scholarly journals Evaluation of VIIRS Neural Network Cloud Detection against Current Operational Cloud Masks

2020 ◽  
Author(s):  
Charles H. White ◽  
Andrew K. Heidinger ◽  
Steven A. Ackerman
Keyword(s):  
Neural Network ◽  
Optical Depth ◽  
Infrared Imaging ◽  
Regional Analysis ◽  
True Positive Rate ◽  
Orthogonal Polarization ◽  
Cloud Detection ◽  
The Neural Network ◽  
Cloud Mask ◽  
The Impact

Abstract. Cloud properties are critical to our understanding of weather and climate variability, but their estimation from satellite imagers is a nontrivial task. In this work, we aim to improve cloud detection which is the most fundamental cloud property. We use a neural network applied to Visible Infrared Imaging Radiometer Suite (VIIRS) measurements to determine whether an imager pixel is cloudy or cloud-free. The neural network is trained and evaluated using four years (2016–2019) of coincident measurements between VIIRS and the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). We successfully address the lack of sun glint in the collocation dataset with a simple semi-supervised learning approach. The results of the neural network are then compared with two operational cloud masks: the MODIS-VIIRS Continuity Cloud Mask (MVCM) and the NOAA Enterprise Cloud Mask (ECM). We find that the neural network outperforms both operational cloud masks in most conditions examined with a few exceptions. The largest improvements we observe occur during the night over snow or ice covered surfaces in the high latitudes. In our analysis, we show that this improvement is not solely due to differences in optical depth-based definitions of a cloud between each mask. We also analyze the differences in true positive rate between day/night and land/water scenes as a function of optical depth. Such differences are a contributor to spatial artifacts in cloud masking and we find that the neural network is the most consistent in cloud detection with respect to optical depth across these conditions. A regional analysis over Greenland illustrates the impact of such differences and shows that they can result in mean cloud fractions with very different spatial and temporal characteristics.

scholarly journals Evaluation of Visible Infrared Imaging Radiometer Suite (VIIRS) neural network cloud detection against current operational cloud masks

2021 ◽  
Vol 14 (5) ◽  
pp. 3371-3394
Author(s):  
Charles H. White ◽  
Andrew K. Heidinger ◽  
Steven A. Ackerman
Keyword(s):  
Neural Network ◽  
Optical Depth ◽  
Infrared Imaging ◽  
Regional Analysis ◽  
True Positive Rate ◽  
Orthogonal Polarization ◽  
Cloud Detection ◽  
The Neural Network ◽  
Cloud Mask ◽  
The Impact

Abstract. Cloud properties are critical to our understanding of weather and climate variability, but their estimation from satellite imagers is a nontrivial task. In this work, we aim to improve cloud detection, which is the most fundamental cloud property. We use a neural network applied to Visible Infrared Imaging Radiometer Suite (VIIRS) measurements to determine whether an imager pixel is cloudy or cloud-free. The neural network is trained and evaluated using 4 years (2016–2019) of coincident measurements between VIIRS and the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). We successfully address the lack of sun glint in the collocation dataset with a simple semi-supervised learning approach. The results of the neural network are then compared with two operational cloud masks: the Continuity MODIS-VIIRS Cloud Mask (MVCM) and the NOAA Enterprise Cloud Mask (ECM). We find that the neural network outperforms both operational cloud masks in most conditions examined with a few exceptions. The largest improvements we observe occur during the night over snow- or ice-covered surfaces in the high latitudes. In our analysis, we show that this improvement is not solely due to differences in optical-depth-based definitions of a cloud between each mask. We also analyze the differences in true-positive rate between day–night and land–water scenes as a function of optical depth. Such differences are a contributor to spatial artifacts in cloud masking, and we find that the neural network is the most consistent in cloud detection with respect to optical depth across these conditions. A regional analysis over Greenland illustrates the impact of such differences and shows that they can result in mean cloud fractions with very different spatial and temporal characteristics.

scholarly journals The Continuity MODIS-VIIRS Cloud Mask

2020 ◽  
Vol 12 (20) ◽  
pp. 3334 ◽  
Author(s):  
Richard A. Frey ◽  
Steven A. Ackerman ◽  
Robert E. Holz ◽  
Steven Dutcher ◽  
Zach Griffith
Keyword(s):  
Infrared Imaging ◽  
Cloud Fraction ◽  
The Arctic ◽  
Orthogonal Polarization ◽  
Cloud Detection ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Hit Rates ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Cloud Mask

This paper introduces the Continuity Moderate Resolution Imaging Spectroradiometer (MODIS)-Visible Infrared Imaging Radiometer Suite (VIIRS) Cloud Mask (MVCM), a cloud detection algorithm designed to facilitate continuity in cloud detection between the MODIS (Moderate Resolution Imaging Spectroradiometer) on the Aqua and Terra platforms and the series of VIIRS (Visible Infrared Imaging Radiometer Suite) instruments, beginning with the Soumi National Polar-orbiting Partnership (SNPP) spacecraft. It is based on the MODIS cloud mask that has been operating since 2000 with the launch of the Terra spacecraft (MOD35) and continuing in 2002 with Aqua (MYD35). The MVCM makes use of fourteen spectral bands that are common to both MODIS and VIIRS so as to create consistent cloud detection between the two instruments and across the years 2000–2020 and beyond. Through comparison data sets, including collocated Aqua MODIS and Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP) from the A-Train, this study was designed to assign statistical consistency benchmarks between the MYD35 and MVCM cloud masks. It is shown that the MVCM produces consistent cloud detection results between Aqua MODIS, SNPP VIIRS, and NOAA-20 VIIRS and that the quality is comparable to the standard Aqua MODIS cloud mask. Globally, comparisons with collocated CALIOP lidar show combined clear and cloudy sky hit rates of 88.2%, 87.5%, 86.8%, and 86.8% for MYD35, MVCM Aqua MODIS, MVCM SNPP VIIRS, and MVCM NOAA-20 VIIRS, respectively, for June through until August, 2018. For the same months and in the same order for 60S–60N, hit rates are 90.7%, 90.5%, 90.1%, and 90.3%. From the time series constructed from gridded daily means of 60S–60N cloud fractions, we found that the mean day-to-day cloud fraction differences/standard deviations in percent to be 0.68/0.55, 0.94/0.64, −0.20/0.50, and 0.44/0.82 for MVCM Aqua MODIS-MVCM SNPP VIIRS day and night, and MVCM NOAA-20 VIIRS-MVCM SNPP VIIRS day and night, respectively. It is seen that the MODIS and VIIRS 1.38 µm cirrus detection bands perform similarly but with MODIS detecting slightly more clouds in the middle to high levels of the troposphere and the VIIRS detecting more in the upper troposphere above 16 km. In the Arctic, MVCM Aqua MODIS and SNPP VIIRS reported cloud fraction differences of 0–3% during the mid-summer season and −3–4% during the mid-winter.

Combined Trajectory Planning and Tracking for Autonomous Vehicles on Deformable Terrains

2020 ◽  
Author(s):  
James Dallas ◽  
Yifan Weng ◽  
Tulga Ersal
Keyword(s):  
Neural Network ◽  
Trajectory Planning ◽  
Autonomous Vehicles ◽  
Performance Metrics ◽  
Soft Soil ◽  
Computation Time ◽  
Operating Conditions ◽  
The Neural Network ◽  
Vehicle Response ◽  
The Impact

Abstract In this work, a novel combined trajectory planner and tracking controller is developed for autonomous vehicles operating on off-road deformable terrains. Common approaches to trajectory planning and tracking often rely on model-dependent schemes, which utilize a simplified model to predict the impact of control inputs to future vehicle response. However, in an off-road context and especially on deformable terrains, accurately modeling the vehicle response for predictive purposes can be challenging due to the complexity of the tire-terrain interaction and limitations of state-of-the-art terramechanics models in terms of operating conditions, computation time, and continuous differentiability. To address this challenge and improve vehicle safety and performance through more accurate prediction of the plant response, in this paper, a nonlinear model predictive control framework is presented that accounts for terrain deformability explicitly using a neural network terramechanics model for deformable terrains. The utility of the proposed scheme is demonstrated on high fidelity simulations for a notional lightweight military vehicle on soft soil. It is shown that the neural network based controller can outperform a baseline Pacejka model based scheme by improving on performance metrics associated with the cost function. In more severe maneuvers, the neural network based controller can achieve sufficient fidelity as compared to the plant to complete maneuvers that lead to failure for the Pacejka based controller. Finally, it is demonstrated that the proposed framework is conducive to real-time implementability.

scholarly journals Development of a neural network model for cloud fraction detection using NASA-Aura OMI VIS radiance measurements

2013 ◽  
Vol 6 (1) ◽  
pp. 1649-1681
Author(s):  
G. Saponaro ◽  
P. Kolmonen ◽  
J. Karhunen ◽  
J. Tamminen ◽  
G. de Leeuw
Keyword(s):  
Neural Network ◽  
Satellite Image ◽  
Cloud Fraction ◽  
Electromagnetic Spectrum ◽  
Cloud Detection ◽  
Ozone Monitoring Instrument ◽  
Neural Network Approach ◽  
Imaging Spectrometer ◽  
Simultaneous Application ◽  
The Neural Network

Abstract. The discrimination of cloudy pixels is required in almost any estimate of a parameter retrieved from a satellite image in the ultraviolet (UV), visual (VIS) or infra-red (IR) parts of the electromagnetic spectrum. Also, the distincion of clouds within satellite imagery and the distribution of their micro-physical properties is essential to the understanding of radiative transfer through the atmosphere. This paper reports the development of neural network algorithms for cloud detection for the NASA-Aura Ozone Monitoring Instrument (OMI). We present and discuss the results obtained by training mathematical neural networks with simultaneous application to OMI and Aqua-MODerate Resolution Imaging Spectrometer (MODIS) data. The neural network delivers cloud fraction estimates in a fast and automated way. The developed neural network approach performs generally well in the training. Highly reflective surfaces, such as ice, snow, sun glint and desert, or atmospheric dust mislead the neural network to a wrong predicted cloud fraction.

scholarly journals The CM-SAF and FUB Cloud Detection Schemes for SEVIRI: Validation with Synoptic Data and Initial Comparison with MODIS and CALIPSO

2009 ◽  
Vol 48 (2) ◽  
pp. 301-316 ◽  
Author(s):  
M. Reuter ◽  
W. Thomas ◽  
P. Albert ◽  
M. Lockhoff ◽  
R. Weber ◽  
...  
Keyword(s):  
Water Cycle ◽  
Regional Scale ◽  
Global Scale ◽  
Detection Methods ◽  
Orthogonal Polarization ◽  
Cloud Detection ◽  
Imaging Spectrometer ◽  
Climate Monitoring ◽  
Initial Comparison ◽  
Cloud Mask

Abstract The Satellite Application Facility on Climate Monitoring (CM-SAF) is aiming to retrieve satellite-derived geophysical parameters suitable for climate monitoring. CM-SAF started routine operations in early 2007 and provides a climatology of parameters describing the global energy and water cycle on a regional scale and partially on a global scale. Here, the authors focus on the performance of cloud detection methods applied to measurements of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on the first Meteosat Second Generation geostationary spacecraft. The retrieved cloud mask is the basis for calculating the cloud fractional coverage (CFC) but is also mandatory for retrieving other geophysical parameters. Therefore, the quality of the cloud detection directly influences climate monitoring of many other parameters derived from spaceborne sensors. CM-SAF products and results of an alternative cloud coverage retrieval provided by the Institut für Weltraumwissenschaften of the Freie Universität in Berlin, Germany (FUB), were validated against synoptic measurements. Furthermore, and on the basis of case studies, an initial comparison was performed of CM-SAF results with results derived from the Moderate Resolution Imaging Spectrometer (MODIS) and from the Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP). Results show that the CFC from CM-SAF and FUB agrees well with synoptic data and MODIS data over midlatitudes but is underestimated over the tropics and overestimated toward the edges of the visible Earth disk.

scholarly journals Language switching training modulates the neural network of non-linguistic cognitive control

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0247100
Author(s):  
Mo Chen ◽  
Fengyang Ma ◽  
Zhaoqi Zhang ◽  
Shuhua Li ◽  
Man Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Cognitive Control ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Brain Regions ◽  
Anterior Cingulate ◽  
Language Switching ◽  
Dorsal Anterior Cingulate Cortex ◽  
The Neural Network ◽  
The Impact

Bilingual language experience, such as switching between languages, has been shown to shape both cognitive and neural mechanisms of non-linguistic cognitive control. However, the neural adaptations induced by language switching remain unclear. Using fMRI, the current study examined the impact of short-term language switching training on the neural network of domain-general cognitive control for unbalanced Chinese-English bilinguals. Effective connectivity maps were constructed by using the extended unified structural equation models (euSEM) within 10 common brain regions involved in both language control and domain-general cognitive control. Results showed that, the dorsal anterior cingulate cortex/pre-supplementary motor area (dACC/pre-SMA) lost connection from the right thalamus after training, suggesting that less neural connectivity was required to complete the same domain-general cognitive control task. These findings not only provide direct evidence for the modulation of language switching training on the neural interaction of domain-general cognitive control, but also have important implications for revealing the potential neurocognitive adaptation effects of specific bilingual language experiences.

scholarly journals A novel method for estimating shortwave direct radiative effect of above-cloud aerosols using CALIOP and MODIS data

2014 ◽  
Vol 7 (6) ◽  
pp. 1777-1789 ◽  
Author(s):  
Z. Zhang ◽  
K. Meyer ◽  
S. Platnick ◽  
L. Oreopoulos ◽  
D. Lee ◽  
...  
Keyword(s):  
Atlantic Ocean ◽  
Optical Depth ◽  
Radiative Forcing ◽  
Orthogonal Polarization ◽  
Radiative Effect ◽  
Cloud Optical Depth ◽  
Modis Data ◽  
Joint Histogram ◽  
The Impact ◽  
Southeastern Atlantic

Abstract. This paper describes an efficient and unique method for computing the shortwave direct radiative effect (DRE) of aerosol residing above low-level liquid-phase clouds using CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) and MODIS (Moderate Resolution Imaging Spectroradiometer) data. It addresses the overlap of aerosol and cloud rigorously by utilizing the joint histogram of cloud optical depth and cloud top pressure while also accounting for subgrid-scale variations of aerosols. The method is computationally efficient because of its use of grid-level cloud and aerosol statistics, instead of pixel-level products, and a precomputed look-up table based on radiative transfer calculations. We verify that for smoke and polluted dust over the southeastern Atlantic Ocean the method yields a seasonal mean instantaneous (approximately 13:30 local time) shortwave DRE of above-cloud aerosol (ACA) that generally agrees with a more rigorous pixel-level computation within 4%. We also estimate the impact of potential CALIOP aerosol optical depth (AOD) retrieval bias of ACA on DRE. We find that the regional and seasonal mean instantaneous DRE of ACA over southeastern Atlantic Ocean would increase, from the original value of 6.4 W m−2 based on operational CALIOP AOD to 9.6 W m−2 if CALIOP AOD retrievals are biased low by a factor of 1.5 (Meyer et al., 2013) and further to 30.9 W m−2 if CALIOP AOD retrievals are biased low by a factor of 5 as suggested in Jethva et al. (2014). In contrast, the instantaneous ACA radiative forcing efficiency (RFE) remains relatively invariant in all cases at about 53 W m−2 AOD−1, suggesting a near-linear relation between the instantaneous RFE and AOD. We also compute the annual mean instantaneous shortwave DRE of light-absorbing aerosols (i.e., smoke and polluted dust) over global oceans based on 4 years of CALIOP and MODIS data. We find that given an above-cloud aerosol type the optical depth of the underlying clouds plays a larger role than above-cloud AOD in the variability of the annual mean shortwave DRE of above-cloud light-absorbing aerosol. While we demonstrate our method using CALIOP and MODIS data, it can also be extended to other satellite data sets.

Bolt Tightening Using Impact Wrench Based on Neural Networks

2000 ◽  
Vol 12 (6) ◽  
pp. 706-711
Author(s):  
Toru Fujinaka ◽  
◽  
Hirofumi Nakano ◽  
Michifumi Yoshioka ◽  
Sigeru Omatu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Clamping Force ◽  
The Neural Network ◽  
The Impact

A method for controlling the tightening operation of bolts using an impact wrench is proposed, where the neural network is employed for achieving proper clamping force. The characteristics of the clamping force depend on the kind of work to which bolts are tightened, thus a neural network is used for classification of the work. The clamping force, which can only be measured during the test run, is estimated online, using another neural network. Then appropriate input to the actuator of the impact wrench is determined, based on the estimated value of the clamping force.

scholarly journals Retrieval and Validation of Cloud Top Temperature from the Geostationary Satellite INSAT-3D

2019 ◽  
Vol 11 (23) ◽  
pp. 2811 ◽  
Author(s):  
Lima ◽  
Prijith ◽  
Sesha Sai ◽  
Rao ◽  
Niranjan ◽  
...  
Keyword(s):  
Geostationary Satellite ◽  
Probability Of Detection ◽  
Quality Data ◽  
Orthogonal Polarization ◽  
Cloud Detection ◽  
Pixel Resolution ◽  
Clear Sky ◽  
Hit Rate ◽  
Cloud Mask ◽  
Cloud Top Temperature

Investigation of cloud top temperature (CTT) and its diurnal variation is highly reliant on high spatial and temporal resolution satellite data, which is lacking over the Indian region. An algorithm has been developed for detection of clouds and retrieval of CTT from the geostationary satellite INSAT-3D. These retrievals are validated (inter-compared) with collocated in-situ (satellite) measurements with specific intent to generate climate-quality data. The cloud detection algorithm employs nine different tests, in accordance with solar illumination, satellite angle and surface type conditions to generate pixel-resolution cloud mask. Validation of cloud mask with cloud-aerosol lidar with orthogonal polarization (CALIOP) shows that probability of detection (POD) of cloudy (clear) sky is 81% (85%), with 83% hit rate. The algorithm is also implemented on similar channels of moderate resolution imaging spectroradiometer (MODIS), which provides 88% (83%) POD of cloudy (clear) sky, with 86% hit rate. CTT retrieval is done at the pixel level, for all cloud pixels, by employing appropriate methods for various types of clouds. Comparison of CTT with radiosonde and cloud-aerosol lidar and infrared pathfinder satellite observations (CALIPSO) shows mean absolute error less than 3%. The study also examines sensitivity of retrieved CTT to the cloud classification scheme and retrieval criteria. Validation results and their close agreements with those of similar satellites demonstrate the reliability of the retrieved product for climate studies.

scholarly journals A practical demonstration on AMSU retrieval precision for upper tropospheric humidity by a non-linear multi-channel regression method

2005 ◽  
Vol 5 (2) ◽  
pp. 451-459 ◽  
Author(s):  
C. Jiménez ◽  
P. Eriksson ◽  
V. O. John ◽  
S. A. Buehler
Keyword(s):  
Neural Network ◽  
Relative Humidity ◽  
Single Channel ◽  
Weighting Function ◽  
Traditional Approach ◽  
Training Dataset ◽  
The Neural Network ◽  
Radiosonde Station ◽  
Neural Network Algorithm ◽  
The Impact

Abstract. A neural network algorithm inverting selected channels from the Advance Microwave Sounding Unit instruments AMSU-A and AMSU-B was applied to retrieve layer averaged relative humidity. The neural network was trained with a global synthetic dataset representing clear-sky conditions. A precision of around 6% was obtained when retrieving global simulated radiances, the precision deteriorated less than 1% when real mid-latitude AMSU radiances were inverted and compared with co-located data from a radiosonde station. The 6% precision outperforms by 1% the reported precision estimate from a linear single-channel regression between radiance and weighting function averaged relative humidity, the more traditional approach to exploit AMSU data. Added advantages are not only a better precision; the AMSU-B humidity information is more optimally exploited by including temperature information from AMSU-A channels; and the layer averaged humidity is a more physical quantity than the weighted humidity, for comparison with other datasets. The training dataset proved adequate for inverting real radiances from a mid-latitude site, but it is limited by not considering the impact of clouds or surface emissivity changes, and further work is needed in this direction for further validation of the precision estimates.

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