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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 145 records · Page 8
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NASA NTRS1992

Signal processing for airborne doppler radar detection of hazardous wind shear as applied to NASA 1991 radar flight experiment data

Radar data collected during the 1991 NASA flight tests have been selectively analyzed to support research directed at developing both improved as well as new algorithms for detecting hazardous low-altitude windshear. Analysis of aircraft attitude data from several flights indicated that platform stability bandwidths were small compared to the data rate bandwidths which should support an assumption that radar returns can be treated as short time stationary. Various approaches at detection of weather returns in the presence of ground clutter are being investigated. Non-coventional clutter rejection through spectrum mode tracking and classification algorithms is a subject of continuing research. Based upon autoregressive modeling of the radar return time sequence, this approach may offer an alternative to overcome errors in conventional pulse-pair estimates. Adaptive filtering is being evaluated as a means of rejecting clutter with emphasis on low signal-to-clutter ratio situations, particularly in the presence of discrete clutter interference. An analysis of out-of-range clutter returns is included to illustrate effects of ground clutter interference due to range aliasing for aircraft on final approach. Data are presented to indicate how aircraft groundspeed might be corrected from the radar data as well as point to an observed problem of groundspeed estimate bias variation with radar antenna scan angle. A description of how recorded clutter return data are mixed with simulated weather returns is included. This enables the researcher to run controlled experiments to test signal processing algorithms. In the summary research efforts involving improved modelling of radar ground clutter returns and a Bayesian approach at hazard factor estimation are mentioned.

Baxa, Ernest G., Jr.↗
NASA NTRS1989

Per-point and per-field contextual classification of multipolarization and multiple incidence angle aircraft L-band radar data

Multipolarized aircraft L-band radar data are classified using two different image classification algorithms: (1) a per-point classifier, and (2) a contextual, or per-field, classifier. Due to the distinct variations in radar backscatter as a function of incidence angle, the data are stratified into three incidence-angle groupings, and training and test data are defined for each stratum. A low-pass digital mean filter with varied window size (i.e., 3x3, 5x5, and 7x7 pixels) is applied to the data prior to the classification. A predominately forested area in northern Florida was the study site. The results obtained by using these image classifiers are then presented and discussed.

Hoffer, Roger M.↗
NASA NTRS2007

Initial Verification of GEOS-4 Aerosols Using CALIPSO and MODIS: Scene Classification

A-train sensors such as MODIS and MISR provide column aerosol properties, and in the process a means of estimating aerosol type (e.g. smoke vs. dust). Correct classification of aerosol type is important because retrievals are often dependent upon selection of the right aerosol model. In addition, aerosol scene classification helps place the retrieved products in context for comparisons and analysis with aerosol transport models. The recent addition of CALIPSO to the A-train now provides a means of classifying aerosol distribution with altitude. CALIPSO level 1 products include profiles of attenuated backscatter at 532 and 1064 nm, and depolarization at 532 nm. Backscatter intensity, wavelength ratio, and depolarization provide information on the vertical profile of aerosol concentration, size, and shape. Thus similar estimates of aerosol type using MODIS or MISR are possible with CALIPSO, and the combination of data from all sensors provides a means of 3D aerosol scene classification. The NASA Goddard Earth Observing System general circulation model and data assimilation system (GEOS-4) provides global 3D aerosol mass for sulfate, sea salt, dust, and black and organic carbon. A GEOS-4 aerosol scene classification algorithm has been developed to provide estimates of aerosol mixtures along the flight track for NASA's Geoscience Laser Altimeter System (GLAS) satellite lidar. GLAS launched in 2003 and did not have the benefit of depolarization measurements or other sensors from the A-train. Aerosol typing from GLAS data alone was not possible, and the GEOS-4 aerosol classifier has been used to identify aerosol type and improve the retrieval of GLAS products. Here we compare 3D aerosol scene classification using CALIPSO and MODIS with the GEOS-4 aerosol classifier. Dust, smoke, and pollution examples will be discussed in the context of providing an initial verification of the 3D GEOS-4 aerosol products. Prior model verification has only been attempted with surface mass comparisons and column optical depth from AERONET and MODIS.

Welton, Ellsworth J.↗
NASA NTRS1993

Initial exploration of the ASRS database

We applied a standard classification algorithm to a subset of NASA/FAA Aviation Safety Reporting System (ASRS) database. The subset concerned incidents of aircraft altitude deviations. We are exploring the database to address questions such as: Are there 'natural' classes into which the data will fall? How would these classes correspond to recognizable events or situations? Beyond addressing these we want the data to determine directions for further study. We conducted experiments on different data subsections and analyzed diagnostics such as 'message length reduction'; these characterize the classification in terms of its significance and information content. The classification effort was successful in revealing relationships in the data that were in consonance with other studies of the data and expert opinion.

Kraft, Richard↗
NASA NTRS1981

Contextual classification of multispectral image data: An unbiased estimator for the context distribution

A key input to a statistical classification algorithm, which exploits the tendency of certain ground cover classes to occur more frequently in some spatial context than in others, is a statistical characterization of the context: the context distribution. An unbiased estimator of the context distribution is discussed which, besides having the advantage of statistical unbiasedness, has the additional advantage over other estimation techniques of being amenable to an adaptive implementation in which the context distribution estimate varies according to local contextual information. Results from applying the unbiased estimator to the contextual classification of three real LANDSAT data sets are presented and contrasted with results from non-contextual classifications and from contextual classifications utilizing other context distribution estimation techniques.

Tilton, J. C.↗
NASA NTRS2017

Nominal 30-M Cropland Extent Map of Continental Africa by Integrating Pixel-Based and Object-Based Algorithms Using Sentinel-2 and Landsat-8 Data on Google Earth Engine

A satellite-derived cropland extent map at high spatial resolution (30-m or better) is a must for food and water security analysis. Precise and accurate global cropland extent maps, indicating cropland and non-cropland areas, is a starting point to develop high-level products such as crop watering methods (irrigated or rainfed), cropping intensities (e.g., single, double, or continuous cropping), crop types, cropland fallows, as well as assessment of cropland productivity (productivity per unit of land), and crop water productivity (productivity per unit of water). Uncertainties associated with the cropland extent map have cascading effects on all higher-level cropland products. However, precise and accurate cropland extent maps at high spatial resolution over large areas (e.g., continents or the globe) are challenging to produce due to the small-holder dominant agricultural systems like those found in most of Africa and Asia. Cloud-based Geospatial computing platforms and multi-date, multi-sensor satellite image inventories on Google Earth Engine offer opportunities for mapping croplands with precision and accuracy over large areas that satisfy the requirements of broad range of applications. Such maps are expected to provide highly significant improvements compared to existing products, which tend to be coarser in resolution, and often fail to capture fragmented small-holder farms especially in regions with high dynamic change within and across years. To overcome these limitations, in this research we present an approach for cropland extent mapping at high spatial resolution (30-m or better) using the 10-day, 10 to 20-m, Sentinel-2 data in combination with 16-day, 30-m, Landsat-8 data on Google Earth Engine (GEE). First, nominal 30-m resolution satellite imagery composites were created from 36,924 scenes of Sentinel-2 and Landsat-8 images for the entire African continent in 2015-2016. These composites were generated using a median-mosaic of five bands (blue, green, red, near-infrared, NDVI) during each of the two periods (period 1: January-June 2016 and period 2: July-December 2015) plus a 30-m slope layer derived from the Shuttle Radar Topographic Mission (SRTM) elevation dataset. Second, we selected Cropland/Non-cropland training samples (sample size 9791) from various sources in GEE to create pixel-based classifications. As supervised classification algorithm, Random Forest (RF) was used as the primary classifier because of its efficiency, and when over-fitting issues of RF happened due to the noise of input training data, Support Vector Machine (SVM) was applied to compensate for such defects in specific areas. Third, the Recursive Hierarchical Segmentation (RHSeg) algorithm was employed to generate an object-oriented segmentation layer based on spectral and spatial properties from the same input data. This layer was merged with the pixel-based classification to improve segmentation accuracy. Accuracies of the merged 30-m crop extent product were computed using an error matrix approach in which 1754 independent validation samples were used. In addition, a comparison was performed with other available cropland maps as well as with LULC maps to show spatial similarity. Finally, the cropland area results derived from the map were compared with UN FAO statistics. The independent accuracy assessment showed a weighted overall accuracy of 94, with a producers accuracy of 85.9 (or omission error of 14.1), and users accuracy of 68.5 (commission error of 31.5) for the cropland class. The total net cropland area (TNCA) of Africa was estimated as 313 Mha for the nominal year 2015.

Cropland mapping; cropland areas; 30-m; Landsat-8;↗
NASA NTRS1975

Investigation of correlation classification techniques

A two-step classification algorithm for processing multispectral scanner data was developed and tested. The first step is a single pass clustering algorithm that assigns each pixel, based on its spectral signature, to a particular cluster. The output of that step is a cluster tape in which a single integer is associated with each pixel. The cluster tape is used as the input to the second step, where ground truth information is used to classify each cluster using an iterative method of potentials. Once the clusters have been assigned to classes the cluster tape is read pixel-by-pixel and an output tape is produced in which each pixel is assigned to its proper class. In addition to the digital classification programs, a method of using correlation clustering to process multispectral scanner data in real time by means of an interactive color video display is also described.

Haskell, R. E.↗
NASA NTRS2010

Detection of Multi-Layer and Vertically-Extended Clouds Using A-Train Sensors

The detection of mUltiple cloud layers using satellite observations is important for retrieval algorithms as well as climate applications. In this paper, we describe a relatively simple algorithm to detect multiple cloud layers and distinguish them from vertically-extended clouds. The algorithm can be applied to coincident passive sensors that derive both cloud-top pressure from the thermal infrared observations and an estimate of solar photon pathlength from UV, visible, or near-IR measurements. Here, we use data from the A-train afternoon constellation of satellites: cloud-top pressure, cloud optical thickness, the multi-layer flag from the Aqua MODerate-resolution Imaging Spectroradiometer (MODIS) and the optical centroid cloud pressure from the Aura Ozone Monitoring Instrument (OMI). For the first time, we use data from the CloudSat radar to evaluate the results of a multi-layer cloud detection scheme. The cloud classification algorithms applied with different passive sensor configurations compare well with each other as well as with data from CloudSat. We compute monthly mean fractions of pixels containing multi-layer and vertically-extended clouds for January and July 2007 at the OMI spatial resolution (l2kmx24km at nadir) and at the 5kmx5km MODIS resolution used for infrared cloud retrievals. There are seasonal variations in the spatial distribution of the different cloud types. The fraction of cloudy pixels containing distinct multi-layer cloud is a strong function of the pixel size. Globally averaged, these fractions are approximately 20% and 10% for OMI and MODIS, respectively. These fractions may be significantly higher or lower depending upon location. There is a much smaller resolution dependence for fractions of pixels containing vertically-extended clouds (approx.20% for OMI and slightly less for MODIS globally), suggesting larger spatial scales for these clouds. We also find higher fractions of vertically-extended clouds over land as compared with ocean, particularly in the tropics and summer hemisphere.

Joiner, J.↗
NASA NTRS2021

The Dark Energy Survey Supernova Programme: Modelling Selection Efficiency and Observed Core-collapse Supernova Contamination

The analysis of current and future cosmological surveys of Type Ia supernovae (SNe Ia) at high redshift depends on the accuratephotometric classification of the SN events detected. Generating realistic simulations of photometric SN surveys constitutes anessential step for training and testing photometric classification algorithms, and for correcting biases introduced by selectioneffects and contamination arising from core-collapse SNe in the photometric SN Ia samples. We use published SN time-seriesspectrophotometric templates, rates, luminosity functions, and empirical relationships between SNe and their host galaxies toconstruct a framework for simulating photometric SN surveys. We present this framework in the context of the Dark EnergySurvey (DES) 5-yr photometric SN sample, comparing our simulations of DES with the observed DES transient populations.We demonstrate excellent agreement in many distributions, including Hubble residuals, between our simulations and data.We estimate the core collapse fraction expected in the DES SN sample after selection requirements are applied and beforephotometric classification. After testing different modelling choices and astrophysical assumptions underlying our simulation,we find that the predicted contamination varies from 7.2 to 11.7 per cent, with an average of 8.8 per cent and an r.m.s. of 1.1 percent. Our simulations are the first to reproduce the observed photometric SN and host galaxy properties in high-redshift surveyswithout fine-tuning the input parameters. The simulation methods presented here will be a critical component of the cosmologyanalysis of the DES photometric SN Ia sample: correcting for biases arising from contamination, and evaluating the associatedsystematic uncertainty.

M Vincenzi↗
DOE OSTI2021

The Dark Energy Survey supernova programme: modelling selection efficiency and observed core-collapse supernova contamination

ABSTRACT The analysis of current and future cosmological surveys of Type Ia supernovae (SNe Ia) at high redshift depends on the accurate photometric classification of the SN events detected. Generating realistic simulations of photometric SN surveys constitutes an essential step for training and testing photometric classification algorithms, and for correcting biases introduced by selection effects and contamination arising from core-collapse SNe in the photometric SN Ia samples. We use published SN time-series spectrophotometric templates, rates, luminosity functions, and empirical relationships between SNe and their host galaxies to construct a framework for simulating photometric SN surveys. We present this framework in the context of the Dark Energy Survey (DES) 5-yr photometric SN sample, comparing our simulations of DES with the observed DES transient populations. We demonstrate excellent agreement in many distributions, including Hubble residuals, between our simulations and data. We estimate the core collapse fraction expected in the DES SN sample after selection requirements are applied and before photometric classification. After testing different modelling choices and astrophysical assumptions underlying our simulation, we find that the predicted contamination varies from 7.2 to 11.7 per cent, with an average of 8.8 per cent and an r.m.s. of 1.1 per cent. Our simulations are the first to reproduce the observed photometric SN and host galaxy properties in high-redshift surveys without fine-tuning the input parameters. The simulation methods presented here will be a critical component of the cosmology analysis of the DES photometric SN Ia sample: correcting for biases arising from contamination, and evaluating the associated systematic uncertainty.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1997

Utility of BRDF Models for Estimating Optimal View Angles in Classification of Remotely Sensed Images

Statistical classification of remotely sensed images attempts to discriminate between surface cover types on the basis of the spectral response recorded by a sensor. It is well known that surfaces reflect incident radiation as a function of wavelength producing a spectral signature specific to the material under investigation. Multispectral and hyperspectral sensors sample the spectral response over tens and even hundreds of wavelength bands to capture the variation of spectral response with wavelength. Classification algorithms then exploit these differences in spectral response to distinguish between materials of interest. Sensors of this type, however, collect detailed spectral information from one direction (usually nadir); consequently, do not consider the directional nature of reflectance potentially detectable at different sensor view angles. Improvements in sensor technology have resulted in remote sensing platforms capable of detecting reflected energy across wavelengths (spectral signatures) and from multiple view angles (angular signatures) in the fore and aft directions. Sensors of this type include: the moderate resolution imaging spectroradiometer (MODIS), the multiangle imaging spectroradiometer (MISR), and the airborne solid-state array spectroradiometer (ASAS). A goal of this paper, then, is to explore the utility of Bidirectional Reflectance Distribution Function (BRDF) models in the selection of optimal view angles for the classification of remotely sensed images by employing a strategy of searching for the maximum difference between surface BRDFs. After a brief discussion of directional reflect ante in Section 2, attention is directed to the Beard-Maxwell BRDF model and its use in predicting the bidirectional reflectance of a surface. The selection of optimal viewing angles is addressed in Section 3, followed by conclusions and future work in Section 4.

Valdez, P. F.↗
NASA NTRS1993

The probabilistic neural network architecture for high speed classification of remotely sensed imagery

In this paper we discuss a neural network architecture (the Probabilistic Neural Net or the PNN) that, to the best of our knowledge, has not previously been applied to remotely sensed data. The PNN is a supervised non-parametric classification algorithm as opposed to the Gaussian maximum likelihood classifier (GMLC). The PNN works by fitting a Gaussian kernel to each training point. The width of the Gaussian is controlled by a tuning parameter called the window width. If very small widths are used, the method is equivalent to the nearest neighbor method. For large windows, the PNN behaves like the GMLC. The basic implementation of the PNN requires no training time at all. In this respect it is far better than the commonly used backpropagation neural network which can be shown to take O(N6) time for training where N is the dimensionality of the input vector. In addition the PNN can be implemented in a feed forward mode in hardware. The disadvantage of the PNN is that it requires all the training data to be stored. Some solutions to this problem are discussed in the paper. Finally, we discuss the accuracy of the PNN with respect to the GMLC and the backpropagation neural network (BPNN). The PNN is shown to be better than GMLC and not as good as the BPNN with regards to classification accuracy.

Chettri, Samir R.↗
DOE OSTI2023

A bi-level data-driven framework for fault-detection and diagnosis of HVAC systems

Long-term operation of heating, ventilation, and air conditioning (HVAC) systems will eventually lead to a range of HVAC system failures, resulting in excessive energy consumption and maintenance costs. Here, to avoid HVAC malfunctioning, fault detection diagnostic (FDD) is utilized as a common practice. Machine learning methods have lately received considerable interest for FDD analysis of HVAC systems due to their high detection accuracy. Meanwhile, HVAC malfunctions are regarded as rare occurrences, hence normal operating data samples are much more accessible than data samples in faulty and malfunctioning conditions. The dominating frequency of normal operation in HVAC datasets has also led to heavily biased classification algorithms within the literature. Moreover, the focus of previous literature has been on increasing the accuracy of the models which leads to a high number of false positives (misleading alarms) in the system. In order to enhance the performance of diagnostic procedures and fill the mentioned gaps, this study proposes a novel data-driven framework. A bi-level machine learning framework is developed for diagnosing faults in air handling units (AHUs) and rooftop units (RTUs) based on principal component analysis (PCA), time series anomaly detection, and random forest (RF). It is shown that PCA can reduce the dataset dimension with one principal component accounting for 95% of data variance. Also, the random forest could classify the faults with 89% precision for single-zone AHU, 85% precision for RTU, and 79% for multi-zone AHU. By proposing this framework, three persistent challenges are addressed: (I) minimizing false positives; (II) accounting for data imbalance; and (III) normal condition monitoring of equipment.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Training Ultrasound Image Classification Deep-Learning Algorithms for Pneumothorax Detection Using a Synthetic Tissue Phantom Apparatus

Ultrasound (US) imaging is a critical tool in emergency and military medicine because of its portability and immediate nature. However, proper image interpretation requires skill, limiting its utility in remote applications for conditions such as pneumothorax (PTX) which requires rapid intervention. Artificial intelligence has the potential to automate ultrasound image analysis for various pathophysiological conditions. Training models require large data sets and a means of troubleshooting in real-time for ultrasound integration deployment, and they also require large animal models or clinical testing. Here, we detail the development of a dynamic synthetic tissue phantom model for PTX and its use in training image classification algorithms. The model comprises a synthetic gelatin phantom cast in a custom 3D-printed rib mold and a lung mimicking phantom. When compared to PTX images acquired in swine, images from the phantom were similar in both PTX negative and positive mimicking scenarios. We then used a deep learning image classification algorithm, which we previously developed for shrapnel detection, to accurately predict the presence of PTX in swine images by only training on phantom image sets, highlighting the utility for a tissue phantom for AI applications.

Boice, Emily N. (ORCID:0000000171802842)↗
DOE OSTI2021

Molecular fingerprint and machine learning to accelerate design of high-performance homochiral metal–organic frameworks

In this report computational screening was employed to calculate the enantioseparation capabilities of 45 functionalized homochiral metal–organic frameworks (FHMOFs), and machine learning (ML) and molecular fingerprint (MF) techniques were used to find new FHMOFs with high performance. With increasing temperature, the enantioselectivities for (R,S)-1,3-dimethyl-1,2-propadiene are improved. The “glove effect” in the chiral pockets was proposed to explain the correlations between the steric effect of functional groups and performance of FHMOFs. Moreover, the neighborhood component analysis and RDKit/MACCS MFs show the highest predictive effect on enantioselectivities among the four ML classification algorithms with nine MFs that were tested. Based on the importance of MF, 85 new FHMOFs were designed, and a newly designed FHMOF, NO 2 -NHOH-FHMOF, with high similarity to the optimal MFs achieved improved chiral separation performance, with enantioselectivities of 85%. The design principles and new chiral pockets obtained by ML and MFs could facilitate the development of new materials for chiral separation.

molecular fingerprint↗
DOE OSTI2022

Insights into Cation Ordering of Double Perovskite Oxides from Machine Learning and Causal Relations

This work investigates origins of cation ordering in double perovskites using first-principles theory computations combined with machine learning (ML) and causal relations. We have considered various oxidation states of A, A', B, and B' from the family of transition metal ions to construct a diverse compositional space. A conventional framework employing traditional ML classification algorithms such as Random Forest (RF) coupled with appropriate features including geometry-driven and key structural modes leads to accurate prediction (~98%) of A-site cation ordering. We have evaluated the accuracy of ML models by employing analyses of decision paths, assignments of probabilistic confidence bound, and finally a direct non-Gaussian acyclic structural equation model to investigate causality. Our study suggests that structural modes are crucial for classifying layered, columnar, and rock-salt ordering. The charge difference between A and A' is the most important feature for predicting clear layered ordering, which in turn depends on the B and B' charge separation. We have also designed mathematical relationships with these features to derive energy differences to form clear layered ordering. Here, the trilinear coupling between tilt, in-phase rotation, and A-site antiferroelectric displacement in the Landau free-energy expansion becomes the necessary condition behind formation of A-site cation ordering.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Machine Learning Analysis of Direct Dynamics Trajectory Outcomes for Thermal Deazetization of 2,3-Diazabicyclo[2.2.1]hept-2-ene

Experimentally, the thermal gas-phase deazetization of 2,3-diazabicyclo[2.2.1]hept-2-ene (1) results in the loss of N2 and the formation of bicyclo products 3 (exo) and 4 (endo) in a nonstatistical ratio, with preference for the exo product. Here, we report unrestricted M06-2X quasiclassical trajectories initialized from the concerted N2 ejection transition state that were able to replicate the experimental preference to form 3. We found that the 3:4 ratio results from the relative amounts of very fast (ballistic) exotype trajectories versus trajectories that lead to the 1,3-diradical intermediate 2. These quasiclassical trajectories provided a set of transition-state vibrational, velocity, momenta, and geometric features for the machine learning analysis. Additionally, a selection of popular supervised classification algorithms (e.g., random forest) provided poor prediction of trajectory outcomes based on only transition-state vibrational quanta and energy features. However, these machine learning models provided more accurate predictions using atomic velocities and atomic positions, attaining ~70% accuracy using initial conditions and between 85 and 95% accuracy at later reaction time steps. This increased accuracy allowed the feature importance analysis to reveal that, at the later-time analysis, the methylene bridge out-of-plane bending is correlated with trajectory outcomes for the formation of either the exo product or toward the diradical intermediate. Possible reasons for the struggle of machine learning algorithms to classify trajectories based on transition-state features is the heavily overlapping feature values, the finite but very large possible vibrational mode combinations, and the possibility of chaos as trajectories propagate. We examined this chaos by comparing a set of nearly identical trajectories that differed by only a very small scaling of the kinetic energies resulting from the transition-state reaction coordinate.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Peak Rain Rate Sensitivity to Observed Cloud Condensation Nuclei and Turbulence in Continental Warm Shallow Clouds During CACTI

Abstract Warm clouds strongly affect Earth's energy budget but remain imperfectly represented in climate models, partly due to the complexity and covariability of relevant processes influencing warm rain. This work presents a detailed analysis of different factors affecting rain rate peak intensity (RR) in continental warm clouds. Clouds were identified with vertically pointing radar and lidar observations and categorized via a temperature‐based cloud type classification algorithm from which warm clouds were isolated. Observations and retrievals of liquid water path (LWP), cloud condensation nuclei concentration (N CCN ), cloud depth, and cloud duration of more than 3,000 separate warm clouds sampled during the Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign are analyzed in this work. Multiple linear regression (MLR) and random forest (RF) models are applied to assess the relative impact of these variables on RR. Overall, RR tends to increase as cloud depth, LWP, and cloud duration increase, or N CCN decreases. Cloud depth affects RR the most while N CCN impacts it the least. When considering over 170 warm clouds observed at least 1 hr in which in‐cloud turbulence is retrieved, the effect of N CCN on RR remains most likely suppressive, but it is not significant at a 75% level for MLR and is highly uncertain for RF. The impact of in‐cloud turbulence depends on the moment and location it is sampled. Cloud base turbulence around the time of RR suppresses RR, while cloud top turbulence effects are inconclusive. Possible difficulties in isolating robust CCN and turbulence effects on RR are discussed.

54 ENVIRONMENTAL SCIENCES↗