctuations in the ice-particle mixing ratios, which in turn are highly correlated to fluctuations in liquid-particle mixing ratios. However, the empirically based T(B)-rain-rate (T(B)-RR) algorithms can still be used as tools for estimating precipitation if the hydrometeor profiles used for T(B)-RR algorithms are not specified in an ad hoc fashion.

Smith, Eric A.↗
NASA NTRS1993

Foundations for statistical-physical precipitation retrieval from passive microwave satellite measurements. II - Emission-source and generalized weighting-function properties of a time-dependent cloud-radiation model

The foremost physical aspects of microphysical-radiative interactions that control the formation and modulation of frequency-dependent passive microwave TB over a continental cold-rain precipitation system are explained within the framework of the essential ingredients of a physically based precipitation-retrieval algorithm. The analysis is based on a modeling study in which a 3D cloud model has provided the objective basis for generating an extensive set of microphysical profiles that describe numerous precipitation features in the course of the evolution of a continental hail storm. A summary of the various components of the algorithm, as well as the surface rain rates, is given. The algorithm employs the cloud model to provide a consistent and objectively generated source of detailed microphysical information as the underpinnings to an inversion-based perturbative retrieval scheme.

Mugnai, Alberto↗
NASA NTRS2021

Evaluation of Clouds, Radiation, and Precipitation in CMIP6 Models Using Global Weather States Derived from ISCCP-H Cloud Property Data

A clustering methodology is applied to cloud optical depth cloud top pressure (TAU-PC) histograms from the new, 1-degree resolution, ISCCP-H dataset, to derive an updated global Weather State (WS) dataset. Then, PC-TAU histograms from current-climate CMIP6 model simulations are assigned to the ISCCP-H WSs along with their concurrent radiation and precipitation properties, to evaluate model cloud, radiation, and precipitation properties in the context of the Weather States. The new ISCCP-H analysis produces WSs that are very similar to those previously found in the lower resolution ISCCP-D dataset. The main difference lies in the splitting of the ISCCP-D thin stratocumulus WS between the ISCCP-H shallow cumulus and stratocumulus WSs, which results in the reduction by one of the total WS number. The evaluation of the CMIP6 models against the ISCCP-H Weather States, shows that, in the ensemble mean, the models are producing an adequate representation of the frequency and geographical distribution of the WSs, with measurable improvements compared to the WSs derived for the CMIP5 ensemble. However, the frequency of shallow cumulus clouds continues to be underestimated, and, in some WSs the good agreement of the ensemble mean with observations comes from averaging models that significantly overpredict and underpredict the ISCCP-H WS frequency. In addition, significant biases exist in the internal cloud properties of the model WSs, such as the model underestimation of cloud fraction in middle-top clouds and secondarily in midlatitude storm and stratocumulus clouds, that result in an underestimation of cloud SW cooling in those regimes.

Clouds↗
NASA NTRS1990

Precipitating cloud vertical structure derived from passive microwave radiometry

A procedure for the retrieval of cloud vertical structure from passive microwave radiometry is demonstrated by using passive microwave radiometry observations made during the Tropical Rainfall Measuring Mission. The procedure uses a set of cloud radiative models, with each model consisting of five vertical layers, specifying a distinct cloud vertical structure in terms of the near-surface parameters. The retrieval procedure is separated into two tasks (1) retrieving a set of geophysical parameters for each cloud radiative model and (2) finding which of the cloud radiative models and its associated retrieved parameters best fit the observed geophysical conditions. It is shown that this retrieval technique can detect differences and similarities between precipitating systems.

Kummerow, Christian D.↗
NASA NTRS1990

Aircraft observations and cloud-microwave radiative model simulations of a deep convective system

A 3D cloud model is used to simulate the storm structure of the June 29, 1986 storm system observed during the Cooperative Huntsville Meteorological Experiment (Dodge et al., 1986), and the results are linked to a microwave radiative transfer model for simulating aircraft observations. The results of cloud and radiative model simulations are then compared with radar data and aircraft microwave observations, respectively. It is shown that the degree of agreement between model simulations and the aircraft microwave observations depends on the model-derived microphysics of cloud and precipitation. It is found that the calculated values of the brightness temperature (Tb) at 92 GHz are close to the observed temperatures, while the calculated Tb values at 18 and 37 GHz are lower than the observed Tb.

Yeh, H.-Y. M.↗
NASA NTRS2006

Precipitation and Latent Heating Distributions from Satellite Passive Microwave Radiometry: Improved Method and Uncertainties - Part 1

A revised Bayesian algorithm for estimating surface rain rate, convective rain proportion, and latent heating profiles from satellite-borne passive microwave radiometer observations over ocean backgrounds is described. The algorithm searches a large database of cloud-radiative model simulations to find cloud profiles that are radiatively consistent with a given set of microwave radiance measurements. The properties of these radiatively consistent profiles are then composited to obtain best estimates of the observed properties. The revised algorithm is supported by an expanded and more physically consistent database of cloud-radiative model simulations. The algorithm also features a better quantification of the convective and nonconvective contributions to total rainfall, a new geographic database, and an improved representation of background radiances in rain-free regions. Bias and random error estimates are derived from applications of the algorithm to synthetic radiance data, based upon a subset of cloud-resolving model simulations, and from the Bayesian formulation itself. Synthetic rain-rate and latent heating estimates exhibit a trend of high (low) bias for low (high) retrieved values. The Bayesian estimates of random error are propagated to represent errors at coarser time and space resolutions, based upon applications of the algorithm to TRMM Microwave Imager (TMI) data. Errors in TMI instantaneous rain-rate estimates at 0.5 -resolution range from approximately 50% at 1 mm/h to 20% at 14 mm/h. Errors in collocated spaceborne radar rain-rate estimates are roughly 50%-80% of the TMI errors at this resolution. The estimated algorithm random error in TMI rain rates at monthly, 2.5deg resolution is relatively small (less than 6% at 5 mm day.1) in comparison with the random error resulting from infrequent satellite temporal sampling (8%-35% at the same rain rate). Percentage errors resulting from sampling decrease with increasing rain rate, and sampling errors in latent heating rates follow the same trend. Averaging over 3 months reduces sampling errors in rain rates to 6%-15% at 5 mm day.1, with proportionate reductions in latent heating sampling errors.

Olson, William S.↗
NASA NTRS2004

Precipitation and Latent Heating Distributions from Satellite Passive Microwave Radiometry: Method and Uncertainties - Part 1

A revised Bayesian algorithm for estimating surface rain rate, convective rain proportion, and latent heating/drying profiles from satellite-borne passive microwave radiometer observations over ocean backgrounds is described. The algorithm searches a large database of cloud-radiative model simulations to find cloud profiles that are radiatively consistent with a given set of microwave radiance measurements. The properties of these radiatively consistent profiles are then composited to obtain best estimates of the observed properties. The revised algorithm is supported by an expanded and more physically consistent database of cloud-radiative model simulations. The algorithm also features a better quantification of the convective and non-convective contributions to total rainfall, a new geographic database, and an improved representation of background radiances in rain-free regions. Bias and random error estimates are derived from applications of the algorithm to synthetic radiance data, based upon a subset of cloud resolving model simulations, and from the Bayesian formulation itself. Synthetic rain rate and latent heating estimates exhibit a trend of high (low) bias for low (high) retrieved values. The Bayesian estimates of random error are propagated to represent errors at coarser time and space resolutions, based upon applications of the algorithm to TRMM Microwave Imager (TMI) data. Errors in instantaneous rain rate estimates at 0.5 deg resolution range from approximately 50% at 1 mm/h to 20% at 14 mm/h. These errors represent about 70-90% of the mean random deviation between collocated passive microwave and spaceborne radar rain rate estimates. The cumulative algorithm error in TMI estimates at monthly, 2.5 deg resolution is relatively small (less than 6% at 5 mm/day) compared to the random error due to infrequent satellite temporal sampling (8-35% at the same rain rate).

Olson, William S.↗
NASA NTRS1991

Atmospheric radiation

Studies of atmospheric radiative processes are summarized for the period 1987-1990. Topics discussed include radiation modeling; clouds and radiation; radiative effects in dynamics and climate; radiation budget and aerosol effects; and gaseous absorption, particulate scattering and surface reflection. It is concluded that the key developments of the period are a defining of the radiative forcing to the climate system by trace gases and clouds, the recognition that cloud microphysics and morphology need to be incorporated not only into radiation models but also climate models, and the isolation of a few important unsolved theoretical problems in atmospheric radiation.

HARSHVARDHAN↗
NASA NTRS1986

A cloud model-radiative model combination for determining microwave TB-rain rate relations

The development of a cloud model-radiative transfer model combination for computing average brightness temperature, T(B), is discussed. The cloud model and radiative transfer model used in this study are described. The relations between rain rate, cloud and rain water, cloud and precipitation ice, and upwelling radiance are investigated. The effects of the rain rate relations on T(B) under different climatological conditions are examined. The model-derived T(B) results are compared to the 92 and 183 GHz aircraft observations of Hakkarinen and Adler (1984, 1986) and the radar-estimated rain rate of Hakkarinen and Adler (1986); good correlation between the data is detected.

Szejwach, Gerard↗
NASA NTRS1992

A cloud and radiation model-based algorithm for rainfall retrieval from SSM/I multispectral microwave measurements

A hybrid statistical-physical retrieval scheme is explored which combines a statistical approach with an approach based on the development of cloud-radiation models designed to simulate precipitating atmospheres. The algorithm employs the detailed microphysical information from a cloud model as input to a radiative transfer model which generates a cloud-radiation model database. Statistical procedures are then invoked to objectively generate an initial guess composite profile data set from the database. The retrieval algorithm has been tested for a tropical typhoon case using Special Sensor Microwave/Imager (SSM/I) data and has shown satisfactory results.

Xiang, Xuwu↗
NASA NTRS1994

Incorporation of multiple cloud layers for ultraviolet radiation modeling studies

Cloud data sets compiled from surface observations were used to develop an algorithm for incorporating multiple cloud layers into a multiple-scattering radiative transfer model. Aerosol extinction and ozone data sets were also incorporated to estimate the seasonally averaged ultraviolet (UV) flux reaching the surface of the Earth in the Detroit, Michigan, region for the years 1979-1991, corresponding to Total Ozone Mapping Spectrometer (TOMS) version 6 ozone observations. The calculated UV spectrum was convolved with an erythema action spectrum to estimate the effective biological exposure for erythema. Calculations show that decreasing the total column density of ozone by 1% leads to an increase in erythemal exposure by approximately 1.1-1.3%, in good agreement with previous studies. A comparison of the UV radiation budget at the surface between a single cloud layer method and a multiple cloud layer method presented here is discussed, along with limitations of each technique. With improved parameterization of cloud properties, and as knowledge of biological effects of UV exposure increase, inclusion of multiple cloud layers may be important in accurately determining the biologically effective UV budget at the surface of the Earth.

Charache, Darryl H.↗
NASA NTRS1988

An earth outgoing longwave radiation climate model. II - Radiation with clouds included

The model of the outgoing longwave radiation (OLWR) of Yang et al. (1987) is modified by accounting for the presence of clouds and their influence on OLWR. Cloud top temperature was adjusted so that the calculation agreed with NOAA scanning radiometer measurements. Cloudy sky cases were calculated for global average, zonal average, and worldwide distributed cases. The results were found to agree well with satellite observations.

Yang, Shi-Keng↗
NASA NTRS2001

Do Fractal Models of Clouds Produces the Right 3D Radiative Effects?

Stochastic fractal models of clouds are often used to study 3D radiative effects and their influence on the remote sensing of cloud properties. Since it is important that the cloud models produce a correct radiative response, some researchers require the model parameters to match observed cloud properties such as scale-independent optical thickness variability. Unfortunately, matching these properties does not necessarily imply that the cloud models will cause the right 3D radiative effects. First, the matched properties alone only influence the 3D effects but do not completely determine them. Second, in many cases the retrieved cloud properties have been already biased by 3D radiative effects, and so the models may not match the true real clouds. Finally, the matched cloud properties cannot be considered independent from the scales at which they have been retrieved. This paper proposes an approach that helps ensure that fractal cloud models are realistic and produce the right 3D effects. The technique compares the results of radiative transfer simulations for the model clouds to new direct observations of 3D radiative effects in satellite images.

Varnai, Tamas↗
DOE OSTI2021

Development of a Random-Forest Cloud-Regime Classification Model Based on Surface Radiation and Cloud Products

Various methods have been developed to characterize cloud type, otherwise referred to as cloud regime. These include manual sky observations, combining radiative and cloud vertical properties observed from satellite, surface-based remote sensing, and digital processing of sky imagers. While each method has inherent advantages and disadvantages, none of these cloud-typing methods actually includes measurements of surface shortwave or longwave radiative fluxes. Here, a method that relies upon detailed, surface-based radiation and cloud measurements and derived data products to train a random-forest machine-learning cloud classification model is introduced. Measurements from five years of data from the ARM Southern Great Plains site were compiled to train and independently evaluate the model classification performance. A cloud-type accuracy of approximately 80% using the random-forest classifier reveals that the model is well suited to predict climatological cloud properties. Furthermore, an analysis of the cloud-type misclassifications is performed. While physical cloud types may be misreported, the shortwave radiative signatures are similar between misclassified cloud types. From this, we assert that the cloud-regime model has the capacity to successfully differentiate clouds with comparable cloud–radiative interactions. Therefore, we conclude that the model can provide useful cloud-property information for fundamental cloud studies, inform renewable energy studies, and be a tool for numerical model evaluation and parameterization improvement, among many other applications.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1994

A model predicting the evolution of ice particle size spectra and radiative properties of cirrus clouds. Part 2: Dependence of absorption and extinction on ice crystal morphology

This study builds upon the microphysical modeling described in Part 1 by deriving formulations for the extinction and absorption coefficients in terms of the size distribution parameters predicted from the micro-physical model. The optical depth and single scatter albedo of a cirrus cloud can then be determined, which, along with the asymmetry parameter, are the input parameters needed by cloud radiation models. Through the use of anomalous diffraction theory, analytical expressions were developed describing the absorption and extinction coefficients and the single scatter albedo as functions of size distribution parameters, ice crystal shapes (or habits), wavelength, and refractive index. The extinction coefficient was formulated in terms of the projected area of the size distribution, while the absorption coefficient was formulated in terms of both the projected area and mass of the size distribution. These properties were formulated as explicit functions of ice crystal geometry and were not based on an 'effective radius.' Based on simulations of the second cirrus case study described in Part 1, absorption coefficients predicted in the near infrared for hexagonal columns and rosettes were up to 47% and 71% lower, respectively, than absorption coefficients predicted by using equivalent area spheres. This resulted in single scatter albedos in the near-infrared that were considerably greater than those predicted by the equivalent area sphere method. Reflectances in this region should therefore be underestimated using the equivalent area sphere approach. Cloud optical depth was found to depend on ice crystal habit. When the simulated cirrus cloud contained only bullet rosettes, the optical depth was 142% greater than when the cloud contained only hexagonal columns. This increase produced a doubling in cloud albedo. In the near-infrared (IR), the single scatter albedo also exhibited a significant dependence on ice crystal habit. More research is needed on the geometrical properties of ice crystals before the influence of ice crystal shape on cirrus radiative properties can be adequately understood. This study provides a way of coupling the radiative properties of absorption, extinction, and single scatter albedo to the microphysical properties of cirrus clouds. The dependence of extinction and absorption on ice crystal shape was not just due to geometrical differences between crystal types, but was also due to the effect these differences had on the evolution of ice particle size spectra. The ice particle growth model in Part 1 and the radiative properties treated here are based on analytical formulations, and thus represent a computationally efficient means of modeling the microphysical and radiative properties of cirrus clouds.

Mitchell, David L.↗
NASA NTRS1989

A self-consistency approach to improve microwave rainfall rate estimation from space

A multichannel statistical approach is used to retrieve rainfall rates from the brightness temperature T(B) observed by passive microwave radiometers flown on a high-altitude NASA aircraft. T(B) statistics are based upon data generated by a cloud radiative model. This model simulates variabilities in the underlying geophysical parameters of interest, and computes their associated T(B) in each of the available channels. By further imposing the requirement that the observed T(B) agree with the T(B) values corresponding to the retrieved parameters through the cloud radiative transfer model, the results can be made to agree quite well with coincident radar-derived rainfall rates. Some information regarding the cloud vertical structure is also obtained by such an added requirement. The applicability of this technique to satellite retrievals is also investigated. Data which might be observed by satellite-borne radiometers, including the effects of nonuniformly filled footprints, are simulated by the cloud radiative model for this purpose.

Kummerow, Christian↗
NASA NTRS2002

A New Look Into the Treatment of Large Drops in Radiative Transfer

One of the weakest links in conventional cloud radiation models is the way a size distribution of cloud particles is mathematically handled: one averages measured drop concentrations over space, evaluates the extinction and scattering cross sections using the mean drop size distribution function and solves the radiative transfer equation with average characteristics. This technique assumes that all drop sizes are well represented in ary given interval along the direction of photon travel. But the concentration of large drops can be so law that this assumption is significantly violated. This is clearly seen if one examines of how the appearance of drops changes with the scale. In the poster we demonstrate the results of our analysis of FSSP data acquired during FIRE'87 and both FSSP and DC1 data from cloud IOP in Spring 2000. The analysis shows that, in general, the average number of drops observed in an interval along the direction of photon travel is proportional to the interval's length (or scale) powered to a drop scaling exponent. For small droplets the scaling exponent is equal to 1, as predicted by a conventional radiative transfer model. However, for large drops, the scaling exponent can fall distinctly below 1. Since the solution of radiative transfer equation depends on the drop scaling exponent, its deviation from unity can lead to a systematic bias in estimation of cloud radiative properties. We discuss the importance of the scaling exponent for the characteristics of the small-scale drop size variability for large and small droplets. Most of the existing cloud radiation models, however, are insensitive to this parameter.

Marshak, Alexander↗
NASA NTRS1987

FIRE - The First ISCCP Regional Experiment

The First International Satellite Cloud Climatology Project Regional Experiment (FIRE) designed to study the roles of clouds, in particular marine stratocumulus and cirrus-cloud systems, in the global climate is discussed. The objectives of FIRE are: (1) to develop a cloud-classification scheme; (2) to validate and improve satellite cloud-retrieval techniques; (3) to improve cloud radiation models; (4) to collect cloud space/time statistics; (5) to improve cloud dynamics models; and (6) to validate and improve GCM cloud parameterizations. The methods used to acquire extended time data and intensive field observations are described. The extended time and intensive field data collected during the FIRE are to be archived in the NASA Pilot Climate Data System at Goddard Space Flight Center.

Cox, Stephen K.↗