Cloud Occurrence Measurements Over Sea during the 2nd 7 Southeast Asian Studies (7SEAS) Field Campaign in Palawan Archipelago

NASA Astrophysics Data System (ADS)

Antioquia, C. T.; Uy, S. N.; Caballa, K.; Lagrosas, N.

2014-12-01

Ground based sky imaging cameras have been used to measure cloud cover over an area to aid in radiation budget models. During daytime, certain clouds tend to help decrease atmospheric temperature by obstructing sunrays in the atmosphere. Thus, the detection of clouds plays an important role in the formulation of radiation budget in the atmosphere. In this study, a wide angled sky imager (GoPro Hero 2) was brought on board M/Y Vasco to detect and quantity cloud occurrence over sea during the 2nd 7SEAS field campaign. The camera is just a part of a number of scientific instruments used to measure weather, aerosol chemistry and solar radiation among others. The data collection started during the departure from Manila Bay on 05 September 2012 and went on until the end of the cruise (29 September 2012). The camera was placed in a weather-proof box that is then affixed on a steel mast where other instruments are also attached during the cruise. The data has a temporal resolution of 1 minute, and each image is 500x666 pixels in size. Fig. 1a shows the track of the ship during the cruise. The red, blue, hue, saturation, and value of the pixels are analysed for cloud occurrence. A pixel is considered to "contain" thick cloud if it passes all four threshold parameters (R-B, R/B, R-B/R+B, HSV; R is the red pixel color value, blue is the blue pixel color value, and HSV is the hue saturation value of the pixel) and considered thin cloud if it passes two or three parameters. Fig. 1b shows the daily analysis of cloud occurrence. Cloud occurrence here is quantified as the ratio of the pixels with cloud to the total number of pixels in the data image. The average cloud cover for the days included in this dataset is 87%. These measurements show a big contrast when compared to cloud cover over land (Manila Observatory) which is usually around 67%. During the duration of the cruise, only one day (September 6) has an average cloud occurrence below 50%; the rest of the days have averages of 66% or higher - 98% being the highest. This result would then give a general trend of how cloud occurrences over land and over sea differ in the South East Asian region. In this study, these cloud occurrences come from local convection and clouds brought about by Southwest Monsoon winds.

A Framework Based on 2-D Taylor Expansion for Quantifying the Impacts of Sub-Pixel Reflectance Variance and Covariance on Cloud Optical Thickness and Effective Radius Retrievals Based on the Bi-Spectral Method

NASA Technical Reports Server (NTRS)

Zhang, Z.; Werner, F.; Cho, H. -M.; Wind, G.; Platnick, S.; Ackerman, A. S.; Di Girolamo, L.; Marshak, A.; Meyer, Kerry

2016-01-01

The bi-spectral method retrieves cloud optical thickness and cloud droplet effective radius simultaneously from a pair of cloud reflectance observations, one in a visible or near-infrared (VISNIR) band and the other in a shortwave infrared (SWIR) band. A cloudy pixel is usually assumed to be horizontally homogeneous in the retrieval. Ignoring sub-pixel variations of cloud reflectances can lead to a significant bias in the retrieved and re. In the literature, the retrievals of and re are often assumed to be independent and considered separately when investigating the impact of sub-pixel cloud reflectance variations on the bi-spectral method. As a result, the impact on is contributed only by the sub-pixel variation of VISNIR band reflectance and the impact on re only by the sub-pixel variation of SWIR band reflectance. In our new framework, we use the Taylor expansion of a two-variable function to understand and quantify the impacts of sub-pixel variances of VISNIR and SWIR cloud reflectances and their covariance on the and re retrievals. This framework takes into account the fact that the retrievals are determined by both VISNIR and SWIR band observations in a mutually dependent way. In comparison with previous studies, it provides a more comprehensive understanding of how sub-pixel cloud reflectance variations impact the and re retrievals based on the bi-spectral method. In particular, our framework provides a mathematical explanation of how the sub-pixel variation in VISNIR band influences the re retrieval and why it can sometimes outweigh the influence of variations in the SWIR band and dominate the error in re retrievals, leading to a potential contribution of positive bias to the re retrieval. We test our framework using synthetic cloud fields from a large-eddy simulation and real observations from Moderate Resolution Imaging Spectroradiometer. The predicted results based on our framework agree very well with the numerical simulations. Our framework can be used to estimate the retrieval uncertainty from sub-pixel reflectance variations in operational satellite cloud products and to help understand the differences in and re retrievals between two instruments.

Retrieval of Cloud Properties for Partially Cloud-Filled Pixels During CRYSTAL-FACE

NASA Astrophysics Data System (ADS)

Nguyen, L.; Minnis, P.; Smith, W. L.; Khaiyer, M. M.; Heck, P. W.; Sun-Mack, S.; Uttal, T.; Comstock, J.

2003-12-01

Partially cloud-filled pixels can be a significant problem for remote sensing of cloud properties. Generally, the optical depth and effective particle sizes are often too small or too large, respectively, when derived from radiances that are assumed to be overcast but contain radiation from both clear and cloud areas within the satellite imager field of view. This study presents a method for reducing the impact of such partially cloud field pixels by estimating the cloud fraction within each pixel using higher resolution visible (VIS, 0.65mm) imager data. Although the nominal resolution for most channels on the Geostationary Operational Environmental Satellite (GOES) imager and the Moderate Resolution Imaging Spectroradiometer (MODIS) on Terra are 4 and 1 km, respectively, both instruments also take VIS channel data at 1 km and 0.25 km, respectively. Thus, it may be possible to obtain an improved estimate of cloud fraction within the lower resolution pixels by using the information contained in the higher resolution VIS data. GOES and MODIS multi-spectral data, taken during the Cirrus Regional Study of Tropical Anvils and Cirrus Layers - Florida Area Cirrus Experiment (CRYSTAL-FACE), are analyzed with the algorithm used for the Atmospheric Radiation Measurement Program (ARM) and the Clouds and Earth's Radiant Energy System (CERES) to derive cloud amount, temperature, height, phase, effective particle size, optical depth, and water path. Normally, the algorithm assumes that each pixel is either entirely clear or cloudy. In this study, a threshold method is applied to the higher resolution VIS data to estimate the partial cloud fraction within each low-resolution pixel. The cloud properties are then derived from the observed low-resolution radiances using the cloud cover estimate to properly extract the radiances due only to the cloudy part of the scene. This approach is applied to both GOES and MODIS data to estimate the improvement in the retrievals for each resolution. Results are compared with the radar reflectivity techniques employed by the NOAA ETL MMCR and the PARSL 94 GHz radars located at the CRYSTAL-FACE Eastern & Western Ground Sites, respectively. This technique is most likely to yield improvements for low and midlevel layer clouds that have little thermal variability in cloud height.

The MODIS Cloud Optical and Microphysical Products: Collection 6 Up-dates and Examples From Terra and Aqua

NASA Technical Reports Server (NTRS)

Platnick, Steven; Meyer, Kerry G.; King, Michael D.; Wind, Galina; Amarasinghe, Nandana; Marchant, Benjamin G.; Arnold, G. Thomas; Zhang, Zhibo; Hubanks, Paul A.; Holz, Robert E.;

The MODIS cloud optical and microphysical products: Collection 6 updates and examples from Terra and Aqua.

PubMed

Platnick, Steven; Meyer, Kerry G; King, Michael D; Wind, Galina; Amarasinghe, Nandana; Marchant, Benjamin; Arnold, G Thomas; Zhang, Zhibo; Hubanks, Paul A; Holz, Robert E; Yang, Ping; Ridgway, William L; Riedi, Jérôme

2017-01-01

The MODIS Level-2 cloud product (Earth Science Data Set names MOD06 and MYD06 for Terra and Aqua MODIS, respectively) provides pixel-level retrievals of cloud-top properties (day and night pressure, temperature, and height) and cloud optical properties (optical thickness, effective particle radius, and water path for both liquid water and ice cloud thermodynamic phases-daytime only). Collection 6 (C6) reprocessing of the product was completed in May 2014 and March 2015 for MODIS Aqua and Terra, respectively. Here we provide an overview of major C6 optical property algorithm changes relative to the previous Collection 5 (C5) product. Notable C6 optical and microphysical algorithm changes include: (i) new ice cloud optical property models and a more extensive cloud radiative transfer code lookup table (LUT) approach, (ii) improvement in the skill of the shortwave-derived cloud thermodynamic phase, (iii) separate cloud effective radius retrieval datasets for each spectral combination used in previous collections, (iv) separate retrievals for partly cloudy pixels and those associated with cloud edges, (v) failure metrics that provide diagnostic information for pixels having observations that fall outside the LUT solution space, and (vi) enhanced pixel-level retrieval uncertainty calculations. The C6 algorithm changes collectively can result in significant changes relative to C5, though the magnitude depends on the dataset and the pixel's retrieval location in the cloud parameter space. Example Level-2 granule and Level-3 gridded dataset differences between the two collections are shown. While the emphasis is on the suite of cloud optical property datasets, other MODIS cloud datasets are discussed when relevant.

A comparison of Aqua MODIS ice and liquid water cloud physical and optical properties between collection 6 and collection 5.1: Pixel-to-pixel comparisons

NASA Astrophysics Data System (ADS)

Yi, Bingqi; Rapp, Anita D.; Yang, Ping; Baum, Bryan A.; King, Michael D.

2017-04-01

We compare differences in ice and liquid water cloud physical and optical properties between Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) collection 6 (C6) and collection 5.1 (C51). The C6 cloud products changed significantly due to improved calibration, improvements based on comparisons with the Cloud-Aerosol Lidar with Orthogonal Polarization, treatment of subpixel liquid water clouds, introduction of a roughened ice habit for C6 rather than the use of smooth ice particles in C51, and more. The MODIS cloud products form a long-term data set for analysis, modeling, and various purposes. Thus, it is important to understand the impact of the changes. Two cases are considered for C6 to C51 comparisons. Case 1 considers pixels with valid cloud retrievals in both C6 and C51, while case 2 compares all valid cloud retrievals in each collection. One year (2012) of level-2 MODIS cloud products are examined, including cloud effective radius (CER), optical thickness (COT), water path, cloud top pressure (CTP), cloud top temperature, and cloud fraction. Large C6-C51 differences are found in the ice CER (regionally, as large as 15 μm) and COT (decrease in annual average by approximately 25%). Liquid water clouds have higher CTP in marine stratocumulus regions in C6 but lower CTP globally (-5 hPa), and there are 66% more valid pixels in C6 (case 2) due to the treatment of pixels with subpixel clouds. Simulated total cloud radiative signatures from C51 and C6 are compared to Clouds and the Earth's Radiant Energy System Energy Balanced And Filled (EBAF) product. The C6 CREs compare more closely with the EBAF than the C51 counterparts.

Improvement in the cloud mask for Terra MODIS mitigated by electronic crosstalk correction in the 6.7 μm and 8.5 μm channels

NASA Astrophysics Data System (ADS)

Sun, Junqiang; Madhavan, S.; Wang, M.

2016-09-01

MODerate resolution Imaging Spectroradiometer (MODIS), a remarkable heritage sensor in the fleet of Earth Observing System for the National Aeronautics and Space Administration (NASA) is in space orbit on two spacecrafts. They are the Terra (T) and Aqua (A) platforms which tracks the Earth in the morning and afternoon orbits. T-MODIS has continued to operate over 15 years easily surpassing the 6 year design life time on orbit. Of the several science products derived from MODIS, one of the primary derivatives is the MODIS Cloud Mask (MOD035). The cloud mask algorithm incorporates several of the MODIS channels in both reflective and thermal infrared wavelengths to identify cloud pixels from clear sky. Two of the thermal infrared channels used in detecting clouds are the 6.7 μm and 8.5 μm. Based on a difference threshold with the 11 μm channel, the 6.7 μm channel helps in identifying thick high clouds while the 8.5 μm channel being useful for identifying thin clouds. Starting 2010, it had been observed in the cloud mask products that several pixels have been misclassified due to the change in the thermal band radiometry. The long-term radiometric changes in these thermal channels have been attributed to the electronic crosstalk contamination. In this paper, the improvement in cloud detection using the 6.7 μm and 8.5 μm channels are demonstrated using the electronic crosstalk correction. The electronic crosstalk phenomena analysis and characterization were developed using the regular moon observation of MODIS and reported in several works. The results presented in this paper should significantly help in improving the MOD035 product, maintaining the long term dataset from T-MODIS which is important for global change monitoring.

A framework for quantifying the impacts of sub-pixel reflectance variance and covariance on cloud optical thickness and effective radius retrievals based on the bi-spectral method

NASA Astrophysics Data System (ADS)

Zhang, Z.; Werner, F.; Cho, H.-M.; Wind, G.; Platnick, S.; Ackerman, A. S.; Di Girolamo, L.; Marshak, A.; Meyer, Kerry

2017-02-01

The so-called bi-spectral method retrieves cloud optical thickness (τ) and cloud droplet effective radius (re) simultaneously from a pair of cloud reflectance observations, one in a visible or near infrared (VIS/NIR) band and the other in a shortwave-infrared (SWIR) band. A cloudy pixel is usually assumed to be horizontally homogeneous in the retrieval. Ignoring sub-pixel variations of cloud reflectances can lead to a significant bias in the retrieved τ and re. In this study, we use the Taylor expansion of a two-variable function to understand and quantify the impacts of sub-pixel variances of VIS/NIR and SWIR cloud reflectances and their covariance on the τ and re retrievals. This framework takes into account the fact that the retrievals are determined by both VIS/NIR and SWIR band observations in a mutually dependent way. In comparison with previous studies, it provides a more comprehensive understanding of how sub-pixel cloud reflectance variations impact the τ and re retrievals based on the bi-spectral method. In particular, our framework provides a mathematical explanation of how the sub-pixel variation in VIS/NIR band influences the re retrieval and why it can sometimes outweigh the influence of variations in the SWIR band and dominate the error in re retrievals, leading to a potential contribution of positive bias to the re retrieval.

A Framework for Quantifying the Impacts of Sub-Pixel Reflectance Variance and Covariance on Cloud Optical Thickness and Effective Radius Retrievals Based on the Bi-Spectral Method.

NASA Technical Reports Server (NTRS)

Zhang, Z; Werner, F.; Cho, H. -M.; Wind, Galina; Platnick, S.; Ackerman, A. S.; Di Girolamo, L.; Marshak, A.; Meyer, Kerry

2017-01-01

The so-called bi-spectral method retrieves cloud optical thickness (t) and cloud droplet effective radius (re) simultaneously from a pair of cloud reflectance observations, one in a visible or near infrared (VIS/NIR) band and the other in a shortwave-infrared (SWIR) band. A cloudy pixel is usually assumed to be horizontally homogeneous in the retrieval. Ignoring sub-pixel variations of cloud reflectances can lead to a significant bias in the retrieved t and re. In this study, we use the Taylor expansion of a two-variable function to understand and quantify the impacts of sub-pixel variances of VIS/NIR and SWIR cloud reflectances and their covariance on the t and re retrievals. This framework takes into account the fact that the retrievals are determined by both VIS/NIR and SWIR band observations in a mutually dependent way. In comparison with previous studies, it provides a more comprehensive understanding of how sub-pixel cloud reflectance variations impact the t and re retrievals based on the bi-spectral method. In particular, our framework provides a mathematical explanation of how the sub-pixel variation in VIS/NIR band influences the re retrieval and why it can sometimes outweigh the influence of variations in the SWIR band and dominate the error in re retrievals, leading to a potential contribution of positive bias to the re retrieval.

Automated cloud and shadow detection and filling using two-date Landsat imagery in the United States

USGS Publications Warehouse

Jin, Suming; Homer, Collin G.; Yang, Limin; Xian, George; Fry, Joyce; Danielson, Patrick; Townsend, Philip A.

2013-01-01

A simple, efficient, and practical approach for detecting cloud and shadow areas in satellite imagery and restoring them with clean pixel values has been developed. Cloud and shadow areas are detected using spectral information from the blue, shortwave infrared, and thermal infrared bands of Landsat Thematic Mapper or Enhanced Thematic Mapper Plus imagery from two dates (a target image and a reference image). These detected cloud and shadow areas are further refined using an integration process and a false shadow removal process according to the geometric relationship between cloud and shadow. Cloud and shadow filling is based on the concept of the Spectral Similarity Group (SSG), which uses the reference image to find similar alternative pixels in the target image to serve as replacement values for restored areas. Pixels are considered to belong to one SSG if the pixel values from Landsat bands 3, 4, and 5 in the reference image are within the same spectral ranges. This new approach was applied to five Landsat path/rows across different landscapes and seasons with various types of cloud patterns. Results show that almost all of the clouds were captured with minimal commission errors, and shadows were detected reasonably well. Among five test scenes, the lowest producer's accuracy of cloud detection was 93.9% and the lowest user's accuracy was 89%. The overall cloud and shadow detection accuracy ranged from 83.6% to 99.3%. The pixel-filling approach resulted in a new cloud-free image that appears seamless and spatially continuous despite differences in phenology between the target and reference images. Our methods offer a straightforward and robust approach for preparing images for the new 2011 National Land Cover Database production.

Method for validating cloud mask obtained from satellite measurements using ground-based sky camera.

PubMed

Letu, Husi; Nagao, Takashi M; Nakajima, Takashi Y; Matsumae, Yoshiaki

2014-11-01

Error propagation in Earth's atmospheric, oceanic, and land surface parameters of the satellite products caused by misclassification of the cloud mask is a critical issue for improving the accuracy of satellite products. Thus, characterizing the accuracy of the cloud mask is important for investigating the influence of the cloud mask on satellite products. In this study, we proposed a method for validating multiwavelength satellite data derived cloud masks using ground-based sky camera (GSC) data. First, a cloud cover algorithm for GSC data has been developed using sky index and bright index. Then, Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data derived cloud masks by two cloud-screening algorithms (i.e., MOD35 and CLAUDIA) were validated using the GSC cloud mask. The results indicate that MOD35 is likely to classify ambiguous pixels as "cloudy," whereas CLAUDIA is likely to classify them as "clear." Furthermore, the influence of error propagations caused by misclassification of the MOD35 and CLAUDIA cloud masks on MODIS derived reflectance, brightness temperature, and normalized difference vegetation index (NDVI) in clear and cloudy pixels was investigated using sky camera data. It shows that the influence of the error propagation by the MOD35 cloud mask on the MODIS derived monthly mean reflectance, brightness temperature, and NDVI for clear pixels is significantly smaller than for the CLAUDIA cloud mask; the influence of the error propagation by the CLAUDIA cloud mask on MODIS derived monthly mean cloud products for cloudy pixels is significantly smaller than that by the MOD35 cloud mask.

Effect of Clouds on Apertures of Space-based Air Fluorescence Detectors

NASA Technical Reports Server (NTRS)

Sokolsky, P.; Krizmanic, J.

2003-01-01

Space-based ultra-high-energy cosmic ray detectors observe fluorescence light from extensive air showers produced by these particles in the troposphere. Clouds can scatter and absorb this light and produce systematic errors in energy determination and spectrum normalization. We study the possibility of using IR remote sensing data from MODIS and GOES satellites to delimit clear areas of the atmosphere. The efficiency for detecting ultra-high-energy cosmic rays whose showers do not intersect clouds is determined for real, night-time cloud scenes. We use the MODIS SST cloud mask product to define clear pixels for cloud scenes along the equator and use the OWL Monte Carlo to generate showers in the cloud scenes. We find the efficiency for cloud-free showers with closest approach of three pixels to a cloudy pixel is 6.5% exclusive of other factors. We conclude that defining a totally cloud-free aperture reduces the sensitivity of space-based fluorescence detectors to unacceptably small levels.

Towards a true aerosol-and-cloud retrieval scheme

NASA Astrophysics Data System (ADS)

Thomas, Gareth; Poulsen, Caroline; Povey, Adam; McGarragh, Greg; Jerg, Matthias; Siddans, Richard; Grainger, Don

2014-05-01

The Optimal Retrieval of Aerosol and Cloud (ORAC) - formally the Oxford-RAL Aerosol and Cloud retrieval - offers a framework that can provide consistent and well characterised properties of both aerosols and clouds from a range of imaging satellite instruments. Several practical issues stand in the way of achieving the potential of this combined scheme however; in particular the sometimes conflicting priorities and requirements of aerosol and cloud retrieval problems, and the question of the unambiguous identification of aerosol and cloud pixels. This presentation will present recent developments made to the ORAC scheme for both aerosol and cloud, and detail how these are being integrated into a single retrieval framework. The implementation of a probabilistic method for pixel identification will also be presented, for both cloud detection and aerosol/cloud type selection. The method is based on Bayesian methods applied the optimal estimation retrieval output of ORAC and is particularly aimed at providing additional information in the so-called "twilight zone", where pixels can't be unambiguously identified as either aerosol or cloud and traditional cloud or aerosol products do not provide results.

Remote Sensing of Lake Ice Phenology in Alaska

NASA Astrophysics Data System (ADS)

Zhang, S.; Pavelsky, T.

2017-12-01

Lake ice phenology (e.g. ice break-up and freeze-up timing) in Alaska is potentially sensitive to climate change. However, there are few current lake ice records in this region, which hinders the comprehensive understanding of interactions between climate change and lake processes. To provide a lake ice database with over a comparatively long time period (2000 - 2017) and large spatial coverage (4000+ lakes) in Alaska, we have developed an algorithm to detect the timing of lake ice using Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data. This approach generally consists of three major steps. First, we use a cloud mask (MOD09GA) to filter out satellite images with heavy cloud contamination. Second, daily MODIS reflectance values (MOD09GQ) of lake surface are used to extract ice pixels from water pixels. The ice status of lakes can be further identified based on the fraction of ice pixels. Third, to improve the accuracy of ice phenology detection, we execute post-processing quality control to reduce false ice events caused by outliers. We validate the proposed algorithm over six lakes by comparing with Landsat-based reference data. Validation results indicate a high correlation between the MODIS results and reference data, with normalized root mean square error (NRMSE) ranging from 1.7% to 4.6%. The time series of this lake ice product is then examined to analyze the spatial and temporal patterns of lake ice phenology.

Cloud cover analysis with Arctic Advanced Very High Resolution Radiometer data. II - Classification with spectral and textural measures

NASA Technical Reports Server (NTRS)

Key, J.

1990-01-01

The spectral and textural characteristics of polar clouds and surfaces for a 7-day summer series of AVHRR data in two Arctic locations are examined, and the results used in the development of a cloud classification procedure for polar satellite data. Since spatial coherence and texture sensitivity tests indicate that a joint spectral-textural analysis based on the same cell size is inappropriate, cloud detection with AVHRR data and surface identification with passive microwave data are first done on the pixel level as described by Key and Barry (1989). Next, cloud patterns within 250-sq-km regions are described, then the spectral and local textural characteristics of cloud patterns in the image are determined and each cloud pixel is classified by statistical methods. Results indicate that both spectral and textural features can be utilized in the classification of cloudy pixels, although spectral features are most useful for the discrimination between cloud classes.

Retrieving Aerosol in a Cloudy Environment: Aerosol Availability as a Function of Spatial and Temporal Resolution

NASA Technical Reports Server (NTRS)

Remer, Lorraine A.; Mattoo, Shana; Levy, Robert C.; Heidinger, Andrew; Pierce, R. Bradley; Chin, Mian

2011-01-01

The challenge of using satellite observations to retrieve aerosol properties in a cloudy environment is to prevent contamination of the aerosol signal from clouds, while maintaining sufficient aerosol product yield to satisfy specific applications. We investigate aerosol retrieval availability at different instrument pixel resolutions, using the standard MODIS aerosol cloud mask applied to MODIS data and a new GOES-R cloud mask applied to GOES data for a domain covering North America and surrounding oceans. Aerosol availability is not the same as the cloud free fraction and takes into account the technqiues used in the MODIS algorithm to avoid clouds, reduce noise and maintain sufficient numbers of aerosol retrievals. The inherent spatial resolution of each instrument, 0.5x0.5 km for MODIS and 1x1 km for GOES, is systematically degraded to 1x1 km, 2x2 km, 4x4 km and 8x8 km resolutions and then analyzed as to how that degradation would affect the availability of an aerosol retrieval, assuming an aerosol product resolution at 8x8 km. The results show that as pixel size increases, availability decreases until at 8x8 km 70% to 85% of the retrievals available at 0.5 km have been lost. The diurnal pattern of aerosol retrieval availability examined for one day in the summer suggests that coarse resolution sensors (i.e., 4x4 km or 8x8 km) may be able to retrieve aerosol early in the morning that would otherwise be missed at the time of current polar orbiting satellites, but not the diurnal aerosol properties due to cloud cover developed during the day. In contrast finer resolution sensors (i.e., 1x1 km or 2x2 km) have much better opportunity to retrieve aerosols in the partly cloudy scenes and better chance of returning the diurnal aerosol properties. Large differences in the results of the two cloud masks designed for MODIS aerosol and GOES cloud products strongly reinforce that cloud masks must be developed with specific purposes in mind and that a generic cloud mask applied to an independent aerosol retrieval will likely fail.

How Often and Why MODIS Cloud Property Retrievals Fail for Liquid-Phase Clouds over Ocean? a Comprehensive Analysis Based on a-Train Observations

NASA Astrophysics Data System (ADS)

Zhang, Z.; Cho, H. M.; Platnick, S. E.; Meyer, K.; Lebsock, M. D.

2014-12-01

The cloud optical thickness (τ) and droplet effective radius (re) are two key cloud parameters retrieved by MODIS (Moderate Resolution Imaging Spectroradiometer). These MODIS cloud products are widely used in a broad range of earth system science applications. In this paper, we present a comprehensive analysis of the failed cloud τ and/or re retrievals for liquid-phase clouds over ocean in the Collection 6 MODIS cloud product. The main findings from this study are summarized as follows: MODIS retrieval failure rates for marine boundary layer (MBL) clouds have a strong dependence on the spectral combination used for retrieval (e.g., 0.86 + 2.1 µm vs. 0.8 + 3.7 µm) and the cloud morphology (i.e., "good" pixels vs. partly cloudy (PCL) pixels). Combining all clear-sky-restoral (CSR) categories (CSR=0,1 and 3), the 0.86 + 2.1 µm and 0.86 + 3.7 µm spectral combinations have an overall failure rate of about 20% and 12%, respectively (See figure below). The PCL pixels (CSR=1 & 3) have significantly higher failure rates and contribute more to the total failure population than the "good" (CSR=0) pixels. The majority of the failed retrievals are caused by the re too large failure, which explains about 85% and 70% of the failed 0.86 + 2.1 µm and 0.86 + 3.7 µm retrievals, respectively. The remaining failures are either due to the re too small failure or τ retrieval failure. The geographical distribution of failure rates has a significant dependence on cloud regime, lower over the coastal stratocumulus cloud regime and higher over the broken trade-wind cumulus cloud regime over open oceans. Enhanced retrieval failure rates are found when MBL clouds have high sub-pixel inhomogeneity , or are located at special Sun-satellite viewing geometries, such as sunglint, large viewing or solar zenith angle, or cloudbow and glory angles, or subject to cloud masking, cloud overlapping and/or cloud phase retrieval issues. About 80% of the failure retrievals can be attributed to at least one or more potential reasons mentioned above. Collocated radar reflectivity observations from CloudSat suggest that the remaining 20% are unlikely to be retrieval artifacts, but reflection of true cloud microphysics, i.e., the true is either truly very small or very large.

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

NASA Technical Reports Server (NTRS)

Joiner, J.; Vasilkov, A. P.; Bhartia, P. K.; Wind, G.; Platnick, S.; Menzel, W. P.

2010-01-01

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.

Automatic Detection of Clouds and Shadows Using High Resolution Satellite Image Time Series

NASA Astrophysics Data System (ADS)

Champion, Nicolas

2016-06-01

Detecting clouds and their shadows is one of the primaries steps to perform when processing satellite images because they may alter the quality of some products such as large-area orthomosaics. The main goal of this paper is to present the automatic method developed at IGN-France for detecting clouds and shadows in a sequence of satellite images. In our work, surface reflectance orthoimages are used. They were processed from initial satellite images using a dedicated software. The cloud detection step consists of a region-growing algorithm. Seeds are firstly extracted. For that purpose and for each input ortho-image to process, we select the other ortho-images of the sequence that intersect it. The pixels of the input ortho-image are secondly labelled seeds if the difference of reflectance (in the blue channel) with overlapping ortho-images is bigger than a given threshold. Clouds are eventually delineated using a region-growing method based on a radiometric and homogeneity criterion. Regarding the shadow detection, our method is based on the idea that a shadow pixel is darker when comparing to the other images of the time series. The detection is basically composed of three steps. Firstly, we compute a synthetic ortho-image covering the whole study area. Its pixels have a value corresponding to the median value of all input reflectance ortho-images intersecting at that pixel location. Secondly, for each input ortho-image, a pixel is labelled shadows if the difference of reflectance (in the NIR channel) with the synthetic ortho-image is below a given threshold. Eventually, an optional region-growing step may be used to refine the results. Note that pixels labelled clouds during the cloud detection are not used for computing the median value in the first step; additionally, the NIR input data channel is used to perform the shadow detection, because it appeared to better discriminate shadow pixels. The method was tested on times series of Landsat 8 and Pléiades-HR images and our first experiments show the feasibility to automate the detection of shadows and clouds in satellite image sequences.

The MODIS cloud optical and microphysical products: Collection 6 updates and examples from Terra and Aqua

PubMed Central

Platnick, Steven; Meyer, Kerry G.; King, Michael D.; Wind, Galina; Amarasinghe, Nandana; Marchant, Benjamin; Arnold, G. Thomas; Zhang, Zhibo; Hubanks, Paul A.; Holz, Robert E.; Yang, Ping; Ridgway, William L.; Riedi, Jérôme

2018-01-01

The MODIS Level-2 cloud product (Earth Science Data Set names MOD06 and MYD06 for Terra and Aqua MODIS, respectively) provides pixel-level retrievals of cloud-top properties (day and night pressure, temperature, and height) and cloud optical properties (optical thickness, effective particle radius, and water path for both liquid water and ice cloud thermodynamic phases–daytime only). Collection 6 (C6) reprocessing of the product was completed in May 2014 and March 2015 for MODIS Aqua and Terra, respectively. Here we provide an overview of major C6 optical property algorithm changes relative to the previous Collection 5 (C5) product. Notable C6 optical and microphysical algorithm changes include: (i) new ice cloud optical property models and a more extensive cloud radiative transfer code lookup table (LUT) approach, (ii) improvement in the skill of the shortwave-derived cloud thermodynamic phase, (iii) separate cloud effective radius retrieval datasets for each spectral combination used in previous collections, (iv) separate retrievals for partly cloudy pixels and those associated with cloud edges, (v) failure metrics that provide diagnostic information for pixels having observations that fall outside the LUT solution space, and (vi) enhanced pixel-level retrieval uncertainty calculations. The C6 algorithm changes collectively can result in significant changes relative to C5, though the magnitude depends on the dataset and the pixel’s retrieval location in the cloud parameter space. Example Level-2 granule and Level-3 gridded dataset differences between the two collections are shown. While the emphasis is on the suite of cloud optical property datasets, other MODIS cloud datasets are discussed when relevant. PMID:29657349

A one year Landsat 8 conterminous United States study of spatial and temporal patterns of cirrus and non-cirrus clouds and implications for the long term Landsat archive.

NASA Astrophysics Data System (ADS)

Kovalskyy, V.; Roy, D. P.

2014-12-01

The successful February 2013 launch of the Landsat 8 satellite is continuing the 40+ year legacy of the Landsat mission. The payload includes the Operational Land Imager (OLI) that has a new 1370 mm band designed to monitor cirrus clouds and the Thermal Infrared Sensor (TIRS) that together provide 30m low, medium and high confidence cloud detections and 30m low and high confidence cirrus cloud detections. A year of Landsat 8 data over the Conterminous United States (CONUS), composed of 11,296 acquisitions, was analyzed comparing the spatial and temporal incidence of these cloud and cirrus states. This revealed (i) 36.5% of observations were detected with high confidence cloud with spatio-temporal patterns similar to those observed by previous Landsat 7 cloud analyses, (ii) 29.2% were high confidence cirrus, (iii) 20.9% were both high confidence cloud and high confidence cirrus, (iv) 8.3% were detected as high confidence cirrus but not as high confidence cloud. The results illustrate the value of the cirrus band for improved Landsat 8 terrestrial monitoring but imply that the historical CONUS Landsat archive has a similar 8% of undetected cirrus contaminated pixels. The implications for long term Landsat time series records, including the global Web Enabled Landsat Data (WELD) product record, are discussed.

Automated, per pixel Cloud Detection from High-Resolution VNIR Data

NASA Technical Reports Server (NTRS)

Varlyguin, Dmitry L.

2007-01-01

CASA is a fully automated software program for the per-pixel detection of clouds and cloud shadows from medium- (e.g., Landsat, SPOT, AWiFS) and high- (e.g., IKONOS, QuickBird, OrbView) resolution imagery without the use of thermal data. CASA is an object-based feature extraction program which utilizes a complex combination of spectral, spatial, and contextual information available in the imagery and the hierarchical self-learning logic for accurate detection of clouds and their shadows.

Multitemporal Snow Cover Mapping in Mountainous Terrain for Landsat Climate Data Record Development

NASA Technical Reports Server (NTRS)

Crawford, Christopher J.; Manson, Steven M.; Bauer, Marvin E.; Hall, Dorothy K.

2013-01-01

A multitemporal method to map snow cover in mountainous terrain is proposed to guide Landsat climate data record (CDR) development. The Landsat image archive including MSS, TM, and ETM+ imagery was used to construct a prototype Landsat snow cover CDR for the interior northwestern United States. Landsat snow cover CDRs are designed to capture snow-covered area (SCA) variability at discrete bi-monthly intervals that correspond to ground-based snow telemetry (SNOTEL) snow-water-equivalent (SWE) measurements. The June 1 bi-monthly interval was selected for initial CDR development, and was based on peak snowmelt timing for this mountainous region. Fifty-four Landsat images from 1975 to 2011 were preprocessed that included image registration, top-of-the-atmosphere (TOA) reflectance conversion, cloud and shadow masking, and topographic normalization. Snow covered pixels were retrieved using the normalized difference snow index (NDSI) and unsupervised classification, and pixels having greater (less) than 50% snow cover were classified presence (absence). A normalized SCA equation was derived to independently estimate SCA given missing image coverage and cloud-shadow contamination. Relative frequency maps of missing pixels were assembled to assess whether systematic biases were embedded within this Landsat CDR. Our results suggest that it is possible to confidently estimate historical bi-monthly SCA from partially cloudy Landsat images. This multitemporal method is intended to guide Landsat CDR development for freshwaterscarce regions of the western US to monitor climate-driven changes in mountain snowpack extent.

The role of cloud contamination, aerosol layer height and aerosol model in the assessment of the OMI near-UV retrievals over the ocean

NASA Astrophysics Data System (ADS)

Gassó, Santiago; Torres, Omar

2016-07-01

Retrievals of aerosol optical depth (AOD) at 388 nm over the ocean from the Ozone Monitoring Instrument (OMI) two-channel near-UV algorithm (OMAERUV) have been compared with independent AOD measurements. The analysis was carried out over the open ocean (OMI and MODerate-resolution Imaging Spectrometer (MODIS) AOD comparisons) and over coastal and island sites (OMI and AERONET, the AErosol RObotic NETwork). Additionally, a research version of the retrieval algorithm (using MODIS and CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) information as constraints) was utilized to evaluate the sensitivity of the retrieval to different assumed aerosol properties. Overall, the comparison resulted in differences (OMI minus independent measurements) within the expected levels of uncertainty for the OMI AOD retrievals (0.1 for AOD < 0.3, 30 % for AOD > 0.3). Using examples from case studies with outliers, the reasons that led to the observed differences were examined with specific purpose to determine whether they are related to instrument limitations (i.e., pixel size, calibration) or algorithm assumptions (such as aerosol shape, aerosol height). The analysis confirms that OMAERUV does an adequate job at rejecting cloudy scenes within the instrument's capabilities. There is a residual cloud contamination in OMI pixels with quality flag 0 (the best conditions for aerosol retrieval according to the algorithm), resulting in a bias towards high AODs in OMAERUV. This bias is more pronounced at low concentrations of absorbing aerosols (AOD 388 nm ˜ < 0.5). For higher aerosol loadings, the bias remains within OMI's AOD uncertainties. In pixels where OMAERUV assigned a dust aerosol model, a fraction of them (< 20 %) had retrieved AODs significantly lower than AERONET and MODIS AODs. In a case study, a detailed examination of the aerosol height from CALIOP and the AODs from MODIS, along with sensitivity tests, was carried out by varying the different assumed parameters in the retrieval (imaginary index of refraction, size distribution, aerosol height, particle shape). It was found that the spherical shape assumption for dust in the current retrieval is the main cause of the underestimate. In addition, it is demonstrated in an example how an incorrect assumption of the aerosol height can lead to an underestimate. Nevertheless, this is not as significant as the effect of particle shape. These findings will be incorporated in a future version of the retrieval algorithm.

Microphysical and macrophysical responses of marine stratocumulus polluted by underlying ships

NASA Astrophysics Data System (ADS)

Christensen, Matthew Wells

Multiple sensors flying in the A-train constellation of satellites were used to determine the extent to which aerosol plumes from ships passing below marine stratocumulus alter the microphysical and macrophysical properties of the clouds. Aerosol plumes generated by ships sometimes influence cloud microphysical properties (effective radius) and, to a largely undetermined extent, cloud macrophysical properties (liquid water path, coverage, depth, precipitation, and longevity). Aerosol indirect effects were brought into focus, using observations from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) and the 94-GHZ radar onboard CloudSat. To assess local cloud scale responses to aerosol, the locations of over one thousand ship tracks coinciding with the radar were meticulously logged by hand from the Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. MODIS imagery was used to distinguish ship tracks that were embedded in closed, open, and unclassifiable mesoscale cellular cloud structures. The impact of aerosol on the microphysical cloud properties in both the closed and open cell regimes were consistent with the changes predicted by the Twomey hypothesis. For the macrophysical changes, differences in the sign and magnitude of these properties were observed between cloud regimes. The results demonstrate that the spatial extent of rainfall (rain cover fraction) and intensity decrease in the clouds contaminated by the ship plume compared to the ambient pristine clouds. Although reductions of precipitation were common amongst the clouds with detectable rainfall (72% of cases), a substantial fraction of ship tracks (28% of cases) exhibited the opposite response. The sign and strength of the response was tied to the type of stratocumulus (e.g., closed vs open cells), depth of the boundary layer, and humidity in the free-troposphere. When closed cellular clouds were identified, liquid water path, drizzle rate, and rain cover fraction (an average relative decrease of 61%) was significantly smaller in the ship-contaminated clouds. Differences in drizzle rate resulted primarily from the reductions in rain cover fraction (i.e., fewer pixels were identified with rain in the clouds polluted by the ship). The opposite occurred in the open cell regime. Ship plumes ingested into this regime resulted in significantly deeper and brighter clouds with higher liquid water amounts and rain rates. Enhanced rain rates (average relative increase of 89%) were primarily due to the changes in intensity (i.e., rain rates on the 1.1 km pixel scale were higher in the ship contaminated clouds) and, to a lesser extent, rain cover fraction. One implication for these differences is that the local aerosol indirect radiative forcing was more than five times larger for ship tracks observed in the open cell regime (-59 W m-2) compared to those identified in the closed cell regime (-12 W m -2). The results presented here underline the need to consider the mesoscale structure of stratocumulus when examining the cloud dynamic response to changes in aerosol concentration. In the final part of the dissertation, the focus shifted to the climate scale to examine the impact of shipping on the Earth's radiation budget. Two studies were employed, in the first; changes to the radiative properties of boundary layer clouds (i.e., cloud top heights less than 3 km) were examined in response to the substantial decreases in ship traffic that resulted from the recent world economic recession in 2008. Differences in the annually averaged droplet effective radius and top of atmosphere outgoing shortwave radiative flux between 2007 and 2009 did not manifest as a clear response in the climate system and, was probably masked either due to competing aerosol cloud feedbacks or by interannual climate variability. In the second study, a method was developed to estimate the radiative forcing from shipping by convolving lanes of densely populated ships onto the global distributions of closed and open cell stratocumulus clouds. Closed cells were observed more than twice as often as open cells. Despite the smaller abundance of open cells, a significant portion of the radiaitve forcing from shipping was claimed by this regime. On the whole, the global radiative forcing from ship tracks was small (approximately -0.45 mW m-2) compared to the radiative forcing associated with the atmospheric buildup of anthropogenic CO2.

Analysis of smoke and cloud impact on seasonal and interannual variations in normalized difference vegetation index in Amazon

NASA Astrophysics Data System (ADS)

Kobayashi, H.; Dye, D. G.

2004-12-01

Normalized difference vegetation index (NDVI) derived from National Oceanic and Atmospheric Administration (NOAA)/Advanced Very High Resolution Radiometer (AVHRR) is a unique measurement of long-term variations in global vegetation dynamics. The NDVI data have been used for the detection of the seasonal and interannual variations in vegetation. However, as reported in several studies, NDVI decreases with the increase in clouds and/or smoke aerosol contaminated in the pixels. This study assesses the smoke and clouds effect on long-term Global Inventory Modeling and Mapping Studies (GIMMS) and Pathfinder AVHRR Land (PAL) NDVI data in Amazon. This knowledge will help developing the correction method in the tropics in the future. To assess the smoke and cloud effects on GIMMS and PAL, we used another satellite-derived data sets; NDVI derived from SPOT/VEGETATION (VGT) data and Aerosol Index (AI) derived from Total Ozone Mapping Spectrometer (TOMS). Since April 1998, VGT has measured the earth surface globally including in Amazon. The advantage of the VGT is that it has blue channel where the smoke and cloud can be easily detected. By analyzing the VGT NDVI and comparing with the AVHRR-based NDVI, we inferred smoke and cloud effect on the AVHRR-based NDVI. From the results of the VGT analysis, we found the large NDVI seasonality in South and Southeastern Amazon. In these areas, the NDVI gradually increased from April to July and decreased from August to October. However the sufficient NDVI data were not existed from August to November when the smoke and cloud pixels were masked using blue reflectance. Thus it is said that the smoke and clouds mainly cause the large decreases in NDVI between August and November and NDVI has little vegetation signature in these months. Also we examined the interannual variations in NDVI and smoke aerosol. Then the decrease in NDVI is well consistent with the increase in the increase in AI. Our results suggest that the months between April and July are the most reliable season to monitor the vegetation.

Improving Pixel Level Cloud Optical Property Retrieval using Monte Carlo Simulations

NASA Technical Reports Server (NTRS)

Oreopoulos, Lazaros; Marshak, Alexander; Cahalan, Robert F.

1999-01-01

The accurate pixel-by-pixel retrieval of cloud optical properties from space is influenced by radiative smoothing due to high order photon scattering and radiative roughening due to low order scattering events. Both are caused by cloud heterogeneity and the three-dimensional nature of radiative transfer and can be studied with the aid of computer simulations. We use Monte Carlo simulations on variable 1-D and 2-D model cloud fields to seek for dependencies of smoothing and roughening phenomena on single scattering albedo, solar zenith angle, and cloud characteristics. The results are discussed in the context of high resolution satellite (such as Landsat) retrieval applications. The current work extends the investigation on the inverse NIPA (Non-local Independent Pixel Approximation) as a tool for removing smoothing and improving retrievals of cloud optical depth. This is accomplished by: (1) Delineating the limits of NIPA applicability; (2) Exploring NIPA parameter dependences on cloud macrostructural features, such as mean cloud optical depth and geometrical thickness, degree of extinction and cloud top height variability. We also compare parameter values from empirical and theoretical considerations; (3) Examining the differences between applying NIPA on radiation quantities vs direct application on optical properties; (4) Studying the radiation budget importance of the NIPA corrections as a function of scale. Finally, we discuss fundamental adjustments that need to be considered for successful radiance inversion at non-conservative wavelengths and oblique Sun angles. These adjustments are necessary to remove roughening signatures which become more prominent with increasing absorption and solar zenith angle.

High-resolution photography of clouds from the surface: Retrieval of optical depth of thin clouds down to centimeter scales: High-Resolution Photography of Clouds

DOE Office of Scientific and Technical Information (OSTI.GOV)

Schwartz, Stephen E.; Huang, Dong; Vladutescu, Daniela Viviana

This article describes the approach and presents initial results, for a period of several minutes in north central Oklahoma, of an examination of clouds by high resolution digital photography from the surface looking vertically upward. A commercially available camera having 35-mm equivalent focal length up to 1200 mm (nominal resolution as fine as 6 µrad, which corresponds to 9 mm for cloud height 1.5 km) is used to obtain a measure of zenith radiance of a 30 m × 30 m domain as a two-dimensional image consisting of 3456 × 3456 pixels (12 million pixels). Downwelling zenith radiance varies substantiallymore » within single images and between successive images obtained at 4-s intervals. Variation in zenith radiance found on scales down to about 10 cm is attributed to variation in cloud optical depth (COD). Attention here is directed primarily to optically thin clouds, COD less than about 2. A radiation transfer model used to relate downwelling zenith radiance to COD and to relate the counts in the camera image to zenith radiance, permits determination of COD on a pixel-by-pixel basis. COD for thin clouds determined in this way exhibits considerable variation, for example, an order of magnitude within 15 m, a factor of 2 within 4 m, and 25% (0.12 to 0.15) over 14 cm. In conclusion, this approach, which examines cloud structure on scales 3 to 5 orders of magnitude finer than satellite products, opens new avenues for examination of cloud structure and evolution.« less

High-resolution photography of clouds from the surface: Retrieval of optical depth of thin clouds down to centimeter scales: High-Resolution Photography of Clouds

DOE PAGES

Schwartz, Stephen E.; Huang, Dong; Vladutescu, Daniela Viviana

2017-03-08

This article describes the approach and presents initial results, for a period of several minutes in north central Oklahoma, of an examination of clouds by high resolution digital photography from the surface looking vertically upward. A commercially available camera having 35-mm equivalent focal length up to 1200 mm (nominal resolution as fine as 6 µrad, which corresponds to 9 mm for cloud height 1.5 km) is used to obtain a measure of zenith radiance of a 30 m × 30 m domain as a two-dimensional image consisting of 3456 × 3456 pixels (12 million pixels). Downwelling zenith radiance varies substantiallymore » within single images and between successive images obtained at 4-s intervals. Variation in zenith radiance found on scales down to about 10 cm is attributed to variation in cloud optical depth (COD). Attention here is directed primarily to optically thin clouds, COD less than about 2. A radiation transfer model used to relate downwelling zenith radiance to COD and to relate the counts in the camera image to zenith radiance, permits determination of COD on a pixel-by-pixel basis. COD for thin clouds determined in this way exhibits considerable variation, for example, an order of magnitude within 15 m, a factor of 2 within 4 m, and 25% (0.12 to 0.15) over 14 cm. In conclusion, this approach, which examines cloud structure on scales 3 to 5 orders of magnitude finer than satellite products, opens new avenues for examination of cloud structure and evolution.« less

Accounting for sub-pixel variability of clouds and/or unresolved spectral variability, as needed, with generalized radiative transfer theory

DOE PAGES

Davis, Anthony B.; Xu, Feng; Collins, William D.

2015-03-01

Atmospheric hyperspectral VNIR sensing struggles with sub-pixel variability of clouds and limited spectral resolution mixing molecular lines. Our generalized radiative transfer model addresses both issues with new propagation kernels characterized by power-law decay in space.

Improvements in Night-Time Low Cloud Detection and MODIS-Style Cloud Optical Properties from MSG SEVIRI

NASA Technical Reports Server (NTRS)

Wind, Galina (Gala); Platnick, Steven; Riedi, Jerome

2011-01-01

The MODIS cloud optical properties algorithm (MOD06IMYD06 for Terra and Aqua MODIS, respectively) slated for production in Data Collection 6 has been adapted to execute using available channels on MSG SEVIRI. Available MODIS-style retrievals include IR Window-derived cloud top properties, using the new Collection 6 cloud top properties algorithm, cloud optical thickness from VISINIR bands, cloud effective radius from 1.6 and 3.7Jlm and cloud ice/water path. We also provide pixel-level uncertainty estimate for successful retrievals. It was found that at nighttime the SEVIRI cloud mask tends to report unnaturally low cloud fraction for marine stratocumulus clouds. A correction algorithm that improves detection of such clouds has been developed. We will discuss the improvements to nighttime low cloud detection for SEVIRI and show examples and comparisons with MODIS and CALIPSO. We will also show examples of MODIS-style pixel-level (Level-2) cloud retrievals for SEVIRI with comparisons to MODIS.

Comparison of computation time and image quality between full-parallax 4G-pixels CGHs calculated by the point cloud and polygon-based method

NASA Astrophysics Data System (ADS)

Nakatsuji, Noriaki; Matsushima, Kyoji

2017-03-01

Full-parallax high-definition CGHs composed of more than billion pixels were so far created only by the polygon-based method because of its high performance. However, GPUs recently allow us to generate CGHs much faster by the point cloud. In this paper, we measure computation time of object fields for full-parallax high-definition CGHs, which are composed of 4 billion pixels and reconstruct the same scene, by using the point cloud with GPU and the polygon-based method with CPU. In addition, we compare the optical and simulated reconstructions between CGHs created by these techniques to verify the image quality.

Development of multi-sensor global cloud and radiance composites for earth radiation budget monitoring from DSCOVR

NASA Astrophysics Data System (ADS)

Khlopenkov, Konstantin; Duda, David; Thieman, Mandana; Minnis, Patrick; Su, Wenying; Bedka, Kristopher

2017-10-01

The Deep Space Climate Observatory (DSCOVR) enables analysis of the daytime Earth radiation budget via the onboard Earth Polychromatic Imaging Camera (EPIC) and National Institute of Standards and Technology Advanced Radiometer (NISTAR). Radiance observations and cloud property retrievals from low earth orbit and geostationary satellite imagers have to be co-located with EPIC pixels to provide scene identification in order to select anisotropic directional models needed to calculate shortwave and longwave fluxes. A new algorithm is proposed for optimal merging of selected radiances and cloud properties derived from multiple satellite imagers to obtain seamless global hourly composites at 5-km resolution. An aggregated rating is employed to incorporate several factors and to select the best observation at the time nearest to the EPIC measurement. Spatial accuracy is improved using inverse mapping with gradient search during reprojection and bicubic interpolation for pixel resampling. The composite data are subsequently remapped into EPIC-view domain by convolving composite pixels with the EPIC point spread function defined with a half-pixel accuracy. PSF-weighted average radiances and cloud properties are computed separately for each cloud phase. The algorithm has demonstrated contiguous global coverage for any requested time of day with a temporal lag of under 2 hours in over 95% of the globe.

Development of Multi-Sensor Global Cloud and Radiance Composites for Earth Radiation Budget Monitoring from DSCOVR

NASA Technical Reports Server (NTRS)

Khlopenkov, Konstantin; Duda, David; Thieman, Mandana; Minnis, Patrick; Su, Wenying; Bedka, Kristopher

2017-01-01

The Deep Space Climate Observatory (DSCOVR) enables analysis of the daytime Earth radiation budget via the onboard Earth Polychromatic Imaging Camera (EPIC) and National Institute of Standards and Technology Advanced Radiometer (NISTAR). Radiance observations and cloud property retrievals from low earth orbit and geostationary satellite imagers have to be co-located with EPIC pixels to provide scene identification in order to select anisotropic directional models needed to calculate shortwave and longwave fluxes. A new algorithm is proposed for optimal merging of selected radiances and cloud properties derived from multiple satellite imagers to obtain seamless global hourly composites at 5-kilometer resolution. An aggregated rating is employed to incorporate several factors and to select the best observation at the time nearest to the EPIC measurement. Spatial accuracy is improved using inverse mapping with gradient search during reprojection and bicubic interpolation for pixel resampling. The composite data are subsequently remapped into EPIC-view domain by convolving composite pixels with the EPIC point spread function (PSF) defined with a half-pixel accuracy. PSF-weighted average radiances and cloud properties are computed separately for each cloud phase. The algorithm has demonstrated contiguous global coverage for any requested time of day with a temporal lag of under 2 hours in over 95 percent of the globe.

Reconciling Simulated and Observed Views of Clouds: MODIS, ISCCP, and the Limits of Instrument Simulators in Climate Models

NASA Technical Reports Server (NTRS)

Pincus, Robert; Platnick, Steven E.; Ackerman, Steve; Hemler, Richard; Hofmann, Patrick

2011-01-01

The properties of clouds that may be observed by satellite instruments, such as optical depth and cloud top pressure, are only loosely related to the way clouds are represented in models of the atmosphere. One way to bridge this gap is through "instrument simulators," diagnostic tools that map the model representation to synthetic observations so that differences between simulator output and observations can be interpreted unambiguously as model error. But simulators may themselves be restricted by limited information available from the host model or by internal assumptions. This work examines the extent to which instrument simulators are able to capture essential differences between MODIS and ISCCP, two similar but independent estimates of cloud properties. We focus on the stark differences between MODIS and ISCCP observations of total cloudiness and the distribution of cloud optical thickness can be traced to different approaches to marginal pixels, which MODIS excludes and ISCCP treats as homogeneous. These pixels, which likely contain broken clouds, cover about 15% of the planet and contain almost all of the optically thinnest clouds observed by either instrument. Instrument simulators can not reproduce these differences because the host model does not consider unresolved spatial scales and so can not produce broken pixels. Nonetheless, MODIS and ISCCP observation are consistent for all but the optically-thinnest clouds, and models can be robustly evaluated using instrument simulators by excluding ambiguous observations.

Retrieval of cloud cover parameters from multispectral satellite images

NASA Technical Reports Server (NTRS)

Arking, A.; Childs, J. D.

1985-01-01

A technique is described for extracting cloud cover parameters from multispectral satellite radiometric measurements. Utilizing three channels from the AVHRR (Advanced Very High Resolution Radiometer) on NOAA polar orbiting satellites, it is shown that one can retrieve four parameters for each pixel: cloud fraction within the FOV, optical thickness, cloud-top temperature and a microphysical model parameter. The last parameter is an index representing the properties of the cloud particle and is determined primarily by the radiance at 3.7 microns. The other three parameters are extracted from the visible and 11 micron infrared radiances, utilizing the information contained in the two-dimensional scatter plot of the measured radiances. The solution is essentially one in which the distributions of optical thickness and cloud-top temperature are maximally clustered for each region, with cloud fraction for each pixel adjusted to achieve maximal clustering.

The Landsat Phenology Study (LaPS): Preliminary CONUS Results for 2008

NASA Astrophysics Data System (ADS)

Henebry, Geoffrey M.; Roy, David P.; Ju, Junchang; Kovalskyy, Valeriy

2010-05-01

Most studies of land surface phenology (LSP) have used time series derived from moderate spatial resolution satellite sensor data (e.g., AVHRR, MODIS, VEGETATION) because these data are freely available and because they provide an acceptable trade-off between higher, near daily, temporal frequency of observation needed to reduce cloud contamination against lower (500m-5km) spatial resolution. The recent opening of the USGS Landsat archive to web-enabled access presents the opportunity to explore how well Landsat time series can portray LSPs at high spatial resolution. The NASA Web-enabled Landsat data (WELD) project (http://landsat.usgs.gov/WELD.php) has produced 30m composited mosaics for all the conterminous US (CONUS) from Landsat 7 ETM+ data. The composited mosaics are generated on monthly, seasonal, and annual basis and include spectral reflectance, normalized difference vegetation index (NDVI), and the acquisition date of each composited pixel. The WELD compositing approach is designed to select valid land surface observations with minimal cloud, snow, and atmospheric contamination. We extracted 30m pixel time series from the twelve monthly WELD composited mosaics for 2008 at 320 locations across the CONUS where we have ground phenological observations that are heterogeneous with respect to the types of plants observed, the phenophases recorded (predominantly spring green-up) and the ground sampling protocols used. The ground data came from several sources, including the cloned lilac/honeysuckle network, the Phenocam network, five LTER sites (H.J. Andrews, Harvard Forest, Jornada, Konza Prairie, and Sevilleta), and a private woodlot in Maine. Temporal profiles of the 30m WELD Landsat NDVI, the green NDVI (GNDVI), the normalized difference infrared index (NDII) derived from the composited reflectances, are compared to the ground observations. Results show that (i) inclusion of the Landsat acquisition date for each pixel improves the characterization of the LSP, (ii) the use of the NDII and GNDVI in conjunction with the NDVI improves identification of the onset both of canopy development and senescence, and (iii) the WELD compositing approach and the resulting mosaics provide a rich new data source for phenological investigations.

The Landsat Phenology Study (LaPS): Preliminary CONUS Results for 2008

NASA Astrophysics Data System (ADS)

Henebry, G. M.; Roy, D.; Ju, J.; Kovalskyy, V.

2009-12-01

Most studies of land surface phenology (LSP) have used time series derived from moderate spatial resolution satellite sensor data (e.g., AVHRR, MODIS, VEGETATION) because these data are freely available and because they provide an acceptable trade-off between higher, near daily, temporal frequency of observation needed to reduce cloud contamination against lower (500m-5km) spatial resolution. The recent opening of the USGS Landsat archive to web-enabled access presents the opportunity to explore how well Landsat time series can portray LSPs at high spatial resolution. The NASA Web-enabled Landsat data (WELD) project has produced 30m composited mosaics for all the conterminous US (CONUS) from Landsat 7 ETM+ data. The composited mosaics are generated on monthly, seasonal, and annual basis and include spectral reflectance, normalized difference vegetation index (NDVI), and the acquisition date of each composited pixel. The WELD compositing approach is designed to select valid land surface observations with minimal cloud, snow, and atmospheric contamination. We extracted 30m pixel time series from the twelve monthly WELD composited mosaics for 2008 at 320 locations across the CONUS where we have ground phenological observations that are heterogeneous with respect to the types of plants observed, the phenophases recorded (predominantly spring green-up) and the ground sampling protocols used. The ground data came from several sources, including the cloned lilac/honeysuckle network, the Phenocam network, five LTER sites (H.J. Andrews, Harvard Forest, Jornada, Konza Prairie, and Sevilleta), and a private woodlot in New Hampshire. Temporal profiles of the 30m WELD Landsat NDVI and the normalized difference infrared index (NDII) derived from the composited reflectances, are compared to the ground observations. Results show that (i) inclusion of the Landsat acquisition date for each pixel improves the characterization of the LSP, (ii) the use of the NDII in conjunction with the NDVI improves identification of the onset both of canopy development and senescence, and (iii) the WELD compositing approach and the resulting mosaics provide a rich new data source for phenological investigations.

A threshold-based cloud mask for the high-resolution visible channel of Meteosat Second Generation SEVIRI

NASA Astrophysics Data System (ADS)

Bley, S.; Deneke, H.

2013-10-01

A threshold-based cloud mask for the high-resolution visible (HRV) channel (1 × 1 km2) of the Meteosat SEVIRI (Spinning Enhanced Visible and Infrared Imager) instrument is introduced and evaluated. It is based on operational EUMETSAT cloud mask for the low-resolution channels of SEVIRI (3 × 3 km2), which is used for the selection of suitable thresholds to ensure consistency with its results. The aim of using the HRV channel is to resolve small-scale cloud structures that cannot be detected by the low-resolution channels. We find that it is of advantage to apply thresholds relative to clear-sky reflectance composites, and to adapt the threshold regionally. Furthermore, the accuracy of the different spectral channels for thresholding and the suitability of the HRV channel are investigated for cloud detection. The case studies show different situations to demonstrate the behavior for various surface and cloud conditions. Overall, between 4 and 24% of cloudy low-resolution SEVIRI pixels are found to contain broken clouds in our test data set depending on considered region. Most of these broken pixels are classified as cloudy by EUMETSAT's cloud mask, which will likely result in an overestimate if the mask is used as an estimate of cloud fraction. The HRV cloud mask aims for small-scale convective sub-pixel clouds that are missed by the EUMETSAT cloud mask. The major limit of the HRV cloud mask is the minimum cloud optical thickness (COT) that can be detected. This threshold COT was found to be about 0.8 over ocean and 2 over land and is highly related to the albedo of the underlying surface.

SeaWiFS Technical Report Series. Volume 7: Cloud screening for polar orbiting visible and infrared (IR) satellite sensors

NASA Technical Reports Server (NTRS)

Darzi, Michael; Hooker, Stanford B. (Editor); Firestone, Elaine R. (Editor)

1992-01-01

Methods for detecting and screening cloud contamination from satellite derived visible and infrared data are reviewed in this document. The methods are applicable to past, present, and future polar orbiting satellite radiometers. Such instruments include the Coastal Zone Color Scanner (CZCS), operational from 1978 through 1986; the Advanced Very High Resolution Radiometer (AVHRR); the Sea-viewing Wide Field-of-view Sensor (SeaWiFS), scheduled for launch in August 1993; and the Moderate Resolution Imaging Spectrometer (IMODIS). Constant threshold methods are the least demanding computationally, and often provide adequate results. An improvement to these methods are the least demanding computationally, and often provide adequate results. An improvement to these methods is to determine the thresholds dynamically by adjusting them according to the areal and temporal distributions of the surrounding pixels. Spatial coherence methods set thresholds based on the expected spatial variability of the data. Other statistically derived methods and various combinations of basic methods are also reviewed. The complexity of the methods is ultimately limited by the computing resources. Finally, some criteria for evaluating cloud screening methods are discussed.

A Bispectral Composite Threshold Approach for Automatic Cloud Detection in VIIRS Imagery

NASA Technical Reports Server (NTRS)

LaFontaine Frank J.; Jedlovec, Gary J.

2015-01-01

The detection of clouds in satellite imagery has a number of important applications in weather and climate studies. The presence of clouds can alter the energy budget of the Earth-atmosphere system through scattering and absorption of shortwave radiation and the absorption and re-emission of infrared radiation at longer wavelengths. The scattering and absorption characteristics of clouds vary with the microphysical properties of clouds, hence the cloud type. Thus, detecting the presence of clouds over a region in satellite imagery is important in order to derive atmospheric or surface parameters that give insight into weather and climate processes. For many applications however, clouds are a contaminant whose presence interferes with retrieving atmosphere or surface information. In these cases, is important to isolate cloud-free pixels, used to retrieve atmospheric thermodynamic information or surface geophysical parameters, from cloudy ones. This abstract describes an application of a two-channel bispectral composite threshold (BCT) approach applied to VIIRS imagery. The simplified BCT approach uses only the 10.76 and 3.75 micrometer spectral channels from VIIRS in two spectral tests; a straight-forward infrared threshold test with the longwave channel and a shortwave - longwave channel difference test. The key to the success of this approach as demonstrated in past applications to GOES and MODIS data is the generation of temporally and spatially dependent thresholds used in the tests from a previous number of days at similar observations to the current data. The paper and subsequent presentation will present an overview of the approach and intercomparison results with other satellites, methods, and against verification data.

The effect of spatial resolution upon cloud optical property retrievals. I - Optical thickness

NASA Technical Reports Server (NTRS)

Feind, Rand E.; Christopher, Sundar A.; Welch, Ronald M.

1992-01-01

High spectral and spatial resolution Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) imagery is used to study the effects of spatial resolution upon fair weather cumulus cloud optical thickness retrievals. As a preprocessing step, a variation of the Gao and Goetz three-band ratio technique is used to discriminate clouds from the background. The combination of the elimination of cloud shadow pixels and using the first derivative of the histogram allows for accurate cloud edge discrimination. The data are progressively degraded from 20 m to 960 m spatial resolution. The results show that retrieved cloud area increases with decreasing spatial resolution. The results also show that there is a monotonic decrease in retrieved cloud optical thickness with decreasing spatial resolution. It is also demonstrated that the use of a single, monospectral reflectance threshold is inadequate for identifying cloud pixels in fair weather cumulus scenes and presumably in any inhomogeneous cloud field. Cloud edges have a distribution of reflectance thresholds. The incorrect identification of cloud edges significantly impacts the accurate retrieval of cloud optical thickness values.

The Role of Cloud Contamination, Aerosol Layer Height and Aerosol Model in the Assessment of the OMI Near-UV Retrievals Over the Ocean

NASA Technical Reports Server (NTRS)

Gasso, Santiago; Torres, Omar

2016-01-01

Retrievals of aerosol optical depth (AOD) at 388 nm over the ocean from the Ozone Monitoring Instrument (OMI) two-channel near-UV algorithm (OMAERUV) have been compared with independent AOD measurements. The analysis was carried out over the open ocean (OMI and MODerate-resolution Imaging Spectrometer (MODIS) AOD comparisons) and over coastal and island sites (OMI and AERONET, the AErosol RObotic NETwork). Additionally, a research version of the retrieval algorithm (using MODIS and CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) information as constraints) was utilized to evaluate the sensitivity of the retrieval to different assumed aerosol properties. Overall, the comparison resulted in differences (OMI minus independent measurements) within the expected levels of uncertainty for the OMI AOD retrievals (0.1 for AOD less than 0.3, 30% for AOD greater than 0.3). Using examples from case studies with outliers, the reasons that led to the observed differences were examined with specific purpose to determine whether they are related to instrument limitations (i.e., pixel size, calibration) or algorithm assumptions (such as aerosol shape, aerosol height). The analysis confirms that OMAERUV does an adequate job at rejecting cloudy scenes within the instrument's capabilities. There is a residual cloud contamination in OMI pixels with quality flag 0 (the best conditions for aerosol retrieval according to the algorithm), resulting in a bias towards high AODs in OMAERUV. This bias is more pronounced at low concentrations of absorbing aerosols (AOD 388 nm approximately less than 0.5). For higher aerosol loadings, the bias remains within OMI's AOD uncertainties. In pixels where OMAERUV assigned a dust aerosol model, a fraction of them (less than 20 %) had retrieved AODs significantly lower than AERONET and MODIS AODs. In a case study, a detailed examination of the aerosol height from CALIOP and the AODs from MODIS, along with sensitivity tests, was carried out by varying the different assumed parameters in the retrieval (imaginary index of refraction, size distribution, aerosol height, particle shape). It was found that the spherical shape assumption for dust in the current retrieval is the main cause of the underestimate. In addition, it is demonstrated in an example how an incorrect assumption of the aerosol height can lead to an underestimate. Nevertheless, this is not as significant as the effect of particle shape. These findings will be incorporated in a future version of the retrieval algorithm.

Discrimination Between Clouds and Snow in Landsat 8 Imagery: an Assessment of Current Methods and a New Approach

NASA Astrophysics Data System (ADS)

Stillinger, T.; Dozier, J.; Phares, N.; Rittger, K.

2015-12-01

Discrimination between snow and clouds poses a serious but tractable challenge to the consistent delivery of high-quality information on mountain snow from remote sensing. Clouds obstruct the surface from the sensor's view, and the similar optical properties of clouds and snow make accurate discrimination difficult. We assess the performance of the current Landsat 8 operational snow and cloud mask products (LDCM CCA and CFmask), along with a new method, using over one million manually identified snow and clouds pixels in Landsat 8 scenes. The new method uses physically based scattering models to generate spectra in each Landsat 8 band, at that scene's solar illumination, for snow and cloud particle sizes that cover the plausible range for each. The modeled spectra are compared to pixels' spectra via several independent ways to identify snow and clouds. The results are synthesized to create a final snow/cloud mask, and the method can be applied to any multispectral imager with bands covering the visible, near-infrared, and shortwave-infrared regions. Each algorithm we tested misidentifies snow and clouds in both directions to varying degrees. We assess performance with measures of Precision, Recall, and the F statistic, which are based on counts of true and false positives and negatives. Tests for significance in differences between spectra in the measured and modeled values among incorrectly identified pixels help ascertain reasons for misidentification. A cloud mask specifically designed to separate snow from clouds is a valuable tool for those interested in remotely sensing snow cover. Given freely available remote sensing datasets and computational tools to feasibly process entire mission histories for an area of interest, enabling researchers to reliably identify and separate snow and clouds increases the usability of the data for hydrological and climatological studies.

Identification Code of Interstellar Cloud within IRAF

NASA Astrophysics Data System (ADS)

Lee, Youngung; Jung, Jae Hoon; Kim, Hyun-Goo

1997-12-01

We present a code which identifies individual clouds in crowded region using IMFORT interface within Image Reduction and Analysis Facility(IRAF). We define a cloud as an object composed of all pixels in longitude, latitude, and velocity that are simply connected and that lie above some threshold temperature. The code searches the whole pixels of the data cube in efficient way to isolate individual clouds. Along with identification of clouds it is designed to estimate their mean values of longitudes, latitudes, and velocities. In addition, a function of generating individual images(or cube data) of identified clouds is added up. We also present identified individual clouds using a 12CO survey data cube of Galactic Anticenter Region(Lee et al. 1997) as a test example. We used a threshold temperature of 5 sigma rms noise level of the data. With a higher threshold temperature, we isolated subclouds of a huge cloud identified originally. As the most important parameter to identify clouds is the threshold value, its effect to the size and velocity dispersion is discussed rigorously.

Comparison of monthly nighttime cloud fraction products from MODIS and AIRS and ground-based camera over Manila Observatory (14.64N, 121.07E)

NASA Astrophysics Data System (ADS)

Gacal, G. F. B.; Lagrosas, N.

2017-12-01

Cloud detection nowadays is primarily achieved by the utilization of various sensors aboard satellites. These include MODIS Aqua, MODIS Terra, and AIRS with products that include nighttime cloud fraction. Ground-based instruments are, however, only secondary to these satellites when it comes to cloud detection. Nonetheless, these ground-based instruments (e.g., LIDARs, ceilometers, and sky-cameras) offer significant datasets about a particular region's cloud cover values. For nighttime operations of cloud detection instruments, satellite-based instruments are more reliably and prominently used than ground-based ones. Therefore if a ground-based instrument for nighttime operations is operated, it ought to produce reliable scientific datasets. The objective of this study is to do a comparison between the results of a nighttime ground-based instrument (sky-camera) and that of MODIS Aqua and MODIS Terra. A Canon Powershot A2300 is placed ontop of Manila Observatory (14.64N, 121.07E) and is configured to take images of the night sky at 5min intervals. To detect pixels with clouds, the pictures are converted to grayscale format. Thresholding technique is used to screen pixels with cloud and pixels without clouds. If the pixel value is greater than 17, it is considered as a cloud; otherwise, a noncloud (Gacal et al., 2016). This algorithm is applied to the data gathered from Oct 2015 to Oct 2016. A scatter plot between satellite cloud fraction in the area covering the area 14.2877N, 120.9869E, 14.7711N and 121.4539E and ground cloud cover is graphed to find the monthly correlation. During wet season (June - November), the satellite nighttime cloud fraction vs ground measured cloud cover produce an acceptable R2 (Aqua= 0.74, Terra= 0.71, AIRS= 0.76). However, during dry season, poor R2 values are obtained (AIRS= 0.39, Aqua & Terra = 0.01). The high correlation during wet season can be attributed to a high probability that the camera and satellite see the same clouds. However during dry season, the satellite sees high altitude clouds and the camera can not detect these clouds from the ground as it relies on city lights reflected from low level clouds. With this acknowledged disparity, the ground-based camera has the advantage of detecting haze and thin clouds near the ground that are hardly or not detected by the satellites.

Marine Stratocumulus Cloud Fields off the Coast of Southern California Observed Using LANDSAT Imagery. Part II: Textural Analysis.

NASA Astrophysics Data System (ADS)

Welch, R. M.; Sengupta, S. K.; Kuo, K. S.

1988-04-01

Statistical measures of the spatial distributions of gray levels (cloud reflectivities) are determined for LANDSAT Multispectral Scanner digital data. Textural properties for twelve stratocumulus cloud fields, seven cumulus fields, and two cirrus fields are examined using the Spatial Gray Level Co-Occurrence Matrix method. The co-occurrence statistics are computed for pixel separations ranging from 57 m to 29 km and at angles of 0°, 45°, 90° and 135°. Nine different textual measures are used to define the cloud field spatial relationships. However, the measures of contrast and correlation appear to be most useful in distinguishing cloud structure.Cloud field macrotexture describes general cloud field characteristics at distances greater than the size of typical cloud elements. It is determined from the spatial asymptotic values of the texture measures. The slope of the texture curves at small distances provides a measure of the microtexture of individual cloud cells. Cloud fields composed primarily of small cells have very steep slopes and reach their asymptotic values at short distances from the origin. As the cells composing the cloud field grow larger, the slope becomes more gradual and the asymptotic distance increases accordingly. Low asymptotic values of correlation show that stratocumulus cloud fields have no large scale organized structure.Besides the ability to distinguish cloud field structure, texture appears to be a potentially valuable tool in cloud classification. Stratocumulus clouds are characterized by low values of angular second moment and large values of entropy. Cirrus clouds appear to have extremely low values of contrast, low values of entropy, and very large values of correlation.Finally, we propose that sampled high spatial resolution satellite data be used in conjunction with coarser resolution operational satellite data to detect and identify cloud field structure and directionality and to locate regions of subresolution scale cloud contamination.

GOES Cloud Detection at the Global Hydrology and Climate Center

NASA Technical Reports Server (NTRS)

Laws, Kevin; Jedlovec, Gary J.; Arnold, James E. (Technical Monitor)

2002-01-01

The bi-spectral threshold (BTH) for cloud detection and height assignment is now operational at NASA's Global Hydrology and Climate Center (GHCC). This new approach is similar in principle to the bi-spectral spatial coherence (BSC) method with improvements made to produce a more robust cloud-filtering algorithm for nighttime cloud detection and subsequent 24-hour operational cloud top pressure assignment. The method capitalizes on cloud and surface emissivity differences from the GOES 3.9 and 10.7-micrometer channels to distinguish cloudy from clear pixels. Separate threshold values are determined for day and nighttime detection, and applied to a 20-day minimum composite difference image to better filter background effects and enhance differences in cloud properties. A cloud top pressure is assigned to each cloudy pixel by referencing the 10.7-micrometer channel temperature to a thermodynamic profile from a locally -run regional forecast model. This paper and supplemental poster will present an objective validation of nighttime cloud detection by the BTH approach in comparison with previous methods. The cloud top pressure will be evaluated by comparing to the NESDIS operational CO2 slicing approach.

3D Cloud Tomography, Followed by Mean Optical and Microphysical Properties, with Multi-Angle/Multi-Pixel Data

NASA Astrophysics Data System (ADS)

Davis, A. B.; von Allmen, P. A.; Marshak, A.; Bal, G.

2010-12-01

The geometrical assumption in all operational cloud remote sensing algorithms is that clouds are plane-parallel slabs, which applies relatively well to the most uniform stratus layers. Its benefit is to justify using classic 1D radiative transfer (RT) theory, where angular details (solar, viewing, azimuthal) are fully accounted for and precise phase functions can be used, to generate the look-up tables used in the retrievals. Unsurprisingly, these algorithms catastrophically fail when applied to cumulus-type clouds, which are highly 3D. This is unfortunate for the cloud-process modeling community that may thrive on in situ airborne data, but would very much like to use satellite data for more than illustrations in their presentations and publications. So, how can we obtain quantitative information from space-based observations of finite aspect ratio clouds? Cloud base/top heights, vertically projected area, mean liquid water content (LWC), and volume-averaged droplet size would be a good start. Motivated by this science need, we present a new approach suitable for sparse cumulus fields where we turn the tables on the standard procedure in cloud remote sensing. We make no a priori assumption about cloud shape, save an approximately flat base, but use brutal approximations about the RT that is necessarily 3D. Indeed, the first order of business is to roughly determine the cloud's outer shape in one of two ways, which we will frame as competing initial guesses for the next phase of shape refinement and volume-averaged microphysical parameter estimation. Both steps use multi-pixel/multi-angle techniques amenable to MISR data, the latter adding a bi-spectral dimension using collocated MODIS data. One approach to rough cloud shape determination is to fit the multi-pixel/multi-angle data with a geometric primitive such as a scalene hemi-ellipsoid with 7 parameters (translation in 3D space, 3 semi-axes, 1 azimuthal orientation); for the radiometry, a simple radiosity-type model is used where the cloud surface "emits" either reflected (sunny-side) or transmitted (shady-side) light at different levels. As it turns out, the reflected/transmitted light ratio yields an approximate cloud optical thickness. Another approach is to invoke tomography techniques to define the volume occupied by the cloud using, as it were, cloud masks for each direction of observation. In the shape and opacity refinement phase, initial guesses along with solar and viewing geometry information are used to predict radiance in each pixel using a fast diffusion model for the 3D RT in MISR's non-absorbing red channel (275 m resolution). Refinement is constrained and stopped when optimal resolution is reached. Finally, multi-pixel/mono-angle MODIS data for the same cloud (at comparable 250 m resolution) reveals the desired droplet size information, hence the volume-averaged LWC. This is an ambitious remote sensing science project drawing on cross-disciplinary expertise gained in medical imaging using both X-ray and near-IR sources and detectors. It is high risk but with potentially high returns not only for the cloud modeling community but also aerosol and surface characterization in the presence of broken 3D clouds.

Cloud cover determination in polar regions from satellite imagery

NASA Technical Reports Server (NTRS)

Barry, R. G.; Key, J.

1989-01-01

The objectives are to develop a suitable validation data set for evaluating the effectiveness of the International Satellite Cloud Climatology Project (ISCCP) algorithm for cloud retrieval in polar regions, to identify limitations of current procedures and to explore potential means to remedy them using textural classifiers, and to compare synoptic cloud data from model runs with observations. Toward the first goal, a polar data set consisting of visible, thermal, and passive microwave data was developed. The AVHRR and SMMR data were digitally merged to a polar stereographic projection with an effective pixel size of 5 sq km. With this data set, two unconventional methods of classifying the imagery for the analysis of polar clouds and surfaces were examined: one based on fuzzy sets theory and another based on a trained neural network. An algorithm for cloud detection was developed from an early test version of the ISCCP algorithm. This algorithm includes the identification of surface types with passive microwave, then temporal tests at each pixel location in the cloud detection phase. Cloud maps and clear sky radiance composites for 5 day periods are produced. Algorithm testing and validation was done with both actural AVHRR/SMMR data, and simulated imagery. From this point in the algorithm, groups of cloud pixels are examined for their spectral and textural characteristics, and a procedure is developed for the analysis of cloud patterns utilizing albedo, IR temperature, and texture. In a completion of earlier work, empirical analyses of arctic cloud cover were explored through manual interpretations of DMSP imagery and compared to U.S. Air Force 3D-nephanalysis. Comparisons of observed cloudiness from existing climatologies to patterns computed by the GISS climate model were also made.

Reconciling Simulated and Observed Views of Clouds: MODIS, ISCCP, and the Limits or Instrument Simulators

NASA Technical Reports Server (NTRS)

Ackerman, Steven A.; Hemler, Richard S.; Hofman, Robert J. Patrick; Pincus, Robert; Platnick, Steven

2011-01-01

The properties of clouds that may be observed by satellite instruments, such as optical depth and cloud top pressure, are only loosely related to the way clouds m-e represented in models of the atmosphere. One way to bridge this gap is through "instrument simulators," diagnostic tools that map the model representation to synthetic observations so that differences between simulator output and observations can be interpreted unambiguously as model error. But simulators may themselves be restricted by limited information available from the host model or by internal assumptions. This paper considers the extent to which instrument simulators are able to capture essential differences between MODIS and ISCCP, two similar but independent estimates of cloud properties. The authors review the measurements and algorithms underlying these two cloud climatologies, introduce a MODIS simulator, and detail data sets developed for comparison with global models using ISCCP and MODIS simulators, In nature MODIS observes less mid-level doudines!> than ISCCP, consistent with the different methods used to determine cloud top pressure; aspects of this difference are reproduced by the simulators running in a climate modeL But stark differences between MODIS and ISCCP observations of total cloudiness and the distribution of cloud optical thickness can be traced to different approaches to marginal pixels, which MODIS excludes and ISCCP treats as homogeneous. These pixels, which likely contain broken clouds, cover about 15 k of the planet and contain almost all of the optically thinnest clouds observed by either instrument. Instrument simulators can not reproduce these differences because the host model does not consider unresolved spatial scales and so can not produce broken pixels. Nonetheless, MODIS and ISCCP observation are consistent for all but the optically-thinnest clouds, and models can be robustly evaluated using instrument simulators by excluding ambiguous observations.

APOLLO_NG - a probabilistic interpretation of the APOLLO legacy for AVHRR heritage channels

NASA Astrophysics Data System (ADS)

Klüser, L.; Killius, N.; Gesell, G.

2015-10-01

The cloud processing scheme APOLLO (AVHRR Processing scheme Over cLouds, Land and Ocean) has been in use for cloud detection and cloud property retrieval since the late 1980s. The physics of the APOLLO scheme still build the backbone of a range of cloud detection algorithms for AVHRR (Advanced Very High Resolution Radiometer) heritage instruments. The APOLLO_NG (APOLLO_NextGeneration) cloud processing scheme is a probabilistic interpretation of the original APOLLO method. It builds upon the physical principles that have served well in the original APOLLO scheme. Nevertheless, a couple of additional variables have been introduced in APOLLO_NG. Cloud detection is no longer performed as a binary yes/no decision based on these physical principles. It is rather expressed as cloud probability for each satellite pixel. Consequently, the outcome of the algorithm can be tuned from being sure to reliably identify clear pixels to conditions of reliably identifying definitely cloudy pixels, depending on the purpose. The probabilistic approach allows retrieving not only the cloud properties (optical depth, effective radius, cloud top temperature and cloud water path) but also their uncertainties. APOLLO_NG is designed as a standalone cloud retrieval method robust enough for operational near-realtime use and for application to large amounts of historical satellite data. The radiative transfer solution is approximated by the same two-stream approach which also had been used for the original APOLLO. This allows the algorithm to be applied to a wide range of sensors without the necessity of sensor-specific tuning. Moreover it allows for online calculation of the radiative transfer (i.e., within the retrieval algorithm) giving rise to a detailed probabilistic treatment of cloud variables. This study presents the algorithm for cloud detection and cloud property retrieval together with the physical principles from the APOLLO legacy it is based on. Furthermore a couple of example results from NOAA-18 are presented.

High-resolution mapping of Martian water ice clouds using Mars Express OMEGA observations - Derivation of the diurnal cloud life cycle

NASA Astrophysics Data System (ADS)

Szantai, Andre; Audouard, Joachim; Madeleine, Jean-Baptiste; Forget, Francois; Pottier, Alizée; Millour, Ehouarn; Gondet, Brigitte; Langevin, Yves; Bibring, Jean-Pierre

2016-10-01

The mapping in space and time of water ice clouds can help to explain the Martian water cycle and atmospheric circulation. For this purpose, an ice cloud index (ICI) corresponding to the depth of a water ice absorption band at 3.4 microns is derived from a series of OMEGA images (spectels) covering 5 Martian years. The ICI values for the corresponding pixels are then binned on a high-resolution regular grid (1° longitude x 1° latitude x 5° Ls x 1 h local time) and averaged. Inside each bin, the cloud cover is calculated by dividing the number of pixels considered as cloudy (after comparison to a threshold) to the number of all (valid) pixelsWe compare the maps of clouds obtained around local time 14:00 with collocated TES cloud observations (which were only obtained around this time of the day). A good agreement is found.Averaged ICI compared to the water ice column variable from the Martian Climate Database (MCD) show a correct correlation (~0.5) , which increases when values limited to the tropics only are compared.The number of gridpoints containing ICI values is small ( ~1%), but by taking several neighbor gridpoints and over longer periods, we can observe a cloud life cycle during daytime. An example in the the tropics, around the northern summer solstice, shows a decrease of cloudiness in the morning followed by an increase in the afternoon.

Development of Multi-Sensor Global Cloud and Radiance Composites for DSCOVR EPIC Imager with Subpixel Definition

NASA Astrophysics Data System (ADS)

Khlopenkov, K. V.; Duda, D. P.; Thieman, M. M.; Sun-Mack, S.; Su, W.; Minnis, P.; Bedka, K. M.

2017-12-01

The Deep Space Climate Observatory (DSCOVR) is designed to study the daytime Earth radiation budget by means of onboard Earth Polychromatic Imaging Camera (EPIC) and National Institute of Standards and Technology Advanced Radiometer (NISTAR). EPIC imager observes in several shortwave bands (317-780 nm), while NISTAR measures the top-of-atmosphere (TOA) whole-disk radiance in shortwave and total broadband windows. Calculation of albedo and outgoing longwave flux requires a high-resolution scene identification such as the radiance observations and cloud property retrievals from low earth orbit and geostationary satellite imagers. These properties have to be co-located with EPIC imager pixels to provide scene identification and to select anisotropic directional models, which are then used to adjust the NISTAR-measured radiance and subsequently obtain the global daytime shortwave and longwave fluxes. This work presents an algorithm for optimal merging of selected radiances and cloud properties derived from multiple satellite imagers to obtain seamless global hourly composites at 5-km resolution. The highest quality observation is selected by means of an aggregated rating which incorporates several factors such as the nearest time relative to EPIC observation, lowest viewing zenith angle, and others. This process provides a smoother transition and avoids abrupt changes in the merged composite data. Higher spatial accuracy in the composite product is achieved by using the inverse mapping with gradient search during reprojection and bicubic interpolation for pixel resampling. The composite data are subsequently remapped into the EPIC-view domain by convolving composite pixels with the EPIC point spread function (PSF) defined with a half-pixel accuracy. Within every EPIC footprint, the PSF-weighted average radiances and cloud properties are computed for each cloud phase and then stored within five data subsets (clear-sky, water cloud, ice cloud, total cloud, and no retrieval). Overall, the composite product has been generated for every EPIC observation from June 2015 to December 2016, typically 300-500 composites per month, which makes it useful for many climate applications.

Evaluation of Decision Trees for Cloud Detection from AVHRR Data

NASA Technical Reports Server (NTRS)

Shiffman, Smadar; Nemani, Ramakrishna

2005-01-01

Automated cloud detection and tracking is an important step in assessing changes in radiation budgets associated with global climate change via remote sensing. Data products based on satellite imagery are available to the scientific community for studying trends in the Earth's atmosphere. The data products include pixel-based cloud masks that assign cloud-cover classifications to pixels. Many cloud-mask algorithms have the form of decision trees. The decision trees employ sequential tests that scientists designed based on empirical astrophysics studies and simulations. Limitations of existing cloud masks restrict our ability to accurately track changes in cloud patterns over time. In a previous study we compared automatically learned decision trees to cloud masks included in Advanced Very High Resolution Radiometer (AVHRR) data products from the year 2000. In this paper we report the replication of the study for five-year data, and for a gold standard based on surface observations performed by scientists at weather stations in the British Islands. For our sample data, the accuracy of automatically learned decision trees was greater than the accuracy of the cloud masks p < 0.001.

Cloud-Induced Uncertainty for Visual Navigation

DTIC Science & Technology

2014-12-26

images at the pixel level. The result is a method that can overlay clouds with various structures on top of any desired image to produce realistic...cloud-shaped structures . The primary contribution of this research, however, is to investigate and quantify the errors in features due to clouds. The...of clouds types, this method does not emulate the true structure of clouds. An alternative popular modern method of creating synthetic clouds is known

NASA/GEWEX shortwave surface radiation budget: Integrated data product with reprocessed radiance, cloud, and meteorology inputs, and new surface albedo treatment

NASA Astrophysics Data System (ADS)

Cox, Stephen J.; Stackhouse, Paul W.; Gupta, Shashi K.; Mikovitz, J. Colleen; Zhang, Taiping

2017-02-01

The NASA/GEWEX Surface Radiation Budget (SRB) project produces shortwave and longwave surface and top of atmosphere radiative fluxes for the 1983-near present time period. Spatial resolution is 1 degree. The current Release 3.0 (available at gewex-srb.larc.nasa.gov) uses the International Satellite Cloud Climatology Project (ISCCP) DX product for pixel level radiance and cloud information. This product is subsampled to 30 km. ISCCP is currently recalibrating and recomputing their entire data series, to be released as the H product, at 10km resolution. The ninefold increase in pixel number will allow SRB a higher resolution gridded product (e.g. 0.5 degree), as well as the production of pixel-level fluxes. Other key input improvements include a detailed aerosol history using the Max Planck Institute Aerosol Climatology (MAC), and temperature and moisture profiles from nnHIRS.

Extending 3D Near-Cloud Corrections from Shorter to Longer Wavelengths

NASA Technical Reports Server (NTRS)

Marshak, Alexander; Evans, K. Frank; Varnai, Tamas; Guoyong, Wen

2014-01-01

Satellite observations have shown a positive correlation between cloud amount and aerosol optical thickness (AOT) that can be explained by the humidification of aerosols near clouds, and/or by cloud contamination by sub-pixel size clouds and the cloud adjacency effect. The last effect may substantially increase reflected radiation in cloud-free columns, leading to overestimates in the retrieved AOT. For clear-sky areas near boundary layer clouds the main contribution to the enhancement of clear sky reflectance at shorter wavelengths comes from the radiation scattered into clear areas by clouds and then scattered to the sensor by air molecules. Because of the wavelength dependence of air molecule scattering, this process leads to a larger reflectance increase at shorter wavelengths, and can be corrected using a simple two-layer model. However, correcting only for molecular scattering skews spectral properties of the retrieved AOT. Kassianov and Ovtchinnikov proposed a technique that uses spectral reflectance ratios to retrieve AOT in the vicinity of clouds; they assumed that the cloud adjacency effect influences the spectral ratio between reflectances at two wavelengths less than it influences the reflectances themselves. This paper combines the two approaches: It assumes that the 3D correction for the shortest wavelength is known with some uncertainties, and then it estimates the 3D correction for longer wavelengths using a modified ratio method. The new approach is tested with 3D radiances simulated for 26 cumulus fields from Large-Eddy Simulations, supplemented with 40 aerosol profiles. The results showed that (i) for a variety of cumulus cloud scenes and aerosol profiles over ocean the 3D correction due to cloud adjacency effect can be extended from shorter to longer wavelengths and (ii) the 3D corrections for longer wavelengths are not very sensitive to unbiased random uncertainties in the 3D corrections at shorter wavelengths.

Estimation of Cirrus and Stratus Cloud Heights Using Landsat Imagery

NASA Technical Reports Server (NTRS)

Inomata, Yasushi; Feind, R. E.; Welch, R. M.

1996-01-01

A new method based upon high-spatial-resolution imagery is presented that matches cloud and shadow regions to estimate cirrus and stratus cloud heights. The distance between the cloud and the matching shadow pattern is accomplished using the 2D cross-correlation function from which the cloud height is derived. The distance between the matching cloud-shadow patterns is verified manually. The derived heights also are validated through comparison with a temperature-based retrieval of cloud height. It is also demonstrated that an estimate of cloud thickness can be retrieved if both the sunside and anti-sunside of the cloud-shadow pair are apparent. The technique requires some intepretation to determine the cloud height level retrieved (i.e., the top, base, or mid-level). It is concluded that the method is accurate to within several pixels, equivalent to cloud height variations of about +/- 250 m. The results show that precise placement of the templates is unnecessary, so that the development of a semi-automated procedure is possible. Cloud templates of about 64 pixels on a side or larger produce consistent results. The procedure was repeated for imagery degraded to simulate lower spatial resolutions. The results suggest that spatial resolution of 150-200 m or better is necessary in order to obtain stable cloud height retrievals.

Detection of long duration cloud contamination in hyper-temporal NDVI imagery

NASA Astrophysics Data System (ADS)

Ali, A.; de Bie, C. A. J. M.; Skidmore, A. K.; Scarrott, R. G.

2012-04-01

NDVI time series imagery are commonly used as a reliable source for land use and land cover mapping and monitoring. However long duration cloud can significantly influence its precision in areas where persistent clouds prevails. Therefore quantifying errors related to cloud contamination are essential for accurate land cover mapping and monitoring. This study aims to detect long duration cloud contamination in hyper-temporal NDVI imagery based land cover mapping and monitoring. MODIS-Terra NDVI imagery (250 m; 16-day; Feb'03-Dec'09) were used after necessary pre-processing using quality flags and upper envelope filter (ASAVOGOL). Subsequently stacked MODIS-Terra NDVI image (161 layers) was classified for 10 to 100 clusters using ISODATA. After classifications, 97 clusters image was selected as best classified with the help of divergence statistics. To detect long duration cloud contamination, mean NDVI class profiles of 97 clusters image was analyzed for temporal artifacts. Results showed that long duration clouds affect the normal temporal progression of NDVI and caused anomalies. Out of total 97 clusters, 32 clusters were found with cloud contamination. Cloud contamination was found more prominent in areas where high rainfall occurs. This study can help to stop error propagation in regional land cover mapping and monitoring, caused by long duration cloud contamination.

Cloud screening Coastal Zone Color Scanner images using channel 5

NASA Technical Reports Server (NTRS)

Eckstein, B. A.; Simpson, J. J.

1991-01-01

Clouds are removed from Coastal Zone Color Scanner (CZCS) data using channel 5. Instrumentation problems require pre-processing of channel 5 before an intelligent cloud-screening algorithm can be used. For example, at intervals of about 16 lines, the sensor records anomalously low radiances. Moreover, the calibration equation yields negative radiances when the sensor records zero counts, and pixels corrupted by electronic overshoot must also be excluded. The remaining pixels may then be used in conjunction with the procedure of Simpson and Humphrey to determine the CZCS cloud mask. These results plus in situ observations of phytoplankton pigment concentration show that pre-processing and proper cloud-screening of CZCS data are necessary for accurate satellite-derived pigment concentrations. This is especially true in the coastal margins, where pigment content is high and image distortion associated with electronic overshoot is also present. The pre-processing algorithm is critical to obtaining accurate global estimates of pigment from spacecraft data.

Development of Multi-Sensor Global Cloud and Radiance Composites for DSCOVR EPIC Imager with Subpixel Definition

NASA Technical Reports Server (NTRS)

Khlopenkov, Konstantin V.; Duda, David; Thieman, Mandana; Sun-mack, Szedung; Su, Wenying; Minnis, Patrick; Bedka, Kristopher

2017-01-01

The Deep Space Climate Observatory (DSCOVR) enables analysis of the daytime Earth radiation budget via the onboard Earth Polychromatic Imaging Camera (EPIC) and National Institute of Standards and Technology Advanced Radiometer (NISTAR). EPIC delivers adequate spatial resolution imagery but only in shortwave bands (317-780 nm), while NISTAR measures the top-of-atmosphere (TOA) whole-disk radiance in shortwave and longwave broadband windows. Accurate calculation of albedo and outgoing longwave flux requires a high-resolution scene identification such as the radiance observations and cloud properties retrievals from low earth orbit (LEO, including NASA Terra and Aqua MODIS, Suomi-NPP VIIRS, and NOAA AVHRR) and geosynchronous (GEO, including GOES east and west, METEOSAT, INSAT-3D, MTSAT-2, and Himawari-8) satellite imagers. The cloud properties are derived using the Clouds and the Earth's Radiant Energy System (CERES) mission Cloud Subsystem group algorithms. These properties have to be co-located with EPIC pixels to provide the scene identification and to select anisotropic directional models (ADMs), which are then used to adjust the NISTAR-measured radiance and subsequently obtain the global daytime shortwave and longwave fluxes. This work presents an algorithm for optimal merging of selected radiance and cloud property parameters derived from multiple satellite imagers to obtain seamless global hourly composites at 5-km resolution. Selection of satellite data for each 5-km pixel is based on an aggregated rating that incorporates five parameters: nominal satellite resolution, pixel time relative to the EPIC time, viewing zenith angle, distance from day/night terminator, and probability of sun glint. To provide a smoother transition in the merged output, in regions where candidate pixel data from two satellite sources have comparable aggregated rating, the selection decision is defined by the cumulative function of the normal distribution so that abrupt changes in the visual appearance of the composite data are avoided. Higher spatial accuracy in the composite product is achieved by using the inverse mapping with gradient search during reprojection and bicubic interpolation for pixel resampling.

A Novel Method for Estimating Shortwave Direct Radiative Effect of Above-cloud Aerosols over Ocean Using CALIOP and MODIS Data

NASA Technical Reports Server (NTRS)

Zhang, Z.; Meyer, K.; Platnick, S.; Oreopoulos, L.; Lee, D.; Yu, H.

2013-01-01

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 and MODIS data. It accounts for the overlapping of aerosol and cloud rigorously by utilizing the joint histogram of cloud optical depth and cloud top pressure. Effects of sub-grid scale cloud and aerosol variations on DRE are accounted for. It is computationally efficient through using grid-level cloud and aerosol statistics, instead of pixel-level products, and a pre-computed look-up table in radiative transfer calculations. We verified that for smoke over the southeast Atlantic Ocean the method yields a seasonal mean instantaneous shortwave DRE that generally agrees with more rigorous pixel-level computation within 4%. We have also computed the annual mean instantaneous shortwave DRE of light-absorbing aerosols (i.e., smoke and polluted dust) over global ocean based on 4 yr of CALIOP and MODIS data. We found that the variability of the annual mean shortwave DRE of above-cloud light-absorbing aerosol is mainly driven by the optical depth of the underlying clouds.

A Novel Method for Estimating Shortwave Direct Radiative Effect of Above-Cloud Aerosols Using CALIOP and MODIS Data

NASA Technical Reports Server (NTRS)

Zhang, Z.; Meyer, K.; Platnick, S.; Oreopoulos, L.; Lee, D.; Yu, H.

2014-01-01

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 and MODIS data. It accounts for the overlapping of aerosol and cloud rigorously by utilizing the joint histogram of cloud optical depth and cloud top pressure. Effects of sub-grid scale cloud and aerosol variations on DRE are accounted for. It is computationally efficient through using grid-level cloud and aerosol statistics, instead of pixel-level products, and a pre-computed look-up table in radiative transfer calculations. We verified that for smoke over the southeast Atlantic Ocean the method yields a seasonal mean instantaneous shortwave DRE that generally agrees with more rigorous pixel-level computation within 4. We have also computed the annual mean instantaneous shortwave DRE of light-absorbing aerosols (i.e., smoke and polluted dust) over global ocean based on 4 yr of CALIOP and MODIS data. We found that the variability of the annual mean shortwave DRE of above-cloud light-absorbing aerosol is mainly driven by the optical depth of the underlying clouds.

A robust threshold-based cloud mask for the HRV channel of MSG SEVIRI

NASA Astrophysics Data System (ADS)

Bley, S.; Deneke, H.

2013-03-01

A robust threshold-based cloud mask for the high-resolution visible (HRV) channel (1 × 1 km2) of the METEOSAT SEVIRI instrument is introduced and evaluated. It is based on operational EUMETSAT cloud mask for the low resolution channels of SEVIRI (3 × 3 km2), which is used for the selection of suitable thresholds to ensure consistency with its results. The aim of using the HRV channel is to resolve small-scale cloud structures which cannot be detected by the low resolution channels. We find that it is of advantage to apply thresholds relative to clear-sky reflectance composites, and to adapt the threshold regionally. Furthermore, the accuracy of the different spectral channels for thresholding and the suitability of the HRV channel are investigated for cloud detection. The case studies show different situations to demonstrate the behaviour for various surface and cloud conditions. Overall, between 4 and 24% of cloudy low-resolution SEVIRI pixels are found to contain broken clouds in our test dataset depending on considered region. Most of these broken pixels are classified as cloudy by EUMETSAT's cloud mask, which will likely result in an overestimate if the mask is used as estimate of cloud fraction.

Reconciling biases and uncertainties of AIRS and MODIS ice cloud properties

NASA Astrophysics Data System (ADS)

Kahn, B. H.; Gettelman, A.

2015-12-01

We will discuss comparisons of collocated Atmospheric Infrared Sounder (AIRS) and Moderate Resolution Imaging Spectroradiometer (MODIS) ice cloud optical thickness (COT), effective radius (CER), and cloud thermodynamic phase retrievals. The ice cloud comparisons are stratified by retrieval uncertainty estimates, horizontal inhomogeneity at the pixel-scale, vertical cloud structure, and other key parameters. Although an estimated 27% globally of all AIRS pixels contain ice cloud, only 7% of them are spatially uniform ice according to MODIS. We find that the correlations of COT and CER between the two instruments are strong functions of horizontal cloud heterogeneity and vertical cloud structure. The best correlations are found in single-layer, horizontally homogeneous clouds over the low-latitude tropical oceans with biases and scatter that increase with scene complexity. While the COT comparisons are unbiased in homogeneous ice clouds, a bias of 5-10 microns remains in CER within the most homogeneous scenes identified. This behavior is entirely consistent with known sensitivity differences in the visible and infrared bands. We will use AIRS and MODIS ice cloud properties to evaluate ice hydrometeor output from climate model output, such as the CAM5, with comparisons sorted into different dynamical regimes. The results of the regime-dependent comparisons will be described and implications for model evaluation and future satellite observational needs will be discussed.

A cloud mask methodology for high resolution remote sensing data combining information from high and medium resolution optical sensors

NASA Astrophysics Data System (ADS)

Sedano, Fernando; Kempeneers, Pieter; Strobl, Peter; Kucera, Jan; Vogt, Peter; Seebach, Lucia; San-Miguel-Ayanz, Jesús

2011-09-01

This study presents a novel cloud masking approach for high resolution remote sensing images in the context of land cover mapping. As an advantage to traditional methods, the approach does not rely on thermal bands and it is applicable to images from most high resolution earth observation remote sensing sensors. The methodology couples pixel-based seed identification and object-based region growing. The seed identification stage relies on pixel value comparison between high resolution images and cloud free composites at lower spatial resolution from almost simultaneously acquired dates. The methodology was tested taking SPOT4-HRVIR, SPOT5-HRG and IRS-LISS III as high resolution images and cloud free MODIS composites as reference images. The selected scenes included a wide range of cloud types and surface features. The resulting cloud masks were evaluated through visual comparison. They were also compared with ad-hoc independently generated cloud masks and with the automatic cloud cover assessment algorithm (ACCA). In general the results showed an agreement in detected clouds higher than 95% for clouds larger than 50 ha. The approach produced consistent results identifying and mapping clouds of different type and size over various land surfaces including natural vegetation, agriculture land, built-up areas, water bodies and snow.

The benefit of limb cloud imaging for tropospheric infrared limb sounding

NASA Astrophysics Data System (ADS)

Adams, S.; Spang, R.; Preusse, P.; Heinemann, G.

2009-03-01

Advances in detector technology enable a new generation of infrared limb sounders to measure 2-D images of the atmosphere. A proposed limb cloud imager (LCI) mode will measure clouds with very high spatial resolution. For the inference of temperature and trace gas distributions, detector pixels of the LCI have to be combined into super-pixels which provide the required signal-to-noise ratio and information content for the retrievals. This study examines the extent to which tropospheric coverage can be improved in comparison to limb sounding using a fixed field of view with the size of the super-pixels, as in conventional limb sounders. The study is based on cloud topographies derived from (a) IR brightness temperatures (BT) of geostationary weather satellites in conjunction with ECMWF temperature profiles and (b) ice and liquid water content data of the Consortium for Small-scale Modeling-Europe (COSMO-EU) of the German Weather Service. Limb cloud images are simulated by matching the cloud topography with the limb sounding line of sight (LOS). The analysis of the BT data shows that the reduction of the spatial sampling along the track has hardly any effect on the gain in information. The comparison between BT and COSMO-EU data identifies the strength of both data sets, which are the representation of the horizontal cloud extent for the BT data and the reproduction of the cloud amount for the COSMO-EU data. The results of the analysis of both data sets show the great advantage of the cloud imager. However, because both cloud data sets do not present the complete fine structure of the real cloud fields in the atmosphere it is assumed that the results tend to underestimate the increase in information. In conclusion, real measurements by such an instrument may result in an even higher benefit for tropospheric limb retrievals.

The benefit of limb cloud imaging for infrared limb sounding of tropospheric trace gases

NASA Astrophysics Data System (ADS)

Adams, S.; Spang, R.; Preusse, P.; Heinemann, G.

2009-06-01

Advances in detector technology enable a new generation of infrared limb sounders to measure 2-D images of the atmosphere. A proposed limb cloud imager (LCI) mode will detect clouds with a spatial resolution unprecedented for limb sounding. For the inference of temperature and trace gas distributions, detector pixels of the LCI have to be combined into super-pixels which provide the required signal-to-noise and information content for the retrievals. This study examines the extent to which tropospheric coverage can be improved in comparison to limb sounding using a fixed field of view with the size of the super-pixels, as in conventional limb sounders. The study is based on cloud topographies derived from (a) IR brightness temperatures (BT) of geostationary weather satellites in conjunction with ECMWF temperature profiles and (b) ice and liquid water content data of the Consortium for Small-scale Modeling-Europe (COSMO-EU) of the German Weather Service. Limb cloud images are simulated by matching the cloud topography with the limb sounding line of sight (LOS). The analysis of the BT data shows that the reduction of the spatial sampling along the track has hardly any effect on the gain in information. The comparison between BT and COSMO-EU data identifies the strength of both data sets, which are the representation of the horizontal cloud extent for the BT data and the reproduction of the cloud amount for the COSMO-EU data. The results of the analysis of both data sets show the great advantage of the cloud imager. However, because both cloud data sets do not present the complete fine structure of the real cloud fields in the atmosphere it is assumed that the results tend to underestimate the increase in information. In conclusion, real measurements by such an instrument may result in an even higher benefit for tropospheric limb retrievals.

Effects of Cloud Horizontal Inhomogeneity and Drizzle on Remote Sensing of Cloud Droplet Effective Radius: Case Studies Based on Large-eddy Simulations

NASA Technical Reports Server (NTRS)

Zhang, Zhibo; Ackerman, Andrew S.; Feingold, Graham; Platnick, Steven; Pincus, Robert; Xue, Huiwen

2012-01-01

This study investigates effects of drizzle and cloud horizontal inhomogeneity on cloud effective radius (re) retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS). In order to identify the relative importance of various factors, we developed a MODIS cloud property retrieval simulator based on the combination of large-eddy simulations (LES) and radiative transfer computations. The case studies based on synthetic LES cloud fields indicate that at high spatial resolution (100 m) 3-D radiative transfer effects, such as illumination and shadowing, can induce significant differences between retrievals ofre based on reflectance at 2.1 m (re,2.1) and 3.7 m (re,3.7). It is also found that 3-D effects tend to have stronger impact onre,2.1 than re,3.7, leading to positive difference between the two (re,3.72.1) from illumination and negative re,3.72.1from shadowing. The cancellation of opposing 3-D effects leads to overall reasonable agreement betweenre,2.1 and re,3.7 at high spatial resolution as far as domain averages are concerned. At resolutions similar to MODIS, however, re,2.1 is systematically larger than re,3.7when averaged over the LES domain, with the difference exhibiting a threshold-like dependence on bothre,2.1and an index of the sub-pixel variability in reflectance (H), consistent with MODIS observations. In the LES cases studied, drizzle does not strongly impact reretrievals at either wavelength. It is also found that opposing 3-D radiative transfer effects partly cancel each other when cloud reflectance is aggregated from high spatial resolution to MODIS resolution, resulting in a weaker net impact of 3-D radiative effects onre retrievals. The large difference at MODIS resolution between re,3.7 and re,2.1 for highly inhomogeneous pixels with H 0.4 can be largely attributed to what we refer to as the plane-parallelrebias, which is attributable to the impact of sub-pixel level horizontal variability of cloud optical thickness onre retrievals and is greater for re,2.1 than re,3.7. These results suggest that there are substantial uncertainties attributable to 3-D radiative effects and plane-parallelre bias in the MODIS re,2.1retrievals for pixels with strong sub-pixel scale variability, and theH index can be used to identify these uncertainties.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Domengie, F., E-mail: florian.domengie@st.com; Morin, P.; Bauza, D.

We propose a model for dark current induced by metallic contamination in a CMOS image sensor. Based on Shockley-Read-Hall kinetics, the expression of dark current proposed accounts for the electric field enhanced emission factor due to the Poole-Frenkel barrier lowering and phonon-assisted tunneling mechanisms. To that aim, we considered the distribution of the electric field magnitude and metal atoms in the depth of the pixel. Poisson statistics were used to estimate the random distribution of metal atoms in each pixel for a given contamination dose. Then, we performed a Monte-Carlo-based simulation for each pixel to set the number of metalmore » atoms the pixel contained and the enhancement factor each atom underwent, and obtained a histogram of the number of pixels versus dark current for the full sensor. Excellent agreement with the dark current histogram measured on an ion-implanted gold-contaminated imager has been achieved, in particular, for the description of the distribution tails due to the pixel regions in which the contaminant atoms undergo a large electric field. The agreement remains very good when increasing the temperature by 15 °C. We demonstrated that the amplification of the dark current generated for the typical electric fields encountered in the CMOS image sensors, which depends on the nature of the metal contaminant, may become very large at high electric field. The electron and hole emissions and the resulting enhancement factor are described as a function of the trap characteristics, electric field, and temperature.« less

Clouds and the Earth's Radiant Energy System (CERES) Algorithm Theoretical Basis Document. Volume 3; Cloud Analyses and Determination of Improved Top of Atmosphere Fluxes (Subsystem 4)

NASA Technical Reports Server (NTRS)

1995-01-01

The theoretical bases for the Release 1 algorithms that will be used to process satellite data for investigation of the Clouds and Earth's Radiant Energy System (CERES) are described. The architecture for software implementation of the methodologies is outlined. Volume 3 details the advanced CERES methods for performing scene identification and inverting each CERES scanner radiance to a top-of-the-atmosphere (TOA) flux. CERES determines cloud fraction, height, phase, effective particle size, layering, and thickness from high-resolution, multispectral imager data. CERES derives cloud properties for each pixel of the Tropical Rainfall Measuring Mission (TRMM) visible and infrared scanner and the Earth Observing System (EOS) moderate-resolution imaging spectroradiometer. Cloud properties for each imager pixel are convolved with the CERES footprint point spread function to produce average cloud properties for each CERES scanner radiance. The mean cloud properties are used to determine an angular distribution model (ADM) to convert each CERES radiance to a TOA flux. The TOA fluxes are used in simple parameterization to derive surface radiative fluxes. This state-of-the-art cloud-radiation product will be used to substantially improve our understanding of the complex relationship between clouds and the radiation budget of the Earth-atmosphere system.

Satellite-Sensor Calibration Verification Using the Cloud-Shadow Method

NASA Technical Reports Server (NTRS)

Reinersman, P.; Carder, K. L.; Chen, F. R.

1995-01-01

An atmospheric-correction method which uses cloud-shaded pixels together with pixels in a neighboring region of similar optical properties is described. This cloud-shadow method uses the difference between the total radiance values observed at the sensor for these two regions, thus removing the nearly identical atmospheric radiance contributions to the two signals (e.g. path radiance and Fresnel-reflected skylight). What remains is largely due to solar photons backscattered from beneath the sea to dominate the residual signal. Normalization by the direct solar irradiance reaching the sea surface and correction for some second-order effects provides the remote-sensing reflectance of the ocean at the location of the neighbor region, providing a known 'ground target' spectrum for use in testing the calibration of the sensor. A similar approach may be useful for land targets if horizontal homogeneity of scene reflectance exists about the shadow. Monte Carlo calculations have been used to correct for adjacency effects and to estimate the differences in the skylight reaching the shadowed and neighbor pixels.

Remote Sensing of Tropical Ecosystems: Atmospheric Correction and Cloud Masking Matter

NASA Technical Reports Server (NTRS)

Hilker, Thomas; Lyapustin, Alexei I.; Tucker, Compton J.; Sellers, Piers J.; Hall, Forrest G.; Wang, Yujie

2012-01-01

Tropical rainforests are significant contributors to the global cycles of energy, water and carbon. As a result, monitoring of the vegetation status over regions such as Amazonia has been a long standing interest of Earth scientists trying to determine the effect of climate change and anthropogenic disturbance on the tropical ecosystems and its feedback on the Earth's climate. Satellite-based remote sensing is the only practical approach for observing the vegetation dynamics of regions like the Amazon over useful spatial and temporal scales, but recent years have seen much controversy over satellite-derived vegetation states in Amazônia, with studies predicting opposite feedbacks depending on data processing technique and interpretation. Recent results suggest that some of this uncertainty could stem from a lack of quality in atmospheric correction and cloud screening. In this paper, we assess these uncertainties by comparing the current standard surface reflectance products (MYD09, MYD09GA) and derived composites (MYD09A1, MCD43A4 and MYD13A2 - Vegetation Index) from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Aqua satellite to results obtained from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm. MAIAC uses a new cloud screening technique, and novel aerosol retrieval and atmospheric correction procedures which are based on time-series and spatial analyses. Our results show considerable improvements of MAIAC processed surface reflectance compared to MYD09/MYD13 with noise levels reduced by a factor of up to 10. Uncertainties in the current MODIS surface reflectance product were mainly due to residual cloud and aerosol contamination which affected the Normalized Difference Vegetation Index (NDVI): During the wet season, with cloud cover ranging between 90 percent and 99 percent, conventionally processed NDVI was significantly depressed due to undetected clouds. A smaller reduction in NDVI due to increased aerosol levels was observed during the dry season, with an inverse dependence of NDVI on aerosol optical thickness (AOT). NDVI observations processed with MAIAC showed highly reproducible and stable inter-annual patterns with little or no dependence on cloud cover, and no significant dependence on AOT (p less than 0.05). In addition to a better detection of cloudy pixels, MAIAC obtained about 20-80 percent more cloud free pixels, depending on season, a considerable amount for land analysis given the very high cloud cover (75-99 percent) observed at any given time in the area. We conclude that a new generation of atmospheric correction algorithms, such as MAIAC, can help to dramatically improve vegetation estimates over tropical rain forest, ultimately leading to reduced uncertainties in satellite-derived vegetation products globally.

Jupiter's Colorful Cloud Belts

NASA Image and Video Library

2018-01-12

Colorful swirling cloud belts dominate Jupiter's southern hemisphere in this image captured by NASA's Juno spacecraft. Jupiter appears in this color-enhanced image as a tapestry of vibrant cloud bands and storms. The dark region in the far left is called the South Temperate Belt. Intersecting the belt is a ghost-like feature of slithering white clouds. This is the largest feature in Jupiter's low latitudes that's a cyclone (rotating with clockwise motion). This image was taken on Dec. 16, 2017 at 10:12 PST (1:12 p.m. EST), as Juno performed its tenth close flyby of Jupiter. At the time the image was taken, the spacecraft was about 8,453 miles (13,604 kilometers) from the tops of the clouds of the planet at a latitude of 27.9 degrees south. The spatial scale in this image is 5.6 miles/pixel (9.1 kilometers/pixel). Citizen scientist Kevin M. Gill processed this image using data from the JunoCam imager. https://photojournal.jpl.nasa.gov/catalog/PIA21974

Automated cloud classification with a fuzzy logic expert system

NASA Technical Reports Server (NTRS)

Tovinkere, Vasanth; Baum, Bryan A.

1993-01-01

An unresolved problem in current cloud retrieval algorithms concerns the analysis of scenes containing overlapping cloud layers. Cloud parameterizations are very important both in global climate models and in studies of the Earth's radiation budget. Most cloud retrieval schemes, such as the bispectral method used by the International Satellite Cloud Climatology Project (ISCCP), have no way of determining whether overlapping cloud layers exist in any group of satellite pixels. One promising method uses fuzzy logic to determine whether mixed cloud and/or surface types exist within a group of pixels, such as cirrus, land, and water, or cirrus and stratus. When two or more class types are present, fuzzy logic uses membership values to assign the group of pixels partially to the different class types. The strength of fuzzy logic lies in its ability to work with patterns that may include more than one class, facilitating greater information extraction from satellite radiometric data. The development of the fuzzy logic rule-based expert system involves training the fuzzy classifier with spectral and textural features calculated from accurately labeled 32x32 regions of Advanced Very High Resolution Radiometer (AVHRR) 1.1-km data. The spectral data consists of AVHRR channels 1 (0.55-0.68 mu m), 2 (0.725-1.1 mu m), 3 (3.55-3.93 mu m), 4 (10.5-11.5 mu m), and 5 (11.5-12.5 mu m), which include visible, near-infrared, and infrared window regions. The textural features are based on the gray level difference vector (GLDV) method. A sophisticated new interactive visual image Classification System (IVICS) is used to label samples chosen from scenes collected during the FIRE IFO II. The training samples are chosen from predefined classes, chosen to be ocean, land, unbroken stratiform, broken stratiform, and cirrus. The November 28, 1991 NOAA overpasses contain complex multilevel cloud situations ideal for training and validating the fuzzy logic expert system.

Limits to Cloud Susceptibility

NASA Technical Reports Server (NTRS)

Coakley, James A., Jr.

2002-01-01

1-kilometer AVHRR observations of ship tracks in low-level clouds off the west coast of the U S. were used to determine limits for the degree to which clouds might be altered by increases in anthropogenic aerosols. Hundreds of tracks were analyzed to determine whether the changes in droplet radii, visible optical depths, and cloud top altitudes that result from the influx of particles from underlying ships were consistent with expectations based on simple models for the indirect effect of aerosols. The models predict substantial increases in sunlight reflected by polluted clouds due to the increases in droplet numbers and cloud liquid water that result from the elevated particle concentrations. Contrary to the model predictions, the analysis of ship tracks revealed a 15-20% reduction in liquid water for the polluted clouds. Studies performed with a large-eddy cloud simulation model suggested that the shortfall in cloud liquid water found in the satellite observations might be attributed to the restriction that the 1-kilometer pixels be completely covered by either polluted or unpolluted cloud. The simulation model revealed that a substantial fraction of the indirect effect is caused by a horizontal redistribution of cloud water in the polluted clouds. Cloud-free gaps in polluted clouds fill in with cloud water while the cloud-free gaps in the surrounding unpolluted clouds remain cloud-free. By limiting the analysis to only overcast pixels, the current study failed to account for the gap-filling predicted by the simulation model. This finding and an analysis of the spatial variability of marine stratus suggest new ways to analyze ship tracks to determine the limit to which particle pollution will alter the amount of sunlight reflected by clouds.

Spectral Cloud-Filtering of AIRS Data: Non-Polar Ocean

NASA Technical Reports Server (NTRS)

Aumann, Hartmut H.; Gregorich, David; Barron, Diana

2004-01-01

The Atmospheric Infrared Sounder (AIRS) is a grating array spectrometer which covers the thermal infrared spectral range between 640 and 1700/cm. In order to retain the maximum radiometric accuracy of the AIRS data, the effects of cloud contamination have to be minimized. We discuss cloud filtering which uses the high spectral resolution of AIRS to identify about 100,000 of 500,000 non-polar ocean spectra per day as relatively "cloud-free". Based on the comparison of surface channels with the NCEP provided global real time sst (rtg.sst), AIRS surface sensitive channels have a cold bias ranging from O.5K during the day to 0.8K during the night. Day and night spatial coherence tests show that the cold bias is due to cloud contamination. During the day the cloud contamination is due to a 2-3% broken cloud cover at the 1-2 km altitude, characteristic of low stratus clouds. The cloud-contamination effects surface sensitive channels only. Cloud contamination can be reduced to 0.2K by combining the spectral filter with a spatial coherence threshold, but the yield drops to 16,000 spectra per day. AIRS was launched in May 2002 on the Earth Observing System (EOS) Aqua satellite. Since September 2002 it has returned 4 million spectra of the globe each day.

Lightweight Electronic Camera for Research on Clouds

NASA Technical Reports Server (NTRS)

Lawson, Paul

2006-01-01

"Micro-CPI" (wherein "CPI" signifies "cloud-particle imager") is the name of a small, lightweight electronic camera that has been proposed for use in research on clouds. It would acquire and digitize high-resolution (3- m-pixel) images of ice particles and water drops at a rate up to 1,000 particles (and/or drops) per second.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bolotnikov, A. E.; Camarda, G. S.; Cui, Y.

Following our successful demonstration of the position-sensitive virtual Frisch-grid detectors, we investigated the feasibility of using high-granularity position sensing to correct response non-uniformities caused by the crystal defects in CdZnTe (CZT) pixelated detectors. The development of high-granularity detectors able to correct response non-uniformities on a scale comparable to the size of electron clouds opens the opportunity of using unselected off-the-shelf CZT material, whilst still assuring high spectral resolution for the majority of the detectors fabricated from an ingot. Here, we present the results from testing 3D position-sensitive 15×15×10 mm 3 pixelated detectors, fabricated with conventional pixel patterns with progressively smallermore » pixel sizes: 1.4, 0.8, and 0.5 mm. We employed the readout system based on the H3D front-end multi-channel ASIC developed by BNL's Instrumentation Division in collaboration with the University of Michigan. We use the sharing of electron clouds among several adjacent pixels to measure locations of interaction points with sub-pixel resolution. By using the detectors with small-pixel sizes and a high probability of the charge-sharing events, we were able to improve their spectral resolutions in comparison to the baseline levels, measured for the 1.4-mm pixel size detectors with small fractions of charge-sharing events. These results demonstrate that further enhancement of the performance of CZT pixelated detectors and reduction of costs are possible by using high spatial-resolution position information of interaction points to correct the small-scale response non-uniformities caused by crystal defects present in most devices.« less

JPSS application in a near real time regional numerical forecast system at CIMSS

NASA Astrophysics Data System (ADS)

Li, J.; Wang, P.; Han, H.; Zhu, F.; Schmit, T. J.; Goldberg, M.

2015-12-01

Observations from next generation of environmental sensors onboard the Suomi National Polar-Orbiting Parnership (S-NPP) and its successor, the Joint Polar Satellite System (JPSS), provide us the critical information for numerical weather forecast (NWP). How to better represent these satellite observations and how to get value added information into NWP system still need more studies. Recently scientists from Cooperative Institute of Meteorological Satellite Studies (CIMSS) at University of Wisconsin-Madison have developed a near realtime regional Satellite Data Assimilation system for Tropical storm forecasts (SDAT) (http://cimss.ssec.wisc.edu/sdat). The system is built with the community Gridpoint Statistical Interpolation (GSI) assimilation and advanced Weather Research Forecast (WRF) model. With GSI, SDAT can assimilate all operational available satellite data including GOES, AMSUA/AMSUB, HIRS, MHS, ATMS, AIRS and IASI radiances and some satellite derived products. In addition, some research products, such as hyperspectral IR retrieved temperature and moisture profiles, GOES imager atmospheric motion vector (AMV) and GOES sounder layer precipitable water (LPW), are also added into the system. Using SDAT as a research testbed, studies have been conducted to show how to improve high impact weather forecast by better handling cloud information in satellite data. Previously by collocating high spatial resolution MODIS data with hyperspectral resolution AIRS data, precise clear pixels of AIRS can be identified and some partially or thin cloud contamination from pixels can be removed by taking advantage of high spatial resolution and high accurate MODIS cloud information. The results have demonstrated that both of these strategies have greatly improved the hurricane track and intensity forecast. We recently have extended these methodologies into processing CrIS/VIIRS data. We also tested similar ideas in microwave sounders by the collocation of AMSU/MODIS and ATMS/VIIRS data. The experiments along with other SDAT progresses will be presented in the meeting.

An Evaluation of Northern Hemisphere Merged Cloud Analyses from the United States Air Force Cloud Depiction Forecasting System II

DTIC Science & Technology

2013-03-01

layering and typing to provide a vertical stratification of the cloud-filled pixels detected in Level 2. Level 3 output is remapped to the standard AFWA...analyses are compared to one another to see if the most recent analysis also has the lowest estimated error. Optimum interpolation (OI) occurs when...NORTHERN HEMISPHERE MERGED CLOUD ANALYSES FROM THE UNITED STATES AIR FORCE CLOUD DEPICTION FORECASTING SYSTEM II by Chandra M. Pasillas March

Emerging Use of Dual Channel Infrared for Remote Sensing of Sea Ice

NASA Astrophysics Data System (ADS)

Lewis, N. S.; Serreze, M. C.; Gallaher, D. W.; Koenig, L.; Schaefer, K. M.; Campbell, G. G.; Thompson, J. A.; Grant, G.; Fetterer, F. M.

2017-12-01

Using GOES-16 data as a proxy for overhead persistent infrared, we examine the feasibility of using a dual channel shortwave / midwave infrared (SWIR/MWIR) approach to detect and chart sea ice in Hudson Bay through a series of images with a temporal scale of less than fifteen minutes. While not traditionally exploited for sea ice remote sensing, the availability of near continuous shortwave and midwave infrared data streams over the Arctic from overhead persistent infrared (OPIR) satellites could provide an invaluable source of information regarding the changing Arctic climate. Traditionally used for the purpose of missile warning and strategic defense, characteristics of OPIR make it an attractive source for Arctic remote sensing as the temporal resolution can provide insight into ice edge melt and motion processes. Fundamentally, the time series based algorithm will discern water/ice/clouds using a SWIR/MWIR normalized difference index. Cloud filtering is accomplished through removing pixels categorized as clouds while retaining a cache of previous ice/water pixels to replace any cloud obscured (and therefore omitted) pixels. Demonstration of the sensitivity of GOES-16 SWIR/MWIR to detect and discern water/ice/clouds provides a justification for exploring the utility of military OPIR sensors for civil and commercial applications. Potential users include the scientific community as well as emergency responders, the fishing industry, oil and gas industries, and transportation industries that are seeking to exploit changing conditions in the Arctic but require more accurate and timely ice charting products.

Evolution of Fuel-Air and Contaminant Clouds Resulting from a Cruise Missile Explosion Scenario

DOE Office of Scientific and Technical Information (OSTI.GOV)

Grossman, A S; Kul, A L

2005-06-22

A low-mach-number hydrodynamics model has been used to simulate the evolution of a fuel-air mixture and contaminant cloud resulting from the detonation of a cruise missile. The detonation has been assumed to be non-nuclear. The cloud evolution has been carried out to a time of 5.5 seconds. At this time the contaminant has completely permeated the initial fuel-air mixture cloud.

Progress in Understanding the Impacts of 3-D Cloud Structure on MODIS Cloud Property Retrievals for Marine Boundary Layer Clouds

NASA Technical Reports Server (NTRS)

Zhang, Zhibo; Werner, Frank; Miller, Daniel; Platnick, Steven; Ackerman, Andrew; DiGirolamo, Larry; Meyer, Kerry; Marshak, Alexander; Wind, Galina; Zhao, Guangyu

2016-01-01

Theory: A novel framework based on 2-D Tayler expansion for quantifying the uncertainty in MODIS retrievals caused by sub-pixel reflectance inhomogeneity. (Zhang et al. 2016). How cloud vertical structure influences MODIS LWP retrievals. (Miller et al. 2016). Observation: Analysis of failed MODIS cloud property retrievals. (Cho et al. 2015). Cloud property retrievals from 15m resolution ASTER observations. (Werner et al. 2016). Modeling: LES-Satellite observation simulator (Zhang et al. 2012, Miller et al. 2016).

Implementation on Landsat Data of a Simple Cloud Mask Algorithm Developed for MODIS Land Bands

NASA Technical Reports Server (NTRS)

Oreopoulos, Lazaros; Wilson, Michael J.; Varnai, Tamas

2010-01-01

This letter assesses the performance on Landsat-7 images of a modified version of a cloud masking algorithm originally developed for clear-sky compositing of Moderate Resolution Imaging Spectroradiometer (MODIS) images at northern mid-latitudes. While data from recent Landsat missions include measurements at thermal wavelengths, and such measurements are also planned for the next mission, thermal tests are not included in the suggested algorithm in its present form to maintain greater versatility and ease of use. To evaluate the masking algorithm we take advantage of the availability of manual (visual) cloud masks developed at USGS for the collection of Landsat scenes used here. As part of our evaluation we also include the Automated Cloud Cover Assesment (ACCA) algorithm that includes thermal tests and is used operationally by the Landsat-7 mission to provide scene cloud fractions, but no cloud masks. We show that the suggested algorithm can perform about as well as ACCA both in terms of scene cloud fraction and pixel-level cloud identification. Specifically, we find that the algorithm gives an error of 1.3% for the scene cloud fraction of 156 scenes, and a root mean square error of 7.2%, while it agrees with the manual mask for 93% of the pixels, figures very similar to those from ACCA (1.2%, 7.1%, 93.7%).

Use of high-granularity CdZnTe pixelated detectors to correct response non-uniformities caused by defects in crystals

DOE PAGES

Bolotnikov, A. E.; Camarda, G. S.; Cui, Y.; ...

2015-09-06

Following our successful demonstration of the position-sensitive virtual Frisch-grid detectors, we investigated the feasibility of using high-granularity position sensing to correct response non-uniformities caused by the crystal defects in CdZnTe (CZT) pixelated detectors. The development of high-granularity detectors able to correct response non-uniformities on a scale comparable to the size of electron clouds opens the opportunity of using unselected off-the-shelf CZT material, whilst still assuring high spectral resolution for the majority of the detectors fabricated from an ingot. Here, we present the results from testing 3D position-sensitive 15×15×10 mm 3 pixelated detectors, fabricated with conventional pixel patterns with progressively smallermore » pixel sizes: 1.4, 0.8, and 0.5 mm. We employed the readout system based on the H3D front-end multi-channel ASIC developed by BNL's Instrumentation Division in collaboration with the University of Michigan. We use the sharing of electron clouds among several adjacent pixels to measure locations of interaction points with sub-pixel resolution. By using the detectors with small-pixel sizes and a high probability of the charge-sharing events, we were able to improve their spectral resolutions in comparison to the baseline levels, measured for the 1.4-mm pixel size detectors with small fractions of charge-sharing events. These results demonstrate that further enhancement of the performance of CZT pixelated detectors and reduction of costs are possible by using high spatial-resolution position information of interaction points to correct the small-scale response non-uniformities caused by crystal defects present in most devices.« less

Low Clouds and Fog Characterization over Iberian Peninsula using Meteosat Second Generation Images

NASA Astrophysics Data System (ADS)

Sánchez, Beatriz; Maqueda, Gregorio

2014-05-01

Fog is defined as a collection of suspended water droplets or ice crystals in the air near the Earth's surface that lead to a reduction of horizontal visibility below 1 km (National Oceanic and Atmospheric Administration, 1995). Fog is a stratiform cloud with similar radiative characteristics, for this reason the difference between fog and low stratus clouds is of little importance for remote sensing applications. Fog and low clouds are important atmospheric phenomena, mainly because of their impact on traffic safety and air quality, acting as an obstruction to traffic at land, sea and in the air. The purpose of this work is to develop the method of fog/low clouds detection and analysis on nighttime using Meteosat Second Generation data. This study is focused on the characterization of these atmospheric phenomena in different study cases over the Iberian Peninsula with distinct orography. Firstly, fog/low clouds detection is implemented as a composition of three infrared channels 12.0, 10.8 and 3.9 µm from SEVIRI radiometer on board European geostationary satellite Meteosat (Meteosat-9). The algorithm of detection makes use of a combination of these channels and their differences by creating RGB composites images. On this way, it displays the spatial coverage and location of fog entities. Secondly, this technique allows separating pixels which are indicated as fog/low clouds from clear pixels, assessing the properties of individual pixels using appropriated thresholds of brightness temperature. Thus, it achieves a full analysis of the extent and distribution of fog and its evolution over time. The results of this study have been checked by using ground-based point measurements available as METAR data. Despite the flaws in this sort of inter-comparison approach, the outcome produces to accurate fog/low clouds detection. This work encompasses the way to obtain spatial information from this atmospheric phenomenon by means of satellite imagery.

High Resolution Integral Field Spectroscopy of Europa's Sodium Clouds: Evidence for a Component with Origins in Iogenic Plasma.

NASA Astrophysics Data System (ADS)

Schmidt, C.; Johnson, R. E.; Mendillo, M.; Baumgardner, J. L.; Moore, L.; O'Donoghue, J.; Leblanc, F.

2015-12-01

With the object of constraining Iogenic contributions and identifying drivers for variability, we report new observations of neutral sodium in Europa's exosphere. An R~20000 integral field spectrograph at McDonald Observatory is used to generate Doppler maps of sodium cloud structures with a resolution of 2.8 km/s/pixel. In the five nights of observations since 2011, measurements on UT 6.15-6.31 May 2015 uniquely feature fast (10s of km/s) neutral sodium clouds extending nearly 100 Europa radii, more distant than in any previous findings. During these measurements, the satellite geometry was favorable for the transfer of Na from Io to Europa, located at 1:55 to 4:00 and 3:38 to 4:39 Jovian local time, respectively. Eastward emission (away from Jupiter) extends 10-20 Europa radii retaining the moon's rest velocity, while westward emission blue-shifts with distance, and a broad range of velocities are measured, reaching at least 70 km/s at 80 Europa radii. These cloud features are distinct from Io's "banana" and "stream" features, the distant Jupiter-orbiting nebula, and from terrestrial OH and Na contaminant emissions. Io's production was quiescent during this observation, following an extremely active phase in February 2015. These results are consistent with previous findings that Europa's Na exosphere has peak emission between midnight and dawn Jovian local time and support the idea that sodium escape from Io can significantly enhance the emission intensity measured at Europa.

Modeling the frequency-dependent detective quantum efficiency of photon-counting x-ray detectors.

PubMed

Stierstorfer, Karl

2018-01-01

To find a simple model for the frequency-dependent detective quantum efficiency (DQE) of photon-counting detectors in the low flux limit. Formula for the spatial cross-talk, the noise power spectrum and the DQE of a photon-counting detector working at a given threshold are derived. Parameters are probabilities for types of events like single counts in the central pixel, double counts in the central pixel and a neighboring pixel or single count in a neighboring pixel only. These probabilities can be derived in a simple model by extensive use of Monte Carlo techniques: The Monte Carlo x-ray propagation program MOCASSIM is used to simulate the energy deposition from the x-rays in the detector material. A simple charge cloud model using Gaussian clouds of fixed width is used for the propagation of the electric charge generated by the primary interactions. Both stages are combined in a Monte Carlo simulation randomizing the location of impact which finally produces the required probabilities. The parameters of the charge cloud model are fitted to the spectral response to a polychromatic spectrum measured with our prototype detector. Based on the Monte Carlo model, the DQE of photon-counting detectors as a function of spatial frequency is calculated for various pixel sizes, photon energies, and thresholds. The frequency-dependent DQE of a photon-counting detector in the low flux limit can be described with an equation containing only a small set of probabilities as input. Estimates for the probabilities can be derived from a simple model of the detector physics. © 2017 American Association of Physicists in Medicine.

Two cloud-based cues for estimating scene structure and camera calibration.

PubMed

Jacobs, Nathan; Abrams, Austin; Pless, Robert

2013-10-01

We describe algorithms that use cloud shadows as a form of stochastically structured light to support 3D scene geometry estimation. Taking video captured from a static outdoor camera as input, we use the relationship of the time series of intensity values between pairs of pixels as the primary input to our algorithms. We describe two cues that relate the 3D distance between a pair of points to the pair of intensity time series. The first cue results from the fact that two pixels that are nearby in the world are more likely to be under a cloud at the same time than two distant points. We describe methods for using this cue to estimate focal length and scene structure. The second cue is based on the motion of cloud shadows across the scene; this cue results in a set of linear constraints on scene structure. These constraints have an inherent ambiguity, which we show how to overcome by combining the cloud motion cue with the spatial cue. We evaluate our method on several time lapses of real outdoor scenes.

A Spectralon BRF Data Base for MISR Calibration Application

NASA Technical Reports Server (NTRS)

Bruegge, C.; Chrien, N.; Haner, D.

1999-01-01

The Multi-angle Imaging SpectroRadiometer (MISR) is an Earth observing sensor which will provide global retrievals of aerosols, clouds, and land surface parameters. Instrument specifications require high accuracy absolute calibration, as well as accurate camera-to-camera, band-to-band and pixel-to-pixel relative response determinations.

A study of the 3D radiative transfer effect in cloudy atmospheres

NASA Astrophysics Data System (ADS)

Okata, M.; Teruyuki, N.; Suzuki, K.

2015-12-01

Evaluation of the effect of clouds in the atmosphere is a significant problem in the Earth's radiation budget study with their large uncertainties of microphysics and the optical properties. In this situation, we still need more investigations of 3D cloud radiative transer problems using not only models but also satellite observational data.For this purpose, we have developed a 3D-Monte-Carlo radiative transfer code that is implemented with various functions compatible with the OpenCLASTR R-Star radiation code for radiance and flux computation, i.e. forward and backward tracing routines, non-linear k-distribution parameterization (Sekiguchi and Nakajima, 2008) for broad band solar flux calculation, and DM-method for flux and TMS-method for upward radiance (Nakajima and Tnaka 1998). We also developed a Minimum cloud Information Deviation Profiling Method (MIDPM) as a method for a construction of 3D cloud field with MODIS/AQUA and CPR/CloudSat data. We then selected a best-matched radar reflectivity factor profile from the library for each of off-nadir pixels of MODIS where CPR profile is not available, by minimizing the deviation between library MODIS parameters and those at the pixel. In this study, we have used three cloud microphysical parameters as key parameters for the MIDPM, i.e. effective particle radius, cloud optical thickness and top of cloud temperature, and estimated 3D cloud radiation budget. We examined the discrepancies between satellite observed and mode-simulated radiances and three cloud microphysical parameter's pattern for studying the effects of cloud optical and microphysical properties on the radiation budget of the cloud-laden atmospheres.

Independent Pixel and Two Dimensional Estimates of LANDSAT-Derived Cloud Field Albedo

NASA Technical Reports Server (NTRS)

Chambers, L. H.; Wielicki, Bruce A.; Evans, K. F.

1996-01-01

A theoretical study has been conducted on the effects of cloud horizontal inhomogeneity on cloud albedo bias. A two-dimensional (2D) version of the Spherical Harmonic Discrete Ordinate Method (SHDOM) is used to estimate the albedo bias of the plane parallel (PP-IPA) and independent pixel (IPA-2D) approximations for a wide range of 2D cloud fields obtained from LANDSAT. They include single layer trade cumulus, open and closed cell broken stratocumulus, and solid stratocumulus boundary layer cloud fields over ocean. Findings are presented on a variety of averaging scales and are summarized as a function of cloud fraction, mean cloud optical depth, cloud aspect ratio, standard deviation of optical depth, and the gamma function parameter Y (a measure of the width of the optical depth distribution). Biases are found to be small for small cloud fraction or mean optical depth, where the cloud fields under study behave linearly. They are large (up to 0.20 for PP-IPA bias, -0.12 for IPA-2D bias) for large v. On a scene average basis PP-IPA bias can reach 0.30, while IPA-2D bias reaches its largest magnitude at -0.07. Biases due to horizontal transport (IPA-2D) are much smaller than PP-IPA biases but account for 20% RMS of the bias overall. Limitations of this work include the particular cloud field set used, assumptions of conservative scattering, constant cloud droplet size, no gas absorption or surface reflectance, and restriction to 2D radiative transport. The LANDSAT data used may also be affected by radiative smoothing.

The neuron net method for processing the clear pixels and method of the analytical formulas for processing the cloudy pixels of POLDER instrument images

NASA Astrophysics Data System (ADS)

Melnikova, I.; Mukai, S.; Vasilyev, A.

Data of remote measurements of reflected radiance with the POLDER instrument on board of ADEOS satellite are used for retrieval of the optical thickness, single scattering albedo and phase function parameter of cloudy and clear atmosphere. The method of perceptron neural network that from input values of multiangle radiance and Solar incident angle allows to obtain surface albedo, the optical thickness, single scattering albedo and phase function parameter in case of clear sky. Two last parameters are determined as optical average for atmospheric column. The calculation of solar radiance with using the MODTRAN-3 code with taking into account multiple scattering is accomplished for neural network learning. All mentioned parameters were randomly varied on the base of statistical models of possible measured parameters variation. Results of processing one frame of remote observation that consists from 150,000 pixels are presented. The methodology elaborated allows operative determining optical characteristics as cloudy as clear atmosphere. Further interpretation of these results gives the possibility to extract the information about total contents of atmospheric aerosols and absorbing gases in the atmosphere and create models of the real cloudiness An analytical method of interpretation that based on asymptotic formulas of multiple scattering theory is applied to remote observations of reflected radiance in case of cloudy pixel. Details of the methodology and error analysis were published and discussed earlier. Here we present results of data processing of pixel size 6x6 km In many studies the optical thickness is evaluated earlier in the assumption of the conservative scattering. But in case of true absorption in clouds the large errors in parameter obtained are possible. The simultaneous retrieval of two parameters at every wavelength independently is the advantage comparing with earlier studies. The analytical methodology is based on the transfer theory asymptotic formula inversion for optically thick stratus clouds. The model of horizontally infinite layer is considered. The slight horizontal heterogeneity is approximately taken into account. Formulas containing only the measured values of two-direction radiance and functions of solar and view angles were derived earlier. The 6 azimuth harmonics of reflection function are taken into account. The simple approximation of the cloud top boarder heterogeneity is used. The clouds, projecting upper the cloud top plane causes the increase of diffuse radiation in the incident flux. It is essential for calculation of radiative characteristics, which depends on lighting conditions. Escape and reflection functions describe this dependence for reflected radiance and local albedo of semi-infinite medium - for irradiance. Thus the functions depending on solar incident angle is to replace by their modifications. Firstly optical thickness of every pixel is obtained with simple formula assuming conservative scattering for all available view directions. Deviations between obtained values may be taken as a measure of the cloud top deviation from the plane. The special parameter is obtained, which takes into account the shadowing effect. Then single scattering albedo and optical thickness (with the true absorption assuming) are obtained for pairs of view directions with equal optical thickness. After that the averaging of values obtained and relative error evaluation is accomplished for all viewing directions of every pixel. The procedure is repeated for all wavelengths and pixels independently.

A New Normalized Difference Cloud Retrieval Technique Applied to Landsat Radiances Over the Oklahoma ARM Site

NASA Technical Reports Server (NTRS)

Orepoulos, Lazaros; Cahalan, Robert; Marshak, Alexander; Wen, Guoyong

1999-01-01

We suggest a new approach to cloud retrieval, using a normalized difference of nadir reflectivities (NDNR) constructed from a non-absorbing and absorbing (with respect to liquid water) wavelength. Using Monte Carlo simulations we show that this quantity has the potential of removing first order scattering effects caused by cloud side illumination and shadowing at oblique Sun angles. Application of the technique to TM (Thematic Mapper) radiance observations from Landsat-5 over the Southern Great Plains site of the ARM (Atmospheric Radiation Measurement) program gives very similar regional statistics and histograms, but significant differences at the pixel level. NDNR can be also combined with the inverse NIPA (Nonlocal Independent Pixel Approximation) of Marshak (1998) which is applied for the first time on overcast Landsat scene subscenes. We demonstrate the sensitivity of the NIPA-retrieved cloud fields on the parameters of the method and discuss practical issues related to the optimal choice of these parameters.

Jovian Tempest

NASA Image and Video Library

2017-11-16

This color-enhanced image of a massive, raging storm in Jupiter's northern hemisphere was captured by NASA's Juno spacecraft during its ninth close flyby of the gas giant planet. The image was taken on Oct. 24, 2017 at 10:32 a.m. PDT (1:32 p.m. EDT). At the time the image was taken, the spacecraft was about 6,281 miles (10,108 kilometers) from the tops of the clouds of Jupiter at a latitude of 41.84 degrees. The spatial scale in this image is 4.2 miles/pixel (6.7 kilometers/pixel). The storm is rotating counter-clockwise with a wide range of cloud altitudes. The darker clouds are expected to be deeper in the atmosphere than the brightest clouds. Within some of the bright "arms" of this storm, smaller clouds and banks of clouds can be seen, some of which are casting shadows to the right side of this picture (sunlight is coming from the left). The bright clouds and their shadows range from approximately 4 to 8 miles (7 to 12 kilometers) in both widths and lengths. These appear similar to the small clouds in other bright regions Juno has detected and are expected to be updrafts of ammonia ice crystals possibly mixed with water ice. Citizen scientists Gerald Eichstädt and Seán Doran processed this image using data from the JunoCam imager. https://photojournal.jpl.nasa.gov/catalog/PIA21971

Detection of diluted contaminants on chicken carcasses using a two-dimensional scatter plot based on a two-dimensional hyperspectral correlation spectrum.

PubMed

Wu, Wei; Chen, Gui-Yun; Wu, Ming-Qing; Yu, Zhen-Wei; Chen, Kun-Jie

2017-03-20

A two-dimensional (2D) scatter plot method based on the 2D hyperspectral correlation spectrum is proposed to detect diluted blood, bile, and feces from the cecum and duodenum on chicken carcasses. First, from the collected hyperspectral data, a set of uncontaminated regions of interest (ROIs) and four sets of contaminated ROIs were selected, whose average spectra were treated as the original spectrum and influenced spectra, respectively. Then, the difference spectra were obtained and used to conduct correlation analysis, from which the 2D hyperspectral correlation spectrum was constructed using the analogy method of 2D IR correlation spectroscopy. Two maximum auto-peaks and a pair of cross peaks appeared at 656 and 474 nm. Therefore, 656 and 474 nm were selected as the characteristic bands because they were most sensitive to the spectral change induced by the contaminants. The 2D scatter plots of the contaminants, clean skin, and background in the 474- and 656-nm space were used to distinguish the contaminants from the clean skin and background. The threshold values of the 474- and 656-nm bands were determined by receiver operating characteristic (ROC) analysis. According to the ROC results, a pixel whose relative reflectance at 656 nm was greater than 0.5 and relative reflectance at 474 nm was lower than 0.3 was judged as a contaminated pixel. A region with more than 50 pixels identified was marked in the detection graph. This detection method achieved a recognition rate of up to 95.03% at the region level and 31.84% at the pixel level. The false-positive rate was only 0.82% at the pixel level. The results of this study confirm that the 2D scatter plot method based on the 2D hyperspectral correlation spectrum is an effective method for detecting diluted contaminants on chicken carcasses.

Model-Based Estimation of Sampling-Caused Uncertainty in Aerosol Remote Sensing for Climate Research Applications

NASA Technical Reports Server (NTRS)

Geogdzhayev, Igor V.; Cairns, Brian; Mishchenko, Michael I.; Tsigaridis, Kostas; van Noije, Twan

2014-01-01

To evaluate the effect of sampling frequency on the global monthly mean aerosol optical thickness (AOT), we use 6 years of geographical coordinates of Moderate Resolution Imaging Spectroradiometer (MODIS) L2 aerosol data, daily global aerosol fields generated by the Goddard Institute for Space Studies General Circulation Model and the chemical transport models Global Ozone Chemistry Aerosol Radiation and Transport, Spectral Radiationtransport Model for Aerosol Species and Transport Model 5, at a spatial resolution between 1.125 deg × 1.125 deg and 2 deg × 3?: the analysis is restricted to 60 deg S-60 deg N geographical latitude. We found that, in general, the MODIS coverage causes an underestimate of the global mean AOT over the ocean. The long-term mean absolute monthly difference between all and dark target (DT) pixels was 0.01-0.02 over the ocean and 0.03-0.09 over the land, depending on the model dataset. Negative DT biases peak during boreal summers, reaching 0.07-0.12 (30-45% of the global long-term mean AOT). Addition of the Deep Blue pixels tempers the seasonal dependence of the DT biases and reduces the mean AOT difference over land by 0.01-0.02. These results provide a quantitative measure of the effect the pixel exclusion due to cloud contamination, ocean sun-glint and land type has on the MODIS estimates of the global monthly mean AOT. We also simulate global monthly mean AOT estimates from measurements provided by pixel-wide along-track instruments such as the Aerosol Polarimetry Sensor and the Cloud-Aerosol LiDAR with Orthogonal Polarization. We estimate the probable range of the global AOT standard error for an along-track sensor to be 0.0005-0.0015 (ocean) and 0.0029-0.01 (land) or 0.5-1.2% and 1.1-4% of the corresponding global means. These estimates represent errors due to sampling only and do not include potential retrieval errors. They are smaller than or comparable to the published estimate of 0.01 as being a climatologically significant change in the global mean AOT, suggesting that sampling density is unlikely to limit the use of such instruments for climate applications at least on a global, monthly scale.

Spring floods prediction with the use of optical satellite data in Québec

NASA Astrophysics Data System (ADS)

Roy, A.; Royer, A.; Turcotte, R.

2009-04-01

The Centre d'expertise hydrique du Québec (CEHQ) operates a distributed hydrological model, which integrates a snow model, for the management of dams in the south of Québec. It appears that the estimation of the water quantity of snowmelt in spring remains a variable with a large uncertainty and induces generally to an important error in stream flow simulation. Therefore, the National snow and ice center (NSIDC) produces, from MODIS (Moderate Resolution Imaging Spectroradiometer) data, continuous and homogeneous spatial snow cover (snow/swow-free) data on the whole territory, but with a cloud contamination. This research aims to improve the prediction of spring floods and the estimation of the rate of discharge by integrating snow cover data in the CEHQ's snow model. The study is done on two watersheds: du Nord river watershed (45,8°N) and Aux Écorces river watershed (47,9°N). The snow model used in the study (SPH-AV) is an implementation developed by the CEHQ of the snowmelt model of HYDROLTEL in is hydrological forecast system to simulate the melted water. The melted water estimated is then used as input in the empirical hydrological model MOHYSE to simulate stream flow. MODIS data are considered valid only when the cloud cover on each pixel of the watersheds is less then 30%. A pixel by pixel correction is applied to the snow pack when there is a difference between satellite snow cover and modeled snow cover. In the case of model shows to much snow, a factor is applied on temperatures by iterative process (starting from the last valid MODIS data) to melt the snow. In the opposite case, the snow quantity added to the last valid MODIS data is found by iterative process so that the pixel's snow water equivalent is equal to the nonzero neighbor minimum value. The study shows, through the simulations done on the two watersheds, the interest of the use of snow/snow-free product for the operational update of snow water equivalent with the objective to improve spring snowmelt stream flow simulations. The binary aspect (snow/snowfree) of the data is however a limit. Alternatives are discussed (passive microwave data). Keywords : satellite snow cover data, MODIS, satellite data integration, snow model, hydrological model, stream flow simulation, flood.

A browser-based 3D Visualization Tool designed for comparing CERES/CALIOP/CloudSAT level-2 data sets.

NASA Astrophysics Data System (ADS)

Chu, C.; Sun-Mack, S.; Chen, Y.; Heckert, E.; Doelling, D. R.

2017-12-01

In Langley NASA, Clouds and the Earth's Radiant Energy System (CERES) and Moderate Resolution Imaging Spectroradiometer (MODIS) are merged with Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) on the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) and CloudSat Cloud Profiling Radar (CPR). The CERES merged product (C3M) matches up to three CALIPSO footprints with each MODIS pixel along its ground track. It then assigns the nearest CloudSat footprint to each of those MODIS pixels. The cloud properties from MODIS, retrieved using the CERES algorithms, are included in C3M with the matched CALIPSO and CloudSat products along with radiances from 18 MODIS channels. The dataset is used to validate the CERES retrieved MODIS cloud properties and the computed TOA and surface flux difference using MODIS or CALIOP/CloudSAT retrieved clouds. This information is then used to tune the computed fluxes to match the CERES observed TOA flux. A visualization tool will be invaluable to determine the cause of these large cloud and flux differences in order to improve the methodology. This effort is part of larger effort to allow users to order the CERES C3M product sub-setted by time and parameter as well as the previously mentioned visualization capabilities. This presentation will show a new graphical 3D-interface, 3D-CERESVis, that allows users to view both passive remote sensing satellites (MODIS and CERES) and active satellites (CALIPSO and CloudSat), such that the detailed vertical structures of cloud properties from CALIPSO and CloudSat are displayed side by side with horizontally retrieved cloud properties from MODIS and CERES. Similarly, the CERES computed profile fluxes whether using MODIS or CALIPSO and CloudSat clouds can also be compared. 3D-CERESVis is a browser-based visualization tool that makes uses of techniques such as multiple synchronized cursors, COLLADA format data and Cesium.

Cloud and DNI nowcasting with MSG/SEVIRI for the optimized operation of concentrating solar power plants

NASA Astrophysics Data System (ADS)

Sirch, Tobias; Bugliaro, Luca; Zinner, Tobias; Möhrlein, Matthias; Vazquez-Navarro, Margarita

2017-02-01

A novel approach for the nowcasting of clouds and direct normal irradiance (DNI) based on the Spinning Enhanced Visible and Infrared Imager (SEVIRI) aboard the geostationary Meteosat Second Generation (MSG) satellite is presented for a forecast horizon up to 120 min. The basis of the algorithm is an optical flow method to derive cloud motion vectors for all cloudy pixels. To facilitate forecasts over a relevant time period, a classification of clouds into objects and a weighted triangular interpolation of clear-sky regions are used. Low and high level clouds are forecasted separately because they show different velocities and motion directions. Additionally a distinction in advective and convective clouds together with an intensity correction for quickly thinning convective clouds is integrated. The DNI is calculated from the forecasted optical thickness of the low and high level clouds. In order to quantitatively assess the performance of the algorithm, a forecast validation against MSG/SEVIRI observations is performed for a period of 2 months. Error rates and Hanssen-Kuiper skill scores are derived for forecasted cloud masks. For a forecast of 5 min for most cloud situations more than 95 % of all pixels are predicted correctly cloudy or clear. This number decreases to 80-95 % for a forecast of 2 h depending on cloud type and vertical cloud level. Hanssen-Kuiper skill scores for cloud mask go down to 0.6-0.7 for a 2 h forecast. Compared to persistence an improvement of forecast horizon by a factor of 2 is reached for all forecasts up to 2 h. A comparison of forecasted optical thickness distributions and DNI against observations yields correlation coefficients larger than 0.9 for 15 min forecasts and around 0.65 for 2 h forecasts.

Methods of editing cloud and atmospheric layer affected pixels from satellite data

NASA Technical Reports Server (NTRS)

Nixon, P. R.; Wiegand, C. L.; Richardson, A. J.; Johnson, M. P. (Principal Investigator)

1982-01-01

Subvisible cirrus clouds (SCi) were easily distinguished in mid-infrared (MIR) TIROS-N daytime data from south Texas and northeast Mexico. The MIR (3.55-3.93 micrometer) pixel digital count means of the SCi affected areas were more than 3.5 standard deviations on the cold side of the scene means. (These standard deviations were made free of the effects of unusual instrument error by factoring out the Ch 3 MIR noise on the basis of detailed examination of noisy and noise-free pixels). SCi affected areas in the IR Ch 4 (10.5-11.5 micrometer) appeared cooler than the general scene, but were not as prominent as in Ch 3, being less than 2 standard deviations from the scene mean. Ch 3 and 4 standard deviations and coefficients of variation are not reliable indicators, by themselves, of the presence of SCi because land features can have similar statistical properties.

The analysis of polar clouds from AVHRR satellite data using pattern recognition techniques

NASA Technical Reports Server (NTRS)

Smith, William L.; Ebert, Elizabeth

1990-01-01

The cloud cover in a set of summertime and wintertime AVHRR data from the Arctic and Antarctic regions was analyzed using a pattern recognition algorithm. The data were collected by the NOAA-7 satellite on 6 to 13 Jan. and 1 to 7 Jul. 1984 between 60 deg and 90 deg north and south latitude in 5 spectral channels, at the Global Area Coverage (GAC) resolution of approximately 4 km. This data embodied a Polar Cloud Pilot Data Set which was analyzed by a number of research groups as part of a polar cloud algorithm intercomparison study. This study was intended to determine whether the additional information contained in the AVHRR channels (beyond the standard visible and infrared bands on geostationary satellites) could be effectively utilized in cloud algorithms to resolve some of the cloud detection problems caused by low visible and thermal contrasts in the polar regions. The analysis described makes use of a pattern recognition algorithm which estimates the surface and cloud classification, cloud fraction, and surface and cloudy visible (channel 1) albedo and infrared (channel 4) brightness temperatures on a 2.5 x 2.5 deg latitude-longitude grid. In each grid box several spectral and textural features were computed from the calibrated pixel values in the multispectral imagery, then used to classify the region into one of eighteen surface and/or cloud types using the maximum likelihood decision rule. A slightly different version of the algorithm was used for each season and hemisphere because of differences in categories and because of the lack of visible imagery during winter. The classification of the scene is used to specify the optimal AVHRR channel for separating clear and cloudy pixels using a hybrid histogram-spatial coherence method. This method estimates values for cloud fraction, clear and cloudy albedos and brightness temperatures in each grid box. The choice of a class-dependent AVHRR channel allows for better separation of clear and cloudy pixels than does a global choice of a visible and/or infrared threshold. The classification also prevents erroneous estimates of large fractional cloudiness in areas of cloudfree snow and sea ice. The hybrid histogram-spatial coherence technique and the advantages of first classifying a scene in the polar regions are detailed. The complete Polar Cloud Pilot Data Set was analyzed and the results are presented and discussed.

Predicting the temporal and spatial probability of orographic cloud cover in the Luquillo Experimental Forest in Puerto Rico using generalized linear (mixed) models.

Treesearch

Wei Wu; Charlesb Hall; Lianjun Zhang

2006-01-01

We predicted the spatial pattern of hourly probability of cloud cover in the Luquillo Experimental Forest (LEF) in North-Eastern Puerto Rico using four different models. The probability of cloud cover (defined as “the percentage of the area covered by clouds in each pixel on the map” in this paper) at any hour and any place is a function of three topographic variables...

Evaluation of the Precision of Satellite-Derived Sea Surface Temperature Fields

NASA Astrophysics Data System (ADS)

Wu, F.; Cornillon, P. C.; Guan, L.

2016-02-01

A great deal of attention has been focused on the temporal accuracy of satellite-derived sea surface temperature (SST) fields with little attention being given to their spatial precision. Specifically, the primary measure of the quality of SST fields has been the bias and variance of selected values minus co-located (in space and time) in situ values. Contributing values, determined by the location of the in situ values and the necessity that the satellite-derived values be cloud free, are generally widely separated in space and time hence provide little information related to the pixel-to-pixel uncertainty in the retrievals. But the main contribution to the uncertainty in satellite-derived SST retrievals relates to atmospheric contamination and because the spatial scales of atmospheric features are, in general, large compared with the pixel separation of modern infra-red sensors, the pixel-to-pixel uncertainty is often smaller than the accuracy determined from in situ match-ups. This makes selection of satellite-derived datasets for the study of submesoscale processes, for which the spatial structure of the upper ocean is significant, problematic. In this presentation we present a methodology to characterize the spatial precision of satellite-derived SST fields. The method is based on an examination of the high wavenumber tail of the 2-D spectrum of SST fields in the Sargasso Sea, a low energy region of the ocean close to the track of the MV Oleander, a container ship making weekly roundtrips between New York and Bermuda, with engine intake temperatures sampled every 75 m along track. Important spectral characteristics are the point at which the satellite-derived spectra separate from the Oleander spectra and the spectral slope following separation. In this presentation a number of high resolution 375 m to 10 km SST datasets are evaluated based on this approach.

A novel method to improve MODIS AOD retrievals in cloudy pixels using an analog ensemble approach

NASA Astrophysics Data System (ADS)

Kumar, R.; Raman, A.; Delle Monache, L.; Alessandrini, S.; Cheng, W. Y. Y.; Gaubert, B.; Arellano, A. F.

2016-12-01

Particulate matter (PM) concentrations are one of the fundamental indicators of air quality. Earth orbiting satellite platforms acquire column aerosol abundance that can in turn provide information about the PM concentrations. One of the serious limitations of column aerosol retrievals from low earth orbiting satellites is that these algorithms are based on clear sky assumptions. They do not retrieve AOD in cloudy pixels. After filtering cloudy pixels, these algorithms also arbitrarily remove brightest and darkest 25% of remaining pixels over ocean and brightest and darkest 50% pixels over land to filter any residual contamination from clouds. This becomes a critical issue especially in regions that experience monsoon, like Asia and North America. In case of North America, monsoon season experiences wide variety of extreme air quality events such as fires in California and dust storms in Arizona. Assessment of these episodic events warrants frequent monitoring of aerosol observations from remote sensing retrievals. In this study, we demonstrate a method to fill in cloudy pixels in Moderate Imaging Resolution Spectroradiometer (MODIS) AOD retrievals based on ensembles generated using an analog-based approach (AnEn). It provides a probabilistic distribution of AOD in cloudy pixels using historical records of model simulations of meteorological predictors such as AOD, relative humidity, and wind speed, and past observational records of MODIS AOD at a given target site. We use simulations from a coupled community weather forecasting model with chemistry (WRF-Chem) run at a resolution comparable to MODIS AOD. Analogs selected from summer months (June, July) of 2011-2013 from model and corresponding observations are used as a training dataset. Then, missing AOD retrievals in cloudy pixels in the last 31 days of the selected period are estimated. Here, we use AERONET stations as target sites to facilitate comparison against in-situ measurements. We use two approaches to evaluate the estimated AOD: 1) by comparing against reanalysis AOD, 2) by inverting AOD to PM10 concentrations and then comparing those with measured PM10. AnEn is an efficient approach to generate an ensemble as it involves only one model run and provides an estimate of uncertainty that complies with the physical and chemical state of the atmosphere.

A Microwave Technique for Mapping Ice Temperature in the Arctic Seasonal Sea Ice Zone

NASA Technical Reports Server (NTRS)

St.Germain, Karen M.; Cavalieri, Donald J.

1997-01-01

A technique for deriving ice temperature in the Arctic seasonal sea ice zone from passive microwave radiances has been developed. The algorithm operates on brightness temperatures derived from the Special Sensor Microwave/Imager (SSM/I) and uses ice concentration and type from a previously developed thin ice algorithm to estimate the surface emissivity. Comparisons of the microwave derived temperatures with estimates derived from infrared imagery of the Bering Strait yield a correlation coefficient of 0.93 and an RMS difference of 2.1 K when coastal and cloud contaminated pixels are removed. SSM/I temperatures were also compared with a time series of air temperature observations from Gambell on St. Lawrence Island and from Point Barrow, AK weather stations. These comparisons indicate that the relationship between the air temperature and the ice temperature depends on ice type.

Extraction of Profile Information from Cloud Contaminated Radiances. Appendixes 2

NASA Technical Reports Server (NTRS)

Smith, W. L.; Zhou, D. K.; Huang, H.-L.; Li, Jun; Liu, X.; Larar, A. M.

2003-01-01

Clouds act to reduce the signal level and may produce noise dependence on the complexity of the cloud properties and the manner in which they are treated in the profile retrieval process. There are essentially three ways to extract profile information from cloud contaminated radiances: (1) cloud-clearing using spatially adjacent cloud contaminated radiance measurements, (2) retrieval based upon the assumption of opaque cloud conditions, and (3) retrieval or radiance assimilation using a physically correct cloud radiative transfer model which accounts for the absorption and scattering of the radiance observed. Cloud clearing extracts the radiance arising from the clear air portion of partly clouded fields of view permitting soundings to the surface or the assimilation of radiances as in the clear field of view case. However, the accuracy of the clear air radiance signal depends upon the cloud height and optical property uniformity across the two fields of view used in the cloud clearing process. The assumption of opaque clouds within the field of view permits relatively accurate profiles to be retrieved down to near cloud top levels, the accuracy near the cloud top level being dependent upon the actual microphysical properties of the cloud. The use of a physically correct cloud radiative transfer model enables accurate retrievals down to cloud top levels and below semi-transparent cloud layers (e.g., cirrus). It should also be possible to assimilate cloudy radiances directly into the model given a physically correct cloud radiative transfer model using geometric and microphysical cloud parameters retrieved from the radiance spectra as initial cloud variables in the radiance assimilation process. This presentation reviews the above three ways to extract profile information from cloud contaminated radiances. NPOESS Airborne Sounder Testbed-Interferometer radiance spectra and Aqua satellite AIRS radiance spectra are used to illustrate how cloudy radiances can be used in the profile retrieval process.

Modelling and testing the x-ray performance of CCD and CMOS APS detectors using numerical finite element simulations

NASA Astrophysics Data System (ADS)

Weatherill, Daniel P.; Stefanov, Konstantin D.; Greig, Thomas A.; Holland, Andrew D.

2014-07-01

Pixellated monolithic silicon detectors operated in a photon-counting regime are useful in spectroscopic imaging applications. Since a high energy incident photon may produce many excess free carriers upon absorption, both energy and spatial information can be recovered by resolving each interaction event. The performance of these devices in terms of both the energy and spatial resolution is in large part determined by the amount of diffusion which occurs during the collection of the charge cloud by the pixels. Past efforts to predict the X-ray performance of imaging sensors have used either analytical solutions to the diffusion equation or simplified monte carlo electron transport models. These methods are computationally attractive and highly useful but may be complemented using more physically detailed models based on TCAD simulations of the devices. Here we present initial results from a model which employs a full transient numerical solution of the classical semiconductor equations to model charge collection in device pixels under stimulation from initially Gaussian photogenerated charge clouds, using commercial TCAD software. Realistic device geometries and doping are included. By mapping the pixel response to different initial interaction positions and charge cloud sizes, the charge splitting behaviour of the model sensor under various illuminations and operating conditions is investigated. Experimental validation of the model is presented from an e2v CCD30-11 device under varying substrate bias, illuminated using an Fe-55 source.

SST algorithm based on radiative transfer model

NASA Astrophysics Data System (ADS)

Mat Jafri, Mohd Z.; Abdullah, Khiruddin; Bahari, Alui

2001-03-01

An algorithm for measuring sea surface temperature (SST) without recourse to the in-situ data for calibration has been proposed. The algorithm which is based on the recorded infrared signal by the satellite sensor is composed of three terms, namely, the surface emission, the up-welling radiance emitted by the atmosphere, and the down-welling atmospheric radiance reflected at the sea surface. This algorithm requires the transmittance values of thermal bands. The angular dependence of the transmittance function was modeled using the MODTRAN code. Radiosonde data were used with the MODTRAN code. The expression of transmittance as a function of zenith view angle was obtained for each channel through regression of the MODTRAN output. The Ocean Color Temperature Scanner (OCTS) data from the Advanced Earth Observation Satellite (ADEOS) were used in this study. The study area covers the seas of the North West of Peninsular Malaysia region. The in-situ data (ship collected SST values) were used for verification of the results. Cloud contaminated pixels were masked out using the standard procedures which have been applied to the Advanced Very High Resolution Radiometer (AVHRR) data. The cloud free pixels at the in-situ sites were extracted for analysis. The OCTS data were then substituted in the proposed algorithm. The appropriate transmittance value for each channel was then assigned in the calculation. Assessment for the accuracy was made by observing the correlation and the rms deviations between the computed and the ship collected values. The results were also compared with the results from OCTS multi- channel sea surface temperature algorithm. The comparison produced high correlation values. The performance of this algorithm is comparable with the established OCTS algorithm. The effect of emissivity on the retrieved SST values was also investigated. SST map was generated and contoured manually.

Near-Real Time Cloud Retrievals from Operational and Research Meteorological Satellites

NASA Technical Reports Server (NTRS)

Minnis, Patrick; Nguyen, Louis; Palilonda, Rabindra; Heck, Patrick W.; Spangenberg, Douglas A.; Doelling, David R.; Ayers, J. Kirk; Smith, William L., Jr.; Khaiyer, Mandana M.; Trepte, Qing Z.;

High Above Jupiter's Clouds

NASA Image and Video Library

2018-01-04

NASA's Juno spacecraft was a little more than one Earth diameter from Jupiter when it captured this mind-bending, color-enhanced view of the planet's tumultuous atmosphere. Jupiter completely fills the image, with only a hint of the terminator (where daylight fades to night) in the upper right corner, and no visible limb (the curved edge of the planet). Juno took this image of colorful, turbulent clouds in Jupiter's northern hemisphere on Dec. 16, 2017 at 9:43 a.m. PST (12:43 p.m. EST) from 8,292 miles (13,345 kilometers) above the tops of Jupiter's clouds, at a latitude of 48.9 degrees. The spatial scale in this image is 5.8 miles/pixel (9.3 kilometers/pixel).. Citizen scientists Gerald Eichstädt and Seán Doran processed this image using data from the JunoCam imager. https://photojournal.jpl.nasa.gov/catalog/PIA21973

Modelled and measured effects of clouds on UV Aerosol Indices on a local, regional, and global scale

NASA Astrophysics Data System (ADS)

Penning de Vries, M.; Wagner, T.

2011-12-01

The UV Aerosol Indices (UVAI) form one of very few available tools in satellite remote sensing that provide information on aerosol absorption. The UVAI are also quite insensitive to surface type and are determined in the presence of clouds - situations where most aerosol retrieval algorithms do not work. The UVAI are most sensitive to elevated layers of absorbing aerosols, such as mineral dust and smoke, but they can also be used to study non-absorbing aerosols, such as sulphate and secondary organic aerosols. Although UVAI are determined for cloud-contaminated pixels, clouds do affect the value of UVAI in several ways: (1) they shield the underlying scene (potentially containing aerosols) from view, (2) they enhance the apparent surface albedo of an elevated aerosol layer, and (3) clouds unpolluted by aerosols also yield non-zero UVAI, here referred to as "cloudUVAI". The main purpose of this paper is to demonstrate that clouds can cause significant UVAI and that this cloudUVAI can be well modelled using simple assumptions on cloud properties. To this aim, we modelled cloudUVAI by using measured cloud optical parameters - either with low spatial resolution from SCIAMACHY, or high resolution from MERIS - as input. The modelled cloudUVAI were compared with UVAI determined from SCIAMACHY reflectances on different spatial (local, regional and global) and temporal scales (single measurement, daily means and seasonal means). The general dependencies of UVAI on cloud parameters were quite well reproduced, but several issues remain unclear: compared to the modelled cloudUVAI, measured UVAI show a bias, in particular for large cloud fractions. Also, the spread in measured UVAI is larger than in modelled cloudUVAI. In addition to the original, Lambert Equivalent Reflector (LER)-based UVAI algorithm, we have also investigated the effects of clouds on UVAI determined using the so-called Modified LER (MLER) algorithm (currently applied to TOMS and OMI data). For medium-sized clouds the MLER algorithm performs better (UVAI are closer to 0), but like for LER UVAI, MLER UVAI can become as large as -1.2 for small clouds and deviate significantly from zero for cloud fractions near 1. The effects of clouds should therefore also be taken into account when MLER UVAI data are used. Because the effects of clouds and aerosols on UVAI are not independent, a simple subtraction of modelled cloudUVAI from measured UVAI does not yield a UVAI representative of a cloud-free scene when aerosols are present. We here propose a first, simple approach for the correction of cloud effects on UVAI. The method is shown to work reasonably well for small to medium-sized clouds located above aerosols.

The Role of Clear Sky Identification in the Study of Cloud Radiative Effects: Combine Analysis from ISCCP and the Scanner of Radiation Budget (ScaRaB)

NASA Technical Reports Server (NTRS)

Rossow, W. B.; Stubenrauch, C. J.; Briand, V.; Hansen, James E. (Technical Monitor)

2001-01-01

Since the effect of clouds on the earth's radiation balance is often estimated as the difference of net radiative fluxes at the top of the atmosphere between all situations and monthly averaged clear sky situations of the same regions, a reliable identification of clear sky is important for the study of cloud radiative effects. The Scanner for Radiation Balance (ScaRaB) radiometer on board the Russian Meteor-3/7 satellite provided earth radiation budget observations from March 1994 to February 1995 with two ERBE-Re broad-band longwave and shortwave channels. Two narrow-band channels, in the infrared atmospheric window and in the visible band, have been added to the ScaRaB instrument to improve the cloud scene identification. The International Satellite Cloud Climatology Project (ISCCP) method for cloud detection and determination of cloud and surface properties uses the same narrow-band channels as ScaRaB, but is employed to a collection of measurements at a better spatial resolution of about 5 km. By applying the original ISCCP algorithms to the ScaRaB data, the clear sky frequency is about 5% lower than the one over quasi-simultaneous original ISCCP data, an indication that the ISCCP cloud detection is quite stable. However, one would expect an about 10 to 20% smaller clear sky occurrence over the larger ScaRaB pixels. Adapting the ISCCP algorithms to the reduced spatial resolution of 60 km and to the different time sampling of the ScaRaB data leads therefore to a reduction of a residual cloud contamination. A sensitivity study with time-space collocated ScaRaB and original ISCCP data at a spatial resolution of 1deg longitude x 1deg latitude shows that the effect of clear sky identification method plays a higher role on the clear sky frequency and therefore on the statistics than on the zonal mean values of the clear sky fluxes. Nevertheless, the zonal outgoing longwave fluxes corresponding to ERBE clear sky are in general about 2 to 10 W/sq m higher than those obtained from the ScaRaB adapted ISCCP clear sky identifications. The latter are close to (about 1 W/sq m higher) fluxes corresponding to clear sky regions from original ISCCP data, whereas ScaRaB clear sky LW fluxes obtained with the original ISCCP identification lie about 1 to 2 W/sq m below. Especially in the tropics where water vapor abundance is high, the ERBE clear sky LW fluxes seem to be systematically overestimated by about 4 W/sq m, and SW fluxes are lower by about 5 to 10 W/sq m. However, the uncertainty in the analysis of monthly mean zonal cloud radiative effects is also produced by the low frequency of clear sky occurrence, illustrated when averaging over pixels or even over regions of 4deg longitude x 5deg latitude, corresponding to the spatial resolution of General Circulation Models. The systematic bias in the clear sky fluxes is not reflected in the zonal cloud radiative effects, because the clear sky regions selected by the different algorithms can occur in different geographic regions with different cloud properties.

The Development of Geo-KOMPSAT-2A (GK-2A) Convective Initiation Algorithm over the Korea peninsular

NASA Astrophysics Data System (ADS)

Kim, H. S.; Chung, S. R.; Lee, B. I.; Baek, S.; Jeon, E.

2016-12-01

The rapid development of convection can bring heavy rainfall that suffers a great deal of damages to society as well as threatens human life. The high accurate forecast of the strong convection is essentially demanded to prevent those disasters from the severe weather. Since a geostationary satellite is the most suitable instrument for monitoring the single cloud's lifecycle from its formation to extinction, it has been attempted to capture the precursor signals of convection clouds by satellite. Keeping pace with the launch of Geo-KOMPSAT-2A (GK-2A) in 2018, we planned to produce convective initiation (CI) defined as the indicator of potential cloud objects to bring heavy precipitation within two hours. The CI algorithm for GK-2A is composed of four stages. The beginning is to subtract mature cloud pixels, a sort of convective cloud mask by visible (VIS) albedo and infrared (IR) brightness temperature thresholds. Then, the remained immature cloud pixels are clustered as a cloud object by watershed techniques. Each clustering object is undergone 'Interest Fields' tests for IR data that reflect cloud microphysical properties at the current and their temporal changes; the cloud depth, updraft strength and production of glaciations. All thresholds of 'Interest fields' were optimized for Korean-type convective clouds. Based on scores from tests, it is decided whether the cloud object would develop as a convective cell or not. Here we show the result of case study in this summer over the Korea peninsular by using Himawari-8 VIS and IR data. Radar echo and data were used for validation. This study suggests that CI products of GK-2A would contribute to enhance accuracy of the very short range forecast over the Korea peninsular.

Sensitivity Study of IROE Cloud Retrievals Using VIIRS M-Bands and Combined VIIRS/CrIS IR Observations

NASA Astrophysics Data System (ADS)

Wang, C.; Platnick, S. E.; Meyer, K.; Ackerman, S. A.; Holz, R.; Heidinger, A.

2017-12-01

The Visible Infrared Imaging Radiometer Suite (VIIRS) on board the Suomi-NPP spacecraft is considered as the next generation of instrument providing operational moderate resolution imaging capabilities after the Moderate Resolution Imaging Spectroradiometer (MODIS) on Terra and Aqua. However, cloud-top property (CTP) retrieval algorithms designed for the two instruments cannot be identical because of the absence of CO2 bands on VIIRS. In this study, we conduct a comprehensive sensitivity study of cloud retrievals utilizing a IR-Optimal Estimation (IROE) based algorithm. With a fast IR radiative transfer model, the IROE simultaneously retrieves cloud-top height (CTH), cloud optical thickness (COT), cloud effective radius (CER) and corresponding uncertainties using a set of IR bands. Three retrieval runs are implemented for this sensitivity study: retrievals using 1) three native VIIRS M-Bands at 750m resolution (8.5-, 11-, and 12-μm), 2) three native VIIRS M-Bands with spectrally integrated CO2 bands from the Cross-Track Infrared Sounder (CrIS), and 3) six MODIS IR bands (8.5-, 11-, 12-, 13.3-, 13.6-, and 13.9-μm). We select a few collocated MODIS and VIIRS granules for pixel-level comparison. Furthermore, aggregated daily and monthly cloud properties from the three runs are also compared. It shows that, the combined VIIRS/CrIS run agrees well with the MODIS-only run except for pixels near cloud edges. The VIIRS-only run is close to its counterparts when clouds are optically thick. However, for optically thin clouds, the VIIRS-only run can be readily influenced by the initial guess. Large discrepancies and uncertainties can be found for optically thin clouds from the VIIRS-only run.

Aerosol climatology using a tunable spectral variability cloud screening of AERONET data

NASA Technical Reports Server (NTRS)

Kaufman, Yoram J.; Gobbi, Gian Paolo; Koren, Ilan

2005-01-01

Can cloud screening of an aerosol data set, affect the aerosol optical thickness (AOT) climatology? Aerosols, humidity and clouds are correlated. Therefore, rigorous cloud screening can systematically bias towards less cloudy conditions, underestimating the average AOT. Here, using AERONET data we show that systematic rejection of variable atmospheric optical conditions can generate such bias in the average AOT. Therefore we recommend (1) to introduce more powerful spectral variability cloud screening and (2) to change the philosophy behind present aerosol climatologies: Instead of systematically rejecting all cloud contaminations, we suggest to intentionally allow the presence of cloud contamination, estimate the statistical impact of the contamination and correct for it. The analysis, applied to 10 AERONET stations with approx. 4 years of data, shows almost no change for Rome (Italy), but up to a change in AOT of 0.12 in Beijing (PRC). Similar technique may be explored for satellite analysis, e.g. MODIS.

The Effect of Cumulus Cloud Field Anisotropy on Domain-Averaged Solar Fluxes and Atmospheric Heating Rates

NASA Technical Reports Server (NTRS)

Hinkelman, Laura M.; Evans, K. Franklin; Clothiaux, Eugene E.; Ackerman, Thomas P.; Stackhouse, Paul W., Jr.

2006-01-01

Cumulus clouds can become tilted or elongated in the presence of wind shear. Nevertheless, most studies of the interaction of cumulus clouds and radiation have assumed these clouds to be isotropic. This paper describes an investigation of the effect of fair-weather cumulus cloud field anisotropy on domain-averaged solar fluxes and atmospheric heating rate profiles. A stochastic field generation algorithm was used to produce twenty three-dimensional liquid water content fields based on the statistical properties of cloud scenes from a large eddy simulation. Progressively greater degrees of x-z plane tilting and horizontal stretching were imposed on each of these scenes, so that an ensemble of scenes was produced for each level of distortion. The resulting scenes were used as input to a three-dimensional Monte Carlo radiative transfer model. Domain-average transmission, reflection, and absorption of broadband solar radiation were computed for each scene along with the average heating rate profile. Both tilt and horizontal stretching were found to significantly affect calculated fluxes, with the amount and sign of flux differences depending strongly on sun position relative to cloud distortion geometry. The mechanisms by which anisotropy interacts with solar fluxes were investigated by comparisons to independent pixel approximation and tilted independent pixel approximation computations for the same scenes. Cumulus anisotropy was found to most strongly impact solar radiative transfer by changing the effective cloud fraction, i.e., the cloud fraction when the field is projected on a surface perpendicular to the direction of the incident solar beam.

The NASA/GEWEX Surface Radiation Budget: Integrated Data Product With Reprocessed Radiance, Cloud, and Meteorology Inputs

NASA Astrophysics Data System (ADS)

Stackhouse, P. W.; Gupta, S. K.; Cox, S. J.; Mikovitz, J. C.; Zhang, T.

2015-12-01

The NASA/GEWEX Surface Radiation Budget (SRB) project produces shortwave and longwave surface and top of atmosphere radiative fluxes for the 1983-near present time period. Spatial resolution is 1 degree. The current release 3.0 (available at gewex-srb.larc.nasa.gov) uses the International Satellite Cloud Climatology Project (ISCCP) DX product for pixel level radiance and cloud information. This product is subsampled to 30 km. ISCCP is currently recalibrating and recomputing their entire data series, to be released as the H product, at 10km resolution. The ninefold increase in pixel number will allow SRB a higher resolution gridded product (e.g. 0.5 degree), as well as the production of pixel-level fluxes. Other key input improvements include a detailed aerosol history using the Max Planck Institut Aerosol Climatology (MAC), temperature and moisture profiles from HIRS, and new topography, surface type, and snow/ice. At the time of abstract submission, results from the year 2007 have been produced. More years will be added as ISCCP reprocessing occurs. Here we present results for the improved GEWEX Shortwave and Longwave algorithm (GSW and GLW) with new ISCCP data, the various other improved input data sets and the incorporation of many additional internal SRB model improvements. Improvements in GSW include an expansion of the number of wavelength bands from five to eighteen, and the inclusion of ice cloud vs. water cloud radiative transfer. The SRB data produced will be released as part of the Release 4.0 Integrated Product, recognizing the interdependence of the radiative fluxes with other GEWEX products providing estimates of the Earth's global water and energy cycle (I.e., ISCCP, SeaFlux, LandFlux, NVAP, etc.).

The Area Coverage of Geophysical Fields as a Function of Sensor Field-of View

NASA Technical Reports Server (NTRS)

Key, Jeffrey R.

1994-01-01

In many remote sensing studies of geophysical fields such as clouds, land cover, or sea ice characteristics, the fractional area coverage of the field in an image is estimated as the proportion of pixels that have the characteristic of interest (i.e., are part of the field) as determined by some thresholding operation. The effect of sensor field-of-view on this estimate is examined by modeling the unknown distribution of subpixel area fraction with the beta distribution, whose two parameters depend upon the true fractional area coverage, the pixel size, and the spatial structure of the geophysical field. Since it is often not possible to relate digital number, reflectance, or temperature to subpixel area fraction, the statistical models described are used to determine the effect of pixel size and thresholding operations on the estimate of area fraction for hypothetical geophysical fields. Examples are given for simulated cumuliform clouds and linear openings in sea ice, whose spatial structures are described by an exponential autocovariance function. It is shown that the rate and direction of change in total area fraction with changing pixel size depends on the true area fraction, the spatial structure, and the thresholding operation used.

Improved ocean-color remote sensing in the Arctic using the POLYMER algorithm

NASA Astrophysics Data System (ADS)

Frouin, Robert; Deschamps, Pierre-Yves; Ramon, Didier; Steinmetz, François

2012-10-01

Atmospheric correction of ocean-color imagery in the Arctic brings some specific challenges that the standard atmospheric correction algorithm does not address, namely low solar elevation, high cloud frequency, multi-layered polar clouds, presence of ice in the field-of-view, and adjacency effects from highly reflecting surfaces covered by snow and ice and from clouds. The challenges may be addressed using a flexible atmospheric correction algorithm, referred to as POLYMER (Steinmetz and al., 2011). This algorithm does not use a specific aerosol model, but fits the atmospheric reflectance by a polynomial with a non spectral term that accounts for any non spectral scattering (clouds, coarse aerosol mode) or reflection (glitter, whitecaps, small ice surfaces within the instrument field of view), a spectral term with a law in wavelength to the power -1 (fine aerosol mode), and a spectral term with a law in wavelength to the power -4 (molecular scattering, adjacency effects from clouds and white surfaces). Tests are performed on selected MERIS imagery acquired over Arctic Seas. The derived ocean properties, i.e., marine reflectance and chlorophyll concentration, are compared with those obtained with the standard MEGS algorithm. The POLYMER estimates are more realistic in regions affected by the ice environment, e.g., chlorophyll concentration is higher near the ice edge, and spatial coverage is substantially increased. Good retrievals are obtained in the presence of thin clouds, with ocean-color features exhibiting spatial continuity from clear to cloudy regions. The POLYMER estimates of marine reflectance agree better with in situ measurements than the MEGS estimates. Biases are 0.001 or less in magnitude, except at 412 and 443 nm, where they reach 0.005 and 0.002, respectively, and root-mean-squared difference decreases from 0.006 at 412 nm to less than 0.001 at 620 and 665 nm. A first application to MODIS imagery is presented, revealing that the POLYMER algorithm is robust when pixels are contaminated by sea ice.

Cloud characterization and clear-sky correction from Landsat-7

USGS Publications Warehouse

Cahalan, Robert F.; Oreopoulos, L.; Wen, G.; Marshak, S.; Tsay, S. -C.; DeFelice, Tom

2001-01-01

Landsat, with its wide swath and high resolution, fills an important mesoscale gap between atmospheric variations seen on a few kilometer scale by local surface instrumentation and the global view of coarser resolution satellites such as MODIS. In this important scale range, Landsat reveals radiative effects on the few hundred-meter scale of common photon mean-free-paths, typical of scattering in clouds at conservative (visible) wavelengths, and even shorter mean-free-paths of absorptive (near-infrared) wavelengths. Landsat also reveals shadowing effects caused by both cloud and vegetation that impact both cloudy and clear-sky radiances. As a result, Landsat has been useful in development of new cloud retrieval methods and new aerosol and surface retrievals that account for photon diffusion and shadowing effects. This paper discusses two new cloud retrieval methods: the nonlocal independent pixel approximation (NIPA) and the normalized difference nadir radiance method (NDNR). We illustrate the improvements in cloud property retrieval enabled by the new low gain settings of Landsat-7 and difficulties found at high gains. Then, we review the recently developed “path radiance” method of aerosol retrieval and clear-sky correction using data from the Department of Energy Atmospheric Radiation Measurement (ARM) site in Oklahoma. Nearby clouds change the solar radiation incident on the surface and atmosphere due to indirect illumination from cloud sides. As a result, if clouds are nearby, this extra side-illumination causes clear pixels to appear brighter, which can be mistaken for extra aerosol or higher surface albedo. Thus, cloud properties must be known in order to derive accurate aerosol and surface properties. A three-dimensional (3D) Monte Carlo (MC) radiative transfer simulation illustrates this point and suggests a method to subtract the cloud effect from aerosol and surface retrievals. The main conclusion is that cloud, aerosol, and surface retrievals are linked and must be treated as a combined system. Landsat provides the range of scales necessary to observe the 3D cloud radiative effects that influence joint surface-atmospheric retrievals.

Automatic Cloud Detection from Multi-Temporal Satellite Images: Towards the Use of PLÉIADES Time Series

NASA Astrophysics Data System (ADS)

Champion, N.

2012-08-01

Contrary to aerial images, satellite images are often affected by the presence of clouds. Identifying and removing these clouds is one of the primary steps to perform when processing satellite images, as they may alter subsequent procedures such as atmospheric corrections, DSM production or land cover classification. The main goal of this paper is to present the cloud detection approach, developed at the French Mapping agency. Our approach is based on the availability of multi-temporal satellite images (i.e. time series that generally contain between 5 and 10 images) and is based on a region-growing procedure. Seeds (corresponding to clouds) are firstly extracted through a pixel-to-pixel comparison between the images contained in time series (the presence of a cloud is here assumed to be related to a high variation of reflectance between two images). Clouds are then delineated finely using a dedicated region-growing algorithm. The method, originally designed for panchromatic SPOT5-HRS images, is tested in this paper using time series with 9 multi-temporal satellite images. Our preliminary experiments show the good performances of our method. In a near future, the method will be applied to Pléiades images, acquired during the in-flight commissioning phase of the satellite (launched at the end of 2011). In that context, this is a particular goal of this paper to show to which extent and in which way our method can be adapted to this kind of imagery.

A cloud shadow detection method combined with cloud height iteration and spectral analysis for Landsat 8 OLI data

NASA Astrophysics Data System (ADS)

Sun, Lin; Liu, Xinyan; Yang, Yikun; Chen, TingTing; Wang, Quan; Zhou, Xueying

2018-04-01

Although enhanced over prior Landsat instruments, Landsat 8 OLI can obtain very high cloud detection precisions, but for the detection of cloud shadows, it still faces great challenges. Geometry-based cloud shadow detection methods are considered the most effective and are being improved constantly. The Function of Mask (Fmask) cloud shadow detection method is one of the most representative geometry-based methods that has been used for cloud shadow detection with Landsat 8 OLI. However, the Fmask method estimates cloud height employing fixed temperature rates, which are highly uncertain, and errors of large area cloud shadow detection can be caused by errors in estimations of cloud height. This article improves the geometry-based cloud shadow detection method for Landsat OLI from the following two aspects. (1) Cloud height no longer depends on the brightness temperature of the thermal infrared band but uses a possible dynamic range from 200 m to 12,000 m. In this case, cloud shadow is not a specific location but a possible range. Further analysis was carried out in the possible range based on the spectrum to determine cloud shadow location. This effectively avoids the cloud shadow leakage caused by the error in the height determination of a cloud. (2) Object-based and pixel spectral analyses are combined to detect cloud shadows, which can realize cloud shadow detection from two aspects of target scale and pixel scale. Based on the analysis of the spectral differences between the cloud shadow and typical ground objects, the best cloud shadow detection bands of Landsat 8 OLI were determined. The combined use of spectrum and shape can effectively improve the detection precision of cloud shadows produced by thin clouds. Several cloud shadow detection experiments were carried out, and the results were verified by the results of artificial recognition. The results of these experiments indicated that this method can identify cloud shadows in different regions with correct accuracy exceeding 80%, approximately 5% of the areas were wrongly identified, and approximately 10% of the cloud shadow areas were missing. The accuracy of this method is obviously higher than the recognition accuracy of Fmask, which has correct accuracy lower than 60%, and the missing recognition is approximately 40%.

Unveiling aerosol-cloud interactions - Part 1: Cloud contamination in satellite products enhances the aerosol indirect forcing estimate

NASA Astrophysics Data System (ADS)

Christensen, Matthew W.; Neubauer, David; Poulsen, Caroline A.; Thomas, Gareth E.; McGarragh, Gregory R.; Povey, Adam C.; Proud, Simon R.; Grainger, Roy G.

2017-11-01

Increased concentrations of aerosol can enhance the albedo of warm low-level cloud. Accurately quantifying this relationship from space is challenging due in part to contamination of aerosol statistics near clouds. Aerosol retrievals near clouds can be influenced by stray cloud particles in areas assumed to be cloud-free, particle swelling by humidification, shadows and enhanced scattering into the aerosol field from (3-D radiative transfer) clouds. To screen for this contamination we have developed a new cloud-aerosol pairing algorithm (CAPA) to link cloud observations to the nearest aerosol retrieval within the satellite image. The distance between each aerosol retrieval and nearest cloud is also computed in CAPA. Results from two independent satellite imagers, the Advanced Along-Track Scanning Radiometer (AATSR) and Moderate Resolution Imaging Spectroradiometer (MODIS), show a marked reduction in the strength of the intrinsic aerosol indirect radiative forcing when selecting aerosol pairs that are located farther away from the clouds (-0.28±0.26 W m-2) compared to those including pairs that are within 15 km of the nearest cloud (-0.49±0.18 W m-2). The larger aerosol optical depths in closer proximity to cloud artificially enhance the relationship between aerosol-loading, cloud albedo, and cloud fraction. These results suggest that previous satellite-based radiative forcing estimates represented in key climate reports may be exaggerated due to the inclusion of retrieval artefacts in the aerosol located near clouds.

Determination of cloud fields from analysis of HIRS2/MSU sounding data. [20 channel infrared and 4 channel microwave atmospheric sounders

NASA Technical Reports Server (NTRS)

Susskind, J.; Reuter, D.

1986-01-01

IR and microwave remote sensing data collected with the HIRS2 and MSU sensors on the NOAA polar-orbiting satellites were evaluated for their effectiveness as bases for determining the cloud cover and cloud physical characteristics. Techniques employed to adjust for day-night alterations in the radiance fields are described, along with computational procedures applied to compare scene pixel values with reference values for clear skies. Sample results are provided for the mean cloud coverage detected over South America and Africa June 1979, with attention given to concurrent surface pressure and cloud top pressure values.

Detecting long-duration cloud contamination in hyper-temporal NDVI imagery

NASA Astrophysics Data System (ADS)

Ali, Amjad; de Bie, C. A. J. M.; Skidmore, A. K.

2013-10-01

Cloud contamination impacts on the quality of hyper-temporal NDVI imagery and its subsequent interpretation. Short-duration cloud impacts are easily removed by using quality flags and an upper envelope filter, but long-duration cloud contamination of NDVI imagery remains. In this paper, an approach that goes beyond the use of quality flags and upper envelope filtering is tested to detect when and where long-duration clouds are responsible for unreliable NDVI readings, so that a user can flag those data as missing. The study is based on MODIS Terra and the combined Terra-Aqua 16-day NDVI product for the south of Ghana, where persistent cloud cover occurs throughout the year. The combined product could be assumed to have less cloud contamination, since it is based on two images per day. Short-duration cloud effects were removed from the two products through using the adaptive Savitzky-Golay filter. Then for each 'cleaned' product an unsupervised classified map was prepared using the ISODATA algorithm, and, by class, plots were prepared to depict changes over time of the means and the standard deviations in NDVI values. By comparing plots of similar classes, long-duration cloud contamination appeared to display a decline in mean NDVI below the lower limit 95% confidence interval with a coinciding increase in standard deviation above the upper limit 95% confidence interval. Regression analysis was carried out per NDVI class in two randomly selected groups in order to statistically test standard deviation values related to long-duration cloud contamination. A decline in seasonal NDVI values (growing season) were below the lower limit of 95% confidence interval as well as a concurrent increase in standard deviation values above the upper limit of the 95% confidence interval were noted in 34 NDVI classes. The regression analysis results showed that differences in NDVI class values between the Terra and the Terra-Aqua imagery were significantly correlated (p < 0.05) with the corresponding standard deviation values of the Terra imagery in case of all NDVI classes of two selected NDVI groups. The method successfully detects long-duration cloud contamination that results in unreliable NDVI values. The approach offers scientists interested in time series analysis a method of masking by area (class) the periods when pre-cleaned NDVI values remain affected by clouds. The approach requires no additional data for execution purposes but involves unsupervised classification of the imagery to carry out the evaluation of class-specific mean NDVI and standard deviation values over time.

Automatic registration of fused lidar/digital imagery (texel images) for three-dimensional image creation

NASA Astrophysics Data System (ADS)

Budge, Scott E.; Badamikar, Neeraj S.; Xie, Xuan

2015-03-01

Several photogrammetry-based methods have been proposed that the derive three-dimensional (3-D) information from digital images from different perspectives, and lidar-based methods have been proposed that merge lidar point clouds and texture the merged point clouds with digital imagery. Image registration alone has difficulty with smooth regions with low contrast, whereas point cloud merging alone has difficulty with outliers and a lack of proper convergence in the merging process. This paper presents a method to create 3-D images that uses the unique properties of texel images (pixel-fused lidar and digital imagery) to improve the quality and robustness of fused 3-D images. The proposed method uses both image processing and point-cloud merging to combine texel images in an iterative technique. Since the digital image pixels and the lidar 3-D points are fused at the sensor level, more accurate 3-D images are generated because registration of image data automatically improves the merging of the point clouds, and vice versa. Examples illustrate the value of this method over other methods. The proposed method also includes modifications for the situation where an estimate of position and attitude of the sensor is known, when obtained from low-cost global positioning systems and inertial measurement units sensors.

Differences in liquid cloud droplet effective radius and number concentration estimates between MODIS Collections 5.1 and 6 over global oceans.

PubMed

Rausch, John; Meyer, Kerry; Bennartz, Ralf; Platnick, Steven

2017-01-01

Differences in cloud droplet effective radius and cloud droplet number concentration (CDNC) estimates inferred from the Aqua MODIS Collections 5.1 and 6 cloud products (MYD06) are examined for warm clouds over global oceans for the year 2008. Individual pixel level retrievals for both collections are aggregated to 1° × 1° and compared globally and regionally for the three main spectral channel pairs used for MODIS cloud optical property retrievals. Comparisons between both collections are performed for cases in which all three effective radii retrievals are classified by the MODIS Cloud Product as valid. The contribution to the observed differences of several key MYD06 Collection 6 algorithm updates are also explored, with a focus on changes to the surface reflectance model, assumed solar irradiance, above cloud emission, cloud top pressure, and pixel registration. Global results show a neutral to positive (> 50 cm -3 ) change for C6-derived CDNC relative to C5.1 for the 1.6 µm and 2.1 µm channel retrievals, corresponding to a neutral to -2 µm difference in droplet effective radius. For 3.7 µm retrievals, CDNC results show a negative change in the tropics, with differences transitioning toward positive values with increasing latitude spanning -25 to +50 cm -3 related to a +2.5 to -1 µm transition in effective radius. Cloud optical thickness differences were small relative to effective radius, and found to not significantly impact CDNC estimates. Regionally, the magnitude and behavior of the annual CDNC cycle are compared for each effective radius retrieval. Results from this study indicate significant intercollection differences in aggregated values of effective radius due to changes to the pre-computed retrieval lookup tables for ocean scenes, changes to retrieved cloud top pressure, solar irradiance, or above cloud thermal emission, depending upon spectral channel. The observed differences between collections may have implications for existing MODIS derived climatologies and validation studies of effective radius and CDNC.

Toward Unified Satellite Climatology of Aerosol Properties. 3. MODIS Versus MISR Versus AERONET

NASA Technical Reports Server (NTRS)

Mishchenko, Michael I.; Liu, Li; Geogdzhayev, Igor V.; Travis, Larry D.; Cairns, Brian; Lacis, Andrew A.

2010-01-01

We use the full duration of collocated pixel-level MODIS-Terra and MISR aerosol optical thickness (AOT) retrievals and level 2 cloud-screened quality-assured AERONET measurements to evaluate the likely individual MODIS and MISR retrieval accuracies globally over oceans and land. We show that the use of quality-assured MODIS AOTs as opposed to the use of all MODIS AOTs has little effect on the resulting accuracy. The MODIS and MISR relative standard deviations (RSTDs) with respect to AERONET are remarkably stable over the entire measurement record and reveal nearly identical overall AOT performances of MODIS and MISR over the entire suite of AERONET sites. This result is used to evaluate the likely pixel-level MODIS and MISR performances on the global basis with respect to the (unknown) actual AOTs. For this purpose, we use only fully compatible MISR and MODIS aerosol pixels. We conclude that the likely RSTDs for this subset of MODIS and MISR AOTs are 73% over land and 30% over oceans. The average RSTDs for the combined [AOT(MODIS)+AOT(MISR)]/2 pixel-level product are close to 66% and 27%, respectively, which allows us to recommend this simple blend as a better alternative to the original MODIS and MISR data. These accuracy estimates still do not represent the totality of MISR and quality-assured MODIS pixel-level AOTs since an unaccounted for and potentially significant source of errors is imperfect cloud screening. Furthermore, many collocated pixels for which one of the datasets reports a retrieval, whereas the other one does not may also be problematic.

VizieR Online Data Catalog: OGLE-III. Magellanic Clouds stellar proper motions (Poleski+, 2012)

NASA Astrophysics Data System (ADS)

Poleski, R.; Soszynski, I.; Udalski, A.; Szymanski, M. K.; Kubiak, M.; Pietrzynski, G.; Wyrzykowski, L.; Ulaczyk, K.

2015-07-01

The OGLE-III project observed the Large Magellanic Cloud, the Small Magellanic Cloud, and the globular cluster 47 Tuc between 2001 and 2009 with the 1.3-m Warsaw telescope, which is situated at the Las Campanas Observatory, Chile. The telescope was equipped with an eight-chip mosaic CCD camera. The field of view was 36'x36' and the pixel scale was 0.26"/pix. I-band filter was used. (5 data files).

An Effective Algorithm Research of Scenario Voxelization Organization and Occlusion Culling

NASA Astrophysics Data System (ADS)

Lai, Guangling; Ding, Lu; Qin, Zhiyuan; Tong, Xiaochong

2016-11-01

Compared with the traditional triangulation approaches, the voxelized point cloud data can reduce the sensitivity of scenario and complexity of calculation. While on the base of the point cloud data, implementation scenario organization could be accomplishment by subtle voxel, but it will add more memory consumption. Therefore, an effective voxel representation method is very necessary. At present, the specific study of voxel visualization algorithm is less. This paper improved the ray tracing algorithm by the characteristics of voxel configuration. Firstly, according to the scope of point cloud data, determined the scope of the pixels on the screen. Then, calculated the light vector came from each pixel. Lastly, used the rules of voxel configuration to calculate all the voxel penetrated through by light. The voxels closest to viewpoint were named visible ones, the rest were all obscured ones. This experimental showed that the method could realize voxelization organization and voxel occlusion culling of implementation scenario efficiently, and increased the render efficiency.

Cloud-based computation for accelerating vegetation mapping and change detection at regional to national scales

Treesearch

Matthew J. Gregory; Zhiqiang Yang; David M. Bell; Warren B. Cohen; Sean Healey; Janet L. Ohmann; Heather M. Roberts

2015-01-01

Mapping vegetation and landscape change at fine spatial scales is needed to inform natural resource and conservation planning, but such maps are expensive and time-consuming to produce. For Landsat-based methodologies, mapping efforts are hampered by the daunting task of manipulating multivariate data for millions to billions of pixels. The advent of cloud-based...

Cloud Motion Vectors from MISR using Sub-pixel Enhancements

NASA Technical Reports Server (NTRS)

Davies, Roger; Horvath, Akos; Moroney, Catherine; Zhang, Banglin; Zhu, Yanqiu

2007-01-01

The operational retrieval of height-resolved cloud motion vectors by the Multiangle Imaging SpectroRadiometer on the Terra satellite has been significantly improved by using sub-pixel approaches to co-registration and disparity assessment, and by imposing stronger quality control based on the agreement between independent forward and aft triplet retrievals. Analysis of the fore-aft differences indicates that CMVs pass the basic operational quality control 67% of the time, with rms differences - in speed of 2.4 m/s, in direction of 17 deg, and in height assignment of 290 m. The use of enhanced quality control thresholds reduces these rms values to 1.5 m/s, 17 deg and 165 m, respectively, at the cost of reduced coverage to 45%. Use of the enhanced thresholds also eliminates a tendency for the rms differences to increase with height. Comparison of CMVs from an earlier operational version that had slightly weaker quality control, with 6-hour forecast winds from the Global Modeling and Assimilation Office yielded very low bias values and an rms vector difference that ranged from 5 m/s for low clouds to 10 m/s for high clouds.

What is the Uncertainty in MODIS Aerosol Optical Depth in the Vicinity of Clouds?

NASA Technical Reports Server (NTRS)

Patadia, Falguni; Levy, Rob; Mattoo, Shana

2017-01-01

MODIS dark-target (DT) algorithm retrieves aerosol optical depth (AOD) using a Look Up Table (LUT) approach. Global comparison of AOD (Collection 6 ) with ground-based sun photometer gives an Estimated Error (EE) of +/-(0.04 + 10%) over ocean. However, EE does not represent per-retrieval uncertainty. For retrievals that are biased high compared to AERONET, here we aim to closely examine the contribution of biases due to presence of clouds and per-pixel retrieval uncertainty. We have characterized AOD uncertainty at 550 nm, due to standard deviation of reflectance in 10 km retrieval region, uncertainty related to gas (H2O, O3) absorption, surface albedo, and aerosol models. The uncertainty in retrieved AOD seems to lie within the estimated over ocean error envelope of +/-(0.03+10%). Regions between broken clouds tend to have higher uncertainty. Compared to C6 AOD, a retrieval omitting observations in the vicinity of clouds (< or = 1 km) is biased by about +/- 0.05. For homogeneous aerosol distribution, clear sky retrievals show near zero bias. Close look at per-pixel reflectance histograms suggests retrieval possibility using median reflectance values.

Daytime sea fog retrieval based on GOCI data: a case study over the Yellow Sea.

PubMed

Yuan, Yibo; Qiu, Zhongfeng; Sun, Deyong; Wang, Shengqiang; Yue, Xiaoyuan

2016-01-25

In this paper, a new daytime sea fog detection algorithm has been developed by using Geostationary Ocean Color Imager (GOCI) data. Based on spectral analysis, differences in spectral characteristics were found over different underlying surfaces, which include land, sea, middle/high level clouds, stratus clouds and sea fog. Statistical analysis showed that the Rrc (412 nm) (Rayleigh Corrected Reflectance) of sea fog pixels is approximately 0.1-0.6. Similarly, various band combinations could be used to separate different surfaces. Therefore, three indices (SLDI, MCDI and BSI) were set to discern land/sea, middle/high level clouds and fog/stratus clouds, respectively, from which it was generally easy to extract fog pixels. The remote sensing algorithm was verified using coastal sounding data, which demonstrated that the algorithm had the ability to detect sea fog. The algorithm was then used to monitor an 8-hour sea fog event and the results were consistent with observational data from buoys data deployed near the Sheyang coast (121°E, 34°N). The goal of this study was to establish a daytime sea fog detection algorithm based on GOCI data, which shows promise for detecting fog separately from stratus.

The spectral signature of cloud spatial structure in shortwave irradiance

PubMed Central

Song, Shi; Schmidt, K. Sebastian; Pilewskie, Peter; King, Michael D.; Heidinger, Andrew K.; Walther, Andi; Iwabuchi, Hironobu; Wind, Gala; Coddington, Odele M.

2017-01-01

In this paper, we used cloud imagery from a NASA field experiment in conjunction with three-dimensional radiative transfer calculations to show that cloud spatial structure manifests itself as a spectral signature in shortwave irradiance fields – specifically in transmittance and net horizontal photon transport in the visible and near-ultraviolet wavelength range. We found a robust correlation between the magnitude of net horizontal photon transport (H) and its spectral dependence (slope), which is scale-invariant and holds for the entire pixel population of a domain. This was surprising at first given the large degree of spatial inhomogeneity. We prove that the underlying physical mechanism for this phenomenon is molecular scattering in conjunction with cloud spatial structure. On this basis, we developed a simple parameterization through a single parameter ε, which quantifies the characteristic spectral signature of spatial inhomogeneities. In the case we studied, neglecting net horizontal photon transport leads to a local transmittance bias of ±12–19 %, even at the relatively coarse spatial resolution of 20 km. Since three-dimensional effects depend on the spatial context of a given pixel in a nontrivial way, the spectral dimension of this problem may emerge as the starting point for future bias corrections. PMID:28824698

The spectral signature of cloud spatial structure in shortwave irradiance.

PubMed

Song, Shi; Schmidt, K Sebastian; Pilewskie, Peter; King, Michael D; Heidinger, Andrew K; Walther, Andi; Iwabuchi, Hironobu; Wind, Gala; Coddington, Odele M

2016-11-08

In this paper, we used cloud imagery from a NASA field experiment in conjunction with three-dimensional radiative transfer calculations to show that cloud spatial structure manifests itself as a spectral signature in shortwave irradiance fields - specifically in transmittance and net horizontal photon transport in the visible and near-ultraviolet wavelength range. We found a robust correlation between the magnitude of net horizontal photon transport ( H ) and its spectral dependence (slope), which is scale-invariant and holds for the entire pixel population of a domain. This was surprising at first given the large degree of spatial inhomogeneity. We prove that the underlying physical mechanism for this phenomenon is molecular scattering in conjunction with cloud spatial structure. On this basis, we developed a simple parameterization through a single parameter ε , which quantifies the characteristic spectral signature of spatial inhomogeneities. In the case we studied, neglecting net horizontal photon transport leads to a local transmittance bias of ±12-19 %, even at the relatively coarse spatial resolution of 20 km. Since three-dimensional effects depend on the spatial context of a given pixel in a nontrivial way, the spectral dimension of this problem may emerge as the starting point for future bias corrections.

Development of the Landsat Data Continuity Mission Cloud Cover Assessment Algorithms

USGS Publications Warehouse

Scaramuzza, Pat; Bouchard, M.A.; Dwyer, John L.

2012-01-01

The upcoming launch of the Operational Land Imager (OLI) will start the next era of the Landsat program. However, the Automated Cloud-Cover Assessment (CCA) (ACCA) algorithm used on Landsat 7 requires a thermal band and is thus not suited for OLI. There will be a thermal instrument on the Landsat Data Continuity Mission (LDCM)-the Thermal Infrared Sensor-which may not be available during all OLI collections. This illustrates a need for CCA for LDCM in the absence of thermal data. To research possibilities for full-resolution OLI cloud assessment, a global data set of 207 Landsat 7 scenes with manually generated cloud masks was created. It was used to evaluate the ACCA algorithm, showing that the algorithm correctly classified 79.9% of a standard test subset of 3.95 109 pixels. The data set was also used to develop and validate two successor algorithms for use with OLI data-one derived from an off-the-shelf machine learning package and one based on ACCA but enhanced by a simple neural network. These comprehensive CCA algorithms were shown to correctly classify pixels as cloudy or clear 88.5% and 89.7% of the time, respectively.

If the MODIS Aerosol Product is so Infested with Cloud Contamination, Why Does Everybody Use the Product?

NASA Technical Reports Server (NTRS)

Remeer, Lorraine A.

2011-01-01

The MODIS aerosol cloud mask is based on a spatial variability test, using the assumption that aerosols are more homogeneous than clouds. On top of this first line of defense are a series of additional tests based on threshold values and ratios of various MODIS channels. The goal is to eliminate clouds and keep the aerosol. How well have we succeeded? There have been several studies showing cloud contamination in the MODIS aerosol product and several alternative cloud masks proposed. There are even "competing" MODIS aerosol products that offer an alternative "cloud free" world. Are these alternative products an improvement to the old standard product? We find there is a trade-off between retrieval availability and cloud contamination, and for many applications it is better to have a little bit of cloud in the product than to not have enough product. I will review the decisions that led us to the present MODIS cloud mask, and show how it is simultaneously too liberal and too conservative, some ideas on how to make it better and why in the end it doesn't matter. I hope to inspire a spirited discussion and will be very willing to take your complaints and suggestions.

Study of texture stitching in 3D modeling of lidar point cloud based on per-pixel linear interpolation along loop line buffer

NASA Astrophysics Data System (ADS)

Xu, Jianxin; Liang, Hong

2013-07-01

Terrestrial laser scanning creates a point cloud composed of thousands or millions of 3D points. Through pre-processing, generating TINs, mapping texture, a 3D model of a real object is obtained. When the object is too large, the object is separated into some parts. This paper mainly focuses on problem of gray uneven of two adjacent textures' intersection. The new algorithm is presented in the paper, which is per-pixel linear interpolation along loop line buffer .The experiment data derives from point cloud of stone lion which is situated in front of west gate of Henan Polytechnic University. The model flow is composed of three parts. First, the large object is separated into two parts, and then each part is modeled, finally the whole 3D model of the stone lion is composed of two part models. When the two part models are combined, there is an obvious fissure line in the overlapping section of two adjacent textures for the two models. Some researchers decrease brightness value of all pixels for two adjacent textures by some algorithms. However, some algorithms are effect and the fissure line still exists. Gray uneven of two adjacent textures is dealt by the algorithm in the paper. The fissure line in overlapping section textures is eliminated. The gray transition in overlapping section become more smoothly.

Spatial distribution analysis of the OMI aerosol layer height: a pixel-by-pixel comparison to CALIOP observations

NASA Astrophysics Data System (ADS)

Chimot, Julien; Pepijn Veefkind, J.; Vlemmix, Tim; Levelt, Pieternel F.

2018-04-01

A global picture of atmospheric aerosol vertical distribution with a high temporal resolution is of key importance not only for climate, cloud formation, and air quality research studies but also for correcting scattered radiation induced by aerosols in absorbing trace gas retrievals from passive satellite sensors. Aerosol layer height (ALH) was retrieved from the OMI 477 nm O2 - O2 band and its spatial pattern evaluated over selected cloud-free scenes. Such retrievals benefit from a synergy with MODIS data to provide complementary information on aerosols and cloudy pixels. We used a neural network approach previously trained and developed. Comparison with CALIOP aerosol level 2 products over urban and industrial pollution in eastern China shows consistent spatial patterns with an uncertainty in the range of 462-648 m. In addition, we show the possibility to determine the height of thick aerosol layers released by intensive biomass burning events in South America and Russia from OMI visible measurements. A Saharan dust outbreak over sea is finally discussed. Complementary detailed analyses show that the assumed aerosol properties in the forward modelling are the key factors affecting the accuracy of the results, together with potential cloud residuals in the observation pixels. Furthermore, we demonstrate that the physical meaning of the retrieved ALH scalar corresponds to the weighted average of the vertical aerosol extinction profile. These encouraging findings strongly suggest the potential of the OMI ALH product, and in more general the use of the 477 nm O2 - O2 band from present and future similar satellite sensors, for climate studies as well as for future aerosol correction in air quality trace gas retrievals.

Generation of the global cloud free data set of MODIS

NASA Astrophysics Data System (ADS)

Oguro, Y.; Tsuchiya, K.

To extract temporal change of the land cover from remotely sensed data from space the generation of the reliable cloud free data set is the first priority item With the objectives of generating accurate global basic data and to find the effects of spectral and spatial resolution differences and observation time an attempt is made to generate reliable global cloud free data set of Terra and Aqua MODIS utilizing personal computers Out of 36 bands seven bands with similar spectral features to those of Landsat TM i e Band 1 through 7 are selected These bands cover the most important spectra to derive landcover features The procedure of the data set generation is as follows 1 Download the global Terra and Aqua MODIS day time data MOD02 Level-1B Calibrated Geolocation Data Set of 250 meter Band 1 and 2 and 500 meter Band 3 through 7 resolution from NASA web site 2 Separate the data into several BSQ Band SeQuential image and several text geolocation information of pixels files 3 The geolocation information is given to the pixels of several kms interval Based on the information resampling of the data are made at 1 2 and 1 4 degrees intervals of latitude and longitude thus the resampled pixels are distributed in the latitude and longitudinal axis plane at 1 4 degrees high resolution and 1 2 degrees low resolution intervals 4 A global data for one day is composed 5 Compute NDVI for each pixel 6 Compare the value of NDVI of successive days and keep the larger NDVI At the same time keep the values of each band of the day of the larger

Marine Boundary Layer Cloud Property Retrievals from High-Resolution ASTER Observations: Case Studies and Comparison with Terra MODIS

NASA Technical Reports Server (NTRS)

Werner, Frank; Wind, Galina; Zhang, Zhibo; Platnick, Steven; Di Girolamo, Larry; Zhao, Guangyu; Amarasinghe, Nandana; Meyer, Kerry

2016-01-01

A research-level retrieval algorithm for cloud optical and microphysical properties is developed for the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) aboard the Terra satellite. It is based on the operational MODIS algorithm. This paper documents the technical details of this algorithm and evaluates the retrievals for selected marine boundary layer cloud scenes through comparisons with the operational MODIS Data Collection 6 (C6) cloud product. The newly developed, ASTERspecific cloud masking algorithm is evaluated through comparison with an independent algorithm reported in Zhao and Di Girolamo (2006). To validate and evaluate the cloud optical thickness (tau) and cloud effective radius (r(sub eff)) from ASTER, the high-spatial-resolution ASTER observations are first aggregated to the same 1000m resolution as MODIS. Subsequently, tau(sub aA) and r(sub eff, aA) retrieved from the aggregated ASTER radiances are compared with the collocated MODIS retrievals. For overcast pixels, the two data sets agree very well with Pearson's product-moment correlation coefficients of R greater than 0.970. However, for partially cloudy pixels there are significant differences between r(sub eff, aA) and the MODIS results which can exceed 10 micrometers. Moreover, it is shown that the numerous delicate cloud structures in the example marine boundary layer scenes, resolved by the high-resolution ASTER retrievals, are smoothed by the MODIS observations. The overall good agreement between the research-level ASTER results and the operational MODIS C6 products proves the feasibility of MODIS-like retrievals from ASTER reflectance measurements and provides the basis for future studies concerning the scale dependency of satellite observations and three-dimensional radiative effects.

Marine boundary layer cloud property retrievals from high-resolution ASTER observations: case studies and comparison with Terra MODIS

NASA Astrophysics Data System (ADS)

Werner, Frank; Wind, Galina; Zhang, Zhibo; Platnick, Steven; Di Girolamo, Larry; Zhao, Guangyu; Amarasinghe, Nandana; Meyer, Kerry

2016-12-01

A research-level retrieval algorithm for cloud optical and microphysical properties is developed for the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) aboard the Terra satellite. It is based on the operational MODIS algorithm. This paper documents the technical details of this algorithm and evaluates the retrievals for selected marine boundary layer cloud scenes through comparisons with the operational MODIS Data Collection 6 (C6) cloud product. The newly developed, ASTER-specific cloud masking algorithm is evaluated through comparison with an independent algorithm reported in [Zhao and Di Girolamo(2006)]. To validate and evaluate the cloud optical thickness (τ) and cloud effective radius (reff) from ASTER, the high-spatial-resolution ASTER observations are first aggregated to the same 1000 m resolution as MODIS. Subsequently, τaA and reff, aA retrieved from the aggregated ASTER radiances are compared with the collocated MODIS retrievals. For overcast pixels, the two data sets agree very well with Pearson's product-moment correlation coefficients of R > 0.970. However, for partially cloudy pixels there are significant differences between reff, aA and the MODIS results which can exceed 10 µm. Moreover, it is shown that the numerous delicate cloud structures in the example marine boundary layer scenes, resolved by the high-resolution ASTER retrievals, are smoothed by the MODIS observations. The overall good agreement between the research-level ASTER results and the operational MODIS C6 products proves the feasibility of MODIS-like retrievals from ASTER reflectance measurements and provides the basis for future studies concerning the scale dependency of satellite observations and three-dimensional radiative effects.

A comparison of performance of automatic cloud coverage assessment algorithm for Formosat-2 image using clustering-based and spatial thresholding methods

NASA Astrophysics Data System (ADS)

Hsu, Kuo-Hsien

2012-11-01

Formosat-2 image is a kind of high-spatial-resolution (2 meters GSD) remote sensing satellite data, which includes one panchromatic band and four multispectral bands (Blue, Green, Red, near-infrared). An essential sector in the daily processing of received Formosat-2 image is to estimate the cloud statistic of image using Automatic Cloud Coverage Assessment (ACCA) algorithm. The information of cloud statistic of image is subsequently recorded as an important metadata for image product catalog. In this paper, we propose an ACCA method with two consecutive stages: preprocessing and post-processing analysis. For pre-processing analysis, the un-supervised K-means classification, Sobel's method, thresholding method, non-cloudy pixels reexamination, and cross-band filter method are implemented in sequence for cloud statistic determination. For post-processing analysis, Box-Counting fractal method is implemented. In other words, the cloud statistic is firstly determined via pre-processing analysis, the correctness of cloud statistic of image of different spectral band is eventually cross-examined qualitatively and quantitatively via post-processing analysis. The selection of an appropriate thresholding method is very critical to the result of ACCA method. Therefore, in this work, We firstly conduct a series of experiments of the clustering-based and spatial thresholding methods that include Otsu's, Local Entropy(LE), Joint Entropy(JE), Global Entropy(GE), and Global Relative Entropy(GRE) method, for performance comparison. The result shows that Otsu's and GE methods both perform better than others for Formosat-2 image. Additionally, our proposed ACCA method by selecting Otsu's method as the threshoding method has successfully extracted the cloudy pixels of Formosat-2 image for accurate cloud statistic estimation.

Merging of multi-temporal SST data at South China Sea

NASA Astrophysics Data System (ADS)

Ng, H. G.; MatJafri, M. Z.; Abdullah, K.; Lim, H. S.

2008-10-01

The sea surface temperature (SST) mapping could be performed with a wide spatial and temporal extent in a reasonable time limit. The space-borne sensor of AVHRR was widely used for the purpose. However, the current SST retrieval techniques for infrared channels were limited only for the cloud-free area, because the electromagnetic waves in the infrared wavelengths could not penetrate the cloud. Therefore, the SST availability was low for the single image. To overcome this problem, we studied to produce the composite of three day's SST map. The diurnal changes of SST data are quite stable through a short period of time if no abrupt natural disaster occurrence. Therefore, the SST data of three consecutive days with nearly coincident daily time were merged in order to create a three day's composite SST data. The composite image could increase the SST availability. In this study, we acquired the level 1b AVHRR (Advanced Very High Resolution Radiometer) images from Malaysia Center of Remote Sensing (MACRES). The images were first preprocessed and the cloud and land areas were masked. We made some modifications on the technique of obtaining the threshold value for cloud masking. The SST was estimated by using the day split MCSST algorithm. The cloud free water pixels availability were computed and compared. The mean of SST for three day's composite data were calculated and a SST map was generated. The cloud free water pixels availability were computed and compared. The SST data availability was increased by merging the SST data.

Pre-Launch Performance Assessment of the VIIRS Land Surface Temperature Environmental Data Record

NASA Astrophysics Data System (ADS)

Hauss, B.; Ip, J.; Agravante, H.

2009-12-01

The Visible/Infrared Imager Radiometer Suite (VIIRS) Land Surface Temperature (LST) Environmental Data Record (EDR) provides the surface temperature of land surface including coastal and inland-water pixels at VIIRS moderate resolution (750m) during both day and night. To predict the LST under optimal conditions, the retrieval algorithm utilizes a dual split-window approach with both Short-wave Infrared (SWIR) channels at 3.70 µm (M12) and 4.05 µm (M13), and Long-wave Infrared (LWIR) channels at 10.76 µm (M15) and 12.01 µm (M16) to correct for atmospheric water vapor. Under less optimal conditions, the algorithm uses a fallback split-window approach with M15 and M16 channels. By comparison, the MODIS generalized split-window algorithm only uses the LWIR bands in the retrieval of surface temperature because of the concern for both solar contamination and large emissivity variations in the SWIR bands. In this paper, we assess whether these concerns are real and whether there is an impact on the precision and accuracy of the LST retrieval. The algorithm relies on the VIIRS Cloud Mask IP for identifying cloudy and ocean pixels, the VIIRS Surface Type EDR for identifying the IGBP land cover type for the pixels, and the VIIRS Aerosol Optical Thickness (AOT) IP for excluding pixels with AOT greater than 1.0. In this paper, we will report the pre-launch performance assessment of the LST EDR based on global synthetic data and proxy data from Terra MODIS. Results of both the split-window and dual split-window algorithms will be assessed by comparison either to synthetic "truth" or results of the MODIS retrieval. We will also show that the results of the assessment with proxy data are consistent with those obtained using the global synthetic data.

An algorithm to detect fire activity using Meteosat: fine tuning and quality assesment

NASA Astrophysics Data System (ADS)

Amraoui, M.; DaCamara, C. C.; Ermida, S. L.

2012-04-01

Hot spot detection by means of sensors on-board geostationary satellites allows studying wildfire activity at hourly and even sub-hourly intervals, an advantage that cannot be met by polar orbiters. Since 1997, the Satellite Application Facility for Land Surface Analysis has been running an operational procedure that allows detecting active fires based on information from Meteosat-8/SEVIRI. This is the so-called Fire Detection and Monitoring (FD&M) product and the procedure takes advantage of the temporal resolution of SEVIRI (one image every 15 min), and relies on information from SEVIRI channels (namely 0.6, 0.8, 3.9, 10.8 and 12.0 μm) together with information on illumination angles. The method is based on heritage from contextual algorithms designed for polar, sun-synchronous instruments, namely NOAA/AVHRR and MODIS/TERRAAQUA. A potential fire pixel is compared with the neighboring ones and the decision is made based on relative thresholds as derived from the pixels in the neighborhood. Generally speaking, the observed fire incidence compares well against hot spots extracted from the global daily active fire product developed by the MODIS Fire Team. However, values of probability of detection (POD) tend to be quite low, a result that may be partially expected by the finer resolution of MODIS. The aim of the present study is to make a systematic assessment of the impacts on POD and False Alarm Ratio (FAR) of the several parameters that are set in the algorithms. Such parameters range from the threshold values of brightness temperature in the IR3.9 and 10.8 channels that are used to select potential fire pixels up to the extent of the background grid and thresholds used to statistically characterize the radiometric departures of a potential pixel from the respective background. The impact of different criteria to identify pixels contaminated by clouds, smoke and sun glint is also evaluated. Finally, the advantages that may be brought to the algorithm by adding contextual tests in the time domain are discussed. The study lays the grounds to the development of improved quality flags that will be integrated in the FD&M product in the nearby future.

Coupling sky images with radiative transfer models: a new method to estimate cloud optical depth

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mejia, Felipe A.; Kurtz, Ben; Murray, Keenan

A method for retrieving cloud optical depth ( τ c) using a UCSD developed ground-based sky imager (USI) is presented. The radiance red–blue ratio (RRBR) method is motivated from the analysis of simulated images of various τ c produced by a radiative transfer model (RTM). From these images the basic parameters affecting the radiance and red–blue ratio (RBR) of a pixel are identified as the solar zenith angle ( θ 0), τ c, solar pixel angle/scattering angle ( θ s), and pixel zenith angle/view angle ( θ z). The effects of these parameters are described and the functions for radiance,more » I λ τ c, θ 0, θ s, θ z , and RBR τ c, θ 0, θ s, θ z are retrieved from the RTM results. RBR, which is commonly used for cloud detection in sky images, provides non-unique solutions for τ c, where RBR increases with τ c up to about τ c = 1 (depending on other parameters) and then decreases. Therefore, the RRBR algorithm uses the measured I λ meas θ s, θ z , in addition to RBR meas θ s, θ z , to obtain a unique solution for τ c. The RRBR method is applied to images of liquid water clouds taken by a USI at the Oklahoma Atmospheric Radiation Measurement (ARM) program site over the course of 220 days and compared against measurements from a microwave radiometer (MWR) and output from the Min et al. (2003) method for overcast skies. τ c values ranged from 0 to 80 with values over 80, being capped and registered as 80. A τ c RMSE of 2.5 between the Min et al. (2003) method and the USI are observed. The MWR and USI  have an RMSE of 2.2, which is well within the uncertainty of the MWR. In conclusion, the procedure developed here provides a foundation to test and develop other cloud detection algorithms.« less

Coupling sky images with radiative transfer models: a new method to estimate cloud optical depth

DOE PAGES

Mejia, Felipe A.; Kurtz, Ben; Murray, Keenan; ...

2016-08-30

A method for retrieving cloud optical depth ( τ c) using a UCSD developed ground-based sky imager (USI) is presented. The radiance red–blue ratio (RRBR) method is motivated from the analysis of simulated images of various τ c produced by a radiative transfer model (RTM). From these images the basic parameters affecting the radiance and red–blue ratio (RBR) of a pixel are identified as the solar zenith angle ( θ 0), τ c, solar pixel angle/scattering angle ( θ s), and pixel zenith angle/view angle ( θ z). The effects of these parameters are described and the functions for radiance,more » I λ τ c, θ 0, θ s, θ z , and RBR τ c, θ 0, θ s, θ z are retrieved from the RTM results. RBR, which is commonly used for cloud detection in sky images, provides non-unique solutions for τ c, where RBR increases with τ c up to about τ c = 1 (depending on other parameters) and then decreases. Therefore, the RRBR algorithm uses the measured I λ meas θ s, θ z , in addition to RBR meas θ s, θ z , to obtain a unique solution for τ c. The RRBR method is applied to images of liquid water clouds taken by a USI at the Oklahoma Atmospheric Radiation Measurement (ARM) program site over the course of 220 days and compared against measurements from a microwave radiometer (MWR) and output from the Min et al. (2003) method for overcast skies. τ c values ranged from 0 to 80 with values over 80, being capped and registered as 80. A τ c RMSE of 2.5 between the Min et al. (2003) method and the USI are observed. The MWR and USI  have an RMSE of 2.2, which is well within the uncertainty of the MWR. In conclusion, the procedure developed here provides a foundation to test and develop other cloud detection algorithms.« less

Developing a confidence metric for the Landsat land surface temperature product

NASA Astrophysics Data System (ADS)

Laraby, Kelly G.; Schott, John R.; Raqueno, Nina

2016-05-01

Land Surface Temperature (LST) is an important Earth system data record that is useful to fields such as change detection, climate research, environmental monitoring, and smaller scale applications such as agriculture. Certain Earth-observing satellites can be used to derive this metric, and it would be extremely useful if such imagery could be used to develop a global product. Through the support of the National Aeronautics and Space Administration (NASA) and the United States Geological Survey (USGS), a LST product for the Landsat series of satellites has been developed. Currently, it has been validated for scenes in North America, with plans to expand to a trusted global product. For ideal atmospheric conditions (e.g. stable atmosphere with no clouds nearby), the LST product underestimates the surface temperature by an average of 0.26 K. When clouds are directly above or near the pixel of interest, however, errors can extend to several Kelvin. As the product approaches public release, our major goal is to develop a quality metric that will provide the user with a per-pixel map of estimated LST errors. There are several sources of error that are involved in the LST calculation process, but performing standard error propagation is a difficult task due to the complexity of the atmospheric propagation component. To circumvent this difficulty, we propose to utilize the relationship between cloud proximity and the error seen in the LST process to help develop a quality metric. This method involves calculating the distance to the nearest cloud from a pixel of interest in a scene, and recording the LST error at that location. Performing this calculation for hundreds of scenes allows us to observe the average LST error for different ranges of distances to the nearest cloud. This paper describes this process in full, and presents results for a large set of Landsat scenes.

Utilization of spectral-spatial characteristics in shortwave infrared hyperspectral images to classify and identify fungi-contaminated peanuts.

PubMed

Qiao, Xiaojun; Jiang, Jinbao; Qi, Xiaotong; Guo, Haiqiang; Yuan, Deshuai

2017-04-01

It's well-known fungi-contaminated peanuts contain potent carcinogen. Efficiently identifying and separating the contaminated can help prevent aflatoxin entering in food chain. In this study, shortwave infrared (SWIR) hyperspectral images for identifying the prepared contaminated kernels. Feature selection method of analysis of variance (ANOVA) and feature extraction method of nonparametric weighted feature extraction (NWFE) were used to concentrate spectral information into a subspace where contaminated and healthy peanuts can have favorable separability. Then, peanut pixels were classified using SVM. Moreover, image segmentation method of region growing was applied to segment the image as kernel-scale patches and meanwhile to number the kernels. The result shows that pixel-wise classification accuracies are 99.13% for breed A, 96.72% for B and 99.73% for C in learning images, and are 96.32%, 94.2% and 97.51% in validation images. Contaminated peanuts were correctly marked as aberrant kernels in both learning images and validation images. Copyright © 2016 Elsevier Ltd. All rights reserved.

Research on snow cover monitoring of Northeast China using Fengyun Geostationary Satellite

NASA Astrophysics Data System (ADS)

Wu, Tong; Gu, Lingjia; Ren, Ruizhi; Zhou, TIngting

2017-09-01

Snow cover information has great significance for monitoring and preventing snowstorms. With the development of satellite technology, geostationary satellites are playing more important roles in snow monitoring. Currently, cloud interference is a serious problem for obtaining accurate snow cover information. Therefore, the cloud pixels located in the MODIS snow products are usually replaced by cloud-free pixels around the day, which ignores snow cover dynamics. FengYun-2(FY-2) is the first generation of geostationary satellite in our country which complements the polar orbit satellite. The snow cover monitoring of Northeast China using FY-2G data in January and February 2016 is introduced in this paper. First of all, geometric and radiometric corrections are carried out for visible and infrared channels. Secondly, snow cover information is extracted according to its characteristics in different channels. Multi-threshold judgment methods for the different land types and similarity separation techniques are combined to discriminate snow and cloud. Furthermore, multi-temporal data is used to eliminate cloud effect. Finally, the experimental results are compared with the MOD10A1 and MYD10A1 (MODIS daily snow cover) product. The MODIS product can provide higher resolution of the snow cover information in cloudless conditions. Multi-temporal FY-2G data can get more accurate snow cover information in cloudy conditions, which is beneficial for monitoring snowstorms and climate changes.

Kriging in the Shadows: Geostatistical Interpolation for Remote Sensing

NASA Technical Reports Server (NTRS)

Rossi, Richard E.; Dungan, Jennifer L.; Beck, Louisa R.

1994-01-01

It is often useful to estimate obscured or missing remotely sensed data. Traditional interpolation methods, such as nearest-neighbor or bilinear resampling, do not take full advantage of the spatial information in the image. An alternative method, a geostatistical technique known as indicator kriging, is described and demonstrated using a Landsat Thematic Mapper image in southern Chiapas, Mexico. The image was first classified into pasture and nonpasture land cover. For each pixel that was obscured by cloud or cloud shadow, the probability that it was pasture was assigned by the algorithm. An exponential omnidirectional variogram model was used to characterize the spatial continuity of the image for use in the kriging algorithm. Assuming a cutoff probability level of 50%, the error was shown to be 17% with no obvious spatial bias but with some tendency to categorize nonpasture as pasture (overestimation). While this is a promising result, the method's practical application in other missing data problems for remotely sensed images will depend on the amount and spatial pattern of the unobscured pixels and missing pixels and the success of the spatial continuity model used.

Development of hybrid fog detection algorithm (FDA) using satellite and ground observation data for nighttime

NASA Astrophysics Data System (ADS)

Kim, So-Hyeong; Han, Ji-Hae; Suh, Myoung-Seok

2017-04-01

In this study, we developed a hybrid fog detection algorithm (FDA) using AHI/Himawari-8 satellite and ground observation data for nighttime. In order to detect fog at nighttime, Dual Channel Difference (DCD) method based on the emissivity difference between SWIR and IR1 is most widely used. DCD is good at discriminating fog from other things (middle/high clouds, clear sea and land). However, it is difficult to distinguish fog from low clouds. In order to separate the low clouds from the pixels that satisfy the thresholds of fog in the DCD test, we conducted supplementary tests such as normalized local standard derivation (NLSD) of BT11 and the difference of fog top temperature (BT11) and air temperature (Ta) from NWP data (SST from OSTIA data). These tests are based on the larger homogeneity of fog top than low cloud tops and the similarity of fog top temperature and Ta (SST). Threshold values for the three tests were optimized through ROC analysis for the selected fog cases. In addition, considering the spatial continuity of fog, post-processing was performed to detect the missed pixels, in particular, at edge of fog or sub-pixel size fog. The final fog detection results are presented by fog probability (0 100 %). Validation was conducted by comparing fog detection probability with the ground observed visibility data from KMA. The validation results showed that POD and FAR are ranged from 0.70 0.94 and 0.45 0.72, respectively. The quantitative validation and visual inspection indicate that current FDA has a tendency to over-detect the fog. So, more works which reducing the FAR is needed. In the future, we will also validate sea fog using CALIPSO data.

Continuous Change Detection and Classification (CCDC) of Land Cover Using All Available Landsat Data

NASA Astrophysics Data System (ADS)

Zhu, Z.; Woodcock, C. E.

2012-12-01

A new algorithm for Continuous Change Detection and Classification (CCDC) of land cover using all available Landsat data is developed. This new algorithm is capable of detecting many kinds of land cover change as new images are collected and at the same time provide land cover maps for any given time. To better identify land cover change, a two step cloud, cloud shadow, and snow masking algorithm is used for eliminating "noisy" observations. Next, a time series model that has components of seasonality, trend, and break estimates the surface reflectance and temperature. The time series model is updated continuously with newly acquired observations. Due to the high variability in spectral response for different kinds of land cover change, the CCDC algorithm uses a data-driven threshold derived from all seven Landsat bands. When the difference between observed and predicted exceeds the thresholds three consecutive times, a pixel is identified as land cover change. Land cover classification is done after change detection. Coefficients from the time series models and the Root Mean Square Error (RMSE) from model fitting are used as classification inputs for the Random Forest Classifier (RFC). We applied this new algorithm for one Landsat scene (Path 12 Row 31) that includes all of Rhode Island as well as much of Eastern Massachusetts and parts of Connecticut. A total of 532 Landsat images acquired between 1982 and 2011 were processed. During this period, 619,924 pixels were detected to change once (91% of total changed pixels) and 60,199 pixels were detected to change twice (8% of total changed pixels). The most frequent land cover change category is from mixed forest to low density residential which occupies more than 8% of total land cover change pixels.

An efficient framework for modeling clouds from Landsat8 images

NASA Astrophysics Data System (ADS)

Yuan, Chunqiang; Guo, Jing

2015-03-01

Cloud plays an important role in creating realistic outdoor scenes for video game and flight simulation applications. Classic methods have been proposed for cumulus cloud modeling. However, these methods are not flexible for modeling large cloud scenes with hundreds of clouds in that the user must repeatedly model each cloud and adjust its various properties. This paper presents a meteorologically based method to reconstruct cumulus clouds from high resolution Landsat8 satellite images. From these input satellite images, the clouds are first segmented from the background. Then, the cloud top surface is estimated from the temperature of the infrared image. After that, under a mild assumption of flat base for cumulus cloud, the base height of each cloud is computed by averaging the top height for pixels on the cloud edge. Then, the extinction is generated from the visible image. Finally, we enrich the initial shapes of clouds using a fractal method and represent the recovered clouds as a particle system. The experimental results demonstrate our method can yield realistic cloud scenes resembling those in the satellite images.

SeaWiFS technical report series. Volume 31: Stray light in the SeaWiFS radiometer

NASA Technical Reports Server (NTRS)

Hooker, Stanford B. (Editor); Firestone, Elaine R. (Editor); Acker, James G. (Editor); Barnes, Robert A.; Holmes, Alan W.; Esaias, Wayne E.

1995-01-01

Some of the measurements from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) will not be useful as ocean measurements. For the ocean data set, there are procedures in place to mask the SeaWiFS measurements of clouds and ice. Land measurements will also be masked using a geographic technique based on each measurment's latitude and longitude. Each of these masks involves a source of light much brighter than the ocean. Because of stray light in the SeaWiFS radiometer, light from these bright sources can contaminate ocean measurements located a variable number of pixels away from a bright source. In this document, the sources of stray light in the sensor are examined, and a method is developed for masking measurements near bright targets for stray light effects. In addition, a procedure is proposed for reducing the effects of stray light in the flight data from SeaWiFS. This correction can also reduce the number of pixels masked for stray light. Without these corrections, local area scenes must be masked 10 pixels before and after bright targets in the along-scan direction. The addition of these corrections reduces the along-scan masks to four pixels before and after bright sources. In the along-track direction, the flight data are not corrected, and are masked two pixels before and after. Laboratory measurements have shown that stray light within the instrument changes in a direct ratio to the intensity of the bright source. The measurements have also shown that none of the bands show peculiarities in their stray light response. In other words, the instrument's response is uniform from band to band. The along-scan correction is based on each band's response to a 1 pixel wide bright sources. Since these results are based solely on preflight laboratory measurements, their successful implementation requires compliance with two additional criteria. First, since SeaWiFS has a large data volume, the correction and masking procedures must be such that they can be converted into computationally fast algorithms. Second, they must be shown to operate properly on flight data. The laboratory results, and the corrections and masking procedures that derive from them, should be considered as zeroeth order estimates of the effects that will be found on orbit.

Preliminary verification for application of a support vector machine-based cloud detection method to GOSAT-2 CAI-2

NASA Astrophysics Data System (ADS)

Oishi, Yu; Ishida, Haruma; Nakajima, Takashi Y.; Nakamura, Ryosuke; Matsunaga, Tsuneo

2018-05-01

The Greenhouse Gases Observing Satellite (GOSAT) was launched in 2009 to measure global atmospheric CO2 and CH4 concentrations. GOSAT is equipped with two sensors: the Thermal And Near infrared Sensor for carbon Observations (TANSO)-Fourier transform spectrometer (FTS) and TANSO-Cloud and Aerosol Imager (CAI). The presence of clouds in the instantaneous field of view of the FTS leads to incorrect estimates of the concentrations. Thus, the FTS data suspected to have cloud contamination must be identified by a CAI cloud discrimination algorithm and rejected. Conversely, overestimating clouds reduces the amount of FTS data that can be used to estimate greenhouse gas concentrations. This is a serious problem in tropical rainforest regions, such as the Amazon, where the amount of useable FTS data is small because of cloud cover. Preparations are continuing for the launch of the GOSAT-2 in fiscal year 2018. To improve the accuracy of the estimates of greenhouse gases concentrations, we need to refine the existing CAI cloud discrimination algorithm: Cloud and Aerosol Unbiased Decision Intellectual Algorithm (CLAUDIA1). A new cloud discrimination algorithm using a support vector machine (CLAUDIA3) was developed and presented in another paper. Although the use of visual inspection of clouds as a standard for judging is not practical for screening a full satellite data set, it has the advantage of allowing for locally optimized thresholds, while CLAUDIA1 and -3 use common global thresholds. Thus, the accuracy of visual inspection is better than that of these algorithms in most regions, with the exception of snow- and ice-covered surfaces, where there is not enough spectral contrast to identify cloud. In other words, visual inspection results can be used as truth data for accuracy evaluation of CLAUDIA1 and -3. For this reason visual inspection can be used for the truth metric for the cloud discrimination verification exercise. In this study, we compared CLAUDIA1-CAI and CLAUDIA3-CAI for various land cover types, and evaluated the accuracy of CLAUDIA3-CAI by comparing both CLAUDIA1-CAI and CLAUDIA3-CAI with visual inspection (400 × 400 pixels) of the same CAI images in tropical rainforests. Comparative results between CLAUDIA1-CAI and CLAUDIA3-CAI for various land cover types indicated that CLAUDIA3-CAI had a tendency to identify bright surface and optically thin clouds. However, CLAUDIA3-CAI had a tendency to misjudge the edges of clouds compared with CLAUDIA1-CAI. The accuracy of CLAUDIA3-CAI was approximately 89.5 % in tropical rainforests, which is greater than that of CLAUDIA1-CAI (85.9 %) for the test cases presented here.

Reduction of time-resolved space-based CCD photometry developed for MOST Fabry Imaging data*

NASA Astrophysics Data System (ADS)

Reegen, P.; Kallinger, T.; Frast, D.; Gruberbauer, M.; Huber, D.; Matthews, J. M.; Punz, D.; Schraml, S.; Weiss, W. W.; Kuschnig, R.; Moffat, A. F. J.; Walker, G. A. H.; Guenther, D. B.; Rucinski, S. M.; Sasselov, D.

2006-04-01

The MOST (Microvariability and Oscillations of Stars) satellite obtains ultraprecise photometry from space with high sampling rates and duty cycles. Astronomical photometry or imaging missions in low Earth orbits, like MOST, are especially sensitive to scattered light from Earthshine, and all these missions have a common need to extract target information from voluminous data cubes. They consist of upwards of hundreds of thousands of two-dimensional CCD frames (or subrasters) containing from hundreds to millions of pixels each, where the target information, superposed on background and instrumental effects, is contained only in a subset of pixels (Fabry Images, defocused images, mini-spectra). We describe a novel reduction technique for such data cubes: resolving linear correlations of target and background pixel intensities. This step-wise multiple linear regression removes only those target variations which are also detected in the background. The advantage of regression analysis versus background subtraction is the appropriate scaling, taking into account that the amount of contamination may differ from pixel to pixel. The multivariate solution for all pairs of target/background pixels is minimally invasive of the raw photometry while being very effective in reducing contamination due to, e.g. stray light. The technique is tested and demonstrated with both simulated oscillation signals and real MOST photometry.

Development and application of a probability distribution retrieval scheme to the remote sensing of clouds and precipitation

NASA Astrophysics Data System (ADS)

McKague, Darren Shawn

2001-12-01

The statistical properties of clouds and precipitation on a global scale are important to our understanding of climate. Inversion methods exist to retrieve the needed cloud and precipitation properties from satellite data pixel-by-pixel that can then be summarized over large data sets to obtain the desired statistics. These methods can be quite computationally expensive, and typically don't provide errors on the statistics. A new method is developed to directly retrieve probability distributions of parameters from the distribution of measured radiances. The method also provides estimates of the errors on the retrieved distributions. The method can retrieve joint distributions of parameters that allows for the study of the connection between parameters. A forward radiative transfer model creates a mapping from retrieval parameter space to radiance space. A Monte Carlo procedure uses the mapping to transform probability density from the observed radiance histogram to a two- dimensional retrieval property probability distribution function (PDF). An estimate of the uncertainty in the retrieved PDF is calculated from random realizations of the radiance to retrieval parameter PDF transformation given the uncertainty of the observed radiances, the radiance PDF, the forward radiative transfer, the finite number of prior state vectors, and the non-unique mapping to retrieval parameter space. The retrieval method is also applied to the remote sensing of precipitation from SSM/I microwave data. A method of stochastically generating hydrometeor fields based on the fields from a numerical cloud model is used to create the precipitation parameter radiance space transformation. The impact of vertical and horizontal variability within the hydrometeor fields has a significant impact on algorithm performance. Beamfilling factors are computed from the simulated hydrometeor fields. The beamfilling factors vary quite a bit depending upon the horizontal structure of the rain. The algorithm is applied to SSM/I images from the eastern tropical Pacific and is compared to PDFs of rain rate computed using pixel-by-pixel retrievals from Wilheit and from Liu and Curry. Differences exist between the three methods, but good general agreement is seen between the PDF retrieval algorithm and the algorithm of Liu and Curry. (Abstract shortened by UMI.)

Forest Biomass Mapping from Stereo Imagery and Radar Data

NASA Astrophysics Data System (ADS)

Sun, G.; Ni, W.; Zhang, Z.

2013-12-01

Both InSAR and lidar data provide critical information on forest vertical structure, which are critical for regional mapping of biomass. However, the regional application of these data is limited by the availability and acquisition costs. Some researchers have demonstrated potentials of stereo imagery in the estimation of forest height. Most of these researches were conducted on aerial images or spaceborne images with very high resolutions (~0.5m). Space-born stereo imagers with global coverage such as ALOS/PRISM have coarser spatial resolutions (2-3m) to achieve wider swath. The features of stereo images are directly affected by resolutions and the approaches use by most of researchers need to be adjusted for stereo imagery with lower resolutions. This study concentrated on analyzing the features of point clouds synthesized from multi-view stereo imagery over forested areas. The small footprint lidar and lidar waveform data were used as references. The triplets of ALOS/PRISM data form three pairs (forward/nadir, backward/nadir and forward/backward) of stereo images. Each pair of the stereo images can be used to generate points (pixels) with 3D coordinates. By carefully co-register these points from three pairs of stereo images, a point cloud data was generated. The height of each point above ground surface was then calculated using DEM from National Elevation Dataset, USGS as the ground surface elevation. The height data were gridded into pixel of different sizes and the histograms of the points within a pixel were analyzed. The average height of the points within a pixel was used as the height of the pixel to generate a canopy height map. The results showed that the synergy of point clouds from different views were necessary, which increased the point density so the point cloud could detect the vertical structure of sparse and unclosed forests. The top layer of multi-layered forest could be captured but the dense forest prevented the stereo imagery to see through. The canopy height map exhibited spatial patterns of roads, forest edges and patches. The linear regression showed that the canopy height map had a good correlation with RH50 of LVIS data at 30m pixel size with a gain of 1.04, bias of 4.3m and R2 of 0.74 (Fig. 1). The canopy height map from PRISM and dual-pol PALSAR data were used together to map biomass in our study area near Howland, Maine, and the results were evaluated using biomass map generated from LVIS waveform data independently. The results showed that adding CHM from PRISM significantly improved biomass accuracy and raised the biomass saturation level of L-band SAR data in forest biomass mapping.

Spectroradiometric calibration of the Thematic Mapper and Multispectral Scanner system

NASA Technical Reports Server (NTRS)

Slater, P. N.; Palmer, J. M. (Principal Investigator)

1984-01-01

Radiometric measurements were taken on the morning of the LANDSAT 5 Thematic Mapper overpass. The sky was cloud free and the sites were dry. Barnes multiband radiometer data were collected for a 4 x 4 pixel area and two fractional pixel areas of slightly higher and lower reflectances than the larger area. Helicopter color photography was obtained of all the ground areas. This photography will allow a detailed reflectance map of the 4 x 4 pixel are to be made and registered to the TM imagery to an accuracy of better than half a pixel. Spectropolarimeter data were also collected of the 4 x 4 pixel area from the helicopter. In addition, ground based solar radiometer data were collected to provide spectral extinction optical thickness valves. The radiative transfer theory used in the development of the Herman code which was used in predicting the TM entrance pupil spectral radiances from the ground based measurements is described.

X-ray analog pixel array detector for single synchrotron bunch time-resolved imaging.

PubMed

Koerner, Lucas J; Gruner, Sol M

2011-03-01

Dynamic X-ray studies can reach temporal resolutions limited by only the X-ray pulse duration if the detector is fast enough to segregate synchrotron pulses. An analog integrating pixel array detector with in-pixel storage and temporal resolution of around 150 ns, sufficient to isolate pulses, is presented. Analog integration minimizes count-rate limitations and in-pixel storage captures successive pulses. Fundamental tests of noise and linearity as well as high-speed laser measurements are shown. The detector resolved individual bunch trains at the Cornell High Energy Synchrotron Source at levels of up to 3.7 × 10(3) X-rays per pixel per train. When applied to turn-by-turn X-ray beam characterization, single-shot intensity measurements were made with a repeatability of 0.4% and horizontal oscillations of the positron cloud were detected.

X-ray analog pixel array detector for single synchrotron bunch time-resolved imaging

PubMed Central

Koerner, Lucas J.; Gruner, Sol M.

2011-01-01

Dynamic X-ray studies can reach temporal resolutions limited by only the X-ray pulse duration if the detector is fast enough to segregate synchrotron pulses. An analog integrating pixel array detector with in-pixel storage and temporal resolution of around 150 ns, sufficient to isolate pulses, is presented. Analog integration minimizes count-rate limitations and in-pixel storage captures successive pulses. Fundamental tests of noise and linearity as well as high-speed laser measurements are shown. The detector resolved individual bunch trains at the Cornell High Energy Synchrotron Source at levels of up to 3.7 × 103 X-rays per pixel per train. When applied to turn-by-turn X-ray beam characterization, single-shot intensity measurements were made with a repeatability of 0.4% and horizontal oscillations of the positron cloud were detected. PMID:21335901

Point-Cloud Compression for Vehicle-Based Mobile Mapping Systems Using Portable Network Graphics

NASA Astrophysics Data System (ADS)

Kohira, K.; Masuda, H.

2017-09-01

A mobile mapping system is effective for capturing dense point-clouds of roads and roadside objects Point-clouds of urban areas, residential areas, and arterial roads are useful for maintenance of infrastructure, map creation, and automatic driving. However, the data size of point-clouds measured in large areas is enormously large. A large storage capacity is required to store such point-clouds, and heavy loads will be taken on network if point-clouds are transferred through the network. Therefore, it is desirable to reduce data sizes of point-clouds without deterioration of quality. In this research, we propose a novel point-cloud compression method for vehicle-based mobile mapping systems. In our compression method, point-clouds are mapped onto 2D pixels using GPS time and the parameters of the laser scanner. Then, the images are encoded in the Portable Networking Graphics (PNG) format and compressed using the PNG algorithm. In our experiments, our method could efficiently compress point-clouds without deteriorating the quality.

Global Single and Multiple Cloud Classification with a Fuzzy Logic Expert System

NASA Technical Reports Server (NTRS)

Welch, Ronald M.; Tovinkere, Vasanth; Titlow, James; Baum, Bryan A.

1996-01-01

An unresolved problem in remote sensing concerns the analysis of satellite imagery containing both single and multiple cloud layers. While cloud parameterizations are very important both in global climate models and in studies of the Earth's radiation budget, most cloud retrieval schemes, such as the bispectral method used by the International Satellite Cloud Climatology Project (ISCCP), have no way of determining whether overlapping cloud layers exist in any group of satellite pixels. Coakley (1983) used a spatial coherence method to determine whether a region contained more than one cloud layer. Baum et al. (1995) developed a scheme for detection and analysis of daytime multiple cloud layers using merged AVHRR (Advanced Very High Resolution Radiometer) and HIRS (High-resolution Infrared Radiometer Sounder) data collected during the First ISCCP Regional Experiment (FIRE) Cirrus 2 field campaign. Baum et al. (1995) explored the use of a cloud classification technique based on AVHRR data. This study examines the feasibility of applying the cloud classifier to global satellite imagery.

Deep Clouds

NASA Image and Video Library

2008-05-27

Bright puffs and ribbons of cloud drift lazily through Saturn's murky skies. In contrast to the bold red, orange and white clouds of Jupiter, Saturn's clouds are overlain by a thick layer of haze. The visible cloud tops on Saturn are deeper in its atmosphere due to the planet's cooler temperatures. This view looks toward the unilluminated side of the rings from about 18 degrees above the ringplane. Images taken using red, green and blue spectral filters were combined to create this natural color view. The images were acquired with the Cassini spacecraft wide-angle camera on April 15, 2008 at a distance of approximately 1.5 million kilometers (906,000 miles) from Saturn. Image scale is 84 kilometers (52 miles) per pixel. http://photojournal.jpl.nasa.gov/catalog/PIA09910

Differences in liquid cloud droplet effective radius and number concentration estimates between MODIS Collections 5.1 and 6 over global oceans

PubMed Central

Rausch, John; Meyer, Kerry; Bennartz, Ralf; Platnick, Steven

2017-01-01

Differences in cloud droplet effective radius and cloud droplet number concentration (CDNC) estimates inferred from the Aqua MODIS Collections 5.1 and 6 cloud products (MYD06) are examined for warm clouds over global oceans for the year 2008. Individual pixel level retrievals for both collections are aggregated to 1° × 1° and compared globally and regionally for the three main spectral channel pairs used for MODIS cloud optical property retrievals. Comparisons between both collections are performed for cases in which all three effective radii retrievals are classified by the MODIS Cloud Product as valid. The contribution to the observed differences of several key MYD06 Collection 6 algorithm updates are also explored, with a focus on changes to the surface reflectance model, assumed solar irradiance, above cloud emission, cloud top pressure, and pixel registration. Global results show a neutral to positive (> 50 cm−3) change for C6-derived CDNC relative to C5.1 for the 1.6 µm and 2.1 µm channel retrievals, corresponding to a neutral to −2 µm difference in droplet effective radius. For 3.7 µm retrievals, CDNC results show a negative change in the tropics, with differences transitioning toward positive values with increasing latitude spanning −25 to +50 cm−3 related to a +2.5 to −1 µm transition in effective radius. Cloud optical thickness differences were small relative to effective radius, and found to not significantly impact CDNC estimates. Regionally, the magnitude and behavior of the annual CDNC cycle are compared for each effective radius retrieval. Results from this study indicate significant intercollection differences in aggregated values of effective radius due to changes to the pre-computed retrieval lookup tables for ocean scenes, changes to retrieved cloud top pressure, solar irradiance, or above cloud thermal emission, depending upon spectral channel. The observed differences between collections may have implications for existing MODIS derived climatologies and validation studies of effective radius and CDNC. PMID:29098040

Differences in Liquid Cloud Droplet Effective Radius and Number Concentration Estimates Between MODIS Collections 5.1 and 6 Over Global Oceans

NASA Technical Reports Server (NTRS)

Rausch, John; Meyer, Kerry; Bennartz, Ralf; Platnick, Steven

2017-01-01

Differences in cloud droplet effective radius and cloud droplet number concentration (CDNC) estimates inferred from the Aqua MODIS Collections 5.1 and 6 cloud products (MYD06) are examined for warm clouds over global oceans for the year 2008. Individual pixel level retrievals for both collections are aggregated to 1 degree x 1 degree and compared globally and regionally for the three main spectral channel pairs used for MODIS cloud optical property retrievals. Comparisons between both collections are performed for cases in which all three effective radii retrievals are classified by the MODIS Cloud Product as valid. The contribution to the observed differences of several key MYD06 Collection 6 algorithm updates are also explored, with a focus on changes to the surface reflectance model, assumed solar irradiance, above cloud emission, cloud top pressure, and pixel registration. Global results show a neutral to positive ( greater than 50cm(exp. -3) change for C6-derived CDNC relative to C5.1 for the 1.6 micrometers and 2.1 micrometers channel retrievals, corresponding to a neutral to -2 micrometers difference in droplet effective radius. For 3.7 micrometer retrievals, CDNC results show a negative change in the tropics, with differences transitioning toward positive values with increasing latitude spanning -25 to +50 cm(exp. -3) related to a +2.5 to -1 micrometers transition in effective radius. Cloud optical thickness differences were small relative to effective radius, and found to not significantly impact CDNC estimates. Regionally, the magnitude and behavior of the annual CDNC cycle are compared for each effective radius retrieval. Results from this study indicate significant intercollection differences in aggregated values of effective radius due to changes to the pre-computed retrieval lookup tables for ocean scenes, changes to retrieved cloud top pressure, solar irradiance, or above cloud thermal emission, depending upon spectral channel. The observed differences between collections may have implications for existing MODIS derived climatologies and validation studies of effective radius and CDNC.

Jupiter Blues

NASA Image and Video Library

2017-11-30

See Jovian clouds in striking shades of blue in this new view taken by NASA's Juno spacecraft. The Juno spacecraft captured this image when the spacecraft was only 11,747 miles (18,906 kilometers) from the tops of Jupiter's clouds -- that's roughly as far as the distance between New York City and Perth, Australia. The color-enhanced image, which captures a cloud system in Jupiter's northern hemisphere, was taken on Oct. 24, 2017 at 10:24 a.m. PDT (1:24 p.m. EDT) when Juno was at a latitude of 57.57 degrees (nearly three-fifths of the way from Jupiter's equator to its north pole) and performing its ninth close flyby of the gas giant planet. The spatial scale in this image is 7.75 miles/pixel (12.5 kilometers/pixel). Because of the Juno-Jupiter-Sun angle when the spacecraft captured this image, the higher-altitude clouds can be seen casting shadows on their surroundings. The behavior is most easily observable in the whitest regions in the image, but also in a few isolated spots in both the bottom and right areas of the image. Citizen scientists Gerald Eichstädt and Seán Doran processed this image using data from the JunoCam imager. https://photojournal.jpl.nasa.gov/catalog/PIA21972

DOE Office of Scientific and Technical Information (OSTI.GOV)

Davies, R.

The spatial autocorrelation functions of broad-band longwave and shortwave radiances measured by the Earth Radiation Budget Experiment (ERBE) are analyzed as a function of view angle in an investigation of the general effects of scene inhomogeneity on radiation. For nadir views, the correlation distance of the autocorrelation function is about 900 km for longwave radiance and about 500 km for shortwave radiance, consistent with higher degrees of freedom in shortwave reflection. Both functions rise monotonically with view angle, but there is a substantial difference in the relative angular dependence of the shortwave and longwave functions, especially for view angles lessmore » than 50 deg. In this range, the increase with angle of the longwave functions is found to depend only on the expansion of pixel area with angle, whereas the shortwave functions show an additional dependence on angle that is attributed to the occlusion of inhomogeneities by cloud height variations. Beyond a view angle of about 50 deg, both longwave and shortwave functions appear to be affected by cloud sides. The shortwave autocorrelation functions do not satisfy the principle of directional reciprocity, thereby proving that the average scene is horizontally inhomogeneous over the scale of an ERBE pixel (1500 sq km). Coarse stratification of the measurements by cloud amount, however, indicates that the average cloud-free scene does satisfy directional reciprocity on this scale.« less

A new NDVI measure that overcomes data sparsity in cloud-covered regions predicts annual variation in ground-based estimates of high arctic plant productivity

NASA Astrophysics Data System (ADS)

Rune Karlsen, Stein; Anderson, Helen B.; van der Wal, René; Bremset Hansen, Brage

2018-02-01

Efforts to estimate plant productivity using satellite data can be frustrated by the presence of cloud cover. We developed a new method to overcome this problem, focussing on the high-arctic archipelago of Svalbard where extensive cloud cover during the growing season can prevent plant productivity from being estimated over large areas. We used a field-based time-series (2000-2009) of live aboveground vascular plant biomass data and a recently processed cloud-free MODIS-Normalised Difference Vegetation Index (NDVI) data set (2000-2014) to estimate, on a pixel-by-pixel basis, the onset of plant growth. We then summed NDVI values from onset of spring to the average time of peak NDVI to give an estimate of annual plant productivity. This remotely sensed productivity measure was then compared, at two different spatial scales, with the peak plant biomass field data. At both the local scale, surrounding the field data site, and the larger regional scale, our NDVI measure was found to predict plant biomass (adjusted R 2 = 0.51 and 0.44, respectively). The commonly used ‘maximum NDVI’ plant productivity index showed no relationship with plant biomass, likely due to some years having very few cloud-free images available during the peak plant growing season. Thus, we propose this new summed NDVI from onset of spring to time of peak NDVI as a proxy of large-scale plant productivity for regions such as the Arctic where climatic conditions restrict the availability of cloud-free images.

Enhancement of the MODIS Snow and Ice Product Suite Utilizing Image Segmentation

NASA Technical Reports Server (NTRS)

Tilton, James C.; Hall, Dorothy K.; Riggs, George A.

2006-01-01

A problem has been noticed with the current NODIS Snow and Ice Product in that fringes of certain snow fields are labeled as "cloud" whereas close inspection of the data indicates that the correct labeling is a non-cloud category such as snow or land. This occurs because the current MODIS Snow and Ice Product generation algorithm relies solely on the MODIS Cloud Mask Product for the labeling of image pixels as cloud. It is proposed here that information obtained from image segmentation can be used to determine when it is appropriate to override the cloud indication from the cloud mask product. Initial tests show that this approach can significantly reduce the cloud "fringing" in modified snow cover labeling. More comprehensive testing is required to determine whether or not this approach consistently improves the accuracy of the snow and ice product.

Identifying fecal matter contamination in produce fields using multispectral reflectance imaging under ambient solar illumination

NASA Astrophysics Data System (ADS)

Everard, Colm D.; Kim, Moon S.; Lee, Hoonsoo; O'Donnell, Colm P.

2016-05-01

An imaging device to detect fecal contamination in fresh produce fields could allow the producer avoid harvesting fecal contaminated produce. E.coli O157:H7 outbreaks have been associated with fecal contaminated leafy greens. In this study, in-field spectral profiles of bovine fecal matter, soil, and spinach leaves are compared. A common aperture imager designed with two identical monochromatic cameras, a beam splitter, and optical filters was used to simultaneously capture two-spectral images of leaves contaminated with both fecal matter and soil. The optical filters where 10 nm full width half maximum bandpass filters, one at 690 nm and the second at 710 nm. These were mounted in front of the object lenses. New images were created using the ratio of these two spectral images on a pixel by pixel basis. Image analysis results showed that the fecal matter contamination could be distinguished from soil and leaf on the ratio images. The use of this technology has potential to allow detection of fecal contamination in produce fields which can be a source of foodbourne illnesses. It has the added benefit of mitigating cross-contamination during harvesting and processing.

Irreducible 3D Radiative Transfer Effects in Multi-angle/Multi-spectral Radio-Polarimetric Signals (Not Noise!) from a Mixture of Clouds and Aerosol in a Single Large-Footprint Pixel

NASA Astrophysics Data System (ADS)

Davis, A. B.; Qu, Z.; Emde, C.; Xu, F.; Marshak, A.

2013-12-01

Although the Glory satellite mission failed at launch, the atmospheric observation strategy implemented in its Aerosol Polarization Sensor (APS) is alive and well since it is at least possible that another one will be built and launched. This strategy is based on APS's along-track scanning spectro-polarimetric measurement system that captures the three main Stokes vector elements (I,Q,U) at a large number (>200) viewing directions for 9 wavelengths emanating from a single pixel that is ~7 km in diameter at nadir and stretches into a ~7 x 20 km^2 ellipse at the most oblique views to be considered (~70 degrees). Two cloud cameras (CCs) were also onboard Glory to provide spatial context. If the relatively large APS footprint is cloud-free or fully-cloudy, then a 1D vector radiative transfer (RT) model is adequate for predicting the APS signals and, upon iteration over its input parameters, aerosol and cloud property retrievals are expected to be of high quality. And this level of accuracy is indeed required to make a real breakthrough in climate modeling where the radiative properties of aerosols and clouds remain one of the main sources of uncertainty. However, the CCs will often show that the APS's field-of-view is a spatially complex cloud scene, but where we are mostly interested in the ambient aerosols. Moreover, it is precisely these aerosols in contact with clouds that will influence their microphysical and optical properties, leading to the manifold indirect aerosol effects on the climate system that need to be far better understood in order to improve their representation in climate models. Therefore, the research presented here addresses the challenge of characterizing simultaneously aerosols and clouds in a single APS observation. Access to polarization can, at least in principle, be used to separate clouds and aerosols using the cloud-bow directions that will often be sampled by APS. In practice, however, we need to assess the extent of 3D polarized RT unfolding inside the APS pixel that cannot be estimated using a linear mixture of 1D vector RT (vRT) computations assuming either aerosol or cloud is present. Differences between the 1D vRT-based prediction and simulated APS data derived from a high-fidelity 3D vRT model is what we call "irreducible" 3D RT effects. To this end, we have used the vMYSTIC Monte Carlo 3D vRT model. Based on computations for a typical scene with a 3D cumulus cloud field embedded in a horizontally uniform aerosol, we find that the irreducible 3D vRT effects are in the APS's signal--not its noise--especially if the aerosol burden is significant. The cloud-bow region, which is key to any practical cloud-aerosol unmixing algorithm, is particularly vulnerable. Moreover, the adopted 1D vRT-based forward model is assumed to be very well informed about the actual aerosol/cloud properties, meaning that the predicted irreducible 3D vRT effects are a best-case scenario. In reality, the problem will be far more severe. We will nonetheless describe a promising path toward a mitigation scheme. We will also assess the impact of the 3D vRT damage on the joint aerosol-cloud property retrieval.

Cloud cover typing from environmental satellite imagery. Discriminating cloud structure with Fast Fourier Transforms (FFT)

NASA Technical Reports Server (NTRS)

Logan, T. L.; Huning, J. R.; Glackin, D. L.

1983-01-01

The use of two dimensional Fast Fourier Transforms (FFTs) subjected to pattern recognition technology for the identification and classification of low altitude stratus cloud structure from Geostationary Operational Environmental Satellite (GOES) imagery was examined. The development of a scene independent pattern recognition methodology, unconstrained by conventional cloud morphological classifications was emphasized. A technique for extracting cloud shape, direction, and size attributes from GOES visual imagery was developed. These attributes were combined with two statistical attributes (cloud mean brightness, cloud standard deviation), and interrogated using unsupervised clustering amd maximum likelihood classification techniques. Results indicate that: (1) the key cloud discrimination attributes are mean brightness, direction, shape, and minimum size; (2) cloud structure can be differentiated at given pixel scales; (3) cloud type may be identifiable at coarser scales; (4) there are positive indications of scene independence which would permit development of a cloud signature bank; (5) edge enhancement of GOES imagery does not appreciably improve cloud classification over the use of raw data; and (6) the GOES imagery must be apodized before generation of FFTs.

Retrievals and Comparisons of Various MODIS-Spectrum Inferred Water Cloud Droplet Effective Radii

NASA Technical Reports Server (NTRS)

Fu-Lung, Chang; Minnis, Patrick; Lin, Bin; Sunny, Sun-Mack; Khaiyer, Mandana M.

2007-01-01

Cloud droplet effective radius retrievals from different Aqua MODIS nearinfrared channels (2.1- micrometer, 3.7- micrometer, and 1.6- micrometer) show considerable differences even among most confident QC pixels. Both Collection 004 and Collection 005 MOD06 show smaller mean effective radii at 3.7- micrometer wavelength than at 2.1- micrometer and 1.6- micrometer wavelengths. Differences in effective radius retrievals between Collection 004 and Collection 005 may be affected by cloud top height/temperature differences, which mainly occur for optically thin clouds. Changes in cloud top height and temperature for thin clouds have different impacts on the effective radius retrievals from 2.1- micrometer, 3.7- micrometer, and 1.6- micrometer channels. Independent retrievals (this study) show, on average, more consistency in the three effective radius retrievals. This study is for Aqua MODIS only.

Evaluation of AIRS cloud properties using MPACE data

NASA Astrophysics Data System (ADS)

Wu, Xuebao; Li, Jun; Menzel, W. Paul; Huang, Allen; Baggett, Kevin; Revercomb, Henry

2005-12-01

Retrieval of cloud properties from the Atmospheric Infrared Sounder (AIRS) aboard the NASA Aqua satellite has been investigated. The cloud products from the collocated MODerate resolution Imaging Spectroradiometer (MODIS) data are used to characterize the AIRS sub-pixel cloud information such as cloud phase, cloud coverage, and cloud layer information. A Minimum Residual (MR) approach is used to retrieve cloud microphysical properties once the cloud top pressure (CTP) and effective cloud amount (ECA) are determined from AIRS CO2 absorption channels between 720 and 790 cm-1. The cloud microphysical properties can be retrieved by minimizing the differences between the observations and the calculations using AIRS longwave window channels between 790 and 1130 cm-1. AIRS is used to derive cloud properties during the Mixed Phase Arctic Cloud Experiment (MPACE) field campaign. Comparison with measurements obtained from lidar data is made for a test day, showing that AIRS cloud property retrievals agree with in situ lidar observations. Due to the large solar zenith angle, the MODIS operational retrieval approach is not able to provide cloud microphysics north of Barrow, Alaska; however, AIRS provides cloud microphysical properties with its high spectral resolution IR measurements.

Monthly and Seasonal Cloud Cover Patterns at the Manila Observatory (14.64°N, 121.08°E)

NASA Astrophysics Data System (ADS)

Antioquia, C. T.; Lagrosas, N.; Caballa, K.

2014-12-01

A ground based sky imaging system was developed at the Manila Observatory in 2012 to measure cloud occurrence and to analyse seasonal variation of cloud cover over Metro Manila. Ground-based cloud occurrence measurements provide more reliable results compared to satellite observations. Also, cloud occurrence data aid in the analysis of radiation budget in the atmosphere. In this study, a GoPro Hero 2 with almost 180o field of view is employed to take pictures of the atmosphere. These pictures are taken continuously, having a temporal resolution of 1min. Atmospheric images from April 2012 to June 2013 (excluding the months of September, October, and November 2012) were processed to determine cloud cover. Cloud cover in an image is measured as the ratio of the number of pixels with clouds present in them to the total number of pixels. The cloud cover values were then averaged over each month to know its monthly and seasonal variation. In Metro Manila, the dry season occurs in the months of November to May of the next year, while the wet season occurs in the months of June to October of the same year. Fig 1 shows the measured monthly variation of cloud cover. No data was collected during the months of September (wherein the camera was used for the 7SEAS field campaign), October, and November 2012 (due to maintenance and repairs). Results show that there is high cloud cover during the wet season months (80% on average) while there is low cloud cover during the dry season months (62% on average). The lowest average cloud cover for a wet season month occurred in June 2012 (73%) while the highest average cloud cover for a wet season month occurred in June 2013 (86%). The variations in cloud cover average in this season is relatively smaller compared to that of the dry season wherein the lowest average cloud cover in a month was during April 2012 (38%) while the highest average cloud cover in a month was during January 2013 (77%); minimum and maximum averages being 39% apart. During the wet season, the cloud occurrence is mainly due to tropical storms, Southwest Monsoon, and local convection processes. In the dry season, less cloud is formed because of cold dry air from Northeast Monsoon (December to February) and generally dry and hot weather (March to May). Regular data collection has been implemented for further long term data analysis.

How to model moon signals using 2-dimensional Gaussian function: Classroom activity for measuring nighttime cloud cover

NASA Astrophysics Data System (ADS)

Gacal, G. F. B.; Lagrosas, N.

2016-12-01

Nowadays, cameras are commonly used by students. In this study, we use this instrument to look at moon signals and relate these signals to Gaussian functions. To implement this as a classroom activity, students need computers, computer software to visualize signals, and moon images. A normalized Gaussian function is often used to represent probability density functions of normal distribution. It is described by its mean m and standard deviation s. The smaller standard deviation implies less spread from the mean. For the 2-dimensional Gaussian function, the mean can be described by coordinates (x0, y0), while the standard deviations can be described by sx and sy. In modelling moon signals obtained from sky-cameras, the position of the mean (x0, y0) is solved by locating the coordinates of the maximum signal of the moon. The two standard deviations are the mean square weighted deviation based from the sum of total pixel values of all rows/columns. If visualized in three dimensions, the 2D Gaussian function appears as a 3D bell surface (Fig. 1a). This shape is similar to the pixel value distribution of moon signals as captured by a sky-camera. An example of this is illustrated in Fig 1b taken around 22:20 (local time) of January 31, 2015. The local time is 8 hours ahead of coordinated universal time (UTC). This image is produced by a commercial camera (Canon Powershot A2300) with 1s exposure time, f-stop of f/2.8, and 5mm focal length. One has to chose a camera with high sensitivity when operated at nighttime to effectively detect these signals. Fig. 1b is obtained by converting the red-green-blue (RGB) photo to grayscale values. The grayscale values are then converted to a double data type matrix. The last conversion process is implemented for the purpose of having the same scales for both Gaussian model and pixel distribution of raw signals. Subtraction of the Gaussian model from the raw data produces a moonless image as shown in Fig. 1c. This moonless image can be used for quantifying cloud cover as captured by ordinary cameras (Gacal et al, 2016). Cloud cover can be defined as the ratio of number of pixels whose values exceeds 0.07 and the total number of pixels. In this particular image, cloud cover value is 0.67.

Reconciling the aerosol-liquid water path relationship in the ECHAM6-HAM GCM and the Aerosol_cci/Cloud_cci (A)ATSR dataset by minimizing the effect of aerosol swelling

NASA Astrophysics Data System (ADS)

Neubauer, D.; Christensen, M.; Lohmann, U.; Poulsen, C. A.

2016-12-01

Studies using present day variability to assess statistical relationships between aerosol and cloud properties find different strengths of these relationships between satellite data and general circulation model (GCM) data. This discrepancy can be explained by structural uncertainties due to differences in the analysis/observational scale and the process scale or spurious relationships between aerosol and cloud properties. Such spurious relationships are the growth of aerosol particles in the humid environment surrounding clouds, misclassification of partly cloudy satellite pixels as cloud free pixels, brightening of aerosol particles by sunlight reflected at cloud edges, or effects of clouds on aerosol like processing of aerosol particles in clouds by nucleation or impact scavenging and subsequent growth by heterogeneous chemistry and release by cloud droplet evaporation or wet scavenging of aerosol particles. To minimize the effects of spatial aggregation and spurious relationships we apply a new nearest neighbour approach to high resolution (A)ATSR datasets from the Aerosol_cci and Cloud_cci projects of the Climate Change Initiative (CCI) programme of ESA. For the ECHAM6-HAM GCM we quantify the impact of using dry aerosol (without aerosol water) in the analysis to mimic the effect of the nearest neighbour approach. The aerosol-liquid water path relationship in ECHAM6-HAM is systematically stronger than in (A)ATSR data and cannot be explained by an overestimation of autoconversion when using diagnostic precipitation but rather by aerosol swelling in regions where humidity is high and clouds are present. When aerosol water is removed from the analysis in ECHAM6-HAM the strength of the aerosol-liquid water path relationship agrees much better with the ones of (A)ATSR or MODIS. We further find that while the observed relationships of different satellite sensors ((A)ATSR vs. MODIS) are not always consistent for tested environmental conditions the relationships in ECHAM6-HAM are missing a strong dependence on environmental conditions which is critical for bridging the gap between satellite and model estimates of aerosol indirect forcing.

A single field of view method for retrieving tropospheric temperature profiles from cloud-contaminated radiance data

NASA Technical Reports Server (NTRS)

Hodges, D. B.

1976-01-01

An iterative method is presented to retrieve single field of view (FOV) tropospheric temperature profiles directly from cloud-contaminated radiance data. A well-defined temperature profile may be calculated from the radiative transfer equation (RTE) for a partly cloudy atmosphere when the average fractional cloud amount and cloud-top height for the FOV are known. A cloud model is formulated to calculate the fractional cloud amount from an estimated cloud-top height. The method is then examined through use of simulated radiance data calculated through vertical integration of the RTE for a partly cloudy atmosphere using known values of cloud-top height(s) and fractional cloud amount(s). Temperature profiles are retrieved from the simulated data assuming various errors in the cloud parameters. Temperature profiles are retrieved from NOAA-4 satellite-measured radiance data obtained over an area dominated by an active cold front and with considerable cloud cover and compared with radiosonde data. The effects of using various guessed profiles and the number of iterations are considered.

Automated detection of cloud and cloud-shadow in single-date Landsat imagery using neural networks and spatial post-processing

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hughes, Michael J.; Hayes, Daniel J

2014-01-01

Use of Landsat data to answer ecological questions is contingent on the effective removal of cloud and cloud shadow from satellite images. We develop a novel algorithm to identify and classify clouds and cloud shadow, \\textsc{sparcs}: Spacial Procedures for Automated Removal of Cloud and Shadow. The method uses neural networks to determine cloud, cloud-shadow, water, snow/ice, and clear-sky membership of each pixel in a Landsat scene, and then applies a set of procedures to enforce spatial rules. In a comparison to FMask, a high-quality cloud and cloud-shadow classification algorithm currently available, \\textsc{sparcs} performs favorably, with similar omission errors for cloudsmore » (0.8% and 0.9%, respectively), substantially lower omission error for cloud-shadow (8.3% and 1.1%), and fewer errors of commission (7.8% and 5.0%). Additionally, textsc{sparcs} provides a measure of uncertainty in its classification that can be exploited by other processes that use the cloud and cloud-shadow detection. To illustrate this, we present an application that constructs obstruction-free composites of images acquired on different dates in support of algorithms detecting vegetation change.« less

Progress towards MODIS and VIIRS Cloud Fraction Data Record Continuity

NASA Astrophysics Data System (ADS)

Ackerman, S. A.; Frey, R.; Holz, R.; Platnick, S. E.; Heidinger, A. K.

2016-12-01

Satellite-derived clear-sky vs. cloudy-sky discrimination at the pixel scale is an important input parameter used in many real-time applications. Cloud fractions, resulting from integrating over time and space, are also critical to the study of recent decadal climate changes. The NASA NPOESS Preparatory Project (NPP) has funded a science team to develop and study the ability to make continuous climate records from MODIS (2000-2020) and VIIRS (2012-2030). The MODAWG project, led by Dr. Steve Platnick of NASA/GSFC, combines elements of the MODIS processing system and the NOAA Algorithm Working Group (AWG) to achieve this goal. This presentation will focus on the cloud masking aspects of MODAWG, derived primarily from the MODIS cloud mask (MOD35). Challenges to continuity of cloud detection due to differences in instrument characteristics will be discussed. Cloud mask results from use of the same (continuity) algorithm will be shown for both MODIS and VIIRS, including comparisons to collocated CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) cloud data.

The Operational MODIS Cloud Optical and Microphysical Property Product: Overview of the Collection 6 Algorithm and Preliminary Results

NASA Technical Reports Server (NTRS)

Platnick, Steven; King, Michael D.; Wind, Galina; Amarasinghe, Nandana; Marchant, Benjamin; Arnold, G. Thomas

2012-01-01

Operational Moderate Resolution Imaging Spectroradiometer (MODIS) retrievals of cloud optical and microphysical properties (part of the archived products MOD06 and MYD06, for MODIS Terra and Aqua, respectively) are currently being reprocessed along with other MODIS Atmosphere Team products. The latest "Collection 6" processing stream, which is expected to begin production by summer 2012, includes updates to the previous cloud retrieval algorithm along with new capabilities. The 1 km retrievals, based on well-known solar reflectance techniques, include cloud optical thickness, effective particle radius, and water path, as well as thermodynamic phase derived from a combination of solar and infrared tests. Being both global and of high spatial resolution requires an algorithm that is computationally efficient and can perform over all surface types. Collection 6 additions and enhancements include: (i) absolute effective particle radius retrievals derived separately from the 1.6 and 3.7 !-lm bands (instead of differences relative to the standard 2.1 !-lm retrieval), (ii) comprehensive look-up tables for cloud reflectance and emissivity (no asymptotic theory) with a wind-speed interpolated Cox-Munk BRDF for ocean surfaces, (iii) retrievals for both liquid water and ice phases for each pixel, and a subsequent determination of the phase based, in part, on effective radius retrieval outcomes for the two phases, (iv) new ice cloud radiative models using roughened particles with a specified habit, (v) updated spatially-complete global spectral surface albedo maps derived from MODIS Collection 5, (vi) enhanced pixel-level uncertainty calculations incorporating additional radiative error sources including the MODIS L1 B uncertainty index for assessing band and scene-dependent radiometric uncertainties, (v) and use of a new 1 km cloud top pressure/temperature algorithm (also part of MOD06) for atmospheric corrections and low cloud non-unity emissivity temperature adjustments.

Investigating the influence of volcanic sulfate aerosol on cloud properties Along A-Train tracks

NASA Astrophysics Data System (ADS)

Mace, G. G.

2017-12-01

Marine boundary layer (MBL) clouds are central actors in the climate system given their extensive coverage on the Earth's surface, their 1-way influence on the radiative balance (cooling), and their intimate coupling between air motions, anthropogenic and natural aerosol sources, and processes within the upper ocean mixed layer. Knowledge of how MBL shallow cumulus clouds respond to changes in aerosol is central to understanding how MBL clouds modulate the climate system. A frequent approach to investigating how sulfate aerosol influences MBL clouds has been to examine sulfate plumes extending downstream of active island volcanoes. This approach is challenging due to modification of the air motions in the plumes downstream of islands and due to the tendency of most researchers to examine only level-2 retrievals ignoring the actual data collected by sensors such as MODIS. Past studies have concluded that sulfate aerosols have large effects consistent with the 1st aerosol indirect effect (AIE). We reason that if such effects are as large as suggested in level-2 retrievals then evidence should also be present in the raw MODIS reflectance data as well as other data sources. In this paper we will build on our recently published work where we tested that hypothesis from data collected near Mount Kilauea during a 3-year period. Separating data into aerosol optical depth (A) quartiles, we found little support for a large 1st AIE response. We did find an unambiguous increase in sub 1km-scale cloud fraction with A. This increase in sub 1 km cloud fraction was entirely consistent with increased reflectance with increasing A that is used, via the level 2 retrievals, to argue for a large AIE response of MBL clouds. While the 1-km pixels became unambiguously brighter, that brightening was due to increased sub 1 km cloud fraction and not necessarily due to changes in pixel-level cloud microphysics. We also found that MBL cloud top heights increase as do surface wind speeds as aerosol increases while the radar reflectivity from CloudSat does not change implying that increased aerosols may have caused invigoration of the MBL clouds with little effect on precipitation. We have since expanded upon this initial analysis by exmaining data near other volcanic islands. These expanded results support our initial findings.

Microlensing towards the Magellanic Clouds and M31: is the quest for MACHOs still open?

NASA Astrophysics Data System (ADS)

Calchi Novati, Sebastiano

2012-03-01

Microlensing is the tool of choice for the search and the analysis of compact halo objects ("MACHOs"), a still viable class of dark matter candidates at the galactic scale. Different analyses point towards an agreement in excluding dark matter MACHOs of less than about 10-1 Modot; it remains however an ongoing debate for values in the mass range (0.1 - 1) Modot. The more robust constraints, though not all in agreement, come from the observational campaigns towards the Magellanic Clouds (the LMC and the SMC). The analyses towards the nearby galaxy of M31, in the so called "pixel lensing" regime, have expanded the perspectives in this field of research. In this contribution first we draw a critical view on recent results and then we focus on the pixel lensing analysis towards M31 of the PLAN collaboration.

Soil, water, and vegetation conditions in south Texas

NASA Technical Reports Server (NTRS)

Wiegand, C. L.; Gausman, H. W.; Leamer, R. W.; Richardson, A. J.; Everitt, J. H.; Gerbermann, A. H. (Principal Investigator)

1976-01-01

The author has identified the following significant results. Software development for a computer-aided crop and soil survey system is nearing completion. Computer-aided variety classification accuracies using LANDSAT-1 MSS data for a 600 hectare citrus farm were 83% for Redblush grapefruit and 91% for oranges. These accuracies indicate that there is good potential for computer-aided inventories of grapefruit and orange citrus orchards with LANDSAT-type MSS data. Mean digital values of clouds differed statistically from those for crop, soil, and water entities, and those for cloud shadows were enough lower than sunlit crop and soil to be distinguishable. The standard errors of estimate for the calibration of computer compatible tape coordinate system (pixel and record) to earth coordinate system (longitude and latitude) for 6 LANDSAT scenes ranged from 0.72 to 1.50 pixels and from 0.58 to 1.75 records.

Downscaling Coarse Actual ET Data Using Land Surface Resistance

NASA Astrophysics Data System (ADS)

Shen, T.

2017-12-01

This study proposed a new approach of downscaling ETWATCH 1km actual evapotranspiration (ET) product to a spatial resolution of 30m using land surface resistance that simulated mainly from monthly Landsat8 data and Jarvis method, which combined the benefits of both high temporal resolution of ETWATCH product and fine spatial resolution of Landsat8. The driving factor, surface resistance (Rs), was chosen for the reason that could reflect the transfer ability of vapor flow over canopy. Combined resistance Rs both upon canopy conditions, atmospheric factors and available water content of soil, which remains stable inside one ETWATCH pixel (1km). In this research, we used ETWATCH 1km ten-day actual ET product from April to October in a total of twenty-one images and monthly 30 meters cloud-free NDVI of 2013 (two images from HJ as a substitute due to cloud contamination) combined meteorological indicators for downscaling. A good agreement and correlation were obtained between the downscaled data and three flux sites observation in the middle reach of Heihe basin. The downscaling results show good consistency with the original ETWATCH 1km data both temporal and spatial scale over different land cover types with R2 ranged from 0.8 to 0.98. Besides, downscaled result captured the progression of vegetation transpiration well. This study proved the practicability of new downscaling method in the water resource management.

Towards global Landsat burned area mapping: revisit time and availability of cloud free observations

NASA Astrophysics Data System (ADS)

Melchiorre, A.; Boschetti, L.

2016-12-01

Global, daily coarse resolution satellite data have been extensively used for systematic burned area mapping (Giglio et al. 2013; Mouillot et al. 2014). The adoption of similar approaches for producing moderate resolution (10 - 30 m) global burned area products would lead to very significant improvements for the wide variety of fire information users. It would meet a demand for accurate burned area perimeters needed for fire management, post-fire assessment and environmental restoration, and would lead to more accurate and precise atmospheric emission estimations, especially over heterogeneous areas (Mouillot et al. 2014; Randerson et al. 2012; van der Werf et al. 2010). The increased spatial resolution clearly benefits mapping accuracy: the reduction of mixed pixels directly translates in increased spectral separation compared to coarse resolution data. As a tradeoff, the lower temporal resolution (e.g. 16 days for Landsat), could potentially cause large omission errors in ecosystems with fast post-fire recovery. The spectral signal due to the fire effects is non-permanent, can be detected for a period ranging from a few weeks in savannas and grasslands, to over a year in forest ecosystems (Roy et al. 2010). Additionally, clouds, smoke, and other optically thick aerosols limit the number of available observations (Roy et al. 2008; Smith and Wooster 2005), exacerbating the issues related to mapping burned areas globally with moderate resolution sensors. This study presents a global analysis of the effect of cloud cover on Landsat data availability over burned areas, by analyzing the MODIS data record of burned area (MCD45) and cloud detections (MOD35), and combining it with the Landsat acquisition calendar and viewing geometry. For each pixel classified as burned in the MCD45 product, the MOD35 data are used to determine how many cloud free observations would have been available on Landsat overpass days, within the period of observability of the burned area spectral signal in the specific ecosystem. If a burned area pixel is covered by clouds on all the post-fire Landsat overpass days, we assume that it would not be detected in a hypothetical Landsat global burned area product. The resulting maps of expected omission errors are combined for the full 15-year MODIS dataset, and summarized by ecoregion and landcover class.

Impact of spatial resolution on cirrus infrared satellite retrievals in the presence of cloud heterogeneity

NASA Astrophysics Data System (ADS)

Fauchez, T.; Platnick, S. E.; Meyer, K.; Zhang, Z.; Cornet, C.; Szczap, F.; Dubuisson, P.

2015-12-01

Cirrus clouds are an important part of the Earth radiation budget but an accurate assessment of their role remains highly uncertain. Cirrus optical properties such as Cloud Optical Thickness (COT) and ice crystal effective particle size are often retrieved with a combination of Visible/Near InfraRed (VNIR) and ShortWave-InfraRed (SWIR) reflectance channels. Alternatively, Thermal InfraRed (TIR) techniques, such as the Split Window Technique (SWT), have demonstrated better accuracy for thin cirrus effective radius retrievals with small effective radii. However, current global operational algorithms for both retrieval methods assume that cloudy pixels are horizontally homogeneous (Plane Parallel Approximation (PPA)) and independent (Independent Pixel Approximation (IPA)). The impact of these approximations on ice cloud retrievals needs to be understood and, as far as possible, corrected. Horizontal heterogeneity effects in the TIR spectrum are mainly dominated by the PPA bias that primarily depends on the COT subpixel heterogeneity; for solar reflectance channels, in addition to the PPA bias, the IPA can lead to significant retrieval errors due to a significant photon horizontal transport between cloudy columns, as well as brightening and shadowing effects that are more difficult to quantify. Furthermore TIR retrievals techniques have demonstrated better retrieval accuracy for thin cirrus having small effective radii over solar reflectance techniques. The TIR range is thus particularly relevant in order to characterize, as accurately as possible, thin cirrus clouds. Heterogeneity effects in the TIR are evaluated as a function of spatial resolution in order to estimate the optimal spatial resolution for TIR retrieval applications. These investigations are performed using a cirrus 3D cloud generator (3DCloud), a 3D radiative transfer code (3DMCPOL), and two retrieval algorithms, namely the operational MODIS retrieval algorithm (MOD06) and a research-level SWT algorithm.

Introducing two Random Forest based methods for cloud detection in remote sensing images

NASA Astrophysics Data System (ADS)

Ghasemian, Nafiseh; Akhoondzadeh, Mehdi

2018-07-01

Cloud detection is a necessary phase in satellite images processing to retrieve the atmospheric and lithospheric parameters. Currently, some cloud detection methods based on Random Forest (RF) model have been proposed but they do not consider both spectral and textural characteristics of the image. Furthermore, they have not been tested in the presence of snow/ice. In this paper, we introduce two RF based algorithms, Feature Level Fusion Random Forest (FLFRF) and Decision Level Fusion Random Forest (DLFRF) to incorporate visible, infrared (IR) and thermal spectral and textural features (FLFRF) including Gray Level Co-occurrence Matrix (GLCM) and Robust Extended Local Binary Pattern (RELBP_CI) or visible, IR and thermal classifiers (DLFRF) for highly accurate cloud detection on remote sensing images. FLFRF first fuses visible, IR and thermal features. Thereafter, it uses the RF model to classify pixels to cloud, snow/ice and background or thick cloud, thin cloud and background. DLFRF considers visible, IR and thermal features (both spectral and textural) separately and inserts each set of features to RF model. Then, it holds vote matrix of each run of the model. Finally, it fuses the classifiers using the majority vote method. To demonstrate the effectiveness of the proposed algorithms, 10 Terra MODIS and 15 Landsat 8 OLI/TIRS images with different spatial resolutions are used in this paper. Quantitative analyses are based on manually selected ground truth data. Results show that after adding RELBP_CI to input feature set cloud detection accuracy improves. Also, the average cloud kappa values of FLFRF and DLFRF on MODIS images (1 and 0.99) are higher than other machine learning methods, Linear Discriminate Analysis (LDA), Classification And Regression Tree (CART), K Nearest Neighbor (KNN) and Support Vector Machine (SVM) (0.96). The average snow/ice kappa values of FLFRF and DLFRF on MODIS images (1 and 0.85) are higher than other traditional methods. The quantitative values on Landsat 8 images show similar trend. Consequently, while SVM and K-nearest neighbor show overestimation in predicting cloud and snow/ice pixels, our Random Forest (RF) based models can achieve higher cloud, snow/ice kappa values on MODIS and thin cloud, thick cloud and snow/ice kappa values on Landsat 8 images. Our algorithms predict both thin and thick cloud on Landsat 8 images while the existing cloud detection algorithm, Fmask cannot discriminate them. Compared to the state-of-the-art methods, our algorithms have acquired higher average cloud and snow/ice kappa values for different spatial resolutions.

Chemical Plume Detection with an Iterative Background Estimation Technique

DTIC Science & Technology

2016-05-17

schemes because of contamination of background statistics by the plume. To mitigate the effects of plume contamination , a first pass of the detector...can be used to create a background mask. However, large diffuse plumes are typically not removed by a single pass. Instead, contamination can be...is estimated using plume-pixels, the covariance matrix is contaminated and detection performance may be significantly reduced. To avoid Further author

@KarlTheFog has been mapped!

USGS Publications Warehouse

Torregrosa, Alicia

2016-01-01

Within the world of mapping, clouds are a pesky interference to be removed from satellite remote sensed imagery.  However, to many of us, that is a waste of pixels. Cloud maps are becoming increasingly valuable in the quest to understand land cover change and surface processes. In coastal California, the dynamic summertime interactions between air masses, the ocean, and topography result in blankets of fog and low clouds flowing into low lying areas of the San Francisco Bay Area. The low clouds and fog advected from the Pacific bring moisture and shade to coastal ecosystems. This acts to reduce temperatures and evapotranspiration stress during the otherwise arid Mediterranean climate season, in turn impacting vegetation distribution, irrigation needs, and urban energy consumption.

Spatial autocorrelation of radiation measured by the Earth Radiation Budget Experiment: Scene inhomogeneity and reciprocity violation

NASA Technical Reports Server (NTRS)

Davies, Roger

1994-01-01

The spatial autocorrelation functions of broad-band longwave and shortwave radiances measured by the Earth Radiation Budget Experiment (ERBE) are analyzed as a function of view angle in an investigation of the general effects of scene inhomogeneity on radiation. For nadir views, the correlation distance of the autocorrelation function is about 900 km for longwave radiance and about 500 km for shortwave radiance, consistent with higher degrees of freedom in shortwave reflection. Both functions rise monotonically with view angle, but there is a substantial difference in the relative angular dependence of the shortwave and longwave functions, especially for view angles less than 50 deg. In this range, the increase with angle of the longwave functions is found to depend only on the expansion of pixel area with angle, whereas the shortwave functions show an additional dependence on angle that is attributed to the occlusion of inhomogeneities by cloud height variations. Beyond a view angle of about 50 deg, both longwave and shortwave functions appear to be affected by cloud sides. The shortwave autocorrelation functions do not satisfy the principle of directional reciprocity, thereby proving that the average scene is horizontally inhomogeneous over the scale of an ERBE pixel (1500 sq km). Coarse stratification of the measurements by cloud amount, however, indicates that the average cloud-free scene does satisfy directional reciprocity on this scale.

A novel point cloud registration using 2D image features

NASA Astrophysics Data System (ADS)

Lin, Chien-Chou; Tai, Yen-Chou; Lee, Jhong-Jin; Chen, Yong-Sheng

2017-01-01

Since a 3D scanner only captures a scene of a 3D object at a time, a 3D registration for multi-scene is the key issue of 3D modeling. This paper presents a novel and an efficient 3D registration method based on 2D local feature matching. The proposed method transforms the point clouds into 2D bearing angle images and then uses the 2D feature based matching method, SURF, to find matching pixel pairs between two images. The corresponding points of 3D point clouds can be obtained by those pixel pairs. Since the corresponding pairs are sorted by their distance between matching features, only the top half of the corresponding pairs are used to find the optimal rotation matrix by the least squares approximation. In this paper, the optimal rotation matrix is derived by orthogonal Procrustes method (SVD-based approach). Therefore, the 3D model of an object can be reconstructed by aligning those point clouds with the optimal transformation matrix. Experimental results show that the accuracy of the proposed method is close to the ICP, but the computation cost is reduced significantly. The performance is six times faster than the generalized-ICP algorithm. Furthermore, while the ICP requires high alignment similarity of two scenes, the proposed method is robust to a larger difference of viewing angle.

The Q Continuum: Encounter with the Cloud Mask

NASA Astrophysics Data System (ADS)

Ackerman, S. A.; Frey, R.; Holz, R.; Philips, C.; Dutcher, S.

2017-12-01

We are developing a common cloud mask for MODIS and VIIRS observations, referred to as the MODIS VIIRS Continuity Mask (MVCM). Our focus is on extending the MODIS-heritage cloud detection approach in order to generate appropriate climate data records for clouds and climate studies. The MVCM is based on heritage from the MODIS cloud mask (MOD35 and MYD35) and employs a series of tests on MODIS reflectances and brightness temperatures. Cloud detection is based on contrasts (i.e., cloud versus background surface) at pixel resolution. The MVCM follows the same approach. These cloud masks use multiple cloud detection tests to indicate the confidence level that the observation is of a clear-sky scene. The outcome of a test ranges from 0 (cloudy) to 1 (clear-sky scene). Because of overlap in the sensitivities of the various spectral tests to the type of cloud, each test is considered in one of several groups. The final cloud mask is determined from the product of the minimum confidence of each group and is referred to as the Q value as defined in Ackerman et al (1998). In MOD35 and MYD35 processing, the Q value is not output, rather predetermined Q values determine the result: If Q ≥ .99 the scene is clear; .95 ≤ Q < .99 the pixel is probably a clear scene, .66 ≤ Q < .95 is probably cloudy and Q < .66 is cloudy. Thus representing Q discretely and not as a continuum. For the MVCM, the numerical value of the Q is output along with the classification of clear, probably clear, probably cloudy, and cloudy. Through comparisons with collocated CALIOP and MODIS observations, we will assess the categorization of the Q values as a function of scene type ). While validation studies have indicated the utility and statistical correctness of the cloud mask approach, the algorithm does not possess immeasurable power and perfection. This comparison will assess the time and space dependence of Q and assure that the laws of physics are followed, at least according to normal human notions. Using CALIOP as representing truth, a receiver operating characteristic curve (ROC) will be analyzed to determine the optimum Q for various scenes and seasons, thus providing a continuum of discriminating thresholds.

Comparison of GOES Cloud Classification Algorithms Employing Explicit and Implicit Physics

NASA Technical Reports Server (NTRS)

Bankert, Richard L.; Mitrescu, Cristian; Miller, Steven D.; Wade, Robert H.

2009-01-01

Cloud-type classification based on multispectral satellite imagery data has been widely researched and demonstrated to be useful for distinguishing a variety of classes using a wide range of methods. The research described here is a comparison of the classifier output from two very different algorithms applied to Geostationary Operational Environmental Satellite (GOES) data over the course of one year. The first algorithm employs spectral channel thresholding and additional physically based tests. The second algorithm was developed through a supervised learning method with characteristic features of expertly labeled image samples used as training data for a 1-nearest-neighbor classification. The latter's ability to identify classes is also based in physics, but those relationships are embedded implicitly within the algorithm. A pixel-to-pixel comparison analysis was done for hourly daytime scenes within a region in the northeastern Pacific Ocean. Considerable agreement was found in this analysis, with many of the mismatches or disagreements providing insight to the strengths and limitations of each classifier. Depending upon user needs, a rule-based or other postprocessing system that combines the output from the two algorithms could provide the most reliable cloud-type classification.

Comparison of the MODIS Multilayer Cloud Detection and Thermodynamic Phase Products with CALIPSO and CloudSat

NASA Technical Reports Server (NTRS)

Platnick, Steven; King, Michael D.; Wind, Gala; Holz, Robert E.; Ackerman, Steven A.; Nagle, Fred W.

2008-01-01

CALIPSO and CloudSat, launched in June 2006, provide global active remote sensing measurements of clouds and aerosols that can be used for validation of a variety of passive imager retrievals derived from instruments flying on the Aqua spacecraft and other A-Train platforms. The most recent processing effort for the MODIS Atmosphere Team, referred to as the "Collection 5" stream, includes a research-level multilayer cloud detection algorithm that uses both thermodynamic phase information derived from a combination of solar and thermal emission bands to discriminate layers of different phases, as well as true layer separation discrimination using a moderately absorbing water vapor band. The multilayer detection algorithm is designed to provide a means of assessing the applicability of 1D cloud models used in the MODIS cloud optical and microphysical product retrieval, which are generated at a 1 h resolution. Using pixel-level collocations of MODIS Aqua, CALIOP, and CloudSat radar measurements, we investigate the global performance of the thermodynamic phase and multilayer cloud detection algorithms.

First Look at the Upper Tropospheric Ozone Mixing Ratio from OMI Estimated using the Cloud Slicing Technique

NASA Technical Reports Server (NTRS)

Bhartia, Pawan K.; Ziemke, Jerry; Chandra, Sushil; Joiner, Joanna; Vassilkov, Alexandra; Taylor, Steven; Yang, Kai; Ahn, Chang-Woo

2004-01-01

The Cloud Slicing technique has emerged as a powerful tool for the study of ozone in the upper troposphere. In this technique one looks at the variation with cloud height of the above-cloud column ozone derived from the backscattered ultraviolet instruments, such as TOMS, to determine the ozone mixing ratio. For this technique to work properly one needs an instrument with relatively good horizontal resolution with very good signal to noise in measuring above-cloud column ozone. In addition, one needs the (radiatively) effective cloud pressure rather than the cloud-top pressure, for the ultraviolet photons received by a satellite instrument are scattered from inside the cloud rather than from the top. For this study we use data from the OMI sensor, which was recently launched on the EOS Aura satellite. OMI is a W-Visible backscattering instrument with a nadir pixel size of 13 x 24 km. The effective cloud pressure is derived from a new algorithm based on Rotational Raman Scattering and O2-O2, absorption in the 340-400 nm band of OMI.

3D Aerosol-Cloud Radiative Interaction Observed in Collocated MODIS and ASTER Images of Cumulus Cloud Fields

NASA Technical Reports Server (NTRS)

Wen, Guoyong; Marshak, Alexander; Cahalan, Robert F.; Remer, Lorraine A.; Kleidman, Richard G.

2007-01-01

3D aerosol-cloud interaction is examined by analyzing two images containing cumulus clouds in biomass burning regions in Brazil. The research consists of two parts. The first part focuses on identifying 3D clo ud impacts on the reflectance of pixel selected for the MODIS aerosol retrieval based purely on observations. The second part of the resea rch combines the observations with radiative transfer computations to identify key parameters in 3D aerosol-cloud interaction. We found that 3D cloud-induced enhancement depends on optical properties of nearb y clouds as well as wavelength. The enhancement is too large to be ig nored. Associated biased error in 1D aerosol optical thickness retrie val ranges from 50% to 140% depending on wavelength and optical prope rties of nearby clouds as well as aerosol optical thickness. We caution the community to be prudent when applying 1D approximations in comp uting solar radiation in dear regions adjacent to clouds or when usin g traditional retrieved aerosol optical thickness in aerosol indirect effect research.

A comparison of the accuracy of pixel based and object based classifications of integrated optical and LiDAR data

NASA Astrophysics Data System (ADS)

Gajda, Agnieszka; Wójtowicz-Nowakowska, Anna

2013-04-01

A comparison of the accuracy of pixel based and object based classifications of integrated optical and LiDAR data Land cover maps are generally produced on the basis of high resolution imagery. Recently, LiDAR (Light Detection and Ranging) data have been brought into use in diverse applications including land cover mapping. In this study we attempted to assess the accuracy of land cover classification using both high resolution aerial imagery and LiDAR data (airborne laser scanning, ALS), testing two classification approaches: a pixel-based classification and object-oriented image analysis (OBIA). The study was conducted on three test areas (3 km2 each) in the administrative area of Kraków, Poland, along the course of the Vistula River. They represent three different dominating land cover types of the Vistula River valley. Test site 1 had a semi-natural vegetation, with riparian forests and shrubs, test site 2 represented a densely built-up area, and test site 3 was an industrial site. Point clouds from ALS and ortophotomaps were both captured in November 2007. Point cloud density was on average 16 pt/m2 and it contained additional information about intensity and encoded RGB values. Ortophotomaps had a spatial resolution of 10 cm. From point clouds two raster maps were generated: intensity (1) and (2) normalised Digital Surface Model (nDSM), both with the spatial resolution of 50 cm. To classify the aerial data, a supervised classification approach was selected. Pixel based classification was carried out in ERDAS Imagine software. Ortophotomaps and intensity and nDSM rasters were used in classification. 15 homogenous training areas representing each cover class were chosen. Classified pixels were clumped to avoid salt and pepper effect. Object oriented image object classification was carried out in eCognition software, which implements both the optical and ALS data. Elevation layers (intensity, firs/last reflection, etc.) were used at segmentation stage due to proper wages usage. Thus a more precise and unambiguous boundaries of segments (objects) were received. As a results of the classification 5 classes of land cover (buildings, water, high and low vegetation and others) were extracted. Both pixel-based image analysis and OBIA were conducted with a minimum mapping unit of 10m2. Results were validated on the basis on manual classification and random points (80 per test area), reference data set was manually interpreted using ortophotomaps and expert knowledge of the test site areas.

PRISM project optical instrument

NASA Technical Reports Server (NTRS)

Taylor, Charles R.

1994-01-01

The scientific goal of the Passively-cooled Reconnaissance of the InterStellar Medium (PRISM) project is to map the emission of molecular hydrogen at 17.035 micrometers and 28.221 micrometers. Since the atmosphere is opaque at these infrared wavelengths, an orbiting telescope is being studied. The availability of infrared focal plane arrays enables infrared imaging spectroscopy at the molecular hydrogen wavelengths. The array proposed for PRISM is 128 pixels square, with a pixel size of 75 micrometers. In order to map the sky in a period of six months, and to resolve the nearer molecular clouds, each pixel must cover 0.5 arcminutes. This sets the focal length at 51.6 cm. In order for the pixel size to be half the diameter of the central diffraction peak at 28 micrometers would require a telescope aperture of 24 cm; an aperture of 60 cm has been selected for the PRISM study for greater light gathering power.

Night Sky Weather Monitoring System Using Fish-Eye CCD

NASA Astrophysics Data System (ADS)

Tomida, Takayuki; Saito, Yasunori; Nakamura, Ryo; Yamazaki, Katsuya

Telescope Array (TA) is international joint experiment observing ultra-high energy cosmic rays. TA employs fluorescence detection technique to observe cosmic rays. In this technique, tho existence of cloud significantly affects quality of data. Therefore, cloud monitoring provides important information. We are developing two new methods for evaluating night sky weather with pictures taken by charge-coupled device (CCD) camera. One is evaluating the amount of cloud with pixels brightness. The other is counting the number of stars with contour detection technique. The results of these methods show clear correlation, and we concluded both the analyses are reasonable methods for weather monitoring. We discuss reliability of the star counting method.

Mapping forest tree species over large areas with partially cloudy Landsat imagery

NASA Astrophysics Data System (ADS)

Turlej, K.; Radeloff, V.

2017-12-01

Forests provide numerous services to natural systems and humankind, but which services forest provide depends greatly on their tree species composition. That makes it important to track not only changes in forest extent, something that remote sensing excels in, but also to map tree species. The main goal of our work was to map tree species with Landsat imagery, and to identify how to maximize mapping accuracy by including partially cloudy imagery. Our study area covered one Landsat footprint (26/28) in Northern Wisconsin, USA, with temperate and boreal forests. We selected this area because it contains numerous tree species and variable forest composition providing an ideal study area to test the limits of Landsat data. We quantified how species-level classification accuracy was affected by a) the number of acquisitions, b) the seasonal distribution of observations, and c) the amount of cloud contamination. We classified a single year stack of Landsat-7, and -8 images data with a decision tree algorithm to generate a map of dominant tree species at the pixel- and stand-level. We obtained three important results. First, we achieved producer's accuracies in the range 70-80% and user's accuracies in range 80-90% for the most abundant tree species in our study area. Second, classification accuracy improved with more acquisitions, when observations were available from all seasons, and is the best when images with up to 40% cloud cover are included. Finally, classifications for pure stands were 10 to 30 percentage points better than those for mixed stands. We conclude that including partially cloudy Landsat imagery allows to map forest tree species with accuracies that were previously only possible for rare years with many cloud-free observations. Our approach thus provides important information for both forest management and science.

The Backscatter Cloudprobe with Polarization Detection: A New Aircraft Ice Water Detector

NASA Astrophysics Data System (ADS)

Freer, M.; Baumgardner, D.; Axisa, D.

2017-12-01

The differentiation of liquid water and ice crystals smaller than 100 um in mixed phase clouds continues to challenge the cloud measurement community. In situ imaging probes now have pixel resolution down to about 5 um, but at least 10 pixels are needed to accurately distinguish a water droplet from an ice crystal. This presents a major obstacle for the understanding of cloud glaciation in general, and the formation and evolution of cloud ice in particular. A new sensor has recently been developed that can detect and quantify supercooled liquid droplets and ice crystals. The Backscatter Cloudprobe with Polarization Detection (BCPD) is a very lightweight, compact and low power optical spectrometer that has already undergone laboratory, wind tunnel and flight tests that have validated its capabilities. The BCPD employs the optical approach with single particles that has been used for years in remote sensing to distinguish liquid water from ice crystals in ensembles of cloud particles. The sensor is mounted inside an aircraft and projects a linearly polarized laser beam to the outside through a heated window. Particles that pass through the sample volume of the laser scatter light and the photons scattered in the back direction pass through another heated window where they are collected and focused onto a beam splitter that directs them onto two photodetectors. The P-detector senses the light with polarization parallel to that of the incident light and the S-Detector measures the light that is perpendicular to that of the laser. The polarization ratio, S/P, is sensitive to the asphericity of a particle and is used to identify liquid water and ice crystals. The BCPD has now been exercised in an icing wind tunnel where it was compared with other cloud spectrometers. It has also been flown on the NCAR C-130 and on a commercial Citation, making measurements in all water, all ice and mixed phase clouds. Results from these three applications clearly show that the BCPD can be employed successfully to derive ice fraction in mixed phase clouds at sizes less than 50 um, a size range that has previously been inaccessible to cloud researchers.

Jupiter's Great Red Spot upper cloud morphology and dynamics from JunoCam images

NASA Astrophysics Data System (ADS)

Sanchez-Lavega, A.; Hueso, R.; Eichstädt, G.; Orton, G.; Rogers, J.; Hansen, C. J.; Momary, T.; Tabataba-Vakili, F.

2017-12-01

We present an analysis of RGB color-composite images of the Great Red Spot (GRS) obtained with JunoCam during Juno's seventh close flyby (PJ7) on July 11, 2017. The images have been projected as 4 cylindrical maps with a resolution of 180 pixels per degree (about 7 km/pixel) spanning a temporal interval of 9 min 41s. The GRS shows a rich variety of cloud morphologies that reveal different dynamical processes in its interior. We consider three major regions. (1) An outer peripheral ring of homogeneous reddish clouds (width about 1,300 km) traces a laminar flow. A family of at least three packets of gravity waves with a mean wavelength of 75 km is present at the internal edge of the ring (in its northern side). They occupy an area of 2,500 km in length (East-West, EW) and 670 km in the North-South (NS) direction. Single clouds in the groups forming the wave have extents of 35 km EW and 70-135 km NS. (2) A large internal region of red clouds (width about 3,200 km) contains three morphologies: (a) fields of bright cumulus-like clusters, (b) long, dark curved filaments (about 7,000 km length with 100 km width), two of them converging into an arrowhead shape, and (c) individual anticyclonic vortices with radius of 500 km that grow due to the radial shear of the wind velocity in the GRS interior as previously measured. A cumulus cluster is conspicuous inside one such anticyclone. Each single cloud element is 50 km in size and the cluster has a 25-30 percent area coverage in cumulus-convective activity, presumably due to ammonia moist convection. (3) A central core has quasi-rectangular shape, extending about 5000 km EW and 3000 km NS, that is confined by elongated clouds distributed along its periphery. Its interior is filled with the redder clouds in the GRS that have a scale 100 km and form a turbulent pattern whose cloud orientations suggest three adjacent areas with alternating cyclonic-cyclonic-anticyclonic vorticity, each with radius 650-850 km.

A Novel Method for Estimating Shortwave Direct Radiative Effect of Above-Cloud Aerosols Using CALIOP and MODIS Data

NASA Technical Reports Server (NTRS)

Zhang, Zhibo; Meyer, Kerry G.; Platnick, Steven; Oreopoulos, Lazaros; Lee, Dongmin; Yu, Hongbin

2014-01-01

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 and MODIS 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 pre-computed look-up table based on radiative transfer calculations. We verify that for smoke over the southeast Atlantic Ocean the method yields a seasonal mean instantaneous (approximately 1:30PM local time) shortwave DRE of above cloud aerosol (ACA) that generally agrees with more rigorous pixel-level computation within 4 percent. 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 southeast 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 retrieval are biased low by a factor of 1.5 (Meyer et al., 2013) and further to 30.9 W m(-2) if CALIOP AOD retrieval 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 the variability of the annual mean shortwave DRE of above-cloud light-absorbing aerosol is mainly driven by the optical depth of the underlying clouds. While we demonstrate our method using CALIOP and MODIS data, it can also be extended to other satellite data sets, as well as climate model outputs.

Planck intermediate results: XXXV. Probing the role of the magnetic field in the formation of structure in molecular clouds

DOE PAGES

Ade, P. A. R.; Aghanim, N.; Alves, M. I. R.; ...

2016-02-09

Within ten nearby (d < 450 pc) Gould belt molecular clouds we evaluate in this paper statistically the relative orientation between the magnetic field projected on the plane of sky, inferred from the polarized thermal emission of Galactic dust observed by Planck at 353 GHz, and the gas column density structures, quantified by the gradient of the column density, N H. The selected regions, covering several degrees in size, are analysed at an effective angular resolution of 10' FWHM, thus sampling physical scales from 0.4 to 40 pc in the nearest cloud. The column densities in the selected regions rangemore » from N H≈ 10 21 to10 23 cm -2, and hence they correspond to the bulk of the molecular clouds. The relative orientation is evaluated pixel by pixel and analysed in bins of column density using the novel statistical tool called “histogram of relative orientations”. Throughout this study, we assume that the polarized emission observed by Planck at 353 GHz is representative of the projected morphology of the magnetic field in each region, i.e., we assume a constant dust grain alignment efficiency, independent of the local environment. Within most clouds we find that the relative orientation changes progressively with increasing N H, from mostly parallel or having no preferred orientation to mostly perpendicular. In simulations of magnetohydrodynamic turbulence in molecular clouds this trend in relative orientation is a signature of Alfvénic or sub-Alfvénic turbulence, implying that the magnetic field is significant for the gas dynamics at the scales probed by Planck. Finally, we compare the deduced magnetic field strength with estimates we obtain from other methods and discuss the implications of the Planck observations for the general picture of molecular cloud formation and evolution.« less

A cloud model based multi-attribute decision making approach for selection and evaluation of groundwater management schemes

NASA Astrophysics Data System (ADS)

Lu, Hongwei; Ren, Lixia; Chen, Yizhong; Tian, Peipei; Liu, Jia

2017-12-01

Due to the uncertainty (i.e., fuzziness, stochasticity and imprecision) existed simultaneously during the process for groundwater remediation, the accuracy of ranking results obtained by the traditional methods has been limited. This paper proposes a cloud model based multi-attribute decision making framework (CM-MADM) with Monte Carlo for the contaminated-groundwater remediation strategies selection. The cloud model is used to handle imprecise numerical quantities, which can describe the fuzziness and stochasticity of the information fully and precisely. In the proposed approach, the contaminated concentrations are aggregated via the backward cloud generator and the weights of attributes are calculated by employing the weight cloud module. A case study on the remedial alternative selection for a contaminated site suffering from a 1,1,1-trichloroethylene leakage problem in Shanghai, China is conducted to illustrate the efficiency and applicability of the developed approach. Totally, an attribute system which consists of ten attributes were used for evaluating each alternative through the developed method under uncertainty, including daily total pumping rate, total cost and cloud model based health risk. Results indicated that A14 was evaluated to be the most preferred alternative for the 5-year, A5 for the 10-year, A4 for the 15-year and A6 for the 20-year remediation.

Venus' night side atmospheric dynamics using near infrared observations from VEx/VIRTIS and TNG/NICS

NASA Astrophysics Data System (ADS)

Mota Machado, Pedro; Peralta, Javier; Luz, David; Gonçalves, Ruben; Widemann, Thomas; Oliveira, Joana

2016-10-01

We present night side Venus' winds based on coordinated observations carried out with Venus Express' VIRTIS instrument and the Near Infrared Camera (NICS) of the Telescopio Nazionale Galileo (TNG). With NICS camera, we acquired images of the continuum K filter at 2.28 μm, which allows to monitor motions at the Venus' lower cloud level, close to 48 km altitude. We will present final results of cloud tracked winds from ground-based TNG observations and from coordinated space-based VEx/VIRTIS observations.The Venus' lower cloud deck is centred at 48 km of altitude, where fundamental dynamical exchanges that help maintain superrotation are thought to occur. The lower Venusian atmosphere is a strong source of thermal radiation, with the gaseous CO2 component allowing radiation to escape in windows at 1.74 and 2.28 μm. At these wavelengths radiation originates below 35 km and unit opacity is reached at the lower cloud level, close to 48 km. Therefore, it is possible to observe the horizontal cloud structure, with thicker clouds seen silhouetted against the bright thermal background from the low atmosphere. By continuous monitoring of the horizontal cloud structure at 2.28 μm (NICS Kcont filter), it is possible to determine wind fields using the technique of cloud tracking. We acquired a series of short exposures of the Venus disk. Cloud displacements in the night side of Venus were computed taking advantage of a phase correlation semi-automated technique. The Venus apparent diameter at observational dates was greater than 32" allowing a high spatial precision. The 0.13" pixel scale of the NICS narrow field camera allowed to resolve ~3-pixel displacements. The absolute spatial resolution on the disk was ~100 km/px at disk center, and the (0.8-1") seeing-limited resolution was ~400 km/px. By co-adding the best images and cross-correlating regions of clouds the effective resolution was significantly better than the seeing-limited resolution. In order to correct for scattered light from the (saturated) day side crescent into the night side, a set of observations with a Br Υ filter were performed. Cloud features are invisible at this wavelength, and this technique allowed for a good correction of scattered light.

North Pacific Cloud Feedbacks Inferred from Synoptic-Scale Dynamic and Thermodynamic Relationships

NASA Technical Reports Server (NTRS)

Norris, Joel R.; Iacobellis, Sam F.

2005-01-01

This study analyzed daily satellite cloud observations and reanalysis dynamical parameters to determine how mid-tropospheric vertical velocity and advection over the sea surface temperature gradient control midlatitude North Pacific cloud properties. Optically thick clouds with high tops are generated by synoptic ascent, but two different cloud regimes occur under synoptic descent. When vertical motion is downward during summer, extensive stratocumulus cloudiness is associated with near surface northerly wind, while frequent cloudless pixels occur with southerly wind. Examinations of ship-reported cloud types indicates that midlatitude stratocumulus breaks up as the the boundary level decouples when it is advected equatorward over warmer water. Cumulus is prevalent under conditions of synoptic descent and cold advection during winter. Poleward advection of subtropical air over colder water causes stratification of the near-surface layer that inhibits upward mixing of moisture and suppresses cloudiness until a fog eventually forms. Averaging of cloud and radiation data into intervals of 500-hPa vertical velocity and advection over the SST gradient enables the cloud response to changes in temperature and the stratification of the lower troposphere to be investigated independent of the dynamics.

AVIRIS calibration using the cloud-shadow method

NASA Technical Reports Server (NTRS)

Carder, K. L.; Reinersman, P.; Chen, R. F.

1993-01-01

More than 90 percent of the signal at an ocean-viewing, satellite sensor is due to the atmosphere, so a 5 percent sensor-calibration error viewing a target that contributes but 10 percent of the signal received at the sensor may result in a target-reflectance error of more than 50 percent. Since prelaunch calibration accuracies of 5 percent are typical of space-sensor requirements, recalibration of the sensor using ground-base methods is required for low-signal target. Known target reflectance or water-leaving radiance spectra and atmospheric correction parameters are required. In this article we describe an atmospheric-correction method that uses cloud shadowed pixels in combination with pixels in a neighborhood region of similar optical properties to remove atmospheric effects from ocean scenes. These neighboring pixels can then be used as known reflectance targets for validation of the sensor calibration and atmospheric correction. The method uses the difference between water-leaving radiance values for these two regions. This allows nearly identical optical contributions to the two signals (e.g., path radiance and Fresnel-reflected skylight) to be removed, leaving mostly solar photons backscattered from beneath the sea to dominate the residual signal. Normalization by incident solar irradiance reaching the sea surface provides the remote-sensing reflectance of the ocean at the location of the neighbor region.

GOME and Sciamachy data access using the Netherlands Sciamachy Data Center

NASA Astrophysics Data System (ADS)

Som de Cerff, Wim; de Vreede, Ernst; van de Vegte, John; van Hees, Ricard; van der Neut, Ian; Stammes, Piet; Pieters, Ankie; van der A, Ronald

2010-05-01

The Netherlands Sciamachy Data Center (NL-SCIA-DC) provides access to satellite data from the GOME and Sciamachy instruments for over 10 years now. GOME and Sciamachy both measure trace gases like Ozone, Methane, NO2 and aerosols, which are important for climate and air quality monitoring. Recently (February 2010) a new release of the NL-SCIA-DC provides an improved processing and archiving structure and an improved user interface. This Java Webstart application allows the user to browse, query and download GOME and Sciamachy data products, including KNMI and SRON GOME and Sciamachy products (cloud products, CH4, NO2, CO). Data can be searched on file and pixel level, and can be graphically displayed. The huge database containing all pixel information of GOME and Sciamachy is unique and allows specific selection, e.g., selecting cloud free pixels. Ordered data is delivered by FTP or email. The data available spans the mission times of GOME and Sciamachy, and is constantly updated as new data becomes available. The data services future upgrades include offering additional functionality to end-users of Sciamachy data. One of the functionalities provided will be the possibility to select and process Sciamachy products using different data processors, using Grid technology. This technology was successfully researched and will be made operationally available in the near future.

Optical properties of aerosol contaminated cloud derived from MODIS instrument

NASA Astrophysics Data System (ADS)

Mei, Linlu; Rozanov, Vladimir; Lelli, Luca; Vountas, Marco; Burrows, John P.

2016-04-01

The presence of absorbing aerosols above/within cloud can reduce the amount of up-welling radiation in visible (VIS) and short-wave infrared and darken the spectral reflectance when compared with a spectrum of a clean cloud observed by satellite instruments (Jethva et al., 2013). Cloud properties retrieval for aerosol contaminated cases is a great challenge. Even small additional injection of aerosol particles into clouds in the cleanest regions of Earth's atmosphere will cause significant effect on those clouds and on climate forcing (Koren et al., 2014; Rosenfeld et al., 2014) because the micro-physical cloud process are non-linear with respect to the aerosol loading. The current cloud products like Moderate Resolution Imaging Spectroradiometer (MODIS) ignoring the aerosol effect for the retrieval, which may cause significant error in the satellite-derived cloud properties. In this paper, a new cloud properties retrieval method, considering aerosol effect, based on the weighting-function (WF) method, is presented. The retrieval results shows that the WF retrieved cloud properties (e.g COT) agrees quite well with MODIS COT product for relative clear atmosphere (AOT ≤ 0.4) while there is a large difference for large aerosol loading. The MODIS COT product is underestimated for at least 2 - 3 times for AOT>0.4, and this underestimation increases with the increase of AOT.

Cloud and surface textural features in polar regions

NASA Technical Reports Server (NTRS)

Welch, Ronald M.; Kuo, Kwo-Sen; Sengupta, Sailes K.

1990-01-01

The study examines the textural signatures of clouds, ice-covered mountains, solid and broken sea ice and floes, and open water. The textural features are computed from sum and difference histogram and gray-level difference vector statistics defined at various pixel displacement distances derived from Landsat multispectral scanner data. Polar cloudiness, snow-covered mountainous regions, solid sea ice, glaciers, and open water have distinguishable texture features. This suggests that textural measures can be successfully applied to the detection of clouds over snow-covered mountains, an ability of considerable importance for the modeling of snow-melt runoff. However, broken stratocumulus cloud decks and thin cirrus over broken sea ice remain difficult to distinguish texturally. It is concluded that even with high spatial resolution imagery, it may not be possible to distinguish broken stratocumulus and thin clouds from sea ice in the marginal ice zone using the visible channel textural features alone.

Comparison of the MODIS Collection 5 Multilayer Cloud Detection Product with CALIPSO

NASA Technical Reports Server (NTRS)

Platnick, Steven; Wind, Gala; King, Michael D.; Holz, Robert E.; Ackerman, Steven A.; Nagle, Fred W.

2010-01-01

CALIPSO, launched in June 2006, provides global active remote sensing measurements of clouds and aerosols that can be used for validation of a variety of passive imager retrievals derived from instruments flying on the Aqua spacecraft and other A-Train platforms. The most recent processing effort for the MODIS Atmosphere Team, referred to as the Collection 5 scream, includes a research-level multilayer cloud detection algorithm that uses both thermodynamic phase information derived from a combination of solar and thermal emission bands to discriminate layers of different phases, as well as true layer separation discrimination using a moderately absorbing water vapor band. The multilayer detection algorithm is designed to provide a means of assessing the applicability of 1D cloud models used in the MODIS cloud optical and microphysical product retrieval, which are generated at a 1 km resolution. Using pixel-level collocations of MODIS Aqua, CALIOP, we investigate the global performance of multilayer cloud detection algorithms (and thermodynamic phase).

Automated Detection of Clouds in Satellite Imagery

NASA Technical Reports Server (NTRS)

Jedlovec, Gary

2010-01-01

Many different approaches have been used to automatically detect clouds in satellite imagery. Most approaches are deterministic and provide a binary cloud - no cloud product used in a variety of applications. Some of these applications require the identification of cloudy pixels for cloud parameter retrieval, while others require only an ability to mask out clouds for the retrieval of surface or atmospheric parameters in the absence of clouds. A few approaches estimate a probability of the presence of a cloud at each point in an image. These probabilities allow a user to select cloud information based on the tolerance of the application to uncertainty in the estimate. Many automated cloud detection techniques develop sophisticated tests using a combination of visible and infrared channels to determine the presence of clouds in both day and night imagery. Visible channels are quite effective in detecting clouds during the day, as long as test thresholds properly account for variations in surface features and atmospheric scattering. Cloud detection at night is more challenging, since only courser resolution infrared measurements are available. A few schemes use just two infrared channels for day and night cloud detection. The most influential factor in the success of a particular technique is the determination of the thresholds for each cloud test. The techniques which perform the best usually have thresholds that are varied based on the geographic region, time of year, time of day and solar angle.

The interpretation of remotely sensed cloud properties from a model paramterization perspective

NASA Technical Reports Server (NTRS)

HARSHVARDHAN; Wielicki, Bruce A.; Ginger, Kathryn M.

1994-01-01

A study has been made of the relationship between mean cloud radiative properties and cloud fraction in stratocumulus cloud systems. The analysis is of several Land Resources Satellite System (LANDSAT) images and three hourly International Satellite Cloud Climatology Project (ISCCP) C-1 data during daylight hours for two grid boxes covering an area typical of a general circulation model (GCM) grid increment. Cloud properties were inferred from the LANDSAT images using two thresholds and several pixel resolutions ranging from roughly 0.0625 km to 8 km. At the finest resolution, the analysis shows that mean cloud optical depth (or liquid water path) increases somewhat with increasing cloud fraction up to 20% cloud coverage. More striking, however, is the lack of correlation between the two quantities for cloud fractions between roughly 0.2 and 0.8. When the scene is essentially overcast, the mean cloud optical tends to be higher. Coarse resolution LANDSAT analysis and the ISCCP 8-km data show lack of correlation between mean cloud optical depth and cloud fraction for coverage less than about 90%. This study shows that there is perhaps a local mean liquid water path (LWP) associated with partly cloudy areas of stratocumulus clouds. A method has been suggested to use this property to construct the cloud fraction paramterization in a GCM when the model computes a grid-box-mean LWP.

Aster Global dem Version 3, and New Aster Water Body Dataset

NASA Astrophysics Data System (ADS)

Abrams, M.

2016-06-01

In 2016, the US/Japan ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) project released Version 3 of the Global DEM (GDEM). This 30 m DEM covers the earth's surface from 82N to 82S, and improves on two earlier versions by correcting some artefacts and filling in areas of missing DEMs by the acquisition of additional data. The GDEM was produced by stereocorrelation of 2 million ASTER scenes and operation on a pixel-by-pixel basis: cloud screening; stacking data from overlapping scenes; removing outlier values, and averaging elevation values. As previously, the GDEM is packaged in ~ 23,000 1 x 1 degree tiles. Each tile has a DEM file, and a NUM file reporting the number of scenes used for each pixel, and identifying the source for fill-in data (where persistent clouds prevented computation of an elevation value). An additional data set was concurrently produced and released: the ASTER Water Body Dataset (AWBD). This is a 30 m raster product, which encodes every pixel as either lake, river, or ocean; thus providing a global inland and shore-line water body mask. Water was identified through spectral analysis algorithms and manual editing. This product was evaluated against the Shuttle Water Body Dataset (SWBD), and the Landsat-based Global Inland Water (GIW) product. The SWBD only covers the earth between about 60 degrees north and south, so it is not a global product. The GIW only delineates inland water bodies, and does not deal with ocean coastlines. All products are at 30 m postings.

Performance measurements of hybrid PIN diode arrays

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jernigan, J.G.; Arens, J.F.; Kramer, G.

We report on the successful effort to develop hybrid PIN diode arrays and to demonstrate their potential as components of vertex detectors. Hybrid pixel arrays have been fabricated by the Hughes Aircraft Co. by bump bonding readout chips developed by Hughes to an array of PIN diodes manufactured by Micron Semiconductor Inc. These hybrid pixel arrays were constructed in two configurations. One array format having 10 {times} 64 pixels, each 120 {mu}m square, and the other format having 256 {times} 256 pixels, each 30 {mu}m square. In both cases, the thickness of the PIN diode layer is 300 {mu}m. Measurementsmore » of detector performance show that excellent position resolution can be achieved by interpolation. By determining the centroid of the charge cloud which spreads charge into a number of neighboring pixels, a spatial resolution of a few microns has been attained. The noise has been measured to be about 300 electrons (rms) at room temperature, as expected from KTC and dark current considerations, yielding a signal-to-noise ratio of about 100 for minimum ionizing particles. 4 refs., 13 figs.« less

Reconstruction of doses and deposition in the western trace from the Chernobyl accident.

PubMed

Sikkeland, T; Skuterud, L; Goltsova, N I; Lindmo, T

1997-05-01

A model is presented for the explosive cloud of particulates that produced the western trace of high radioactive ground contamination in the Chernobyl accident on 26 April 1986. The model was developed to reproduce measured dose rates and nuclide contamination and to relate estimated doses to observed changes in: (1) infrared emission from the foliage and (2) morphological and histological structures of individual pines. Dominant factors involved in ground contamination were initial cloud shape, particle size distribution, and rate of particle fallout. At time of formation, the cloud was assumed to be parabolical and to contain a homogeneous distribution of spherically shaped fuel particulates having a log-normal size distribution. The particulates were dispersed by steady winds and diffusion that produced a straight line deposition path. The analysis indicates that two clouds, denoted by Cloud I and Cloud II, were involved. Fallout from the former dominated the far field region and fallout from latter the region near the reactor. At formation they had a full width at half maximum of 1800 m and 500 m, respectively. For wind velocities of 5-10 m s(-1) the particulates' radial distribution at formation had a standard deviation and mode of 1.8 microm and 0.5 microm, respectively. This distribution corresponds to a release of 390 GJ in the runaway explosion. The clouds' height and mass are not uniquely determined but are coupled together. For an initial height of 3,600 m, Cloud I contained about 400 kg fuel. For Cloud II the values were, respectively, 1,500 m and 850 kg. Loss of activities from the clouds is found to be small. Values are obtained for the rate of radionuclide migration from the deposit. Various types of biological damage to pines, as reported in the literature, are shown to be mainly due to ionizing radiation from the deposit by Cloud II. A formula is presented for the particulate size distribution in the trace area.

Analysis of the VIIRS cloud mask, comparison with the NAVOCEANO cloud mask, and how they complement each other

NASA Astrophysics Data System (ADS)

Cayula, Jean-François P.; May, Douglas A.; McKenzie, Bruce D.

2014-05-01

The Visible Infrared Imaging Radiometer Suite (VIIRS) Cloud Mask (VCM) Intermediate Product (IP) has been developed for use with Suomi National Polar-orbiting Partnership (NPP) VIIRS Environmental Data Record (EDR) products. In particular, the VIIRS Sea Surface Temperature (SST) EDR relies on VCM to identify cloud contaminated observations. Unfortunately, VCM does not appear to perform as well as cloud detection algorithms for SST. This may be due to similar but different goals of the two algorithms. VCM is concerned with detecting clouds while SST is interested in identifying clear observations. The result is that in undetermined cases VCM defaults to "clear," while the SST cloud detection defaults to "cloud." This problem is further compounded because classic SST cloud detection often flags as "cloud" all types of corrupted data, thus making a comparison with VCM difficult. The Naval Oceanographic Office (NAVOCEANO), which operationally produces a VIIRS SST product, relies on cloud detection from the NAVOCEANO Cloud Mask (NCM), adapted from cloud detection schemes designed for SST processing. To analyze VCM, the NAVOCEANO SST process was modified to attach the VCM flags to all SST retrievals. Global statistics are computed for both day and night data. The cases where NCM and/or VCM tag data as cloud-contaminated or clear can then be investigated. By analyzing the VCM individual test flags in conjunction with the status of NCM, areas where VCM can complement NCM are identified.

Jupiter's Swirling South Pole

NASA Image and Video Library

2018-01-18

This image of Jupiter's swirling south polar region was captured by NASA's Juno spacecraft as it neared completion of its tenth close flyby of the gas giant planet. The "empty" space above and below Jupiter in this color-enhanced image can trick the mind, causing the viewer to perceive our solar system's largest planet as less colossal than it is. In reality, Jupiter is wide enough to fit 11 Earths across its clouded disk. The spacecraft captured this image on Dec. 16, 2017, at 11:07 PST (2:07 p.m. EST) when the spacecraft was about 64,899 miles (104,446 kilometers) from the tops of the clouds of the planet at a latitude of 83.9 degrees south -- almost directly over Jupiter's south pole. The spatial scale in this image is 43.6 miles/pixel (70.2 kilometers/pixel). Citizen scientist Gerald Eichstädt processed this image using data from the JunoCam imager. https://photojournal.jpl.nasa.gov/catalog/PIA21975

IPRT polarized radiative transfer model intercomparison project - Three-dimensional test cases (phase B)

NASA Astrophysics Data System (ADS)

Emde, Claudia; Barlakas, Vasileios; Cornet, Céline; Evans, Frank; Wang, Zhen; Labonotte, Laurent C.; Macke, Andreas; Mayer, Bernhard; Wendisch, Manfred

2018-04-01

Initially unpolarized solar radiation becomes polarized by scattering in the Earth's atmosphere. In particular molecular scattering (Rayleigh scattering) polarizes electromagnetic radiation, but also scattering of radiation at aerosols, cloud droplets (Mie scattering) and ice crystals polarizes. Each atmospheric constituent produces a characteristic polarization signal, thus spectro-polarimetric measurements are frequently employed for remote sensing of aerosol and cloud properties. Retrieval algorithms require efficient radiative transfer models. Usually, these apply the plane-parallel approximation (PPA), assuming that the atmosphere consists of horizontally homogeneous layers. This allows to solve the vector radiative transfer equation (VRTE) efficiently. For remote sensing applications, the radiance is considered constant over the instantaneous field-of-view of the instrument and each sensor element is treated independently in plane-parallel approximation, neglecting horizontal radiation transport between adjacent pixels (Independent Pixel Approximation, IPA). In order to estimate the errors due to the IPA approximation, three-dimensional (3D) vector radiative transfer models are required. So far, only a few such models exist. Therefore, the International Polarized Radiative Transfer (IPRT) working group of the International Radiation Commission (IRC) has initiated a model intercomparison project in order to provide benchmark results for polarized radiative transfer. The group has already performed an intercomparison for one-dimensional (1D) multi-layer test cases [phase A, 1]. This paper presents the continuation of the intercomparison project (phase B) for 2D and 3D test cases: a step cloud, a cubic cloud, and a more realistic scenario including a 3D cloud field generated by a Large Eddy Simulation (LES) model and typical background aerosols. The commonly established benchmark results for 3D polarized radiative transfer are available at the IPRT website (http://www.meteo.physik.uni-muenchen.de/ iprt).

Cloud Detection with the Earth Polychromatic Imaging Camera (EPIC)

NASA Technical Reports Server (NTRS)

Meyer, Kerry; Marshak, Alexander; Lyapustin, Alexei; Torres, Omar; Wang, Yugie

2011-01-01

The Earth Polychromatic Imaging Camera (EPIC) on board the Deep Space Climate Observatory (DSCOVR) would provide a unique opportunity for Earth and atmospheric research due not only to its Lagrange point sun-synchronous orbit, but also to the potential for synergistic use of spectral channels in both the UV and visible spectrum. As a prerequisite for most applications, the ability to detect the presence of clouds in a given field of view, known as cloud masking, is of utmost importance. It serves to determine both the potential for cloud contamination in clear-sky applications (e.g., land surface products and aerosol retrievals) and clear-sky contamination in cloud applications (e.g., cloud height and property retrievals). To this end, a preliminary cloud mask algorithm has been developed for EPIC that applies thresholds to reflected UV and visible radiances, as well as to reflected radiance ratios. This algorithm has been tested with simulated EPIC radiances over both land and ocean scenes, with satisfactory results. These test results, as well as algorithm sensitivity to potential instrument uncertainties, will be presented.

Spectral analysis of Uranus’ 2014 bright storm with VLT/SINFONI

NASA Astrophysics Data System (ADS)

Irwin, Patrick Gerard Joseph; Fletcher, Leigh N.; Read, Peter L.; Tice, Dane; de Pater, Imke; Orton, Glenn S.; Teanby, Nicholas A.; Davis, Gary R.

2015-11-01

Observations by amateur observers of an extremely bright storm system in Uranus’ atmosphere in September 2014 triggered an international campaign to view this feature with many telescopes across the world. Near infrared observations of the storm system were acquired in October/November 2014 with SINFONI on ESO’s Very Large Telescope (VLT) in Chile. SINFONI is an Integral Field Unit spectrometer, recording 64 × 64 pixel images with 2048 wavelengths/pixel using adaptive optics. H-band (1.43 - 1.87 µm) image 'cubes' were obtained at spatial resolutions of ˜ 0.1″ per pixel. The observations show that the centre of the storm feature shifts markedly with increasing altitude, moving in the retrograde direction and slightly poleward with increasing altitude. A faint 'tail' of more reflective material was also seen to the immediate south of the storm, which again trails in the retrograde direction. The observed spectra were analysed with the radiative transfer and retrieval code, NEMESIS (Irwin et al., 2008). We find that the storm is well-modelled using either two main cloud layers of a 5-layer aerosol model based on Sromovsky et al. (2011) or employing the simpler two-cloud-layer model of Tice et al. (2013). The deep component appears to be caused by a brightening (i.e. an increase in reflectivity) and increase in altitude of the main tropospheric cloud deck at 2 - 3 bars for both models, while the upper component of the feature appears to be due to either a thickening of the tropospheric haze of the 2-layer model or a vertical extension of the upper tropospheric cloud of the 5-layer model, assumed to be composed of methane ice and based at the assumed methane condensation level at 1.23 bar. For the 5-layer model we also found this methane ice cloud to be responsible for the faint ‘tail’ seen to the feature’s south and the brighter polar ‘hood’ seen in all observations polewards of ˜ 45°N.During the twelve days between our sets of observations the higher-altitude component of the feature was observed to have brightened significantly and moved to even higher altitudes, while the deeper component faded.

Quantifying Forest and Coastal Disturbance from Industrial Mining Using Satellite Time Series Analysis Under Very Cloudy Conditions

NASA Astrophysics Data System (ADS)

Alonzo, M.; Van Den Hoek, J.; Ahmed, N.

2015-12-01

The open-pit Grasberg mine, located in the highlands of Western Papua, Indonesia, and operated by PT Freeport Indonesia (PT-FI), is among the world's largest in terms of copper and gold production. Over the last 27 years, PT-FI has used the Ajkwa River to transport an estimated 1.3 billion tons of tailings from the mine into the so-called Ajkwa Deposition Area (ADA). The ADA is the product of aggradation and lateral expansion of the Ajkwa River into the surrounding lowland rainforest and mangroves, which include species important to the livelihoods of indigenous Papuans. Mine tailings that do not settle in the ADA disperse into the Arafura Sea where they increase levels of suspended particulate matter (SPM) and associated concentrations of dissolved copper. Despite the mine's large-scale operations, ecological impact of mine tailings deposition on the forest and estuarial ecosystems have received minimal formal study. While ground-based inquiries are nearly impossible due to access restrictions, assessment via satellite remote sensing is promising but hindered by extreme cloud cover. In this study, we characterize ridgeline-to-coast environmental impacts along the Ajkwa River, from the Grasberg mine to the Arafura Sea between 1987 and 2014. We use "all available" Landsat TM and ETM+ images collected over this time period to both track pixel-level vegetation disturbance and monitor changes in coastal SPM levels. Existing temporal segmentation algorithms are unable to assess both acute and protracted trajectories of vegetation change due to pervasive cloud cover. In response, we employ robust, piecewise linear regression on noisy vegetation index (NDVI) data in a manner that is relatively insensitive to atmospheric contamination. Using this disturbance detection technique we constructed land cover histories for every pixel, based on 199 image dates, to differentiate processes of vegetation decline, disturbance, and regrowth. Using annual reports from PT-FI, we show that the changing extent and spatial patterns of riparian vegetation disturbance directly correlate with yearly tailings production rates. While the rate of vegetation disturbance decreased after 1998, SPM levels along the Arafura coast increased, suggesting the failure of PT-FI to fully confine tailings to the ADA.

[A cloud detection algorithm for MODIS images combining Kmeans clustering and multi-spectral threshold method].

PubMed

Wang, Wei; Song, Wei-Guo; Liu, Shi-Xing; Zhang, Yong-Ming; Zheng, Hong-Yang; Tian, Wei

2011-04-01

An improved method for detecting cloud combining Kmeans clustering and the multi-spectral threshold approach is described. On the basis of landmark spectrum analysis, MODIS data is categorized into two major types initially by Kmeans method. The first class includes clouds, smoke and snow, and the second class includes vegetation, water and land. Then a multi-spectral threshold detection is applied to eliminate interference such as smoke and snow for the first class. The method is tested with MODIS data at different time under different underlying surface conditions. By visual method to test the performance of the algorithm, it was found that the algorithm can effectively detect smaller area of cloud pixels and exclude the interference of underlying surface, which provides a good foundation for the next fire detection approach.

Waves on Saturn

NASA Technical Reports Server (NTRS)

2005-01-01

An up-close look at Saturn's atmosphere shows wavelike structures in the planet's constantly changing clouds.

Feathery striations in the lower right appear to be small-scale waves propagating at a higher altitude than the other cloud features.

The image was taken with the Cassini spacecraft wide-angle camera on April 14, 2005, through a filter sensitive to wavelengths of infrared light centered at 727 nanometers and at a distance of approximately 386,000 kilometers (240,000 miles) from Saturn. The image scale is 19 kilometers (12 miles) per pixel.

A Comparison of Seasonal and Interannual Variability of Soil Dust Aerosols Over the Atlantic Ocean as Inferred by the Toms AI and AVHRR AOT Retrievals

NASA Technical Reports Server (NTRS)

Cakmur, R. V.; Miller, R. L.; Tegen, Ina; Hansen, James E. (Technical Monitor)

2001-01-01

The seasonal cycle and interannual variability of two estimates of soil (or 'mineral') dust aerosols are compared: Advanced Very High Resolution Radiometer (AVHRR) aerosol optical thickness (AOT) and Total Ozone Mapping Spectrometer (TOMS) aerosol index (AI), Both data sets, comprising more than a decade of global, daily images, are commonly used to evaluate aerosol transport models. The present comparison is based upon monthly averages, constructed from daily images of each data set for the period between 1984 and 1990, a period that excludes contamination from volcanic eruptions. The comparison focuses upon the Northern Hemisphere subtropical Atlantic Ocean, where soil dust aerosols make the largest contribution to the aerosol load, and are assumed to dominate the variability of each data set. While each retrieval is sensitive to a different aerosol radiative property - absorption for the TOMS AI versus reflectance for the AVHRR AOT - the seasonal cycles of dust loading implied by each retrieval are consistent, if seasonal variations in the height of the aerosol layer are taken into account when interpreting the TOMS AI. On interannual time scales, the correlation is low at most locations. It is suggested that the poor interannual correlation is at least partly a consequence of data availability. When the monthly averages are constructed using only days common to both data sets, the correlation is substantially increased: this consistency suggests that both TOMS and AVHRR accurately measure the aerosol load in any given scene. However, the two retrievals have only a few days in common per month so that these restricted monthly averages have a large uncertainty. Calculations suggest that at least 7 to 10 daily images are needed to estimate reliably the average dust load during any particular month, a threshold that is rarely satisfied by the AVHRR AOT due to the presence of clouds in the domain. By rebinning each data set onto a coarser grid, the availability of the AVHRR AOT is increased during any particular month, along with its interannual correlation with the TOMS AI The latter easily exceeds the sampling threshold due to its greater ability to infer the aerosol load in the presence of clouds. Whether the TOMS AI should be regarded as a more reliable indicator of interannual variability depends upon the extent of contamination by sub-pixel clouds.

Estimating Cloud optical thickness from SEVIRI, for air quality research, by implementing a semi-analytical cloud retrieval algorithm

NASA Astrophysics Data System (ADS)

Pandey, Praveen; De Ridder, Koen; van Looy, Stijn; van Lipzig, Nicole

2010-05-01

Clouds play an important role in Earth's climate system. As they affect radiation hence photolysis rate coefficients (ozone formation),they also affect the air quality at the surface of the earth. Thus, a satellite remote sensing technique is used to retrieve the cloud properties for air quality research. The geostationary satellite, Meteosat Second Generation (MSG) has onboard, the Spinning Enhanced Visible and Infrared Imager (SEVIRI). The channels in the wavelength 0.6 µm and 1.64 µm are used to retrieve cloud optical thickness (COT). The study domain is over Europe covering a region between 35°N-70°N and 5°W-30°E, centred over Belgium. The steps involved in pre-processing the EUMETSAT level 1.5 images are described, which includes, acquisition of digital count number, radiometric conversion using offsets and slopes, estimation of radiance and calculation of reflectance. The Sun-earth-satellite geometry also plays an important role. A semi-analytical cloud retrieval algorithm (Kokhanovsky et al., 2003) is implemented for the estimation of COT. This approach doesn't involve the conventional look-up table approach, hence it makes the retrieval independent of numerical radiative transfer solutions. The semi-analytical algorithm is implemented on a monthly dataset of SEVIRI level 1.5 images. Minimum reflectance in the visible channel, at each pixel, during the month is accounted as the surface albedo of the pixel. Thus, monthly variation of COT over the study domain is prepared. The result so obtained, is compared with the COT products of Satellite Application Facility on Climate Monitoring (CM SAF). Henceforth, an approach to assimilate the COT for air quality research is presented. Address of corresponding author: Praveen Pandey, VITO- Flemish Institute for Technological Research, Boeretang 200, B 2400, Mol, Belgium E-mail: praveen.pandey@vito.be

SU-C-206-01: Impact of Charge Sharing Effect On Sub-Pitch Resolution for CZT-Based Photon Counting CT Systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zheng, X; Cheng, Z; Deen, J

Purposes: Photon counting CT is a new imaging technology that can provide tissue composition information such as calcium/iodine content quantification. Cadmium zinc telluride CZT is considered a good candidate the photon counting CT due to its relatively high atomic number and band gap. One potential challenge is the degradation of both spatial and energy resolution as the fine electrode pitch is deployed (<50 µm). We investigated the extent of charge sharing effect as functions of gap width, bias voltage and depth-of-interaction (DOI). Methods: The initial electron cloud size and diffusion process were modeled analytically. The valid range of charge sharingmore » effect refers to the range over which both signals of adjacent electrodes are above the triggering threshold (10% of the amplitude of 60keV X-ray photons). The intensity ratios of output in three regions (I1/I2/I3: left pixel, gap area and right pixel) were calculated. With Gaussian white noises modeled (a SNR of 5 based upon the preliminary experiments), the sub-pitch resolution as a function of the spatial position in-between two pixels was studied. Results: The valid range of charge sharing increases linearly with depth-of-interaction (DOI) but decreases with gap width and bias voltage. For a 1.5mm thickness CZT detector (pitch: 50µm, bias: 400 V), the range increase from ∼90µm up to ∼110µm. Such an increase can be attributed to a longer travel distance and the associated electron cloud broadening. The achievable sub-pitch resolution is in the range of ∼10–30µm. Conclusion: The preliminary results demonstrate that sub-pixel spatial resolution can be achieved using the ratio of amplitudes of two neighboring pixels. Such ratio may also be used to correct charge loss and help improve energy resolution of a CZT detector. The impact of characteristic X-rays hitting adjacent pixels (i.e., multiple interaction) on charge sharing is currently being investigated.« less

First observations of tracking clouds using scanning ARM cloud radars

DOE PAGES

Borque, Paloma; Giangrande, Scott; Kollias, Pavlos

2014-12-01

Tracking clouds using scanning cloud radars can help to document the temporal evolution of cloud properties well before large drop formation (‘‘first echo’’). These measurements complement cloud and precipitation tracking using geostationary satellites and weather radars. Here, two-dimensional (2-D) Along-Wind Range Height Indicator (AW-RHI) observations of a population of shallow cumuli (with and without precipitation) from the 35-GHz scanning ARM cloud radar (SACR) at the DOE Atmospheric Radiation Measurements (ARM) program Southern Great Plains (SGP) site are presented. Observations from the ARM SGP network of scanning precipitation radars are used to provide the larger scale context of the cloud fieldmore » and to highlight the advantages of the SACR to detect the numerous, small, non-precipitating cloud elements. A new Cloud Identification and Tracking Algorithm (CITA) is developed to track cloud elements. In CITA, a cloud element is identified as a region having a contiguous set of pixels exceeding a preset reflectivity and size threshold. The high temporal resolution of the SACR 2-D observations (30 sec) allows for an area superposition criteria algorithm to match cloud elements at consecutive times. Following CITA, the temporal evolution of cloud element properties (number, size, and maximum reflectivity) is presented. The vast majority of the designated elements during this cumulus event were short-lived non-precipitating clouds having an apparent life cycle shorter than 15 minutes. The advantages and disadvantages of cloud tracking using an SACR are discussed.« less

First observations of tracking clouds using scanning ARM cloud radars

DOE Office of Scientific and Technical Information (OSTI.GOV)

Borque, Paloma; Giangrande, Scott; Kollias, Pavlos

Tracking clouds using scanning cloud radars can help to document the temporal evolution of cloud properties well before large drop formation (‘‘first echo’’). These measurements complement cloud and precipitation tracking using geostationary satellites and weather radars. Here, two-dimensional (2-D) Along-Wind Range Height Indicator (AW-RHI) observations of a population of shallow cumuli (with and without precipitation) from the 35-GHz scanning ARM cloud radar (SACR) at the DOE Atmospheric Radiation Measurements (ARM) program Southern Great Plains (SGP) site are presented. Observations from the ARM SGP network of scanning precipitation radars are used to provide the larger scale context of the cloud fieldmore » and to highlight the advantages of the SACR to detect the numerous, small, non-precipitating cloud elements. A new Cloud Identification and Tracking Algorithm (CITA) is developed to track cloud elements. In CITA, a cloud element is identified as a region having a contiguous set of pixels exceeding a preset reflectivity and size threshold. The high temporal resolution of the SACR 2-D observations (30 sec) allows for an area superposition criteria algorithm to match cloud elements at consecutive times. Following CITA, the temporal evolution of cloud element properties (number, size, and maximum reflectivity) is presented. The vast majority of the designated elements during this cumulus event were short-lived non-precipitating clouds having an apparent life cycle shorter than 15 minutes. The advantages and disadvantages of cloud tracking using an SACR are discussed.« less

An Integrated Photogrammetric and Photoclinometric Approach for Pixel-Resolution 3d Modelling of Lunar Surface

NASA Astrophysics Data System (ADS)

Liu, W. C.; Wu, B.

2018-04-01

High-resolution 3D modelling of lunar surface is important for lunar scientific research and exploration missions. Photogrammetry is known for 3D mapping and modelling from a pair of stereo images based on dense image matching. However dense matching may fail in poorly textured areas and in situations when the image pair has large illumination differences. As a result, the actual achievable spatial resolution of the 3D model from photogrammetry is limited by the performance of dense image matching. On the other hand, photoclinometry (i.e., shape from shading) is characterised by its ability to recover pixel-wise surface shapes based on image intensity and imaging conditions such as illumination and viewing directions. More robust shape reconstruction through photoclinometry can be achieved by incorporating images acquired under different illumination conditions (i.e., photometric stereo). Introducing photoclinometry into photogrammetric processing can therefore effectively increase the achievable resolution of the mapping result while maintaining its overall accuracy. This research presents an integrated photogrammetric and photoclinometric approach for pixel-resolution 3D modelling of the lunar surface. First, photoclinometry is interacted with stereo image matching to create robust and spatially well distributed dense conjugate points. Then, based on the 3D point cloud derived from photogrammetric processing of the dense conjugate points, photoclinometry is further introduced to derive the 3D positions of the unmatched points and to refine the final point cloud. The approach is able to produce one 3D point for each image pixel within the overlapping area of the stereo pair so that to obtain pixel-resolution 3D models. Experiments using the Lunar Reconnaissance Orbiter Camera - Narrow Angle Camera (LROC NAC) images show the superior performances of the approach compared with traditional photogrammetric technique. The results and findings from this research contribute to optimal exploitation of image information for high-resolution 3D modelling of the lunar surface, which is of significance for the advancement of lunar and planetary mapping.

Optical observations of electrical activity in cloud discharges

NASA Astrophysics Data System (ADS)

Vayanganie, S. P. A.; Fernando, M.; Sonnadara, U.; Cooray, V.; Perera, C.

2018-07-01

Temporal variation of the luminosity of seven natural cloud-to-cloud lightning channels were studied, and results were presented. They were recorded by using a high-speed video camera with the speed of 5000 fps (frames per second) and the pixel resolution of 512 × 512 in three locations in Sri Lanka in the tropics. Luminosity variation of the channel with time was obtained by analyzing the image sequences. Recorded video frames together with the luminosity variation were studied to understand the cloud discharge process. Image analysis techniques also used to understand the characteristics of channels. Cloud flashes show more luminosity variability than ground flashes. Most of the time it starts with a leader which do not have stepping process. Channel width and standard deviation of intensity variation across the channel for each cloud flashes was obtained. Brightness variation across the channel shows a Gaussian distribution. The average time duration of the cloud flashes which start with non stepped leader was 180.83 ms. Identified characteristics are matched with the existing models to understand the process of cloud flashes. The fact that cloud discharges are not confined to a single process have been further confirmed from this study. The observations show that cloud flash is a basic lightning discharge which transfers charge between two charge centers without using one specific mechanism.

From One Pixel to One Earth: Building a Living Atlas in the Cloud to Analyze and Monitor Global Patterns

NASA Astrophysics Data System (ADS)

Moody, D.; Brumby, S. P.; Chartrand, R.; Franco, E.; Keisler, R.; Kelton, T.; Kontgis, C.; Mathis, M.; Raleigh, D.; Rudelis, X.; Skillman, S.; Warren, M. S.; Longbotham, N.

2016-12-01

The recent computing performance revolution has driven improvements in sensor, communication, and storage technology. Historical, multi-decadal remote sensing datasets at the petabyte scale are now available in commercial clouds, with new satellite constellations generating petabytes per year of high-resolution imagery with daily global coverage. Cloud computing and storage, combined with recent advances in machine learning and open software, are enabling understanding of the world at an unprecedented scale and detail. We have assembled all available satellite imagery from the USGS Landsat, NASA MODIS, and ESA Sentinel programs, as well as commercial PlanetScope and RapidEye imagery, and have analyzed over 2.8 quadrillion multispectral pixels. We leveraged the commercial cloud to generate a tiled, spatio-temporal mosaic of the Earth for fast iteration and development of new algorithms combining analysis techniques from remote sensing, machine learning, and scalable compute infrastructure. Our data platform enables processing at petabytes per day rates using multi-source data to produce calibrated, georeferenced imagery stacks at desired points in time and space that can be used for pixel level or global scale analysis. We demonstrate our data platform capability by using the European Space Agency's (ESA) published 2006 and 2009 GlobCover 20+ category label maps to train and test a Land Cover Land Use (LCLU) classifier, and generate current self-consistent LCLU maps in Brazil. We train a standard classifier on 2006 GlobCover categories using temporal imagery stacks, and we validate our results on co-registered 2009 Globcover LCLU maps and 2009 imagery. We then extend the derived LCLU model to current imagery stacks to generate an updated, in-season label map. Changes in LCLU labels can now be seamlessly monitored for a given location across the years in order to track, for example, cropland expansion, forest growth, and urban developments. An example of change monitoring is illustrated in the included figure showing rainfed cropland change in the Mato Grosso region of Brazil between 2006 and 2009.

Vorticity and energy diagnostics from the 2000 Cassini Jupiter flyby

NASA Astrophysics Data System (ADS)

Young, R. M. B.; Read, P. L.; Armstrong, D.; Lancaster, A.

2011-10-01

The Cassini spacecraft flew by Jupiter in December 2000, returning hundreds of images near closest approach [1]. We have been analysing the images spanning four Jupiter rotation periods at closest approach using automated cloud tracking software to obtain horizontal velocity fields. Our method has some advantages over other methods used for this purpose in that it accounts for both cloud deformation and rotation in addition to the standard translation. We shall present detailed horizontal velocity vectors and related vorticity and energy fields over four Jupiter rotation periods. We also intend to produce derived energy and turbulence diagnostics that will help us to understand the interplay between processes acting on different length scales. It may also be possible to relate these diagnostics to 'zonostrophic' jets and small-scale turbulence studied in the laboratory using the Coriolis rotating tank, work itself motivated by jets in giant planet atmospheres [2]. In the future we intend to combine velocity fields with temperature data to produce fully-3D velocity and potential vorticity fields for Jupiter's troposphere and stratosphere. The cloud tracking method is based on correlation image velocimetry (CIV) and was originally developed by the Coriolis facility team at LEGI, Université de Grenoble [3], where it is used to extract velocity fields from data obtained in their 13m diameter rotating tank experiment. The method has two stages. First, velocity vectors are calculated using translation only, where the velocity is defined by the highest correlation between two images taken 63 minutes apart of a small pixel patch moving within a larger search box. In the second stage the correlation analysis is repeated, but instead of just translation of the pixel patch, rotation and deformation (shearing, stretching) are taken into account. We use the first stage velocity field as an estimate of the velocity vector and search within a small window around this, including sub-pixel translations, to refine the velocity. We have also been involved with a collaborative effort comparing methods used for cloud tracking in planetary atmospheres [4], and will summarise the progress of this work as well.

OMMYDCLD: a New A-train Cloud Product that Co-locates OMI and MODIS Cloud and Radiance Parameters onto the OMI Footprint

NASA Technical Reports Server (NTRS)

Fisher, Brad; Joiner, Joanna; Vasilkov, Alexander; Veefkind, Pepijn; Platnick, Steven; Wind, Galina

2014-01-01

Clouds cover approximately 60% of the earth's surface. When obscuring the satellite's field of view (FOV), clouds complicate the retrieval of ozone, trace gases and aerosols from data collected by earth observing satellites. Cloud properties associated with optical thickness, cloud pressure, water phase, drop size distribution (DSD), cloud fraction, vertical and areal extent can also change significantly over short spatio-temporal scales. The radiative transfer models used to retrieve column estimates of atmospheric constituents typically do not account for all these properties and their variations. The OMI science team is preparing to release a new data product, OMMYDCLD, which combines the cloud information from sensors on board two earth observing satellites in the NASA A-Train: Aura/OMI and Aqua/MODIS. OMMYDCLD co-locates high resolution cloud and radiance information from MODIS onto the much larger OMI pixel and combines it with parameters derived from the two other OMI cloud products: OMCLDRR and OMCLDO2. The product includes histograms for MODIS scientific data sets (SDS) provided at 1 km resolution. The statistics of key data fields - such as effective particle radius, cloud optical thickness and cloud water path - are further separated into liquid and ice categories using the optical and IR phase information. OMMYDCLD offers users of OMI data cloud information that will be useful for carrying out OMI calibration work, multi-year studies of cloud vertical structure and in the identification and classification of multi-layer clouds.

Ten Years of Cloud Optical and Microphysical Retrievals from MODIS

NASA Technical Reports Server (NTRS)

Platnick, Steven; King, Michael D.; Wind, Galina; Hubanks, Paul; Arnold, G. Thomas; Amarasinghe, Nandana

2010-01-01

The MODIS cloud optical properties algorithm (MOD06/MYD06 for Terra and Aqua MODIS, respectively) has undergone extensive improvements and enhancements since the launch of Terra. These changes have included: improvements in the cloud thermodynamic phase algorithm; substantial changes in the ice cloud light scattering look up tables (LUTs); a clear-sky restoral algorithm for flagging heavy aerosol and sunglint; greatly improved spectral surface albedo maps, including the spectral albedo of snow by ecosystem; inclusion of pixel-level uncertainty estimates for cloud optical thickness, effective radius, and water path derived for three error sources that includes the sensitivity of the retrievals to solar and viewing geometries. To improve overall retrieval quality, we have also implemented cloud edge removal and partly cloudy detection (using MOD35 cloud mask 250m tests), added a supplementary cloud optical thickness and effective radius algorithm over snow and sea ice surfaces and over the ocean, which enables comparison with the "standard" 2.1 11m effective radius retrieval, and added a multi-layer cloud detection algorithm. We will discuss the status of the MOD06 algorithm and show examples of pixellevel (Level-2) cloud retrievals for selected data granules, as well as gridded (Level-3) statistics, notably monthly means and histograms (lD and 2D, with the latter giving correlations between cloud optical thickness and effective radius, and other cloud product pairs).

Scattering property based contextual PolSAR speckle filter

NASA Astrophysics Data System (ADS)

Mullissa, Adugna G.; Tolpekin, Valentyn; Stein, Alfred

2017-12-01

Reliability of the scattering model based polarimetric SAR (PolSAR) speckle filter depends upon the accurate decomposition and classification of the scattering mechanisms. This paper presents an improved scattering property based contextual speckle filter based upon an iterative classification of the scattering mechanisms. It applies a Cloude-Pottier eigenvalue-eigenvector decomposition and a fuzzy H/α classification to determine the scattering mechanisms on a pre-estimate of the coherency matrix. The H/α classification identifies pixels with homogeneous scattering properties. A coarse pixel selection rule groups pixels that are either single bounce, double bounce or volume scatterers. A fine pixel selection rule is applied to pixels within each canonical scattering mechanism. We filter the PolSAR data and depending on the type of image scene (urban or rural) use either the coarse or fine pixel selection rule. Iterative refinement of the Wishart H/α classification reduces the speckle in the PolSAR data. Effectiveness of this new filter is demonstrated by using both simulated and real PolSAR data. It is compared with the refined Lee filter, the scattering model based filter and the non-local means filter. The study concludes that the proposed filter compares favorably with other polarimetric speckle filters in preserving polarimetric information, point scatterers and subtle features in PolSAR data.

A two-step framework for reconstructing remotely sensed land surface temperatures contaminated by cloud

NASA Astrophysics Data System (ADS)

Zeng, Chao; Long, Di; Shen, Huanfeng; Wu, Penghai; Cui, Yaokui; Hong, Yang

2018-07-01

Land surface temperature (LST) is one of the most important parameters in land surface processes. Although satellite-derived LST can provide valuable information, the value is often limited by cloud contamination. In this paper, a two-step satellite-derived LST reconstruction framework is proposed. First, a multi-temporal reconstruction algorithm is introduced to recover invalid LST values using multiple LST images with reference to corresponding remotely sensed vegetation index. Then, all cloud-contaminated areas are temporally filled with hypothetical clear-sky LST values. Second, a surface energy balance equation-based procedure is used to correct for the filled values. With shortwave irradiation data, the clear-sky LST is corrected to the real LST under cloudy conditions. A series of experiments have been performed to demonstrate the effectiveness of the developed approach. Quantitative evaluation results indicate that the proposed method can recover LST in different surface types with mean average errors in 3-6 K. The experiments also indicate that the time interval between the multi-temporal LST images has a greater impact on the results than the size of the contaminated area.

Uncertainty of Passive Imager Cloud Optical Property Retrievals to Instrument Radiometry and Model Assumptions: Examples from MODIS

NASA Technical Reports Server (NTRS)

Platnick, Steven; Wind, Galina; Meyer, Kerry; Amarasinghe, Nandana; Arnold, G. Thomas; Zhang, Zhibo; King, Michael D.

2013-01-01

The optical and microphysical structure of clouds is of fundamental importance for understanding a variety of cloud radiation and precipitation processes. With the advent of MODIS on the NASA EOS Terra and Aqua platforms, simultaneous global-daily 1 km retrievals of cloud optical thickness (COT) and effective particle radius (CER) are provided, as well as the derived water path (WP). The cloud product (MOD06/MYD06 for MODIS Terra and Aqua, respectively) provides separate retrieval datasets for various two-channel retrievals, typically a VISNIR channel paired with a 1.6, 2.1, and 3.7 m spectral channel. The MOD06 forward model is derived from on a homogeneous plane-parallel cloud. In Collection 5 processing (completed in 2007 with a modified Collection 5.1 completed in 2010), pixel-level retrieval uncertainties were calculated for the following non-3-D error sources: radiometry, surface spectral albedo, and atmospheric corrections associated with model analysis uncertainties (water vapor only). The latter error source includes error correlation across the retrieval spectral channels. Estimates of uncertainty in 1 aggregated (Level-3) means were also provided assuming unity correlation between error sources for all pixels in a grid for a single day, and zero correlation of error sources from one day to the next. I n Collection 6 (expected to begin in late summer 2013) we expanded the uncertainty analysis to include: (a) scene-dependent calibration uncertainty that depends on new band and detector-specific Level 1B uncertainties, (b) new model error sources derived from the look-up tables which includes sensitivities associated with wind direction over the ocean and uncertainties in liquid water and ice effective variance, (c) thermal emission uncertainties in the 3.7 m band associated with cloud and surface temperatures that are needed to extract reflected solar radiation from the total radiance signal, (d) uncertainty in the solar spectral irradiance at 3.7 m, and (e) addition of stratospheric ozone uncertainty in visible atmospheric corrections. A summary of the approach and example Collection 6 results will be shown.

Uncertainty of passive imager cloud retrievals to instrument radiometry and model assumptions: Examples from MODIS Collection 6

NASA Astrophysics Data System (ADS)

Platnick, S.; Wind, G.; Amarasinghe, N.; Arnold, G. T.; Zhang, Z.; Meyer, K.; King, M. D.

2013-12-01

The optical and microphysical structure of clouds is of fundamental importance for understanding a variety of cloud radiation and precipitation processes. With the advent of MODIS on the NASA EOS Terra and Aqua platforms, simultaneous global/daily 1km retrievals of cloud optical thickness (COT) and effective particle radius (CER) are provided, as well as the derived water path (WP). The cloud product (MOD06/MYD06 for MODIS Terra and Aqua, respectively) provides separate retrieval datasets for various two-channel retrievals, typically a VIS/NIR channel paired with a 1.6, 2.1, and 3.7 μm spectral channel. The MOD06 forward model is derived from a homogeneous plane-parallel cloud. In Collection 5 processing (completed in 2007 with a modified Collection 5.1 completed in 2010), pixel-level retrieval uncertainties were calculated for the following non-3-D error sources: radiometry, surface spectral albedo, and atmospheric corrections associated with model analysis uncertainties (water vapor only). The latter error source includes error correlation across the retrieval spectral channels. Estimates of uncertainty in 1° aggregated (Level-3) means were also provided assuming unity correlation between error sources for all pixels in a grid for a single day, and zero correlation of error sources from one day to the next. In Collection 6 (expected to begin in late summer 2013) we expanded the uncertainty analysis to include: (a) scene-dependent calibration uncertainty that depends on new band and detector-specific Level 1B uncertainties, (b) new model error sources derived from the look-up tables which includes sensitivities associated with wind direction over the ocean and uncertainties in liquid water and ice effective variance, (c) thermal emission uncertainties in the 3.7 μm band associated with cloud and surface temperatures that are needed to extract reflected solar radiation from the total radiance signal, (d) uncertainty in the solar spectral irradiance at 3.7 μm, and (e) addition of stratospheric ozone uncertainty in visible atmospheric corrections. A summary of the approach and example Collection 6 results will be shown.

Detection of contaminated pixels based on the short-term continuity of NDVI and correction using spatio-temporal continuity

NASA Astrophysics Data System (ADS)

Cho, A.-Ra; Suh, Myoung-Seok

2013-08-01

The present study developed and assessed a correction technique (CSaTC: Correction based on Spatial and Temporal Continuity) for the detection and correction of contaminated Normalized Difference Vegetation Index (NDVI) time series data. Global Inventory Modeling and Mapping Studies (GIMMS) NDVI data from 1982 to 2006 with a 15-day period and an 8-km spatial resolution was used. CSaTC utilizes short-term continuity of vegetation to detect contaminated pixels, and then, corrects the detected pixels using the spatio-temporal continuity of vegetation. CSaTC was applied to the NDVI data over the East Asian region, which exhibits diverse seasonal and interannual variations in vegetation activities. The correction skill of CSaTC was compared to two previously applied methods, IDR (iterative Interpolation for Data Reconstruction) and Park et al. (2011) using GIMMS NDVI data. CSaTC reasonably resolved the overcorrection and spreading phenomenon caused by excessive correction of Park et al. (2011). The validation using the simulated NDVI time series data showed that CSaTC shows a systematically better correction skill in bias and RMSE irrespective of phenology types of vegetation and noise levels. In general, CSaTC showed a good recovery of the contaminated data appearing over the short-term period on a level similar to that obtained using the IDR technique. In addition, it captured the multi-peak of NDVI, and the germination and defoliating patterns more accurately than that by IDR, which overly compensates for seasons with a high temporal variation and where NDVI data exhibit multi-peaks.

Semantic Segmentation of Building Elements Using Point Cloud Hashing

NASA Astrophysics Data System (ADS)

Chizhova, M.; Gurianov, A.; Hess, M.; Luhmann, T.; Brunn, A.; Stilla, U.

2018-05-01

For the interpretation of point clouds, the semantic definition of extracted segments from point clouds or images is a common problem. Usually, the semantic of geometrical pre-segmented point cloud elements are determined using probabilistic networks and scene databases. The proposed semantic segmentation method is based on the psychological human interpretation of geometric objects, especially on fundamental rules of primary comprehension. Starting from these rules the buildings could be quite well and simply classified by a human operator (e.g. architect) into different building types and structural elements (dome, nave, transept etc.), including particular building parts which are visually detected. The key part of the procedure is a novel method based on hashing where point cloud projections are transformed into binary pixel representations. A segmentation approach released on the example of classical Orthodox churches is suitable for other buildings and objects characterized through a particular typology in its construction (e.g. industrial objects in standardized enviroments with strict component design allowing clear semantic modelling).

Optical properties of marine stratocumulus clouds modified by ship track effluents

NASA Technical Reports Server (NTRS)

King, Michael D.; Nakajima, Teruyuki; Radke, Lawrence F.

1990-01-01

Results are presented from multispectral radiation measurements made within a marine stratocumulus cloud layer modified by ship-track effluents. The measurements showed that, compared with nearby noncontaminated clouds not affected by pollution, the upwelling intensity field of the modified stratocumulus clouds increased at a nonabsorbing wavelength in the visible region and decreased in the NIR, where absorption by liquid water is significant. The observations are consistent with an increased optical thickness, a reduced effective radius of the cloud droplets, and a reduced absorption in the contaminated cloud layer compared to a noncontaminated cloud.

3D cloud detection and tracking system for solar forecast using multiple sky imagers

DOE PAGES

Peng, Zhenzhou; Yu, Dantong; Huang, Dong; ...

2015-06-23

We propose a system for forecasting short-term solar irradiance based on multiple total sky imagers (TSIs). The system utilizes a novel method of identifying and tracking clouds in three-dimensional space and an innovative pipeline for forecasting surface solar irradiance based on the image features of clouds. First, we develop a supervised classifier to detect clouds at the pixel level and output cloud mask. In the next step, we design intelligent algorithms to estimate the block-wise base height and motion of each cloud layer based on images from multiple TSIs. Thus, this information is then applied to stitch images together intomore » larger views, which are then used for solar forecasting. We examine the system’s ability to track clouds under various cloud conditions and investigate different irradiance forecast models at various sites. We confirm that this system can 1) robustly detect clouds and track layers, and 2) extract the significant global and local features for obtaining stable irradiance forecasts with short forecast horizons from the obtained images. Finally, we vet our forecasting system at the 32-megawatt Long Island Solar Farm (LISF). Compared with the persistent model, our system achieves at least a 26% improvement for all irradiance forecasts between one and fifteen minutes.« less

DeepSAT's CloudCNN: A Deep Neural Network for Rapid Cloud Detection from Geostationary Satellites

NASA Astrophysics Data System (ADS)

Kalia, S.; Li, S.; Ganguly, S.; Nemani, R. R.

2017-12-01

Cloud and cloud shadow detection has important applications in weather and climate studies. It is even more crucial when we introduce geostationary satellites into the field of terrestrial remotesensing. With the challenges associated with data acquired in very high frequency (10-15 mins per scan), the ability to derive an accurate cloud/shadow mask from geostationary satellite data iscritical. The key to the success for most of the existing algorithms depends on spatially and temporally varying thresholds, which better capture local atmospheric and surface effects.However, the selection of proper threshold is difficult and may lead to erroneous results. In this work, we propose a deep neural network based approach called CloudCNN to classifycloud/shadow from Himawari-8 AHI and GOES-16 ABI multispectral data. DeepSAT's CloudCNN consists of an encoder-decoder based architecture for binary-class pixel wise segmentation. We train CloudCNN on multi-GPU Nvidia Devbox cluster, and deploy the prediction pipeline on NASA Earth Exchange (NEX) Pleiades supercomputer. We achieved an overall accuracy of 93.29% on test samples. Since, the predictions take only a few seconds to segment a full multi-spectral GOES-16 or Himawari-8 Full Disk image, the developed framework can be used for real-time cloud detection, cyclone detection, or extreme weather event predictions.

Modeling the Impact of Drizzle and 3D Cloud Structure on Remote Sensing of Effective Radius

NASA Technical Reports Server (NTRS)

Platnick, Steven; Zinner, Tobias; Ackerman, S.

2008-01-01

Remote sensing of cloud particle size with passive sensors like MODIS is an important tool for cloud microphysical studies. As a measure of the radiatively relevant droplet size, effective radius can be retrieved with different combinations of visible through shortwave infrared channels. MODIS observations sometimes show significantly larger effective radii in marine boundary layer cloud fields derived from the 1.6 and 2.1 pm channel observations than for 3.7 pm retrievals. Possible explanations range from 3D radiative transport effects and sub-pixel cloud inhomogeneity to the impact of drizzle formation on the droplet distribution. To investigate the potential influence of these factors, we use LES boundary layer cloud simulations in combination with 3D Monte Carlo simulations of MODIS observations. LES simulations of warm cloud spectral microphysics for cases of marine stratus and broken stratocumulus, each for two different values of cloud condensation nuclei density, produce cloud structures comprising droplet size distributions with and without drizzle size drops. In this study, synthetic MODIS observations generated from 3D radiative transport simulations that consider the full droplet size distribution will be generated for each scene. The operational MODIS effective radius retrievals will then be applied to the simulated reflectances and the results compared with the LES microphysics.

Cloud Detection from Satellite Imagery: A Comparison of Expert-Generated and Automatically-Generated Decision Trees

NASA Technical Reports Server (NTRS)

Shiffman, Smadar

2004-01-01

Automated cloud detection and tracking is an important step in assessing global climate change via remote sensing. Cloud masks, which indicate whether individual pixels depict clouds, are included in many of the data products that are based on data acquired on- board earth satellites. Many cloud-mask algorithms have the form of decision trees, which employ sequential tests that scientists designed based on empirical astrophysics studies and astrophysics simulations. Limitations of existing cloud masks restrict our ability to accurately track changes in cloud patterns over time. In this study we explored the potential benefits of automatically-learned decision trees for detecting clouds from images acquired using the Advanced Very High Resolution Radiometer (AVHRR) instrument on board the NOAA-14 weather satellite of the National Oceanic and Atmospheric Administration. We constructed three decision trees for a sample of 8km-daily AVHRR data from 2000 using a decision-tree learning procedure provided within MATLAB(R), and compared the accuracy of the decision trees to the accuracy of the cloud mask. We used ground observations collected by the National Aeronautics and Space Administration Clouds and the Earth s Radiant Energy Systems S COOL project as the gold standard. For the sample data, the accuracy of automatically learned decision trees was greater than the accuracy of the cloud masks included in the AVHRR data product.

View angle dependence of cloud optical thicknesses retrieved by MODIS

NASA Technical Reports Server (NTRS)

Marshak, Alexander; Varnai, Tamas

2005-01-01

This study examines whether cloud inhomogeneity influences the view angle dependence of MODIS cloud optical thickness (tau) retrieval results. The degree of cloud inhomogeneity is characterized through the local gradient in 11 microns brightness temperature. The analysis of liquid phase clouds in a one year long global dataset of Collection 4 MODIS data reveals that while optical thickness retrievals give remarkably consistent results for all view directions if clouds are homogeneous, they give much higher tau-values for oblique views than for overhead views if clouds are inhomogeneous and the sun is fairly oblique. For solar zenith angles larger than 55deg, the mean optical thickness retrieved for the most inhomogeneous third of cloudy pixels is more than 30% higher for oblique views than for overhead views. After considering a variety of possible scenarios, the paper concludes that the most likely reason for the increase lies in three-dimensional radiative interactions that are not considered in current, one-dimensional retrieval algorithms. Namely, the radiative effect of cloud sides viewed at oblique angles seems to contribute most to the enhanced tau-values. The results presented here will help understand cloud retrieval uncertainties related to cloud inhomogeneity. They complement the uncertainty estimates that will start accompanying MODIS cloud products in Collection 5 and may eventually help correct for the observed view angle dependent biases.

Send in the Clouds

NASA Image and Video Library

2017-01-02

Floating high above the hydrocarbon lakes, wispy clouds have finally started to return to Titan's northern latitudes Clouds like these disappeared from Titan's (3,200 miles or 5,150 kilometers across) northern reaches for several years (from about 2010 to 2014). Now they have returned, but in far smaller numbers than expected. Since clouds can quickly appear and disappear, Cassini scientists regularly monitor the large moon, in the hopes of observing cloud activity. They are especially interested in comparing these observations to predictions of how cloud cover should change with Saturn's seasons. Titan's clear skies are not what researchers expected. This view looks toward the Saturn-facing side of Titan. North on Titan is up and rotated 3 degrees to the left. The image was taken with the Cassini spacecraft narrow-angle camera on Oct. 29, 2016 using a spectral filter that preferentially admits wavelengths of near-infrared light centered at 938 nanometers. The view was obtained at a distance of approximately 545,000 miles (878,000 kilometers) from Titan. Image scale is 3 miles (5 kilometers) per pixel. http://photojournal.jpl.nasa.gov/catalog/PIA20516

Refinement of the CALIOP cloud mask algorithm

NASA Astrophysics Data System (ADS)

Katagiri, Shuichiro; Sato, Kaori; Ohta, Kohei; Okamoto, Hajime

2018-04-01

A modified cloud mask algorithm was applied to the CALIOP data to have more ability to detect the clouds in the lower atmosphere. In this algorithm, we also adopt the fully attenuation discrimination and the remain noise estimation using the data obtained at an altitude of 40 km to avoid contamination of stratospheric aerosols. The new cloud mask shows an increase in the lower cloud fraction. Comparison of the results to the data observed with a PML ground observation was also made.

Cloud cameras at the Pierre Auger Observatory

NASA Astrophysics Data System (ADS)

Winnick, Michael G.

2010-06-01

This thesis presents the results of measurements made by infrared cloud cameras installed at the Pierre Auger Observatory in Argentina. These cameras were used to record cloud conditions during operation of the observatory's fluorescence detectors. As cloud may affect the measurement of fluorescence from cosmic ray extensive air showers, the cloud cameras provide a record of which measurements have been interfered with by cloud. Several image processing algorithms were developed, along with a methodology for the detection of cloud within infrared images taken by the cloud cameras. A graphical user interface (GUI) was developed to expediate this, as a large number of images need to be checked for cloud. A cross-check between images recorded by three of the observatory's cloud cameras is presented, along with a comparison with independent cloud measurements made by LIDAR. Despite the cloud cameras and LIDAR observing different areas of the sky, a good agreement is observed in the measured cloud fraction between the two instruments, particularly on very clear and overcast nights. Cloud information recorded by the cloud cameras, with cloud height information measured by the LIDAR, was used to identify those extensive air showers that were obscured by cloud. These events were used to study the effectiveness of standard quality cuts at removing cloud afflicted events. Of all of the standard quality cuts studied in this thesis, the LIDAR cloud fraction cut was the most effective at preferentially removing cloud obscured events. A 'cloudy pixel' veto is also presented, whereby cloud obscured measurements are excluded during the standard hybrid analysis, and new extensive air shower reconstructed parameters determined. The application of such a veto would provide a slight increase to the number of events available for higher level analysis.

Robust Likelihoods for Inflationary Gravitational Waves from Maps of Cosmic Microwave Background Polarization

NASA Technical Reports Server (NTRS)

Switzer, Eric Ryan; Watts, Duncan J.

2016-01-01

The B-mode polarization of the cosmic microwave background provides a unique window into tensor perturbations from inflationary gravitational waves. Survey effects complicate the estimation and description of the power spectrum on the largest angular scales. The pixel-space likelihood yields parameter distributions without the power spectrum as an intermediate step, but it does not have the large suite of tests available to power spectral methods. Searches for primordial B-modes must rigorously reject and rule out contamination. Many forms of contamination vary or are uncorrelated across epochs, frequencies, surveys, or other data treatment subsets. The cross power and the power spectrum of the difference of subset maps provide approaches to reject and isolate excess variance. We develop an analogous joint pixel-space likelihood. Contamination not modeled in the likelihood produces parameter-dependent bias and complicates the interpretation of the difference map. We describe a null test that consistently weights the difference map. Excess variance should either be explicitly modeled in the covariance or be removed through reprocessing the data.

Spatio-energetic cross talk in photon counting detectors: Detector model and correlated Poisson data generator.

PubMed

Taguchi, Katsuyuki; Polster, Christoph; Lee, Okkyun; Stierstorfer, Karl; Kappler, Steffen

2016-12-01

An x-ray photon interacts with photon counting detectors (PCDs) and generates an electron charge cloud or multiple clouds. The clouds (thus, the photon energy) may be split between two adjacent PCD pixels when the interaction occurs near pixel boundaries, producing a count at both of the pixels. This is called double-counting with charge sharing. (A photoelectric effect with K-shell fluorescence x-ray emission would result in double-counting as well). As a result, PCD data are spatially and energetically correlated, although the output of individual PCD pixels is Poisson distributed. Major problems include the lack of a detector noise model for the spatio-energetic cross talk and lack of a computationally efficient simulation tool for generating correlated Poisson data. A Monte Carlo (MC) simulation can accurately simulate these phenomena and produce noisy data; however, it is not computationally efficient. In this study, the authors developed a new detector model and implemented it in an efficient software simulator that uses a Poisson random number generator to produce correlated noisy integer counts. The detector model takes the following effects into account: (1) detection efficiency; (2) incomplete charge collection and ballistic effect; (3) interaction with PCDs via photoelectric effect (with or without K-shell fluorescence x-ray emission, which may escape from the PCDs or be reabsorbed); and (4) electronic noise. The correlation was modeled by using these two simplifying assumptions: energy conservation and mutual exclusiveness. The mutual exclusiveness is that no more than two pixels measure energy from one photon. The effect of model parameters has been studied and results were compared with MC simulations. The agreement, with respect to the spectrum, was evaluated using the reduced χ 2 statistics or a weighted sum of squared errors, χ red 2 (≥1), where χ red 2 =1 indicates a perfect fit. The model produced spectra with flat field irradiation that qualitatively agree with previous studies. The spectra generated with different model and geometry parameters allowed for understanding the effect of the parameters on the spectrum and the correlation of data. The agreement between the model and MC data was very strong. The mean spectra with 90 keV and 140 kVp agreed exceptionally well: χ red 2 values were 1.049 with 90 keV data and 1.007 with 140 kVp data. The degrees of cross talk (in terms of the relative increase from single pixel irradiation to flat field irradiation) were 22% with 90 keV and 19% with 140 kVp for MC simulations, while they were 21% and 17%, respectively, for the model. The covariance was in strong agreement qualitatively, although it was overestimated. The noisy data generation was very efficient, taking less than a CPU minute as opposed to CPU hours for MC simulators. The authors have developed a novel, computationally efficient PCD model that takes into account double-counting and resulting spatio-energetic correlation between PCD pixels. The MC simulation validated the accuracy.

Use of In Situ and Airborne Multiangle Data to Assess MODIS- and Landsat-based Estimates of Surface Albedo

NASA Technical Reports Server (NTRS)

Roman, Miguel O.; Gatebe, Charles K.; Shuai, Yanmin; Wang, Zhuosen; Gao, Feng; Masek, Jeff; Schaaf, Crystal B.

2012-01-01

The quantification of uncertainty of global surface albedo data and products is a critical part of producing complete, physically consistent, and decadal land property data records for studying ecosystem change. A current challenge in validating satellite retrievals of surface albedo is the ability to overcome the spatial scaling errors that can contribute on the order of 20% disagreement between satellite and field-measured values. Here, we present the results from an uncertain ty analysis of MODerate Resolution Imaging Spectroradiometer (MODIS) and Landsat albedo retrievals, based on collocated comparisons with tower and airborne multi-angular measurements collected at the Atmospheric Radiation Measurement Program s (ARM) Cloud and Radiation Testbed (CART) site during the 2007 Cloud and Land Surface Interaction Campaign (CLAS33 IC 07). Using standard error propagation techniques, airborne measurements obtained by NASA s Cloud Absorption Radiometer (CAR) were used to quantify the uncertainties associated with MODIS and Landsat albedos across a broad range of mixed vegetation and structural types. Initial focus was on evaluating inter-sensor consistency through assessments of temporal stability, as well as examining the overall performance of satellite-derived albedos obtained at all diurnal solar zenith angles. In general, the accuracy of the MODIS and Landsat albedos remained under a 10% margin of error in the SW(0.3 - 5.0 m) domain. However, results reveal a high degree of variability in the RMSE (root mean square error) and bias of albedos in both the visible (0.3 - 0.7 m) and near-infrared (0.3 - 5.0 m) broadband channels; where, in some cases, retrieval uncertainties were found to be in excess of 20%. For the period of CLASIC 07, the primary factors that contributed to uncertainties in the satellite-derived albedo values include: (1) the assumption of temporal stability in the retrieval of 500 m MODIS BRDF values over extended periods of cloud-contaminated observations; and (2) the assumption of spatial 45 and structural uniformity at the Landsat (30 m) pixel scale.

Apollo 15 contamination photography

NASA Technical Reports Server (NTRS)

Naumann, R. J.

1972-01-01

The problem of optical contamination in the form of particulates in the vicinity of a spacecraft has been a source of concern for any astronomical experiment that must be performed in sunlight. This concern prompted a photographic photometric experiment on Apollo 15 to measure the brightness of the residual contamination cloud as well as the cloud produced by dumping waste water overboard. An upper limit of 10 to the minus 12.3 power B (B designates the brightness of the solar disc) was placed on the residual cloud at a 90 deg sun angle, which is comparable to the zodiacal light. The brightness of the cloud produced by the waste dump was estimated to be 10 to the minus 9.2 power B. It was observed to decrease rapidly to 10 to the -11.6 power B in minutes, then fluctuate in brightness for at least 25 minutes as additional material left the spacecraft. The cloud was observed to consist of individually resolved particle tracks estimated to be particles ranging from millimeters to centimeters in diameter in addition to a background of unresolved particles with an average diameter of 10.5 microns. Most of the tracks proceeded in straight-line paths from the dump nozzle. Several tracks violated this direction, apparently having been scattered by collisions. A few tracks appeared to have definite curvatures, which are believed to be caused by charged particle interactions.

Measurement of Aerosol and Cloud Particles with PACS and HARP Hyperangular Imaging Polarimeters

NASA Astrophysics Data System (ADS)

Martins, J.; Fernandez-Borda, R.; Remer, L. A.; Sparr, L.; Buczkowski, S.; Munchak, L. A.

2013-12-01

PACS is new hyper-angular imaging polarimeter for aeorosol and cloud measurerents designed to meet the requirements of the proposed ACE decadal survey mission. The full PACS system consists of three wide field of view (110deg cross track) telescopes covering the UV, VNIR, and SWIR spectral ranges with angular coverage between +55 deg forward to -55deg backwards. The angular density can be selected to cover up to 100 different viewing angles at selected wavelengths. PACS_VNIR is a prototype airborne instrument designed to demonstrate PACS capability by deploying just one of the three wavelength modules of the full PACS. With wavelengths at 470, 550, 675, 760 and 875nm, PACS_VNIR flew for the first time during the PODEX experiment in January/February 2013 aboard the NASA ER-2 aircraft. PACS SWIR (1.64, 1.88, 2.1, and 2.25um) is currently under construction and should be operational in the lab by Fall/2013. PACS_ UV has been fully designed, but is not yet under construction. During the PODEX flights PACS_VNIR collected data for aerosol and clouds over variable surface types including, water, vegetation, urban areas, and snow. The data is currently being calibrated, geolocated and prepared for the inversion of geophysical parameters including water cloud size distribution and aerosol microphysical parameters. The large density of angles in PACS allows for the characterization of cloudbow features in relatively high spatial resolution in a pixel to pixel basis. This avoids the need for assumptions of cloud homogeneity over any distance. The hyperangle capability also allows detailed observation of cloud ice particles, surface characterization, and optimum selection of the number of angles desired for aerosol retrievals. The aerosol and cloud retrieval algorithms under development for the retrieval of particle microphysical properties from the PACS data will be discussed in this presentation. As an extension of the PACS concept we are currently developing the HARP (Hyper-Angular Rainbow Polarimeter) Cubesat satellite funded by the NASA/ESTO/InVEST program. HARP will demonstrate the PACS concept from space and will allow for high resolution angular measurements of polarized radiances over different aerosol and cloud scenarios. The HARP concept and strategy will be presented and discussed as part of the general PACS measurement strategy.

Sahara Dust

Atmospheric Science Data Center

2013-04-15

article title:  Casting Light and Shadows on a Saharan Dust Storm     ... ocean and dust layer, which are visible in shades of blue and tan, respectively. In the lower panel, heights derived from automated ... cast by the cirrus clouds onto the dust (indicated by blue and cyan pixels) provide sufficient spatial contrast for a retrieval of ...

Sampling errors for a nadir viewing instrument on the International Space Station

NASA Astrophysics Data System (ADS)

Berger, H. I.; Pincus, R.; Evans, F.; Santek, D.; Ackerman, S.; Ackerman, S.

2001-12-01

In an effort to improve the observational charactarization of ice clouds in the earth's atmosphere, we are developing a sub-millimeter wavelength radiometer which we propose to fly on the International Space Station for two years. Our goal is to accurately measure the ice water path and mass-weighted particle size at the finest possible temporal and spatial resolution. The ISS orbit precesses, sampling through the dirunal cycle every 16 days, but technological constraints limit our instrument to a single pixel viewed near nadir. We discuss sampling errors associated with this instrument/platform configuration. We use as "truth" the ISCCP dataset of pixel-level cloud optical retrievals, which acts as a proxy for ice water path; this dataset is sampled according to the orbital characteristics of the space station, and the statistics computed from the sub-sampled population are compared with those from the full dataset. We explore the tradeoffs in average sampling error as a function of the averaging time and spatial scale, and explore the possibility of resolving the dirunal cycle.

Change detection using landsat time series: A review of frequencies, preprocessing, algorithms, and applications

NASA Astrophysics Data System (ADS)

Zhu, Zhe

2017-08-01

The free and open access to all archived Landsat images in 2008 has completely changed the way of using Landsat data. Many novel change detection algorithms based on Landsat time series have been developed We present a comprehensive review of four important aspects of change detection studies based on Landsat time series, including frequencies, preprocessing, algorithms, and applications. We observed the trend that the more recent the study, the higher the frequency of Landsat time series used. We reviewed a series of image preprocessing steps, including atmospheric correction, cloud and cloud shadow detection, and composite/fusion/metrics techniques. We divided all change detection algorithms into six categories, including thresholding, differencing, segmentation, trajectory classification, statistical boundary, and regression. Within each category, six major characteristics of different algorithms, such as frequency, change index, univariate/multivariate, online/offline, abrupt/gradual change, and sub-pixel/pixel/spatial were analyzed. Moreover, some of the widely-used change detection algorithms were also discussed. Finally, we reviewed different change detection applications by dividing these applications into two categories, change target and change agent detection.

The life cycle of a tornadic cloud as seen from a geosynchronous satellite

NASA Technical Reports Server (NTRS)

Hung, R. J.; Dodge, J. C.; Smith, R. E.

1983-01-01

The life span of a severe storm is on the order of a few hours. Rapid-scan infrared and visible observations from geosynchronous satellites can be useful for studying the life cycle of a severe convective storm. By using artificial colors for pixels representing blackbody temperatures of the cloud top, convective storms can be observed throughout their life cycles. In this paper clouds associated with a tornadic storm, the Ringwood, OK tornado on May 29, 1977, are compared with those without a tornadic storm to illustrate how the infrared and visible observations from a geosynchronous satellite can be used to study the differences in their life cycles. The instability of the air mass and the meteorological background are discussed based on balloon observations.

Dreamy Swirls on Saturn

NASA Image and Video Library

2017-09-12

NASA's Cassini spacecraft gazed toward the northern hemisphere of Saturn to spy subtle, multi-hued bands in the clouds there. This view looks toward the terminator -- the dividing line between night and day -- at lower left. The sun shines at low angles along this boundary, in places highlighting vertical structure in the clouds. Some vertical relief is apparent in this view, with higher clouds casting shadows over those at lower altitude. Images taken with the Cassini spacecraft narrow-angle camera using red, green and blue spectral filters were combined to create this natural-color view. The images were acquired on Aug. 31, 2017, at a distance of approximately 700,000 miles (1.1 million kilometers) from Saturn. Image scale is about 4 miles (6 kilometers) per pixel. https://photojournal.jpl.nasa.gov/catalog/PIA21888

ARC-1989-A89-7007

NASA Image and Video Library

1989-08-21

The bright cirrus-like clouds of Neptune change rapidly, often forming and dissipation over periods of several to tens of hours. In this sequence spanning two rotations of Neptune (about 36 hours) Voyager 2 observed cloud evolution in the region around the Great Dark Spot (GDS) at an effective resolution of about 100 km (62 miles) per pixel. The surprisingly rapid changes which occur over the 18 hours separating each panel shows that in this region Neptune's weather is perhaps as dynamic and variable as that of the Earth. However, the scale is immense by our standards--the Earth and the GDS are of similar size -- and in Neptune's frigid atmosphere, where temperatures are as low as 55 degree Kelvin (-360F), the cirrus clouds are composed of frozen methane rather than Earth's crystalse of water ice.

Resolution Enhancement of MODIS-derived Water Indices for Studying Persistent Flooding

NASA Astrophysics Data System (ADS)

Underwood, L. W.; Kalcic, M. T.; Fletcher, R. M.

2012-12-01

Monitoring coastal marshes for persistent flooding and salinity stress is a high priority issue in Louisiana. Remote sensing can identify environmental variables that can be indicators of marsh habitat conditions, and offer timely and relatively accurate information for aiding wetland vegetation management. Monitoring activity accuracy is often limited by mixed pixels which occur when areas represented by the pixel encompasses more than one cover type. Mixtures of marsh grasses and open water in 250m Moderate Resolution Imaging Spectroradiometer (MODIS) data can impede flood area estimation. Flood mapping of such mixtures requires finer spatial resolution data to better represent the cover type composition within 250m MODIS pixel. Fusion of MODIS and Landsat can improve both spectral and temporal resolution of time series products to resolve rapid changes from forcing mechanisms like hurricane winds and storm surge. For this study, using a method for estimating sub-pixel values from a MODIS time series of a Normalized Difference Water Index (NDWI), using temporal weighting, was implemented to map persistent flooding in Louisiana coastal marshes. Ordinarily NDWI computed from daily 250m MODIS pixels represents a mixture of fragmented marshes and water. Here, sub-pixel NDWI values were derived for MODIS data using Landsat 30-m data. Each MODIS pixel was disaggregated into a mixture of the eight cover types according to the classified image pixels falling inside the MODIS pixel. The Landsat pixel means for each cover type inside a MODIS pixel were computed for the Landsat data preceding the MODIS image in time and for the Landsat data succeeding the MODIS image. The Landsat data were then weighted exponentially according to closeness in date to the MODIS data. The reconstructed MODIS data were produced by summing the product of fractional cover type with estimated NDWI values within each cover type. A new daily time series was produced using both the reconstructed 250-m MODIS, with enhanced features, and the approximated daily 30-m high-resolution image based on Landsat data. The algorithm was developed and tested over the Calcasieu-Sabine Basin, which was heavily inundated by storm surge from Hurricane Ike to study the extent and duration of flooding following the storm. Time series for 2000-2009, covering flooding events by Hurricane Rita in 2005 and Hurricane Ike in 2008, were derived. High resolution images were formed for all days in 2008 between the first cloud free Landsat scene and the last cloud-free Landsat scene. To refine and validate flooding maps, each time series was compared to Louisiana Coastwide Reference Monitoring System (CRMS) station water levels adjusted to marsh to optimize thresholds for MODIS-derived time series of NDWI. Seasonal fluctuations were adjusted by subtracting ten year average NDWI for marshes, excluding the hurricane events. Results from different NDWI indices and a combination of indices were compared. Flooding persistence that was mapped with higher-resolution data showed some improvement over the original MODIS time series estimates. The advantage of this novel technique is that improved mapping of extent and duration of inundation can be provided.

Resolution Enhancement of MODIS-Derived Water Indices for Studying Persistent Flooding

NASA Technical Reports Server (NTRS)

Underwood, L. W.; Kalcic, Maria; Fletcher, Rose

2012-01-01

Monitoring coastal marshes for persistent flooding and salinity stress is a high priority issue in Louisiana. Remote sensing can identify environmental variables that can be indicators of marsh habitat conditions, and offer timely and relatively accurate information for aiding wetland vegetation management. Monitoring activity accuracy is often limited by mixed pixels which occur when areas represented by the pixel encompasses more than one cover type. Mixtures of marsh grasses and open water in 250m Moderate Resolution Imaging Spectroradiometer (MODIS) data can impede flood area estimation. Flood mapping of such mixtures requires finer spatial resolution data to better represent the cover type composition within 250m MODIS pixel. Fusion of MODIS and Landsat can improve both spectral and temporal resolution of time series products to resolve rapid changes from forcing mechanisms like hurricane winds and storm surge. For this study, using a method for estimating sub-pixel values from a MODIS time series of a Normalized Difference Water Index (NDWI), using temporal weighting, was implemented to map persistent flooding in Louisiana coastal marshes. Ordinarily NDWI computed from daily 250m MODIS pixels represents a mixture of fragmented marshes and water. Here, sub-pixel NDWI values were derived for MODIS data using Landsat 30-m data. Each MODIS pixel was disaggregated into a mixture of the eight cover types according to the classified image pixels falling inside the MODIS pixel. The Landsat pixel means for each cover type inside a MODIS pixel were computed for the Landsat data preceding the MODIS image in time and for the Landsat data succeeding the MODIS image. The Landsat data were then weighted exponentially according to closeness in date to the MODIS data. The reconstructed MODIS data were produced by summing the product of fractional cover type with estimated NDWI values within each cover type. A new daily time series was produced using both the reconstructed 250-m MODIS, with enhanced features, and the approximated daily 30-m high-resolution image based on Landsat data. The algorithm was developed and tested over the Calcasieu-Sabine Basin, which was heavily inundated by storm surge from Hurricane Ike to study the extent and duration of flooding following the storm. Time series for 2000-2009, covering flooding events by Hurricane Rita in 2005 and Hurricane Ike in 2008, were derived. High resolution images were formed for all days in 2008 between the first cloud free Landsat scene and the last cloud-free Landsat scene. To refine and validate flooding maps, each time series was compared to Louisiana Coastwide Reference Monitoring System (CRMS) station water levels adjusted to marsh to optimize thresholds for MODIS-derived time series of NDWI. Seasonal fluctuations were adjusted by subtracting ten year average NDWI for marshes, excluding the hurricane events. Results from different NDWI indices and a combination of indices were compared. Flooding persistence that was mapped with higher-resolution data showed some improvement over the original MODIS time series estimates. The advantage of this novel technique is that improved mapping of extent and duration of inundation can be provided.

NASA/GEWEX Surface Radiation Budget: Integrated Data Product With Reprocessed Radiance, Cloud, and Meteorology Inputs, and New Surface Albedo Treatment

NASA Technical Reports Server (NTRS)

Cox, Stephen J.; Stackhouse, Paul W., Jr.; Gupta, Shashi K.; Mikovitz, J. Colleen; Zhang, Taiping

2016-01-01

The NASA/GEWEX Surface Radiation Budget (SRB) project produces shortwave and longwave surface and top of atmosphere radiative fluxes for the 1983-near present time period. Spatial resolution is 1 degree. The current release 3.0 (available at gewex-srb.larc.nasa.gov) uses the International Satellite Cloud Climatology Project (ISCCP) DX product for pixel level radiance and cloud information. This product is subsampled to 30 km. ISCCP is currently recalibrating and recomputing their entire data series, to be released as the H product, at 10km resolution. The ninefold increase in pixel number will allow SRB a higher resolution gridded product (e.g. 0.5 degree), as well as the production of pixel-level fluxes. In addition to the input data improvements, several important algorithm improvements have been made. Most notable has been the adaptation of Angular Distribution Models (ADMs) from CERES to improve the initial calculation of shortwave TOA fluxes, from which the surface flux calculations follow. Other key input improvements include a detailed aerosol history using the Max Planck Institut Aerosol Climatology (MAC), temperature and moisture profiles from HIRS, and new topography, surface type, and snow/ice. Here we present results for the improved GEWEX Shortwave and Longwave algorithm (GSW and GLW) with new ISCCP data, the various other improved input data sets and the incorporation of many additional internal SRB model improvements. As of the time of abstract submission, results from 2007 have been produced with ISCCP H availability the limiting factor. More SRB data will be produced as ISCCP reprocessing continues. The SRB data produced will be released as part of the Release 4.0 Integrated Product, recognizing the interdependence of the radiative fluxes with other GEWEX products providing estimates of the Earth's global water and energy cycle (I.e., ISCCP, SeaFlux, LandFlux, NVAP, etc.).

DOE Office of Scientific and Technical Information (OSTI.GOV)

Weiss, Joel T.; Becker, Julian; Shanks, Katherine S.

There is a compelling need for a high frame rate imaging detector with a wide dynamic range, from single x-rays/pixel/pulse to >10{sup 6} x-rays/pixel/pulse, that is capable of operating at both x-ray free electron laser (XFEL) and 3rd generation sources with sustained fluxes of > 10{sup 11} x-rays/pixel/s [1, 2, 3]. We propose to meet these requirements with the High Dynamic Range Pixel Array Detector (HDR-PAD) by (a) increasing the speed of charge removal strategies [4], (b) increasing integrator range by implementing adaptive gain [5], and (c) exploiting the extended charge collection times of electron-hole pair plasma clouds that formmore » when a sufficiently large number of x-rays are absorbed in a detector sensor in a short period of time [6]. We have developed a measurement platform similar to the one used in [6] to study the effects of high electron-hole densities in silicon sensors using optical lasers to emulate the conditions found at XFELs. Characterizations of the employed tunable wavelength laser with picosecond pulse duration have shown Gaussian focal spots sizes of 6 ± 1 µm rms over the relevant spectrum and 2 to 3 orders of magnitude increase in available intensity compared to previous measurements presented in [6]. Results from measurements on a typical pixelated silicon diode intended for use with the HDR-PAD (150 µm pixel size, 500 µm thick sensor) are presented.« less

Limb darkening in Venus night-side disk as viewed from Akatsuki IR2

NASA Astrophysics Data System (ADS)

Satoh, Takehiko; Nakakushi, Takashi; Sato, Takao M.; Hashimoto, George L.

2017-10-01

Night-side hemisphere of Venus exhibits dark and bright regions as a result of spatially inhomogeneous cloud opacity which is illuminated by infrared radiation from deeper atmosphere. The 2-μm camera (IR2) onboard Akatsuki, Japan's Venus Climate Orbiter, is equipped with three narrow-band filters (1.735, 2.26, and 2.32 μm) to image Venus night-side disk in well-known transparency windows of CO2 atmosphere (Allen and Crawford 1984). In general, a cloud feature appears brightest when it is in the disk center and becomes darker as the zenith angle of emergent light increases. Such limb darkening was observed with Galileo/NIMS and mathematically approximated (Carlson et al., 1993). Limb-darkening correction helps to identify branches, in a 1.74-μm vs. 2.3-μm radiances scatter plot, each of which corresponds to a group of aerosols with similar properties. We analyzed Akatsuki/IR2 images to characterize the limb darkening for three night-side filters.There is, however, contamination from the intense day-side disk blurred by IR2's point spread function (PSF). It is found that infrared light can be multiplly reflected within the Si substrate of IR2 detector (1024x1024 pixels PtSi array), causing elongated tail in the actual PSF. We treated this in two different ways. One is to mathematically approximate the PSF (with a combination of modified Lorentz functions) and another is to differentiate 2.26-μm image from 2.32-μm image so that the blurred light pattern can directly be obtained. By comparing results from these two methods, we are able to reasonablly clean up the night-side images and limb darkening is extracted. Physical interpretation of limb darkening, as well as "true" time variations of cloud brightness will be presented/discussed.

Quantifying the Floe Size Distribution in the Marginal Ice Zone from Satellite Imagery for use in Model Development and Validation

NASA Astrophysics Data System (ADS)

Schweiger, A. J.; Stern, H. L.; Stark, M.; Zhang, J.; Hwang, P.; Steele, M.

2013-12-01

Several key processes in the Marginal Ice Zone (MIZ) of the Arctic Ocean are related to the size of the ice floes, whose diameters range from meters to tens of kilometers. The floe size distribution (FSD) influences mechanical properties of the ice and thus its response to winds, currents, and waves, which is likely to modify the air-sea momentum transfer. The FSD also influences the air-sea heat transfer and the response of the MIZ ice cover to the thermal forcing. The FSD also has a significant role in lateral melting. No existing sea-ice/ocean models currently simulate the FSD in the MIZ. Significant uncertainties in FSD-related processes hinder model incorporation of the FSD, and model development must heavily depend on observations of the FSD for parameterization, calibration, and validation. To support the development and implementation of the FSD in the Marginal Ice Zone Modeling and Assimilation System (MIZMAS), we have conducted an analysis of the FSD in the Beaufort and Chukchi seas using three sources of satellite imagery: NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) on the Terra and Aqua satellites, the Canadian Space Agency's synthetic aperture radar (SAR) on RADARSAT, and declassified National Technical Means imagery from the Global Fiducials Library (GFL) of the U.S. Geological Survey. The MODIS visible and short-wave infrared bands have a pixel size of 250 meters, and are only useful in cloud-free regions. The SAR imagery is unaffected by clouds and darkness, and has a pixel size of 50 meters. The GFL visible imagery, with a pixel size of 1 meter, is only useful in cloud-free regions. The resolution and spatial extent of the various image products allows us to identify ice floes of all sizes from 10 meters to 100 kilometers. The general procedure for identifying ice floes in the imagery is as follows: delineate cloud-free regions (if necessary); choose a threshold to separate ice from water, and create a binary image; apply the morphological erosion operation to separate floes that touch each other; identify contiguous sets of pixels (floes) by a recursive algorithm; and apply the morphological dilation operation to restore (approximately) the floes to their original sizes and shapes. Once the floes in an image have been identified, any number of properties may be calculated: the centroid, length, width, area, perimeter, orientation, convexity, etc. We calculate the mean caliper diameter as a simple, single measure of the size of a floe. We report results on the observed FSD in the Beaufort and Chukchi seas, including its seasonal evolution and spatial variability. We outline how the results will be used in model development and validation of the FSD in the MIZMAS.

Sub-pixel localization of highways in AVIRIS images

NASA Technical Reports Server (NTRS)

Salu, Yehuda

1995-01-01

Roads and highways show up clearly in many bands of AVIRIS images. A typical lane in the U.S. is 12 feet wide, and the total width of a four lane highway, including 18 feet of paved shoulders, is 19.8 m. Such a highway will cover only a portion of any 20x20 m AVIRIS pixel that it traverses. The other portion of these pixels wil be usually covered by vegetation. An interesting problem is to precisely determine the location of a highway within the AVIRIS pixels that it traverses. This information may be used for alignment and spatial calibration of AVIRIS images. Also, since the reflection properties of highway surfaces do not change with time, and they can be determined once and for all, such information can be of help in calculating and filtering out the atmospheric noise that contaminates AVIRIS measurements. The purpose of this report is to describe a method for sub-pixel localization of highways.

Expected Performance of the Upcoming Surface Water and Ocean Topography Mission Measurements of River Height, Width, and Slope

NASA Astrophysics Data System (ADS)

Wei, R.; Frasson, R. P. M.; Williams, B. A.; Rodriguez, E.; Pavelsky, T.; Altenau, E. H.; Durand, M. T.

2017-12-01

The upcoming Surface Water and Ocean Topography (SWOT) mission will measure river widths and water surface elevations of rivers wider than 100 m. In preparation for the SWOT mission, the Jet Propulsion Laboratory built the SWOT hydrology simulator with the intent of generating synthetic SWOT overpasses over rivers with realistic error characteristics. These synthetic overpasses can be used to guide the design of processing methods and data products, as well as develop data assimilation techniques that will incorporate the future SWOT data into hydraulic and hydrologic models as soon as the satellite becomes operational. SWOT simulator uses as inputs water depth, river bathymetry, and the surrounding terrain digital elevation model to create simulated interferograms of the study area. Next, the simulator emulates the anticipated processing of SWOT data by attempting to geolocate and classify the radar returns. The resulting cloud of points include information on water surface elevation, pixel area, and surface classification (land vs water). Finally, we process the pixel clouds by grouping pixels into equally spaced nodes located at the river centerline. This study applies the SWOT simulator to six different rivers: Sacramento River, Tanana River, Saint Lawrence River, Platte River, Po River, and Amazon River. This collection of rivers covers a range of size, slope, and planform complexity with the intent of evaluating the impact of river width, slope, planform complexity, and surrounding topography on the anticipated SWOT height, width, and slope error characteristics.

X-Ray Spectroscopy of Optically Bright Planets using the Chandra Observatory

NASA Technical Reports Server (NTRS)

Ford, P. G.; Elsner, R. F.

2005-01-01

Since its launch in July 1999, Chandra's Advanced CCD Imaging Spectrometer (ACIS) has observed several planets (Venus, Mars, Jupiter and Saturn) and 6 comets. At 0.5 arc-second spatial resolution, ACIS detects individual x-ray photons with good quantum efficiency (25% at 0.6 KeV) and energy resolution (20% FWHM at 0.6 KeV). However, the ACIS CCDs are also sensitive to optical and near-infrared light, which is absorbed by optical blocking filters (OBFs) that eliminate optical contamination from all but the brightest extended sources, e.g., planets. .Jupiter at opposition subseconds approx.45 arc-seconds (90 CCD pixels.) Since Chandra is incapable of tracking a moving target, the planet takes 10 - 20 kiloseconds to move across the most sensitive ACIS CCD, after which the observatory must be re-pointed. Meanwhile, the OBF covering that CCD adds an opt,ical signal equivalent to approx.110 eV to each pixel that lies within thc outline of the Jovian disk. This has three consequences: (1) the observatory must be pointed away from Jupiter while CCD bias maps are constructed; (2) most x-rays from within the optical image will be misidentified as charged-particle background and ignored; and (3) those x-rays that are reported will bc assigned anomalously high energies. The same also applies to thc other planets, but is less serious since they are either dimmer at optical wavelengths, or they show less apparent motion across the sky, permitting reduced CCD exposure times: the optical contamination from Saturn acids approx.15 eV per pixel, and from Mars and Venus approx.31 eV. After analyzing a series of short .Jupiter observations in December 2000, ACIS parameters were optimized for the February 2003 opposition. CCD bias maps were constructed while Chandra pointed away from Jupiter, and the subsequent observations employed on-board software to ignore any pixel that contained less charge than that expected from optical leakage. In addition, ACIS was commanded to report 5 x 5 arrays of pixel values surrounding each x-ray event, and the outlying values were employed during ground processing to correct for the optical contamination.

Photogrammetric Analysis of Rotor Clouds Observed during T-REX

NASA Astrophysics Data System (ADS)

Romatschke, U.; Grubišić, V.

2017-12-01

Stereo photogrammetric analysis is a rarely utilized but highly valuable tool for studying smaller, highly ephemeral clouds. In this study, we make use of data that was collected during the Terrain-induced Rotor Experiment (T-REX), which took place in Owens Valley, eastern California, in the spring of 2006. The data set consists of matched digital stereo photographs obtained at high temporal (on the order of seconds) and spatial resolution (limited by the pixel size of the cameras). Using computer vision techniques we have been able to develop algorithms for camera calibration, automatic feature matching, and ultimately reconstruction of 3D cloud scenes. Applying these techniques to images from different T-REX IOPs we capture the motion of clouds in several distinct mountain wave scenarios ranging from short lived lee wave clouds on an otherwise clear sky day to rotor clouds formed in an extreme turbulence environment with strong winds and high cloud coverage. Tracking the clouds in 3D space and time allows us to quantify phenomena such as vertical and horizontal movement of clouds, turbulent motion at the upstream edge of rotor clouds, the structure of the lifting condensation level, extreme wind shear, and the life cycle of clouds in lee waves. When placed into context with the existing literature that originated from the T-REX field campaign, our results complement and expand our understanding of the complex dynamics observed in a variety of different lee wave settings.

A Thermal Infrared Cloud Mapper

NASA Astrophysics Data System (ADS)

Mallama, A.; Degnan, J. J.

2001-12-01

A thermal infrared imager for mapping the changing cloud cover over a ground based observing site has been developed. There are two main components to our instrument. One is a commercially made uncooled 10 micron thermal infrared detector that outputs a 120x120 pixel thermogram. The other is a convex electroplated reflector, which is situated beneath the detector and in its field of view. The resulting image covers the sky from zenith down to about 10 degrees elevation. The self-reflection of the camera and supporting vanes is removed by interpolation. Atmospheric transparency is distinguished by the difference between the sky temperature and the ambient air temperature. Clear sky is indicated by pixels having a difference of about 20 degrees C or more. The qualitative results 'clear, haze and cloud' have proven to be very reliable during two years of development and testing. Quantitative information, such as the extinction coefficient, is also available though it is not exact. The uncertainty is probably due to variability of the lapse rate under different atmospheric conditions. Software has been written for PC/DOS and VME/LynxOS (similar to Linux) systems in the C programming language. Functionality includes serial communication with the detector, analysis of the thermogram, mapping of cloud cover, data display, and file I/O. The main elements of cost in this system were for the thermal infrared detector and for the machining of an 18-inch diameter stainless steel mandrel. The latter is needed to produce an electroplated reflector. We have had good success with the gold and rhodium reflectors that have been generated. The reflectors themselves are relatively inexpensive now that the mandrel is available.

Mt-Insar Landslide Monitoring with the Aid of Homogeneous Pixels Filter

NASA Astrophysics Data System (ADS)

Liu, X. J.; Zhao, C. Y.; Wang, B. H.; Zhu, W. F.

2018-04-01

SAR interferograms are often contaminated by random noises related to temporal decorrelation, geometrical decorrelation and thermal noises, which makes the fringes obscured and greatly decreases the density of the coherent target and the accuracy of InSAR deformation results, especially for the landslide monitoring in vegetated region and in rainy season. Two different SAR interferogram filtering methods, that is Goldstein filter and homogeneous pixels filter, for one specific landslide are compared. The results show that homogeneous pixels filter is better than Goldstein one for small-scale loess landslide monitoring, which can increase the density of monitoring points. Moreover, the precision of InSAR result can reach millimeter by comparing with GPS time series measurements.

AIRS Subpixel Cloud Characterization Using MODIS Cloud Products.

NASA Astrophysics Data System (ADS)

Li, Jun; Menzel, W. Paul; Sun, Fengying; Schmit, Timothy J.; Gurka, James

2004-08-01

The Moderate Resolution Imaging Spectroradiometer (MODIS) and the Atmospheric Infrared Sounder (AIRS) measurements from the Earth Observing System's (EOS's) Aqua satellite enable improved global monitoring of the distribution of clouds. MODIS is able to provide, at high spatial resolution (1 5 km), a cloud mask, surface and cloud types, cloud phase, cloud-top pressure (CTP), effective cloud amount (ECA), cloud particle size (CPS), and cloud optical thickness (COT). AIRS is able to provide CTP, ECA, CPS, and COT at coarser spatial resolution (13.5 km at nadir) but with much better accuracy using its high-spectral-resolution measurements. The combined MODIS AIRS system offers the opportunity for improved cloud products over those possible from either system alone. The key steps for synergistic use of imager and sounder radiance measurements are 1) collocation in space and time and 2) imager cloud amount, type, and phase determination within the sounder pixel. The MODIS and AIRS measurements from the EOS Aqua satellite provide the opportunity to study the synergistic use of advanced imager and sounder measurements. As the first step, the MODIS classification procedure is applied to identify various surface and cloud types within an AIRS footprint. Cloud-layer information (lower, midlevel, or high clouds) and phase information (water, ice, or mixed-phase clouds) within the AIRS footprint are sorted and characterized using MODIS 1-km-spatial-resolution data. The combined MODIS and AIRS data for various scenes are analyzed to study the utility of the synergistic use of high-spatial-resolution imager products and high-spectral-resolution sounder radiance measurements. There is relevance to the optimal use of data from the Advanced Baseline Imager (ABI) and Hyperspectral Environmental Suite (HES) systems, which are to fly on the Geostationary Operational Environmental Satellite (GOES)-R.

Estimating the Direct Radiative Effect of Absorbing Aerosols Overlying Marine Boundary Layer Clouds in the Southeast Atlantic Using MODIS and CALIOP

NASA Technical Reports Server (NTRS)

Meyer, Kerry; Platnick, Steven; Oreopoulos, Lazaros; Lee, Dongmin

2013-01-01

Absorbing aerosols such as smoke strongly absorb solar radiation, particularly at ultraviolet and visible/near-infrared (VIS/NIR) wavelengths, and their presence above clouds can have considerable implications. It has been previously shown that they have a positive (i.e., warming) direct aerosol radiative effect (DARE) when overlying bright clouds. Additionally, they can cause biased passive instrument satellite retrievals in techniques that rely on VIS/NIR wavelengths for inferring the cloud optical thickness (COT) and effective radius (re) of underlying clouds, which can in turn yield biased above-cloud DARE estimates. Here we investigate Moderate Resolution Imaging Spectroradiometer (MODIS) cloud optical property retrieval biases due to overlying absorbing aerosols observed by Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) and examine the impact of these biases on above-cloud DARE estimates. The investigation focuses on a region in the southeast Atlantic Ocean during August and September (2006-2011), where smoke from biomass burning in southern Africa overlies persistent marine boundary layer stratocumulus clouds. Adjusting for above-cloud aerosol attenuation yields increases in the regional mean liquid COT (averaged over all ocean-only liquid clouds) by roughly 6%; mean re increases by roughly 2.6%, almost exclusively due to the COT adjustment in the non-orthogonal retrieval space. It is found that these two biases lead to an underestimate of DARE. For liquid cloud Aqua MODIS pixels with CALIOP-observed above-cloud smoke, the regional mean above-cloud radiative forcing efficiency (DARE per unit aerosol optical depth (AOD)) at time of observation (near local noon for Aqua overpass) increases from 50.9Wm(sup-2)AOD(sup-1) to 65.1Wm(sup-2)AOD(sup -1) when using bias-adjusted instead of nonadjusted MODIS cloud retrievals.

Transitions of cloud-topped marine boundary layers characterized by AIRS, MODIS, and a large eddy simulation model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yue, Qing; Kahn, Brian; Xiao, Heng

2013-08-16

Cloud top entrainment instability (CTEI) is a hypothesized positive feedback between entrainment mixing and evaporative cooling near the cloud top. Previous theoretical and numerical modeling studies have shown that the persistence or breakup of marine boundary layer (MBL) clouds may be sensitive to the CTEI parameter. Collocated thermodynamic profile and cloud observations obtained from the Atmospheric Infrared Sounder (AIRS) and Moderate Resolution Imaging Spectroradiometer (MODIS) instruments are used to quantify the relationship between the CTEI parameter and the cloud-topped MBL transition from stratocumulus to trade cumulus in the northeastern Pacific Ocean. Results derived from AIRS and MODIS are compared withmore » numerical results from the UCLA large eddy simulation (LES) model for both well-mixed and decoupled MBLs. The satellite and model results both demonstrate a clear correlation between the CTEI parameter and MBL cloud fraction. Despite fundamental differences between LES steady state results and the instantaneous snapshot type of observations from satellites, significant correlations for both the instantaneous pixel-scale observations and the long-term averaged spatial patterns between the CTEI parameter and MBL cloud fraction are found from the satellite observations and are consistent with LES results. This suggests the potential of using AIRS and MODIS to quantify global and temporal characteristics of the cloud-topped MBL transition.« less

H31G-1596: DeepSAT's CloudCNN: A Deep Neural Network for Rapid Cloud Detection from Geostationary Satellites

NASA Technical Reports Server (NTRS)

Kalia, Subodh; Ganguly, Sangram; Li, Shuang; Nemani, Ramakrishna R.

2017-01-01

Cloud and cloud shadow detection has important applications in weather and climate studies. It is even more crucial when we introduce geostationary satellites into the field of terrestrial remote sensing. With the challenges associated with data acquired in very high frequency (10-15 mins per scan), the ability to derive an accurate cloud shadow mask from geostationary satellite data is critical. The key to the success for most of the existing algorithms depends on spatially and temporally varying thresholds,which better capture local atmospheric and surface effects.However, the selection of proper threshold is difficult and may lead to erroneous results. In this work, we propose a deep neural network based approach called CloudCNN to classify cloudshadow from Himawari-8 AHI and GOES-16 ABI multispectral data. DeepSAT's CloudCNN consists of an encoderdecoder based architecture for binary-class pixel wise segmentation. We train CloudCNN on multi-GPU Nvidia Devbox cluster, and deploy the prediction pipeline on NASA Earth Exchange (NEX) Pleiades supercomputer. We achieved an overall accuracy of 93.29% on test samples. Since, the predictions take only a few seconds to segment a full multispectral GOES-16 or Himawari-8 Full Disk image, the developed framework can be used for real-time cloud detection, cyclone detection, or extreme weather event predictions.

Characterization of Earth as an exoplanet on the basis of VIRTIS-Venus Express data analysis.

NASA Astrophysics Data System (ADS)

Oliva, Fabrizio; Piccioni, Giuseppe; D'Aversa, Emiliano; Bellucci, Giancarlo; Sindoni, Giuseppe; Grassi, Davide; Filacchione, Gianrico; Tosi, Federico; Capaccioni, Fabrizio

2017-04-01

The Visible and InfraRed Thermal Imaging Spectrometer (VIRTIS, Piccioni et al., 2007) on board the Venus Express spacecraft observed the planet Earth several times in the course of the mission. In particular, a subset of 48 observations has been taken from a distance at which our planet is imaged at sub-pixel size, as exoplanets are observed using current technologies. We studied this full subset to understand which spectral signatures, related to different surface and cloud types, can be identified from the integrated planet spectrum. As expected, we found that the cloud coverage has a key role in the identification of surface features and that vegetation is very difficult to be detected. To validate our results we built a simple tool capable to simulate observations of an Earth-like planet as seen from a VIRTIS-like spectrometer in the 0.3 - 5.0 μm range. The illumination and viewing geometries, along with the spectrometer instantaneous field of view and spectral grid and sampling, can be defined by the user. The spectral endmembers used to generate the planet have been selected from an observation of Earth registered from the instrument VIRTIS on board the ESA mission Rosetta, with similar characteristics, during the third flyby of the spacecraft around our planet, occurred in November 2009. Hence, we simulated planets made of: vegetation, desert, ocean, water ice clouds and liquid water clouds. Using different amounts for each spectral class we inferred the percentages that are required to identify each class when all the spectral information is integrated into a single pixel. The outcome of this analysis confirms that clouds are not a negligible issue in the research for spectral signatures, in particular those related to the habitability of a planet and its climate conditions, even when the cloud coverage is not so high. Acknowledgements: This study has been performed within the WOW project financed by INAF and thanks to the support from the Italian Space Agency to VIRTIS Venus Express and Rosetta. References Piccioni, G., et al., 2007. VIRTIS: The Visible and Infrared Thermal Imaging Spectrometer. ESA Special Publication, SP-1295, 1-27.

Aerosol and Cloud Interaction Observed From High Spectral Resolution Lidar Data

NASA Technical Reports Server (NTRS)

Su, Wenying; Schuster, Gregory L.; Loeb, Norman G.; Rogers, Raymond R.; Ferrare, Richard A.; Hostetler, Chris A.; Hair, Johnathan W.; Obland, Michael D.

2008-01-01

Recent studies utilizing satellite retrievals have shown a strong correlation between aerosol optical depth (AOD) and cloud cover. However, these retrievals from passive sensors are subject to many limitations, including cloud adjacency (or 3D) effects, possible cloud contamination, uncertainty in the AOD retrieval. Some of these limitations do not exist in High Spectral Resolution Lidar (HSRL) observations; for instance, HSRL observations are not a ected by cloud adjacency effects, are less prone to cloud contamination, and offer accurate aerosol property measurements (backscatter coefficient, extinction coefficient, lidar ratio, backscatter Angstrom exponent,and aerosol optical depth) at a neospatial resolution (less than 100 m) in the vicinity of clouds. Hence, the HSRL provides an important dataset for studying aerosol and cloud interaction. In this study, we statistically analyze aircraft-based HSRL profiles according to their distance from the nearest cloud, assuring that all profile comparisons are subject to the same large-scale meteorological conditions. Our results indicate that AODs from HSRL are about 17% higher in the proximity of clouds (approximately 100 m) than far away from clouds (4.5 km), which is much smaller than the reported cloud 3D effect on AOD retrievals. The backscatter and extinction coefficients also systematically increase in the vicinity of clouds, which can be explained by aerosol swelling in the high relative humidity (RH) environment and/or aerosol growth through in cloud processing (albeit not conclusively). On the other hand, we do not observe a systematic trend in lidar ratio; we hypothesize that this is caused by the opposite effects of aerosol swelling and aerosol in-cloud processing on the lidar ratio. Finally, the observed backscatter Angstrom exponent (BAE) does not show a consistent trend because of the complicated relationship between BAE and RH. We demonstrate that BAE should not be used as a surrogate for Angstrom exponent, especially at high RH.

The primordial deuterium abundance at zabs = 2.504 from a high signal-to-noise spectrum of Q1009+2956

NASA Astrophysics Data System (ADS)

Zavarygin, E. O.; Webb, J. K.; Dumont, V.; Riemer-Sørensen, S.

2018-04-01

The spectrum of the zem = 2.63 quasar Q1009+2956 has been observed extensively on the Keck telescope. The Lyman limit absorption system zabs = 2.504 was previously used to measure D/H by Burles & Tytler using a spectrum with signal to noise approximately 60 per pixel in the continuum near Ly α at zabs = 2.504. The larger dataset now available combines to form an exceptionally high signal to noise spectrum, around 147 per pixel. Several heavy element absorption lines are detected in this LLS, providing strong constraints on the kinematic structure. We explore a suite of absorption system models and find that the deuterium feature is likely to be contaminated by weak interloping Ly α absorption from a low column density H I cloud, reducing the expected D/H precision. We find D/H =2.48^{+0.41}_{-0.35} × 10^{-5} for this system. Combining this new measurement with others from the literature and applying the method of Least Trimmed Squares to a statistical sample of 15 D/H measurements results in a "reliable" sample of 13 values. This sample yields a primordial deuterium abundance of (D/H)p = (2.545 ± 0.025) × 10-5. The corresponding mean baryonic density of the Universe is Ωbh2 = 0.02174 ± 0.00025. The quasar absorption data is of the same precision as, and marginally inconsistent with, the 2015 CMB Planck (TT+lowP+lensing) measurement, Ωbh2 = 0.02226 ± 0.00023. Further quasar and more precise nuclear data are required to establish whether this is a random fluctuation.

Development of 2D deconvolution method to repair blurred MTSAT-1R visible imagery

NASA Astrophysics Data System (ADS)

Khlopenkov, Konstantin V.; Doelling, David R.; Okuyama, Arata

2014-09-01

Spatial cross-talk has been discovered in the visible channel data of the Multi-functional Transport Satellite (MTSAT)-1R. The slight image blurring is attributed to an imperfection in the mirror surface caused either by flawed polishing or a dust contaminant. An image processing methodology is described that employs a two-dimensional deconvolution routine to recover the original undistorted MTSAT-1R data counts. The methodology assumes that the dispersed portion of the signal is small and distributed randomly around the optical axis, which allows the image blurring to be described by a point spread function (PSF) based on the Gaussian profile. The PSF is described by 4 parameters, which are solved using a maximum likelihood estimator using coincident collocated MTSAT-2 images as truth. A subpixel image matching technique is used to align the MTSAT-2 pixels into the MTSAT-1R projection and to correct for navigation errors and cloud displacement due to the time and viewing geometry differences between the two satellite observations. An optimal set of the PSF parameters is derived by an iterative routine based on the 4-dimensional Powell's conjugate direction method that minimizes the difference between PSF-corrected MTSAT-1R and collocated MTSAT-2 images. This iterative approach is computationally intensive and was optimized analytically as well as by coding in assembly language incorporating parallel processing. The PSF parameters were found to be consistent over the 5-days of available daytime coincident MTSAT-1R and MTSAT-2 images, and can easily be applied to the MTSAT-1R imager pixel level counts to restore the original quality of the entire MTSAT-1R record.

Discrimination of Biomass Burning Smoke and Clouds in MAIAC Algorithm

NASA Technical Reports Server (NTRS)

Lyapustin, A.; Korkin, S.; Wang, Y.; Quayle, B.; Laszlo, I.

2012-01-01

The multi-angle implementation of atmospheric correction (MAIAC) algorithm makes aerosol retrievals from MODIS data at 1 km resolution providing information about the fine scale aerosol variability. This information is required in different applications such as urban air quality analysis, aerosol source identification etc. The quality of high resolution aerosol data is directly linked to the quality of cloud mask, in particular detection of small (sub-pixel) and low clouds. This work continues research in this direction, describing a technique to detect small clouds and introducing the smoke test to discriminate the biomass burning smoke from the clouds. The smoke test relies on a relative increase of aerosol absorption at MODIS wavelength 0.412 micrometers as compared to 0.47-0.67 micrometers due to multiple scattering and enhanced absorption by organic carbon released during combustion. This general principle has been successfully used in the OMI detection of absorbing aerosols based on UV measurements. This paper provides the algorithm detail and illustrates its performance on two examples of wildfires in US Pacific North-West and in Georgia/Florida of 2007.

Simultaneously inferring above-cloud absorbing aerosol optical thickness and underlying liquid phase cloud optical and microphysical properties using MODIS

NASA Astrophysics Data System (ADS)

Meyer, Kerry; Platnick, Steven; Zhang, Zhibo

2015-06-01

The regional haze over the southeast (SE) Atlantic Ocean induced by biomass burning in southern Africa can be problematic for passive imager-based retrievals of the underlying quasi-permanent marine boundary layer (MBL) clouds and for estimates of top-of-atmosphere (TOA) aerosol direct radiative effect (DRE). Here an algorithm is introduced to simultaneously retrieve above-cloud aerosol optical thickness (AOT), the cloud optical thickness (COT), and cloud effective particle radius (CER) of the underlying MBL clouds while also providing pixel-level estimates of retrieval uncertainty. This approach utilizes reflectance measurements at six Moderate Resolution Imaging Spectroradiometer (MODIS) channels from the visible to the shortwave infrared. Retrievals are run under two aerosol model assumptions on 8 years (2006-2013) of June-October Aqua MODIS data over the SE Atlantic, from which a regional cloud and above-cloud aerosol climatology is produced. The cloud retrieval methodology is shown to yield COT and CER consistent with those from the MODIS operational cloud product (MOD06) when forcing AOT to zero, while the full COT-CER-AOT retrievals that account for the above-cloud aerosol attenuation increase regional monthly mean COT and CER by up to 9% and 2%, respectively. Retrieved AOT is roughly 3 to 5 times larger than the collocated 532 nm Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) retrievals, though closer agreement is observed with the CALIOP 1064 nm retrievals, a result consistent with previous case study analyses. Regional cloudy-sky above-cloud aerosol DRE calculations are also performed that illustrate the importance of the aerosol model assumption and underlying cloud retrievals.

Contaminant transport in wetland flows with bulk degradation and bed absorption

NASA Astrophysics Data System (ADS)

Wang, Ping; Chen, G. Q.

2017-09-01

Ecological degradation and absorption are ubiquitous and exert considerable influence on the contaminant transport in natural and constructed wetland flows. It creates an increased demand on models to accurately characterize the spatial concentration distribution of the transport process. This work extends a method of spatial concentration moments by considering the non-uniform longitudinal solute displacements along the vertical direction, and analytically determines the spatial concentration distribution in the very initial stage since source release with effects of bulk degradation and bed absorption. The present method is demonstrated to bear a more accurate prediction especially in the initial stage through convergence analysis of Hermite polynomials. Results reveal that contaminant cloud shows to be more contracted and reformed by bed absorption with increasing damping factor of wetland flows. Tremendous vertical concentration variation especially in the downstream of the contaminant cloud remains great even at asymptotic large times. Spatial concentration evolution by the extended method other than the mean by previous studies is potential for various implements associated with contaminant transport with strict environmental standards.

Laboratory experiments of relevance to the space station environment

NASA Technical Reports Server (NTRS)

Caledonia, G. E.

1988-01-01

It has been found that the interaction between orbital vehicles and the ambient environment produces a contaminant cloud which can cause deletrious effects to spacecraft materials and equipment, create increased radiative backgrounds that would interfere with observational instrumentation, and enhance surface charging. A brief overview of the phenomena that produce the contaminant cloud is presented along with a review of physical data required to characterize it. Laboratory techniques which can be utilized to provide the required data are described. In particular, several oxygen beam apparati are discussed.

Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network-Cloud Classification System

NASA Astrophysics Data System (ADS)

Hong, Yang

Precipitation estimation from satellite information (VISIBLE , IR, or microwave) is becoming increasingly imperative because of its high spatial/temporal resolution and board coverage unparalleled by ground-based data. After decades' efforts of rainfall estimation using IR imagery as basis, it has been explored and concluded that the limitations/uncertainty of the existing techniques are: (1) pixel-based local-scale feature extraction; (2) IR temperature threshold to define rain/no-rain clouds; (3) indirect relationship between rain rate and cloud-top temperature; (4) lumped techniques to model high variability of cloud-precipitation processes; (5) coarse scales of rainfall products. As continuing studies, a new version of Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network (PERSIANN), called Cloud Classification System (CCS), has been developed to cope with these limitations in this dissertation. CCS includes three consecutive components: (1) a hybrid segmentation algorithm, namely Hierarchically Topographical Thresholding and Stepwise Seeded Region Growing (HTH-SSRG), to segment satellite IR images into separated cloud patches; (2) a 3D feature extraction procedure to retrieve both pixel-based local-scale and patch-based large-scale features of cloud patch at various heights; (3) an ANN model, Self-Organizing Nonlinear Output (SONO) network, to classify cloud patches into similarity-based clusters, using Self-Organizing Feature Map (SOFM), and then calibrate hundreds of multi-parameter nonlinear functions to identify the relationship between every cloud types and their underneath precipitation characteristics using Probability Matching Method and Multi-Start Downhill Simplex optimization techniques. The model was calibrated over the Southwest of United States (100°--130°W and 25°--45°N) first and then adaptively adjusted to the study region of North America Monsoon Experiment (65°--135°W and 10°--50°N) using observations from Geostationary Operational Environmental Satellite (GOES) IR imagery, Next Generation Radar (NEXRAD) rainfall network, and Tropical Rainfall Measurement Mission (TRMM) microwave rain rate estimates. CCS functions as a distributed model that first identifies cloud patches and then dispatches different but the best matching cloud-precipitation function for each cloud patch to estimate instantaneous rain rate at high spatial resolution (4km) and full temporal resolution of GOES IR images (every 30-minute). Evaluated over a range of spatial and temporal scales, the performance of CCS compared favorably with GOES Precipitation Index (GPI), Universal Adjusted GPI (UAGPI), PERSIANN, and Auto-Estimator (AE) algorithms, consistently. Particularly, the large number of nonlinear functions and optimum IR-rain rate thresholds of CCS model are highly variable, reflecting the complexity of dominant cloud-precipitation processes from cloud patch to cloud patch over various regions. As a result, CCS can more successfully capture variability in rain rate at small scales than existing algorithms and potentially provides rainfall product from GOES IR-NEXARD-TRMM TMI (SSM/I) at 0.12° x 0.12° and 3-hour resolution with relative low standard error (˜=3.0mm/hr) and high correlation coefficient (˜=0.65).

Possible influences of Asian dust aerosols on cloud properties and radiative forcing observed from MODIS and CERES

NASA Astrophysics Data System (ADS)

Huang, Jianping; Minnis, Patrick; Lin, Bing; Wang, Tianhe; Yi, Yuhong; Hu, Yongxiang; Sun-Mack, Sunny; Ayers, Kirk

2006-03-01

The effects of dust storms on cloud properties and Radiative Forcing (RF) are analyzed over Northwestern China from April 2001 to June 2004 using data collected by the MODerate Resolution Imaging Spectroradiometer (MODIS) and Clouds and the Earth's Radiant Energy System (CERES) instruments on the Aqua and Terra satellites. On average, ice cloud effective particle diameter, optical depth and ice water path of cirrus clouds under dust polluted conditions are 11%, 32.8%, and 42% less, respectively, than those derived from ice clouds in dust-free atmospheric environments. Due to changes in cloud microphysics, the instantaneous net RF is increased from -161.6 W/m2 for dust-free clouds to -118.6 W/m2 for dust-contaminated clouds.

Extension of surface data by use of meteorological satellites

NASA Technical Reports Server (NTRS)

Giddings, L. E.

1976-01-01

Ways of using meteorological satellite data to extend surface data are summarized. Temperature models are prepared from infrared data from ITOS/NOAA, NIMBUS, SMS/GOES, or future LANDSAT satellites. Using temperatures for surface meteorological stations as anchors, an adjustment is made to temperature values for each pixel in the model. The result is an image with an estimated temperature for each pixel. This provides an economical way of producing detailed temperature information for data-sparse areas, such as are found in underdeveloped countries. Related uses of these satellite data are also given, including the use of computer prepared cloud-free composites to extend climatic zones, and their use in discrimination of reflectivity-thermal regime zones.

The Advanced ACTPol 27/39 GHz Array

NASA Astrophysics Data System (ADS)

Simon, S. M.; Beall, J. A.; Cothard, N. F.; Duff, S. M.; Gallardo, P. A.; Ho, S. P.; Hubmayr, J.; Koopman, B. J.; McMahon, J. J.; Nati, F.; Niemack, M. D.; Staggs, S. T.; Vavagiakis, E. M.; Wollack, E. J.

2018-05-01

Advanced ACTPol (AdvACT) will observe the temperature and polarization of the cosmic microwave background (CMB) at multiple frequencies and high resolution to place improved constraints on inflation, dark matter, and dark energy. Foregrounds from synchrotron and dust radiation are a source of contamination that must be characterized and removed across a wide range of frequencies. AdvACT will thus observe at five frequency bands from 27 to 230 GHz. We discuss the design of the pixels and feedhorns for the 27/39 GHz multichroic array for AdvACT, which will target the synchrotron radiation that dominates at these frequencies. To gain 35% in mapping speed in the 39 GHz band where the foreground signals are faintest, the pixel number was increased through reducing the pixel diameter to 1.08λ at the lowest frequency, which represents a 22% decrease in size compared to our previously most tightly packed pixels.

MODIS Retrievals of Cloud Optical Thickness and Particle Radius

NASA Technical Reports Server (NTRS)

Platnick, S.; King, M. D.; Ackerman, S. A.; Gray, M.; Moody, E.; Arnold, G. T.; Einaudi, Franco (Technical Monitor)

2000-01-01

The Moderate Resolution Imaging Spectroradiometer (MODIS) provides an unprecedented opportunity for global cloud studies with 36 spectral bands from the visible through the infrared, and spatial resolution from 250 m to 1 km at nadir. In particular, all solar window bands useful for simultaneous retrievals of cloud optical thickness and particle size (0.67, 0.86, 1.2, 1.6, 2.1, and 3.7 micron bands) are now available on a single satellite instrument/platform for the first time. An operational algorithm for the retrieval of these optical and cloud physical properties (including water path) have been developed for both liquid and ice phase clouds. The product is archived into two categories: pixel-level retrievals at 1 km spatial resolution (referred to as a Level-2 product) and global gridded statistics (Level-3 product). An overview of the MODIS cloud retrieval algorithm and early level-2 and -3 results will be presented. A number of MODIS cloud validation activities are being planned, including the recent Southern Africa Regional Science Initiative 2000 (SAFARI-2000) dry season campaign conducted in August/September 2000. The later part of the experiment concentrated on MODIS validation in the Namibian stratocumulus regime off the southwest coast of Africa. Early retrieval results from this regime will be discussed.

Effects of 3-D clouds on atmospheric transmission of solar radiation: Cloud type dependencies inferred from A-train satellite data

NASA Astrophysics Data System (ADS)

Ham, Seung-Hee; Kato, Seiji; Barker, Howard W.; Rose, Fred G.; Sun-Mack, Sunny

2014-01-01

Three-dimensional (3-D) effects on broadband shortwave top of atmosphere (TOA) nadir radiance, atmospheric absorption, and surface irradiance are examined using 3-D cloud fields obtained from one hour's worth of A-train satellite observations and one-dimensional (1-D) independent column approximation (ICA) and full 3-D radiative transfer simulations. The 3-D minus ICA differences in TOA nadir radiance multiplied by π, atmospheric absorption, and surface downwelling irradiance, denoted as πΔI, ΔA, and ΔT, respectively, are analyzed by cloud type. At the 1 km pixel scale, πΔI, ΔA, and ΔT exhibit poor spatial correlation. Once averaged with a moving window, however, better linear relationships among πΔI, ΔA, and ΔT emerge, especially for moving windows larger than 5 km and large θ0. While cloud properties and solar geometry are shown to influence the relationships amongst πΔI, ΔA, and ΔT, once they are separated by cloud type, their linear relationships become much stronger. This suggests that ICA biases in surface irradiance and atmospheric absorption can be approximated based on ICA biases in nadir radiance as a function of cloud type.

On predicting contamination levels of HALOE optics aboard UARS using direct simulation Monte Carlo

NASA Technical Reports Server (NTRS)

Woronowicz, Michael S.; Rault, Didier F. G.

1993-01-01

A three-dimensional version of the direct simulation Monte Carlo method is adapted to assess the contamination environment surrounding a highly detailed model of the Upper Atmosphere Research Satellite. Emphasis is placed on simulating a realistic, worst-case set of flowfield and surface conditions and geometric orientations in order to estimate an upper limit for the cumulative level of volatile organic molecular deposits at the aperture of the Halogen Occultation Experiment. Problems resolving species outgassing and vent flux rates that varied over many orders of magnitude were handled using species weighting factors. Results relating to contaminant cloud structure, cloud composition, and statistics of simulated molecules impinging on the target surface are presented, along with data related to code performance. Using procedures developed in standard contamination analyses, the cumulative level of volatile organic deposits on HALOE's aperture over the instrument's 35-month nominal data collection period is estimated to be about 2700A.

Study of the effect of cloud inhomogeneity on the earth radiation budget experiment

NASA Technical Reports Server (NTRS)

Smith, Phillip J.

1988-01-01

The Earth Radiation Budget Experiment (ERBE) is the most recent and probably the most intensive mission designed to gather precise measurements of the Earth's radiation components. The data obtained from ERBE is of great importance for future climatological studies. A statistical study reveals that the ERBE scanner data are highly correlated and that instantaneous measurements corresponding to neighboring pixels contain almost the same information. Analyzing only a fraction of the data set when sampling is suggested and applications of this strategy are given in the calculation of the albedo of the Earth and of the cloud-forcing over ocean.

Finding Blackbody Temperature and Emissivity on a Sub-Pixel Scale

NASA Astrophysics Data System (ADS)

Bernstein, D. J.; Bausell, J.; Grigsby, S.; Kudela, R. M.

2015-12-01

Surface temperature and emissivity provide important insight into the ecosystem being remotely sensed. Dozier (1981) proposed a an algorithm to solve for percent coverage and temperatures of two different surface types (e.g. sea surface, cloud cover, etc.) within a given pixel, with a constant value for emissivity assumed. Here we build on Dozier (1981) by proposing an algorithm that solves for both temperature and emissivity of a water body within a satellite pixel by assuming known percent coverage of surface types within the pixel. Our algorithm generates thermal infrared (TIR) and emissivity end-member spectra for the two surface types. Our algorithm then superposes these end-member spectra on emissivity and TIR spectra emitted from four pixels with varying percent coverage of different surface types. The algorithm was tested preliminarily (48 iterations) using simulated pixels containing more than one surface type, with temperature and emissivity percent errors of ranging from 0 to 1.071% and 2.516 to 15.311% respectively[1]. We then tested the algorithm using a MASTER image from MASTER collected as part of the NASA Student Airborne Research Program (NASA SARP). Here the temperature of water was calculated to be within 0.22 K of in situ data. The algorithm calculated emissivity of water with an accuracy of 0.13 to 1.53% error for Salton Sea pixels collected with MASTER, also collected as part of NASA SARP. This method could improve retrievals for the HyspIRI sensor. [1] Percent error for emissivity was generated by averaging percent error across all selected bands widths.

Sloped terrain segmentation for autonomous drive using sparse 3D point cloud.

PubMed

Cho, Seoungjae; Kim, Jonghyun; Ikram, Warda; Cho, Kyungeun; Jeong, Young-Sik; Um, Kyhyun; Sim, Sungdae

2014-01-01

A ubiquitous environment for road travel that uses wireless networks requires the minimization of data exchange between vehicles. An algorithm that can segment the ground in real time is necessary to obtain location data between vehicles simultaneously executing autonomous drive. This paper proposes a framework for segmenting the ground in real time using a sparse three-dimensional (3D) point cloud acquired from undulating terrain. A sparse 3D point cloud can be acquired by scanning the geography using light detection and ranging (LiDAR) sensors. For efficient ground segmentation, 3D point clouds are quantized in units of volume pixels (voxels) and overlapping data is eliminated. We reduce nonoverlapping voxels to two dimensions by implementing a lowermost heightmap. The ground area is determined on the basis of the number of voxels in each voxel group. We execute ground segmentation in real time by proposing an approach to minimize the comparison between neighboring voxels. Furthermore, we experimentally verify that ground segmentation can be executed at about 19.31 ms per frame.

Neptune Clouds and Methane, from Ground-Based Visible and Near-Infrared Spectroscopy with Adaptive Optics

NASA Astrophysics Data System (ADS)

Tice, D. S.; Irwin, P. G. J.; Houghton, R. W. C.; Fletcher, L. N.; Clarke, F.; Hurley, J.; Thatte, N.; Tecza, M.

2013-09-01

Observations of Neptune were made in June/July 2012 with the SWIFT integral field spectrometer at the Palomar Observatory's 200-inch Hale Telescope. Spectral resolutions for observations between 0.65 μm and 1.0 μm were R ≥ 3250. Palomar's PALM-3000 adaptive optics system enabled images of the full Neptunian disc to be recorded at a spatial scale of 0.08"·pixel^-1 with a seeing of approximately 0.30" - 0.40". Retrievals of cloud properties and methane abundance in the highly dynamic atmosphere were obtained with the general-purpose retrieval tool, NEMESIS. The short wavelengths of the observations allowed for good characterisation of the scattering particles' optical properties in the many cloud and haze layers of the upper Neptunian atmosphere. A region of relatively low methane absorption and high collision-induced hydrogen quadrupole absorption at 825 nm further constrains spectral properties of clouds as distinguished from those of methane absorption.

Characterizing Subpixel Spatial Resolution of a Hybrid CMOS Detector

NASA Astrophysics Data System (ADS)

Bray, Evan; Burrows, Dave; Chattopadhyay, Tanmoy; Falcone, Abraham; Hull, Samuel; Kern, Matthew; McQuaide, Maria; Wages, Mitchell

2018-01-01

The detection of X-rays is a unique process relative to other wavelengths, and allows for some novel features that increase the scientific yield of a single observation. Unlike lower photon energies, X-rays liberate a large number of electrons from the silicon absorber array of the detector. This number is usually on the order of several hundred to a thousand for moderate-energy X-rays. These electrons tend to diffuse outward into what is referred to as the charge cloud. This cloud can then be picked up by several pixels, forming a specific pattern based on the exact incident location. By conducting the first ever “mesh experiment" on a hybrid CMOS detector (HCD), we have experimentally determined the charge cloud shape and used it to characterize responsivity of the detector with subpixel spatial resolution.

JunoCam's Images of Jupiter

NASA Astrophysics Data System (ADS)

Hansen, C. J.; Ravine, M. A.; Caplinger, M. A.; Orton, G. S.; Ingersoll, A. P.; Jensen, E.; Lipkaman, L.; Krysak, D.; Zimdar, R.; Bolton, S. J.

2016-12-01

JunoCam is a visible imager on the Juno spacecraft in orbit around Jupiter. It is a wide angle camera (58 deg field of view) with 4 color filters: red, green and blue (RGB) and methane at 889 nm, designed for optimal imaging of Jupiter's poles. Juno's elliptical polar orbit will offer unique views of Jupiter's polar regions with a spatial scale of 50 km/pixel. At closest approach the images will have a spatial scale of 3 km/pixel. As a push-frame imager on a rotating spacecraft, JunoCam uses time-delayed integration to take advantage of the spacecraft spin to extend integration time to increase signal. Images of Jupiter's poles reveal a largely uncharted region of Jupiter, as nearly all earlier spacecraft have orbited or flown by in the equatorial plane. Most of the images of Jupiter will be acquired in the +/-2 hours surrounding closest approach. The polar vortex, polar cloud morphology, and winds will be investigated. RGB color images of the aurora will be acquired if detectable. Stereo images and images taken with the methane filter will allow us to estimate cloud-top heights. Images of the cloud-tops will aid in understanding the data collected by other instruments on Juno that probe deeper in the atmosphere. During the two months that Jupiter is too close to the sun for ground-based observers to collect data, JunoCam will take images routinely to monitor large-scale features. Occasional, opportunistic images of the Galilean moons will be acquired.

Calibration Image of Earth by Mars Color Imager

NASA Technical Reports Server (NTRS)

2005-01-01

Three days after the Mars Reconnaissance Orbiter's Aug. 12, 2005, launch, the NASA spacecraft was pointed toward Earth and the Mars Color Imager camera was powered up to acquire a suite of color and ultraviolet images of Earth and the Moon. When it gets to Mars, the Mars Color Imager's main objective will be to obtain daily global color and ultraviolet images of the planet to observe martian meteorology by documenting the occurrence of dust storms, clouds, and ozone. This camera will also observe how the martian surface changes over time, including changes in frost patterns and surface brightness caused by dust storms and dust devils.

The purpose of acquiring an image of Earth and the Moon just three days after launch was to help the Mars Color Imager science team obtain a measure, in space, of the instrument's sensitivity, as well as to check that no contamination occurred on the camera during launch. Prior to launch, the team determined that, three days out from Earth, the planet would only be about 4.77 pixels across, and the Moon would be less than one pixel in size, as seen from the Mars Color Imager's wide-angle perspective. If the team waited any longer than three days to test the camera's performance in space, Earth would be too small to obtain meaningful results.

The images were acquired by turning Mars Reconnaissance Orbiter toward Earth, then slewing the spacecraft so that the Earth and Moon would pass before each of the five color and two ultraviolet filters of the Mars Color Imager. The distance to Earth was about 1,170,000 kilometers (about 727,000 miles).

This image shows a color composite view of Mars Color Imager's image of Earth. As expected, it covers only five pixels. This color view has been enlarged five times. The Sun was illuminating our planet from the left, thus only one quarter of Earth is seen from this perspective. North America was in daylight and facing toward the camera at the time the picture was taken; the data from the camera were being transmitted in real time to the Deep Space Network antennas in Goldstone, California.

An Assessment of Differences Between Cloud Effective Particle Radius Retrievals for Marine Water Clouds from Three MODIS Spectral Bands

NASA Technical Reports Server (NTRS)

Platnick, Steven; Zhang, Zhibo

2011-01-01

The Moderate Resolution Imaging Spectroradiometer (MODIS) cloud product provides three separate 1 km resolution retrievals of cloud particle effective radii (r (sub e)), derived from 1.6, 2.1 and 3.7 micron band observations. In this study, differences among the three size retrievals for maritime water clouds (designated as r (sub e), 1.6 r (sub e), 2.1 and r (sub e),3.7) were systematically investigated through a series of case studies and global analyses. Substantial differences are found between r (sub e),3.7 and r (sub e),2.1 retrievals (delta r (sub e),3.7-2.l), with a strong dependence on cloud regime. The differences are typically small, within +/- 2 micron, over relatively spatially homogeneous coastal stratocumulus cloud regions. However, for trade wind cumulus regimes, r (sub e),3.7 was found to be substantially smaller than r (sub e),2.1, sometimes by more than 10 micron. The correlation of delta r(sub e),3.7-2.1 with key cloud parameters, including the cloud optical thickness (tau), r (sub e) and a cloud horizontal heterogeneity index (H-sigma) derived from 250 m resolution MODIS 0.86 micron band observations, were investigated using one month of MODIS Terra data. It was found that differences among the three r (sub e) retrievals for optically thin clouds (tau <5) are highly variable, ranging from - 15 micron to 10 micron, likely due to the large MODIS retrieval uncertainties when the cloud is thin. The delta r (sub e),3.7-2.1 exhibited a threshold-like dependence on both r (sub e),2.l and H-sigma. The re,3.7 is found to agree reasonably well with re,2.! when re,2.l is smaller than about 15J-Lm, but becomes increasingly smaller than re,2.1 once re,2.! exceeds this size. All three re retrievals showed little dependence when H-sigma < 0.3 (defined as standard deviation divided by the mean for the 250 m pixels within a 1 km pixel retrieval). However, for H-=sigma >0.3, both r (sub e),1.6 and r (sub e),2.1 were seen to increase quickly with H-sigma. On the other hand, r (sub e),3.7 statistics showed little dependence on H-sigma and remained relatively stable over the whole range of H-sigma values. Potential contributing causes to the substantial r (sub e),3.7 and r (sub e),2.1 differences are discussed. In particular, based on both 1-D and 3-D radiative transfer simulations, we have elucidated mechanisms by which cloud heterogeneity and 3-D radiative effects can cause large differences between r (sub e),3.7 and r (sub e),2.l retrievals for highly inhomogeneous clouds. Our results suggest that the contrast in observed delta r (sub e)3.7-2.1 between cloud regimes is correlated with increases in both cloud r (sub e) and H-sigma. We also speculate that in some highly inhomogeneous drizzling clouds, vertical structure induced by drizzle and 3-D radiative effects might operate together to cause dramatic differences between r (sub e),3.7 and r (sub e),2.1 retrievals.

Zonal Aerosol Direct and Indirect Radiative Forcing using Combined CALIOP, CERES, CloudSat, and CERES Data

NASA Astrophysics Data System (ADS)

Miller, W. F.; Kato, S.; Rose, F. G.; Sun-Mack, S.

2009-12-01

Under the NASA Energy and Water Cycle System (NEWS) program, cloud and aerosol properties derived from CALIPSO, CloudSat, and MODIS data then matched to the CERES footprint are used for irradiance profile computations. Irradiance profiles are included in the publicly available product, CCCM. In addition to the MODIS and CALIPSO generated aerosol, aerosol optical thickness is calculated over ocean by processing MODIS radiance through the Stowe-Ignatov algorithm. The CERES cloud mask and properties algorithm are use with MODIS radiance to provide additional cloud information to accompany the actively sensed data. The passively sensed data is the only input to the standard CERES radiative flux products. The combined information is used as input to the NASA Langley Fu-Liou radiative transfer model to determine vertical profiles and Top of Atmosphere shortwave and longwave flux for pristine, all-sky, and aerosol conditions for the special data product. In this study, the three sources of aerosol optical thickness will be compared directly and their influence on the calculated and measured TOA fluxes. Earlier studies indicate that the largest uncertainty in estimating direct aerosol forcing using aerosol optical thickness derived from passive sensors is caused by cloud contamination. With collocated CALIPSO data, we are able to estimate frequency of occurrence of cloud contamination, effect on the aerosol optical thickness and direct radiative effect estimates.

Relating rainfall characteristics to cloud top temperatures at different scales

NASA Astrophysics Data System (ADS)

Klein, Cornelia; Belušić, Danijel; Taylor, Christopher

2017-04-01

Extreme rainfall from mesoscale convective systems (MCS) poses a threat to lives and livelihoods of the West African population through increasingly frequent devastating flooding and loss of crops. However, despite the significant impact of such extreme events, the dominant processes favouring their occurrence are still under debate. In the data-sparse West African region, rainfall radar data from the Tropical Rainfall Measuring Mission (TRMM) gives invaluable information on the distribution and frequency of extreme rainfall. The TRMM 2A25 product provides a 15-year dataset of snapshots of surface rainfall from 2-4 overpasses per day. Whilst this sampling captures the overall rainfall characteristics, it is neither long nor frequent enough to diagnose changes in MCS properties, which may be linked to the trend towards rainfall intensification in the region. On the other hand, Meteosat geostationary satellites provide long-term sub-hourly records of cloud top temperatures, raising the possibility of combining these with the high-quality rainfall data from TRMM. In this study, we relate TRMM 2A25 rainfall to Meteosat Second Generation (MSG) cloud top temperatures, which are available from 2004 at 15 minutes intervals, to get a more detailed picture of the structure of intense rainfall within the life cycle of MCS. We find TRMM rainfall intensities within an MCS to be strongly coupled with MSG cloud top temperatures: the probability for extreme rainfall increases from <10% for minimum temperatures warmer than -40°C to over 70% when temperatures drop below -70°C, confirming the potential in analysing cloud-top temperatures as a proxy for extreme rain. The sheer size of MCS raises the question which scales of sub-cloud structures are more likely to be associated with extreme rain than others. In the end, this information could help to associate scale changes in cloud top temperatures with processes that affect the probability of extreme rain. We use 2D continuous wavelets to decompose cloud top temperatures into power spectra at scales between 15 and 200km. From these, cloud sub-structures are identified as circular areas of respective scale with local power maxima in their centre. These areas are then mapped onto coinciding TRMM rainfall, allowing us to assign rainfall fields to sub-cloud features of different scales. We find a higher probability for extreme rainfall for cloud features above a scale of 30km, with features 100km contributing most to the number of extreme rainfall pixels. Over the average diurnal cycle, the number of smaller cloud features between 15-60km shows an increase between 15 - 1700UTC, gradually developing into larger ones. The maximum of extreme rainfall pixels around 1900UTC coincides with a peak for scales 100km, suggesting a dominant role of these scales for intense rain for the analysed cloud type. Our results demonstrate the suitability of 2D wavelet decomposition for the analysis of sub-cloud structures and their relation to rainfall characteristics, and help us to understand long-term changes in the properties of MCS.

Controlled generation of large volumes of atmospheric clouds in a ground-based environmental chamber

NASA Technical Reports Server (NTRS)

Hettel, H. J.; Depena, R. G.; Pena, J. A.

1975-01-01

Atmospheric clouds were generated in a 23,000 cubic meter environmental chamber as the first step in a two part study on the effects of contaminants on cloud formation. The generation procedure was modeled on the terrestrial generation mechanism so that naturally occurring microphysics mechanisms were operative in the cloud generation process. Temperature, altitude, liquid water content, and convective updraft velocity could be selected independently over the range of terrestrially realizable clouds. To provide cloud stability, a cotton muslin cylinder 29.3 meters in diameter and 24.2 meters high was erected within the chamber and continuously wetted with water at precisely the same temperature as the cloud. The improved instrumentation which permitted fast, precise, and continual measurements of cloud temperature and liquid water content is described.

Improved simulation of aerosol, cloud, and density measurements by shuttle lidar

NASA Technical Reports Server (NTRS)

Russell, P. B.; Morley, B. M.; Livingston, J. M.; Grams, G. W.; Patterson, E. W.

1981-01-01

Data retrievals are simulated for a Nd:YAG lidar suitable for early flight on the space shuttle. Maximum assumed vertical and horizontal resolutions are 0.1 and 100 km, respectively, in the boundary layer, increasing to 2 and 2000 km in the mesosphere. Aerosol and cloud retrievals are simulated using 1.06 and 0.53 microns wavelengths independently. Error sources include signal measurement, conventional density information, atmospheric transmission, and lidar calibration. By day, tenuous clouds and Saharan and boundary layer aerosols are retrieved at both wavelengths. By night, these constituents are retrieved, plus upper tropospheric, stratospheric, and mesospheric aerosols and noctilucent clouds. Density, temperature, and improved aerosol and cloud retrievals are simulated by combining signals at 0.35, 1.06, and 0.53 microns. Particlate contamination limits the technique to the cloud free upper troposphere and above. Error bars automatically show effect of this contamination, as well as errors in absolute density nonmalization, reference temperature or pressure, and the sources listed above. For nonvolcanic conditions, relative density profiles have rms errors of 0.54 to 2% in the upper troposphere and stratosphere. Temperature profiles have rms errors of 1.2 to 2.5 K and can define the tropopause to 0.5 km and higher wave structures to 1 or 2 km.

Spatial and Temporal Distribution of Clouds Observed by MODIS Onboard the Terra and Aqua Satellites

NASA Technical Reports Server (NTRS)

King, Michael D.; Platnick, Steven; Menzel, W. Paul; Ackerman, Steven A.; Hubanks, Paul A.

2012-01-01

The Moderate Resolution Imaging Spectroradiometer (MODIS) was developed by NASA and launched aboard the Terra spacecraft on December 18, 1999 and Aqua spacecraft on May 4, 2002. A comprehensive set of remote sensing algorithms for the retrieval of cloud physical and optical properties have enabled over twelve years of continuous observations of cloud properties from Terra and over nine years from Aqua. The archived products from these algorithms include 1 km pixel-level (Level-2) and global gridded Level-3 products. In addition to an extensive cloud mask, products include cloud-top properties (temperature, pressure, effective emissivity), cloud thermodynamic phase, cloud optical and microphysical parameters (optical thickness, effective particle radius, water path), as well as derived statistics. Results include the latitudinal distribution of cloud optical and radiative properties for both liquid water and ice clouds, as well as latitudinal distributions of cloud top pressure and cloud top temperature. MODIS finds the cloud fraction, as derived by the cloud mask, is nearly identical during the day and night, with only modest diurnal variation. Globally, the cloud fraction derived by the MODIS cloud mask is approx.67%, with somewhat more clouds over land during the afternoon and less clouds over ocean in the afternoon, with very little difference in global cloud cover between Terra and Aqua. Overall, cloud fraction over land is approx.55%, with a distinctive seasonal cycle, whereas the ocean cloudiness is much higher, around 72%, with much reduced seasonal variation. Cloud top pressure and temperature have distinct spatial and temporal patterns, and clearly reflect our understanding of the global cloud distribution. High clouds are especially prevalent over the northern hemisphere continents between 30 and 50 . Aqua and Terra have comparable zonal cloud top pressures, with Aqua having somewhat higher clouds (cloud top pressures lower by 100 hPa) over land due to afternoon deep convection. The coldest cloud tops (colder than 230 K) generally occur over Antarctica and the high clouds in the tropics (ITCZ and the deep convective clouds over the western tropical Pacific and Indian sub-continent).

Effect of CALIPSO Cloud Aerosol Discrimination (CAD) Confidence Levels on Observations of Aerosol Properties near Clouds

NASA Technical Reports Server (NTRS)

Yang, Weidong; Marshak, Alexander; Varnai, Tamas; Liu, Zhaoyan

2012-01-01

CALIPSO aerosol backscatter enhancement in the transition zone between clouds and clear sky areas is revisited with particular attention to effects of data selection based on the confidence level of cloud-aerosol discrimination (CAD). The results show that backscatter behavior in the transition zone strongly depends on the CAD confidence level. Higher confidence level data has a flatter backscatter far away from clouds and a much sharper increase near clouds (within 4 km), thus a smaller transition zone. For high confidence level data it is shown that the overall backscatter enhancement is more pronounced for small clear-air segments and horizontally larger clouds. The results suggest that data selection based on CAD reduces the possible effects of cloud contamination when studying aerosol properties in the vicinity of clouds.

The NASA/GEWEX Surface Radiation Budget Release 4 Integrated Product: An Assessment of Improvements in Algorithms and Inputs

NASA Astrophysics Data System (ADS)

Stackhouse, P. W., Jr.; Cox, S. J.; Mikovitz, J. C.; Zhang, T.; Gupta, S. K.

2016-12-01

The NASA/GEWEX Surface Radiation Budget (SRB) project produces, validates and analyzes shortwave and longwave surface and top of atmosphere radiative fluxes for the 1983-near present time period. The current release 3.0/3.1 consists of 1x1 degree radiative fluxes (available at gewex-srb.larc.nasa.gov) and is produced using the International Satellite Cloud Climatology Project (ISCCP) DX product for pixel level radiance and cloud information. This ISCCP DX product is subsampled to 30 km. ISCCP is currently recalibrating and reprocessing their entire data series, to be released as the H product series, with its highest resolution at 10km pixel resolution. The nine-fold increase in number of pixels will allow SRB to produce a higher resolution gridded product (e.g. 0.5 degree or higher), as well as the production of pixel-level fluxes. Other key input improvements include a detailed aerosol history using the Max Planck Institute Aerosol Climatology (MAC), temperature and moisture profiles from HIRS, and new topography, surface type, and snow/ice maps. Here we present results for the improved GEWEX Shortwave and Longwave algorithm (GSW and GLW) with new ISCCP data (for at least 5 years, 2005-2009), various other improved input data sets and incorporation of many additional internal SRB model improvements. We assess the radiative fluxes from new SRB products and contrast these at various resolutions. All these fluxes are compared to both surface measurements and to CERES SYN1Deg and EBAF data products for assessment of the effect of improvements. The SRB data produced will be released as part of the Release 4.0 Integrated Product that shares key input and output quantities with other GEWEX global products providing estimates of the Earth's global water and energy cycle (i.e., ISCCP, SeaFlux, LandFlux, NVAP, etc.).

NIMS Observes the Structure and Composition of Jupiter Clouds

NASA Image and Video Library

1998-03-26

With the NIMS instrument high quality observations are being obtained from all parts of Jupiter. The images in the upper panel are taken at a wavelength of 4.8 microns. At this wavelength thermal radiation from about 100 km deep below the visible cloud deck is escaping, allowing us to study the deep atmospheric region. The overlying cloud deck absorbs a part of the radiation, but there are places where it is thin and more radiation can escape. These are called hot spot regions. Many hotspots regions occur in a zone between the equator and 15 degrees north latitude, the North Equatorial Belt (NEB), but thermal radiation is seen from much of the planet. The uniqueness of NIMS is that it is capable of observing the same spatial region at a maximum of 408 different wavelengths between 0.7 and 5.2 micron simultaneously. Every picture element (pixel) contains a spectrum of up to 408 wavelengths. The gases that compose the atmosphere leave there traces in the spectra. In this particular case, 48 wavelengths were available between 4.6 and 5.2 micron, and we see spectral signatures of water, ammonia, and phosphine. Also, the total amount of radiation is determined by the amount of overlying cloud, characterized by the cloud opacity. By means of model calculations, we can determine the amount of water and the cloud opacity for each individual spectrum. The amount ammonia and phosphine is more difficult to obtain because its influence on the spectra is weaker. The results of these calculations are shown in the form of maps in the next two panels. With NIMS, we can now have a detailed look at the spatial distribution of the water and ammonia amounts and the cloud opacity in the atmosphere. Not all the pixels from the observations have good spectra, so for some data points no reliable determination of the water and cloud opacity could be made. We find that the atmosphere is extremely dry in, and close to, the hot spot, with relative humidities between 0.02 % and 10 %, with the dryest places being inside the hot spot. This corroborates the in-situ Galileo Entry Probe measurements. The Probe entered the atmosphere, on December 5 1995, in a hot spot region. Whereas the Probe obtained only a very localized snapshot, with NIMS we can do observations of larger areas and over longer periods. The spatial distribution of water is more complex than expected. More detailed investigations will be necessary to fully understand these results. http://photojournal.jpl.nasa.gov/catalog/PIA01224

Estimation of photosynthetically available radiation (PAR) from OCEANSAT-I OCM using a simple atmospheric radiative transfer model

NASA Astrophysics Data System (ADS)

Tripathy, Madhumita; Raman, Mini; Chauhan, Prakash

2015-10-01

Photosynthetically available radiation (PAR) is an important variable for radiation budget, marine and terrestrial ecosystem models. OCEANSAT-1 Ocean Color Monitor (OCM) PAR was estimated using two different methods under both clear and cloudy sky conditions. In the first approach, aerosol optical depth (AOD) and cloud optical depth (COD) were estimated from OCEANSAT-1 OCM TOA (top-of-atmosphere) radiance data on a pixel by pixel basis and PAR was estimated from extraterrestrial solar flux for fifteen spectral bands using a radiative transfer model. The second approach used TOA radiances measured by OCM in the PAR spectral range to compute PAR. This approach also included surface albedo and cloud albedo as inputs. Comparison between OCEANSAT-1 OCM PAR at noon with in situ measured PAR shows that root mean square difference was 5.82% for the method I and 7.24% for the method II in daily time scales. Results indicate that methodology adopted to estimate PAR from OCEANSAT-1 OCM can produce reasonably accurate PAR estimates over the tropical Indian Ocean region. This approach can be extended to OCEANSAT-2 OCM and future OCEANSAT-3 OCM data for operational estimation of PAR for regional marine ecosystem applications.

Near-Cloud Aerosol Properties from the 1 Km Resolution MODIS Ocean Product

NASA Technical Reports Server (NTRS)

Varnai, Tamas; Marshak, Alexander

2014-01-01

This study examines aerosol properties in the vicinity of clouds by analyzing high-resolution atmospheric correction parameters provided in the MODIS (Moderate Resolution Imaging Spectroradiometer) ocean color product. The study analyzes data from a 2 week long period of September in 10 years, covering a large area in the northeast Atlantic Ocean. The results indicate that on the one hand, the Quality Assessment (QA) flags of the ocean color product successfully eliminate cloud-related uncertainties in ocean parameters such as chlorophyll content, but on the other hand, using the flags introduces a sampling bias in atmospheric products such as aerosol optical thickness (AOT) and Angstrom exponent. Therefore, researchers need to select QA flags by balancing the risks of increased retrieval uncertainties and sampling biases. Using an optimal set of QA flags, the results reveal substantial increases in optical thickness near clouds-on average the increase is 50% for the roughly half of pixels within 5 km from clouds and is accompanied by a roughly matching increase in particle size. Theoretical simulations show that the 50% increase in 550nm AOT changes instantaneous direct aerosol radiative forcing by up to 8W/m2 and that the radiative impact is significantly larger if observed near-cloud changes are attributed to aerosol particles as opposed to undetected cloud particles. These results underline that accounting for near-cloud areas and understanding the causes of near-cloud particle changes are critical for accurate calculations of direct aerosol radiative forcing.

Improving Scene Classifications with Combined Active/Passive Measurements

NASA Astrophysics Data System (ADS)

Hu, Y.; Rodier, S.; Vaughan, M.; McGill, M.

The uncertainties in cloud and aerosol physical properties derived from passive instruments such as MODIS are not insignificant And the uncertainty increases when the optical depths decrease Lidar observations do much better for the thin clouds and aerosols Unfortunately space-based lidar measurements such as the one onboard CALIPSO satellites are limited to nadir view only and thus have limited spatial coverage To produce climatologically meaningful thin cloud and aerosol data products it is necessary to combine the spatial coverage of MODIS with the highly sensitive CALIPSO lidar measurements Can we improving the quality of cloud and aerosol remote sensing data products by extending the knowledge about thin clouds and aerosols learned from CALIPSO-type of lidar measurements to a larger portion of the off-nadir MODIS-like multi-spectral pixels To answer the question we studied the collocated Cloud Physics Lidar CPL with Modis-Airborne-Simulation MAS observations and established an effective data fusion technique that will be applied in the combined CALIPSO MODIS cloud aerosol product algorithms This technique performs k-mean and Kohonen self-organized map cluster analysis on the entire swath of MAS data as well as on the combined CPL MAS data at the nadir track Interestingly the clusters generated from the two approaches are almost identical It indicates that the MAS multi-spectral data may have already captured most of the cloud and aerosol scene types such as cloud ice water phase multi-layer information aerosols

Cloud Overlapping Detection Algorithm Using Solar and IR Wavelengths With GOSE Data Over ARM/SGP Site

NASA Technical Reports Server (NTRS)

Kawamoto, Kazuaki; Minnis, Patrick; Smith, William L., Jr.

2001-01-01

One of the most perplexing problems in satellite cloud remote sensing is the overlapping of cloud layers. Although most techniques assume a 1-layer cloud system in a given retrieval of cloud properties, many observations are affected by radiation from more than one cloud layer. As such, cloud overlap can cause errors in the retrieval of many properties including cloud height, optical depth, phase, and particle size. A variety of methods have been developed to identify overlapped clouds in a given satellite imager pixel. Baum el al. (1995) used CO2 slicing and a spatial coherence method to demonstrate a possible analysis method for nighttime detection of multilayered clouds. Jin and Rossow (1997) also used a multispectral CO2 slicing technique for a global analysis of overlapped cloud amount. Lin et al. (1999) used a combination infrared, visible, and microwave data to detect overlapped clouds over water. Recently, Baum and Spinhirne (2000) proposed 1.6 and 11 microns. bispectral threshold method. While all of these methods have made progress in solving this stubborn problem, none have yet proven satisfactory for continuous and consistent monitoring of multilayer cloud systems. It is clear that detection of overlapping clouds from passive instruments such as satellite radiometers is in an immature stage of development and requires additional research. Overlapped cloud systems also affect the retrievals of cloud properties over the ARM domains (e.g., Minnis et al 1998) and hence should identified as accurately as possible. To reach this goal, it is necessary to determine which information can be exploited for detecting multilayered clouds from operational meteorological satellite data used by ARM. This paper examines the potential information available in spectral data available on the Geostationary Operational Environmental Satellite (GOES) imager and the NOAA Advanced Very High Resolution Radiometer (AVHRR) used over the ARM SGP and NSA sites to study the capability of detecting overlapping clouds

Cloud Overlapping Detection Algorithm Using Solar and IR Wavelengths with GOES Data Over ARM/SGP Site

NASA Technical Reports Server (NTRS)

Kawamoto, K.; Minnis, P.; Smith, W. L., Jr.

2001-01-01

One of the most perplexing problems in satellite cloud remote sensing is the overlapping of cloud layers. Although most techniques assume a one layer cloud system in a given retrieval of cloud properties, many observations are affected by radiation from more than one cloud layer. As such, cloud overlap can cause errors in the retrieval of many properties including cloud height, optical depth, phase, and particle size. A variety of methods have been developed to identify overlapped clouds in a given satellite imager pixel. Baum et al used CO2 slicing and a spatial coherence method to demonstrate a possible analysis method for nighttime detection of multilayered clouds. Jin and Rossow also used a multispectral CO2 slicing technique for a global analysis of overlapped cloud amount. Lin et al. used a combination infrared (IR), visible (VIS), and microwave data to detect overlapped clouds over water. Recently, Baum and Spinhirne proposed a 1.6 and 11 micron bispectral threshold method. While all of these methods have made progress in solving this stubborn problem none have yet proven satisfactory for continuous and consistent monitoring of multilayer cloud systems. It is clear that detection of overlapping clouds from passive instruments such as satellite radiometers is in an immature stage of development and requires additional research. Overlapped cloud systems also affect the retrievals of cloud properties over the Atmospheric Radiation Measurement (ARM) domains and hence should be identified as accurately as possible. To reach this goal, it is necessary to determine which information can be exploited for detecting multilayered clouds from operational meteorological satellite data used by ARM. This paper examines the potential information available in spectral data available on the Geostationary Operational Environmental Satellite (GOES) imager and the National Oceanic Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) used over the ARM Program's Southern Great Plains (SGP), and North Slope of Alaska (NSA) sites to study the capability of detecting overlapping clouds.

Overview of 3D-TRACE, a NASA Initiative in Three-Dimensional Tomography of the Aerosol-Cloud Environment

NASA Astrophysics Data System (ADS)

Davis, Anthony; Diner, David; Yanovsky, Igor; Garay, Michael; Xu, Feng; Bal, Guillaume; Schechner, Yoav; Aides, Amit; Qu, Zheng; Emde, Claudia

2013-04-01

Remote sensing is a key tool for sorting cloud ensembles by dynamical state, aerosol environments by source region, and establishing causal relationships between aerosol amounts, type, and cloud microphysics-the so-called indirect aerosol climate impacts, and one of the main sources of uncertainty in current climate models. Current satellite imagers use data processing approaches that invariably start with cloud detection/masking to isolate aerosol air-masses from clouds, and then rely on one-dimensional (1D) radiative transfer (RT) to interpret the aerosol and cloud measurements in isolation. Not only does this lead to well-documented biases for the estimates of aerosol radiative forcing and cloud optical depths in current missions, but it is fundamentally inadequate for future missions such as EarthCARE where capturing the complex, three-dimensional (3D) interactions between clouds and aerosols is a primary objective. In order to advance the state of the art, the next generation of satellite information processing systems must incorporate technologies that will enable the treatment of the atmosphere as a fully 3D environment, represented more realistically as a continuum. At one end, there is an optically thin background dominated by aerosols and molecular scattering that is strongly stratified and relatively homogeneous in the horizontal. At the other end, there are optically thick embedded elements, clouds and aerosol plumes, which can be more or less uniform and quasi-planar or else highly 3D with boundaries in all directions; in both cases, strong internal variability may be present. To make this paradigm shift possible, we propose to combine the standard models for satellite signal prediction physically grounded in 1D and 3D RT, both scalar and vector, with technologies adapted from biomedical imaging, digital image processing, and computer vision. This will enable us to demonstrate how the 3D distribution of atmospheric constituents, and their associated microphysical properties, can be reconstructed from multi-angle/multi-spectral imaging radiometry and, more and more, polarimetry. Specific technologies of interest are computed tomography (reconstruction from projections), optical tomography (using cross-pixel radiation transport in the diffusion limit), stereoscopy (depth/height retrievals), blind source and scale separation (signal unmixing), and disocclusion (information recovery in the presence of obstructions). Later on, these potentially powerful inverse problem solutions will be fully integrated in a versatile satellite data analysis toolbox. At present, we can report substantial progress at the component level. Specifically, we will focus on the most elementary problems in atmospheric tomography with an emphasis on the vastly under-exploited class of multi-pixel techniques. One basic problem is to infer the outer shape and mean opacity of 3D clouds, along with a bulk measure of cloud particle size. Another is to separate high and low cloud layers based on their characteristically different spatial textures. Yet another is to reconstruct the 3D spatial distribution of aerosol density based on passive imaging. This suite of independent feasibility studies amounts to a compelling proofof- concept for the ambitious 3D-Tomographic Reconstruction of the Aerosol-Cloud Environment (3D-TRACE) project as a whole.

Regional distribution of the high-altitude clouds over the Indian subcontinent and surrounding oceanic regions based on seven years of satellite observations

NASA Astrophysics Data System (ADS)

Meenu, S.; Rajeev, K.; Parameswaran, K.; Suresh Raju, C.

2006-12-01

Quantitative estimates of the spatio-temporal variations in deep convective events over the Indian subcontinent, Arabian Sea, Bay of Bengal, and tropical Indian Ocean are carried out using the data obtained from Advanced Very High Resolution Radiometer (AVHRR) onboard NOAA-14 and NOAA-16 during the period 1996-2003. Pixels having thermal IR brightness temperature (BT) less than 245K are considered as high altitude clouds and those having BT<220 K are considered as very high altitude clouds. Very deep convective clouds are observed over north Bay of Bengal during the Asian summer monsoon season when the mean cloud top temperature reaches as low as 190K. Over the Head Bay of Bengal (HBoB) from June to September, more than 50% of the observed clouds are deep convective type and more than half of these deep convective clouds are very deep convective clouds. Histogram analysis of the cloud top temperatures during this period shows that over HBoB the most prominent cloud top temperature of the deep convective clouds is ~205K over the HBoB while that over southeast Arabian Sea (SEAS) is ~220K. This indicates that most probably the cloud top altitude over HBoB is ~2 km larger than that over SEAS during the Asian summer monsoon period. Another remarkable feature observed during the Asian summer monsoon period is the significantly low values of deep convective clouds observed over the south Bay of Bengal close to Srilanka, which appears as a large pool of reduced cloud amount surrounded by regions of large-scale deep convection. Over both SEAS and HBoB, the total, deep convective and very deep convective cloud amounts as well as their corresponding cloud top temperatures (or the altitude of the cloud top) undergo large seasonal variations, while such variations are less prominent over the eastern equatorial Indian Ocean.

Planetary astronomy

NASA Technical Reports Server (NTRS)

1976-01-01

Color and spectral data from spectrometer observations and computerized analyses of asteroid spectra are discussed. Potential occultations of bright asteroids by the moon are summarized. Analysis of anisotropic scattering within Saturn's rings indicates that mineral contamination of the 120 particles cannot exceed 5 percent by weight, and that the rings formed from particle breakup rather than from particle condensation. Raman probe applications to Jupiter and Uranus atmospheres indicate the presence of aerosol particles. A review of Mariner 9 Mars cloud topography data establishes that most blue clouds are orographic uplift clouds composed of condensates, and that sporadic red clouds are associated with blue clouds or volcanoes and thus probably do not represent dust storm phenomena.

Drivers in the Scaling Between Precipitation and Cloud Radiative Impacts in Deep Convection

NASA Astrophysics Data System (ADS)

Rapp, A. D.; Sun, L.; Smalley, K.

2017-12-01

The coupling between changes in radiation and precipitation has been demonstrated by a number of studies and suggests an important link between cloud and precipitation processes for defining climate sensitivity. Precipitation and radiative fluxes from CloudSat/CALIPSO retrieval products are used to examine the relationship between precipitation and cloud radiative impacts through two dimensionless parameters. The surface radiative cooling impact, Rc, represents the ratio of the surface shortwave cloud radiative effect to latent heating (LH) from precipitation. The atmospheric radiative heating impact, Rh, represents the ratio of the atmospheric cloud radiative effect to LH from precipitation. Together, these parameters describe the relationship between precipitation processes and how efficiently clouds cools the surface or heats the atmosphere. Deep convective clouds are identified using the 2B-GEOPROF-LIDAR joint radar-lidar product and the cloud radiative impact parameters are calculated from the 2B-FLXHR-LIDAR fluxes and 2C-RAIN-PROFILE precipitation. Deep convective clouds will be sampled according to their dynamic and thermodynamic regimes to provide insights into the factors that control the scaling between precipitation and radiative impacts. Preliminary results from analysis of precipitating deep convective pixels indicates a strong increase (decrease) in the ratio of atmospheric heating (surface cooling) and precipitation with thermodynamic environment, especially increasing water vapor; however, it remains to be seen whether these results hold when integrated over an entire deep convective cloud system. Analysis of the dependence of Rc and Rh on the cloud horizontal and vertical structure is also planned, which should lead to a better understanding of the role of non-precipitating anvil characteristics in modulating the relationship between precipitation and surface and atmospheric radiative effects.

The EOS CERES Global Cloud Mask

NASA Technical Reports Server (NTRS)

Berendes, T. A.; Welch, R. M.; Trepte, Q.; Schaaf, C.; Baum, B. A.

1996-01-01

To detect long-term climate trends, it is essential to produce long-term and consistent data sets from a variety of different satellite platforms. With current global cloud climatology data sets, such as the International Satellite Cloud Climatology Experiment (ISCCP) or CLAVR (Clouds from Advanced Very High Resolution Radiometer), one of the first processing steps is to determine whether an imager pixel is obstructed between the satellite and the surface, i.e., determine a cloud 'mask.' A cloud mask is essential to studies monitoring changes over ocean, land, or snow-covered surfaces. As part of the Earth Observing System (EOS) program, a series of platforms will be flown beginning in 1997 with the Tropical Rainfall Measurement Mission (TRMM) and subsequently the EOS-AM and EOS-PM platforms in following years. The cloud imager on TRMM is the Visible/Infrared Sensor (VIRS), while the Moderate Resolution Imaging Spectroradiometer (MODIS) is the imager on the EOS platforms. To be useful for long term studies, a cloud masking algorithm should produce consistent results between existing (AVHRR) data, and future VIRS and MODIS data. The present work outlines both existing and proposed approaches to detecting cloud using multispectral narrowband radiance data. Clouds generally are characterized by higher albedos and lower temperatures than the underlying surface. However, there are numerous conditions when this characterization is inappropriate, most notably over snow and ice of the cloud types, cirrus, stratocumulus and cumulus are the most difficult to detect. Other problems arise when analyzing data from sun-glint areas over oceans or lakes over deserts or over regions containing numerous fires and smoke. The cloud mask effort builds upon operational experience of several groups that will now be discussed.

Influence of Subpixel Scale Cloud Top Structure on Reflectances from Overcast Stratiform Cloud Layers

NASA Technical Reports Server (NTRS)

Loeb, N. G.; Varnai, Tamas; Winker, David M.

1998-01-01

Recent observational studies have shown that satellite retrievals of cloud optical depth based on plane-parallel model theory suffer from systematic biases that depend on viewing geometry, even when observations are restricted to overcast marine stratus layers, arguably the closest to plane parallel in nature. At moderate to low sun elevations, the plane-parallel model significantly overestimates the reflectance dependence on view angle in the forward-scattering direction but shows a similar dependence in the backscattering direction. Theoretical simulations are performed that show that the likely cause for this discrepancy is because the plane-parallel model assumption does not account for subpixel, scale variations in cloud-top height (i.e., "cloud bumps"). Monte Carlo simulation, comparing ID model radiances to radiances from overcast cloud field with 1) cloud-top height variation, but constant cloud volume extinction; 2) flat tops but horizontal variations in cloud volume extinction; and 3) variations in both cloud top height and cloud extinction are performed over a approximately equal to 4 km x 4 km domain (roughly the size of an individual GAC AVHRR pixel). The comparisons show that when cloud-top height variations are included, departures from 1D theory are remarkably similar (qualitatively) to those obtained observationally. In contrast, when clouds are assumed flat and only cloud extinction is variable, reflectance differences are much smaller and do not show any view-angle dependence. When both cloud-top height and cloud extinction variations are included, however, large increases in cloud extinction variability can enhance reflectance difference. The reason 3D-1D reflectance differences are more sensitive to cloud-top height variations in the forward-scattering direction (at moderate to low, sun elevations) is because photons leaving the cloud field in that direction experience fewer scattering events (low-order scattering) and are restricted to the topmost portions of the cloud. While reflectance deviations from 1D theory are much larger for bumpy clouds than for flat clouds with variable cloud extinction, differences in cloud albedo are comparable for these two cases.

An Investigation of the Characterization of Cloud Contamination in Hyperspectral Radiances

NASA Technical Reports Server (NTRS)

McCarty, William; Jedlovec, Gary J.; LeMarshall, John

2007-01-01

In regions lacking direct observations, the assimilation of radiances from infrared and microwave sounders is the primary method for characterizing the atmosphere in the analysis process. In recent years, technological advances have led to the launching of more advanced sounders, particularly in the thermal infrared spectrum. With the advent of these hyperspectral sounders, the amount of data available for the analysis process has and will continue to be dramatically increased. However, the utilization of infrared radiances in variational assimilation can be problematic in the presence of clouds; specifically the assessment of the presence of clouds in an instantaneous field of view (IFOV) and the contamination in the individual channels within the IFOV. Various techniques have been developed to determine if a channel is contaminated by clouds. The work presented in this paper and subsequent presentation will investigate traditional techniques and compare them to a new technique, the C02 sorting technique, which utilizes the high spectral resolution of the Atmospheric Infrared Sounder (AIRS) within the framework of the Gridpoint Statistical Interpolation (GSI) 3DVAR system. Ultimately, this work is done in preparation for the assessment of short-term forecast impacts with the regional assimilation of AIRS radiances within the analysis fields of the Weather Research and Forecast Nonhydrostatic Mesoscale Model (WRF-NMM) at the NASA Short-term Prediction Research and Transition (SPORT) Center.

Argus+: The Future of Wide-Field, Spectral-Line Imaging at 3-mm with the Green Bank Telescope

NASA Astrophysics Data System (ADS)

Maddalena, Ronald; Frayer, David; Lockman, Felix; O'Neil, Karen; White, Steven; Argus+ Collaboration

2018-01-01

The Robert C Byrd Green Bank Telescope has met its design goal of providing high-quality observations at 115 GHz. Observers also have access to the new, 16-pixel, 3-mm Argus receiver, which is providing high-dynamic range images over wide fields for the multitude of spectral lines between 85 and 115 GHz, including CO, 13CO, C18O, SiO, HCN, HCO+, HNC, N2H+, and CS. The small number of pixels in Argus limits its ability to map many of the most interesting objects whose extent exceeds many arc-minutes. The successful performance of Argus, and its modular design, demonstrates that receivers with many more pixels could be built for the GBT. A 12 x 12 array of the Argus design would have mapping speeds about nine times faster than Argus without suffering any degradation in performance for the outer pixels in the array. We present our plans to build the next-generation Argus instrument (Argus+) with 144-pixels, a footprint 5’x5’, and 7" resolution at 110 GHz. The project will be a collaboration between the Green Bank Observatory and university groups, who will supply key components. The key science drivers for Argus+ are studies of molecular filaments in the Milky Way, studies of molecular clouds in nearby galaxies, and the observations of rapidly evolving solar system objects.

The Teton-Yellowstone Tornado of 21 July 1987

NASA Technical Reports Server (NTRS)

Fujita, T. Theodore

1989-01-01

The Teton-Yellowstone Tornado, rated F4, crossed the Continental Divide at 3070 m, leaving behind a damage swath 39.2-km long and 2.5-km wide. A detailed damage analysis by using stereo-pair and color photos revealed the existence of four spinup swirl marks and 72 microburst outflows inside the damage area. The tornado was spawned by a mesocyclone that formed at the intersection of a mesohigh boundary and a warm front. The parent cloud of the tornado, tracked on eight infrared-temperature maps from GOES East and West, moved at 25 m s-1 and the number of cold temperature pixels below -60 C reached a distinct peak during the tornado time. Identified and tracked also are two warm spots enclosed inside the cold anvil cloud. On the basis of their identity and movement, an attempt was made to explain the cause of these spots as being the stratospheric cirrus clouds.

Region-Based Prediction for Image Compression in the Cloud.

PubMed

Begaint, Jean; Thoreau, Dominique; Guillotel, Philippe; Guillemot, Christine

2018-04-01

Thanks to the increasing number of images stored in the cloud, external image similarities can be leveraged to efficiently compress images by exploiting inter-images correlations. In this paper, we propose a novel image prediction scheme for cloud storage. Unlike current state-of-the-art methods, we use a semi-local approach to exploit inter-image correlation. The reference image is first segmented into multiple planar regions determined from matched local features and super-pixels. The geometric and photometric disparities between the matched regions of the reference image and the current image are then compensated. Finally, multiple references are generated from the estimated compensation models and organized in a pseudo-sequence to differentially encode the input image using classical video coding tools. Experimental results demonstrate that the proposed approach yields significant rate-distortion performance improvements compared with the current image inter-coding solutions such as high efficiency video coding.

A conceptual method for monitoring locust habitat

USGS Publications Warehouse

Howard, Stephen M.; Loveland, Thomas R.; Ohlen, Donald O.; Moore, Donald G.; Gallo, Kevin P.; Olsson, Jonathon

1987-01-01

A procedure to map and monitor vegetation conditions in near-real time was developed at the United States Geological Survey;s Earth Resources Observation Systems Data Center for use in locust control efforts. Meteorological satellite dat were acquired daily for 3 weeks in October and November 1986 over a 1.4-million-square-kilometer study area centered on Botswana in southern Africa. Advanced Very High Resolution Radiometer data were screened to remove cloud-contaminated data and registered to a 1-kilometer geographic base. Each day the normalized difference vegetation index (NDVI) was calculated to determine the presence and relative amounts of green vegetation in the area. Over a 10-day cycle, subsequent dates of NDVI data were composited to fill in data removed by the cloud-screening process. At any pixel location, the maximum NDVI value was retained. At the end of the 10-day cycle, a composite vegetation-greenness map was produced and another cycle started. Greenness-change maps were produced by comparing two 10-day composite greenness images. Automated map production procedures were used to merge the NDVI image data with cartographic data (boundaries, roads, tick marks) digitized from 1:1,000,000-scale operational navigation charts. The vegetation-greenness map shoes the current distribution of vegetation in the region and can be used to locate potential locust breeding area. The change map shows areas where increases and decreases in greenness have occurred between processing cycles. Significant areas of locust damage in remote regions are characterized by an unexpected decrease in greenness. These maps can be used by locust control teams to efficiently target areas for reconnaissance. In general, the procedures and products have utility for resource managers who are required to monitor vegetation resources over large geographic regions.

The JCMT Gould Belt Survey: the effect of molecular contamination in SCUBA-2 observations of Orion A

NASA Astrophysics Data System (ADS)

Coudé, S.; Bastien, P.; Kirk, H.; Johnstone, D.; Drabek-Maunder, E.; Graves, S.; Hatchell, J.; Chapin, E. L.; Gibb, A. G.; Matthews, B.; JCMT Gould Belt Survey Team

2016-04-01

Thermal emission from cold dust grains in giant molecular clouds can be used to probe the physical properties, such as density, temperature and emissivity in star-forming regions. We present the Submillimetre Common-User Bolometer Array (SCUBA-2) shared-risk observations at 450 and 850 μm of the Orion A molecular cloud complex taken at the James Clerk Maxwell Telescope (JCMT). Previous studies showed that molecular emission lines can contribute significantly to the measured fluxes in those continuum bands. We use the Heterodyne Array Receiver Programme 12CO J = 3-2 integrated intensity map for Orion A in order to evaluate the molecular line contamination and its effects on the SCUBA-2 maps. With the corrected fluxes, we have obtained a new spectral index α map for the thermal emission of dust in the well-known integral-shaped filament. Furthermore, we compare a sample of 33 sources, selected over the Orion A molecular cloud complex for their high 12CO J = 3-2 line contamination, to 27 previously identified clumps in OMC 4. This allows us to quantify the effect of line contamination on the ratio of 850-450 μm flux densities and how it modifies the deduced spectral index of emissivity β for the dust grains. We also show that at least one Spitzer-identified protostellar core in OMC 5 has a 12CO J = 3-2 contamination level of 16 per cent. Furthermore, we find the strongest contamination level (44 per cent) towards a young star with disc near OMC 2. This work is part of the JCMT Gould Belt Legacy Survey.

APOLLO_NG - a probabilistic interpretation of the APOLLO legacy for AVHRR heritage channels

NASA Astrophysics Data System (ADS)

Klüser, L.; Killius, N.; Gesell, G.

2015-04-01

The cloud processing scheme APOLLO (Avhrr Processing scheme Over cLouds, Land and Ocean) has been in use for cloud detection and cloud property retrieval since the late 1980s. The physics of the APOLLO scheme still build the backbone of a range of cloud detection algorithms for AVHRR (Advanced Very High Resolution Radiometer) heritage instruments. The APOLLO_NG (APOLLO_NextGeneration) cloud processing scheme is a probabilistic interpretation of the original APOLLO method. While building upon the physical principles having served well in the original APOLLO a couple of additional variables have been introduced in APOLLO_NG. Cloud detection is not performed as a binary yes/no decision based on these physical principals but is expressed as cloud probability for each satellite pixel. Consequently the outcome of the algorithm can be tuned from clear confident to cloud confident depending on the purpose. The probabilistic approach allows to retrieving not only the cloud properties (optical depth, effective radius, cloud top temperature and cloud water path) but also their uncertainties. APOLLO_NG is designed as a standalone cloud retrieval method robust enough for operational near-realtime use and for the application with large amounts of historical satellite data. Thus the radiative transfer solution is approximated by the same two stream approach which also had been used for the original APOLLO. This allows the algorithm to be robust enough for being applied to a wide range of sensors without the necessity of sensor-specific tuning. Moreover it allows for online calculation of the radiative transfer (i.e. within the retrieval algorithm) giving rise to a detailed probabilistic treatment of cloud variables. This study presents the algorithm for cloud detection and cloud property retrieval together with the physical principles from the APOLLO legacy it is based on. Furthermore a couple of example results from on NOAA-18 are presented.

A GUI visualization system for airborne lidar image data to reconstruct 3D city model

NASA Astrophysics Data System (ADS)

Kawata, Yoshiyuki; Koizumi, Kohei

2015-10-01

A visualization toolbox system with graphical user interfaces (GUIs) was developed for the analysis of LiDAR point cloud data, as a compound object oriented widget application in IDL (Interractive Data Language). The main features in our system include file input and output abilities, data conversion capability from ascii formatted LiDAR point cloud data to LiDAR image data whose pixel value corresponds the altitude measured by LiDAR, visualization of 2D/3D images in various processing steps and automatic reconstruction ability of 3D city model. The performance and advantages of our graphical user interface (GUI) visualization system for LiDAR data are demonstrated.

Spatial and Temporal Distribution of Cloud Properties Observed by MODIS: Preliminary Level-3 Results from the Collection 5 Reprocessing

NASA Technical Reports Server (NTRS)

King, Michael D.; Platnick, Steven; Hubanks, Paul; Pincus, Robert

2006-01-01

The Moderate Resolution Imaging Spectroradiometer (MODIS) was developed by NASA and launched onboard the Terra spacecraft on December 18, 1999 and Aqua spacecraft on May 4, 2002. It achieved its final orbit and began Earth observations on February 24, 2000 for Terra and June 24, 2002 for Aqua. A comprehensive set of operational algorithms for the retrieval of cloud physical and optical properties (optical thickness, effective particle radius, water path, thermodynamic phase) have recently been updated and are being used in the new "Collection 5" processing stream being produced by the MODIS Adaptive Processing System (MODAPS) at NASA GSFC. All Terra and Aqua data are undergoing Collection 5 reprocessing with an expected completion date by the end of 2006. The archived products from these algorithms include 1 km pixel-level (Level-2) and global gridded Level-3 products. The cloud products have applications in climate change studies, climate modeling, numerical weather prediction, as well as fundamental atmospheric research. In this talk, we will summarize the available Level-3 cloud properties and their associated statistical data sets, and show preliminary Terra and Aqua results from the available Collection 5 reprocessing effort. Anticipated results include the latitudinal distribution of cloud optical and radiative properties for both liquid water and ice clouds, as well as joint histograms of cloud optical thickness and effective radius for selected geographical locations around the world.

Automatic cloud coverage assessment of Formosat-2 image

NASA Astrophysics Data System (ADS)

Hsu, Kuo-Hsien

2011-11-01

Formosat-2 satellite equips with the high-spatial-resolution (2m ground sampling distance) remote sensing instrument. It has been being operated on the daily-revisiting mission orbit by National Space organization (NSPO) of Taiwan since May 21 2004. NSPO has also serving as one of the ground receiving stations for daily processing the received Formosat- 2 images. The current cloud coverage assessment of Formosat-2 image for NSPO Image Processing System generally consists of two major steps. Firstly, an un-supervised K-means method is used for automatically estimating the cloud statistic of Formosat-2 image. Secondly, manual estimation of cloud coverage from Formosat-2 image is processed by manual examination. Apparently, a more accurate Automatic Cloud Coverage Assessment (ACCA) method certainly increases the efficiency of processing step 2 with a good prediction of cloud statistic. In this paper, mainly based on the research results from Chang et al, Irish, and Gotoh, we propose a modified Formosat-2 ACCA method which considered pre-processing and post-processing analysis. For pre-processing analysis, cloud statistic is determined by using un-supervised K-means classification, Sobel's method, Otsu's method, non-cloudy pixels reexamination, and cross-band filter method. Box-Counting fractal method is considered as a post-processing tool to double check the results of pre-processing analysis for increasing the efficiency of manual examination.

On the Use of Deep Convective Clouds to Calibrate AVHRR Data

NASA Technical Reports Server (NTRS)

Doelling, David R.; Nguyen, Louis; Minnis, Patrick

2004-01-01

Remote sensing of cloud and radiation properties from National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) satellites requires constant monitoring of the visible sensors. NOAA satellites do not have onboard visible calibration and need to be calibrated vicariously in order to determine the calibration and the degradation rate. Deep convective clouds are extremely bright and cold, are at the tropopause, have nearly a Lambertian reflectance, and provide predictable albedos. The use of deep convective clouds as calibration targets is developed into a calibration technique and applied to NOAA-16 and NOAA-17. The technique computes the relative gain drift over the life-span of the satellite. This technique is validated by comparing the gain drifts derived from inter-calibration of coincident AVHRR and Moderate-Resolution Imaging Spectroradiometer (MODIS) radiances. A ray-matched technique, which uses collocated, coincident, and co-angled pixel satellite radiance pairs is used to intercalibrate MODIS and AVHRR. The deep convective cloud calibration technique was found to be independent of solar zenith angle, by using well calibrated Visible Infrared Scanner (VIRS) radiances onboard the Tropical Rainfall Measuring Mission (TRMM) satellite, which precesses through all solar zenith angles in 23 days.

Determination of effective droplet radius and optical depth of liquid water clouds over a tropical site in northern Thailand using passive microwave soundings, aircraft measurements and spectral irradiance data

NASA Astrophysics Data System (ADS)

Nimnuan, P.; Janjai, S.; Nunez, M.; Pratummasoot, N.; Buntoung, S.; Charuchittipan, D.; Chanyatham, T.; Chantraket, P.; Tantiplubthong, N.

2017-08-01

This paper presents an algorithm for deriving the effective droplet radius and optical depth of liquid water clouds using ground-based measurements, aircraft observations and an adiabatic model of cloud liquid water. The algorithm derives cloud effective radius and cloud optical depth over a tropical site at Omkoi (17.80°N, 98.43°E), Thailand. Monthly averages of cloud optical depth are highest in April (54.5), which is the month with the lowest average cloud effective radius (4.2 μm), both occurring before the start of the rainy season and at the end of the high contamination period. By contrast, the monsoon period extending from May to October brings higher cloud effective radius and lower cloud optical depth to the region on average. At the diurnal scale there is a gradual increase in average cloud optical depth and decrease in cloud effective radius as the day progresses.

Integrated Efforts for Analysis of Geophysical Measurements and Models.

DTIC Science & Technology

1997-09-26

12b. DISTRIBUTION CODE 13. ABSTRACT ( Maximum 200 words) This contract supported investigations of integrated applications of physics, ephemerides...REGIONS AND GPS DATA VALIDATIONS 20 2.5 PL-SCINDA: VISUALIZATION AND ANALYSIS TECHNIQUES 22 2.5.1 View Controls 23 2.5.2 Map Selection...and IR data, about cloudy pixels. Clustering and maximum likelihood classification algorithms categorize up to four cloud layers into stratiform or

Direct simulation Monte Carlo prediction of on-orbit contaminant deposit levels for HALOE

NASA Technical Reports Server (NTRS)

Woronowicz, Michael S.; Rault, Didier F. G.

1994-01-01

A three-dimensional version of the direct simulation Monte Carlo method is adapted to assess the contamination environment surrounding a highly detailed model of the Upper Atmosphere Research Satellite. Emphasis is placed on simulating a realistic, worst-case set of flow field and surface conditions and geometric orientations for the satellite in order to estimate an upper limit for the cumulative level of volatile organic molecular deposits at the aperture of the Halogen Occultation Experiment. A detailed description of the adaptation of this solution method to the study of the satellite's environment is also presented. Results pertaining to the satellite's environment are presented regarding contaminant cloud structure, cloud composition, and statistics of simulated molecules impinging on the target surface, along with data related to code performance. Using procedures developed in standard contamination analyses, along with many worst-case assumptions, the cumulative upper-limit level of volatile organic deposits on HALOE's aperture over the instrument's 35-month nominal data collection period is estimated at about 13,350 A.

ARC-1989-AC89-7038

NASA Image and Video Library

1989-08-26

P-34709 Range: 157,000 kilometers (98,000 miles) This Voyager 2 high resolution color image, taken 2 hours before closest approach, provides obvious evidence of vertical relief in Neptune's bright cloud streaks. These clouds were observed at a latitude of 29° N near Neptune's east terminator. The linear cloud forms are stretched approximately along lines of constant latitude and the sun is toward the lower left. The bright sides of the clouds that face the sun are brighter than the surrounding cloud deck because they are more directly exposed to the sun. Shadows can be seen on the side directly opposite the sun. These shadows are less distinct at short wavelengths (violet filter) and more distinct at long wavelengths (orange filter). This can be understood if the underlying cloud deck on which the shadow is cast is at a relatively great depth, in which case scattering by molecules in the overlying atmopsphere will diffuse light into the shadow. Because molecules scatter blue light much more efficiently than red light, the shadows will be darkest at the longest (reddest) wavelengths, and will appear blue under white illumination. The resolution of this image is 11 kilometers (6.8 miles per pixel). The width of the cloud streaks range from 50 to 200 kilometers (31 to 124 miles), and their shadow widths range from 30 to 50 kilometers (18 to 31 miles). Cloud heights appear to be of the order of 50 kilometers (31 miles). This corresponds to 2 scale heights.

A Comparison of Several Techniques to Assign Heights to Cloud Tracers.

NASA Astrophysics Data System (ADS)

Nieman, Steven J.; Schmetz, Johannes; Menzel, W. Paul

1993-09-01

Satellite-derived cloud-motion vector (CMV) production has been troubled by inaccurate height assignment of cloud tracers, especially in thin semitransparent clouds. This paper presents the results of an intercomparison of current operational height assignment techniques. Currently, heights are assigned by one of three techniques when the appropriate spectral radiance measurements are available. The infrared window (IRW) technique compares measured brightness temperatures to forecast temperature profiles and thus infers opaque cloud levels. In semitransparent or small subpixel clouds, the carbon dioxide (CO2) technique uses the ratio of radiances from different layers of the atmosphere to infer the correct cloud height. In the water vapor (H2O) technique, radiances influenced by upper-tropospheric moisture and IRW radiances are measured for several pixels viewing different cloud amounts, and their linear relationship is used to extrapolate the correct cloud height. The results presented in this paper suggest that the H2O technique is a viable alternative to the CO2 technique for inferring the heights of semitransparent cloud elements. This is important since future National Environmental Satellite, Data, and Information Service (NESDIS) operations will have to rely on H20-derived cloud-height assignments in the wind field determinations with the next operational geostationary satellite. On a given day, the heights from the two approaches compare to within 60 110 hPa rms; drier atmospheric conditions tend to reduce the effectiveness of the H2O technique. By inference one can conclude that the present height algorithms used operationally at NESDIS (with the C02 technique) and at the European Satellite Operations Center (ESOC) (with their version of the H20 technique) are providing similar results. Sample wind fields produced with the ESOC and NESDIS algorithms using Meteosat-4 data show good agreement.

The Effects of an Absorbing Smoke Layer on MODIS Marine Boundary Layer Cloud Optical Property Retrievals and Radiative Forcing

NASA Technical Reports Server (NTRS)

Meyer, Kerry; Platnick, Steven

2012-01-01

Clouds, aerosols, and their interactions are widely considered to be key uncertainty components in our current understanding of the Earth's atmosphere and radiation budget. The work presented here is focused on the quasi-permanent marine boundary layer . (MBL) clouds off the southern Atlantic coast of Africa and the effects on MODIS cloud optical property retrievals (MOD06) of an overlying absorbing smoke layer. During much of August and September, a persistent smoke layer resides over this region, produced from extensive biomass burning throughout the southern African savanna. The resulting absorption, which increases with decreasing wavelength, potentially introduces biases into the MODIS cloud optical property retrievals of the underlying MBL clouds. This effect is more pronounced in the cloud optical thickness retrievals, which over ocean are derived from the wavelength channel centered near 0.86 micron (effective particle size retrievals are derived from the longer-wavelength near-IR channels at 1.6, 2.1, and 3.7 microns). Here, the spatial distributions of the scalar statistics of both the cloud and aerosol layers are first determined from the CALIOP 5 km layer products. Next, the MOD06 look-up tables (LUTs) are adjusted by inserting an absorbing smoke layer of varying optical thickness over the cloud. Retrievals are subsequently performed for a subset of MODIS pixels collocated with the CALIOP ground track, using smoke optical thickness from the CALIOP 5km aerosol layer product to select the appropriate LUT. The resulting differences in cloud optical property retrievals due to the inclusion of the smoke layer in the LUTs will be examined. In addition, the direct radiative forcing of this smoke layer will be investigated from the perspective of the cloud optical property retrieval differences.

An Extension of the Split Window Technique for the Retrieval of Precipitable Water: Experimental Verification

DTIC Science & Technology

1988-09-23

DOWNGRADING SCHEDULE D~istribution Unlimited 4. PERFORMING ORGANIZATiON REPORT NUMVBER(S) 5. MONITORiG ORGANIZATION REPORT NUMBER(S) AFGL-TR-88-0237...Collocations were performed on launch sites of the cloud contamination, aerosol problems, collocation 1200 UT radiosondes on 25 Aug 1987. Statistics were...al (1987) and Thomason, 1987). In this imagery opaque clouds to this problem appear white, low clouds and fog appear bright red against a brown

Effect of Thin Cirrus Clouds on Dust Optical Depth Retrievals From MODIS Observations

NASA Technical Reports Server (NTRS)

Feng, Qian; Hsu, N. Christina; Yang, Ping; Tsay, Si-Chee

2011-01-01

The effect of thin cirrus clouds in retrieving the dust optical depth from MODIS observations is investigated by using a simplified aerosol retrieval algorithm based on the principles of the Deep Blue aerosol property retrieval method. Specifically, the errors of the retrieved dust optical depth due to thin cirrus contamination are quantified through the comparison of two retrievals by assuming dust-only atmospheres and the counterparts with overlapping mineral dust and thin cirrus clouds. To account for the effect of the polarization state of radiation field on radiance simulation, a vector radiative transfer model is used to generate the lookup tables. In the forward radiative transfer simulations involved in generating the lookup tables, the Rayleigh scattering by atmospheric gaseous molecules and the reflection of the surface assumed to be Lambertian are fully taken into account. Additionally, the spheroid model is utilized to account for the nonsphericity of dust particles In computing their optical properties. For simplicity, the single-scattering albedo, scattering phase matrix, and optical depth are specified a priori for thin cirrus clouds assumed to consist of droxtal ice crystals. The present results indicate that the errors in the retrieved dust optical depths due to the contamination of thin cirrus clouds depend on the scattering angle, underlying surface reflectance, and dust optical depth. Under heavy dusty conditions, the absolute errors are comparable to the predescribed optical depths of thin cirrus clouds.

A method for quantifying cloud immersion in a tropical mountain forest using time-lapse photography

USGS Publications Warehouse

Bassiouni, Maoya; Scholl, Martha A.; Torres-Sanchez, Angel J.; Murphy, Sheila F.

2017-01-01

Quantifying the frequency, duration, and elevation range of fog or cloud immersion is essential to estimate cloud water deposition in water budgets and to understand the ecohydrology of cloud forests. The goal of this study was to develop a low-cost and high spatial-coverage method to detect occurrence of cloud immersion within a mountain cloud forest by using time-lapse photography. Trail cameras and temperature/relative humidity sensors were deployed at five sites covering the elevation range from the assumed lifting condensation level to the mountain peaks in the Luquillo Mountains of Puerto Rico. Cloud-sensitive image characteristics (contrast, the coefficient of variation and the entropy of pixel luminance, and image colorfulness) were used with a k-means clustering approach to accurately detect cloud-immersed conditions in a time series of images from March 2014 to May 2016. Images provided hydrologically meaningful cloud-immersion information while temperature-relative humidity data were used to refine the image analysis using dew point information and provided temperature gradients along the elevation transect. Validation of the image processing method with human-judgment based classification generally indicated greater than 90% accuracy. Cloud-immersion frequency averaged 80% at sites above 900 m during nighttime hours and 49% during daytime hours, and was consistent with diurnal patterns of cloud immersion measured in a previous study. Results for the 617 m site demonstrated that cloud immersion in the Luquillo Mountains rarely occurs at the previously-reported cloud base elevation of about 600 m (11% during nighttime hours and 5% during daytime hours). The framework presented in this paper will be used to monitor at a low cost and high spatial resolution the long-term variability of cloud-immersion patterns in the Luquillo Mountains, and can be applied to ecohydrology research at other cloud-forest sites or in coastal ecosystems with advective sea fog.

Atmospheric Correction at AERONET Locations: A New Science and Validation Data Set

NASA Technical Reports Server (NTRS)

Wang, Yujie; Lyapustin, Alexei; Privette, Jeffery L.; Morisette, Jeffery T.; Holben, Brent

2008-01-01

This paper describes an AERONET-based Surface Reflectance Validation Network (ASRVN) and its dataset of spectral surface bidirectional reflectance and albedo based on MODIS TERRA and AQUA data. The ASRVN is an operational data collection and processing system. It receives 50x50 square kilometer subsets of MODIS L1B data from MODAPS and AERONET aerosol and water vapor information. Then it performs an accurate atmospheric correction for about 100 AERONET sites based on accurate radiative transfer theory with high quality control of the input data. The ASRVN processing software consists of L1B data gridding algorithm, a new cloud mask algorithm based on a time series analysis, and an atmospheric correction algorithm. The atmospheric correction is achieved by fitting the MODIS top of atmosphere measurements, accumulated for 16-day interval, with theoretical reflectance parameterized in terms of coefficients of the LSRT BRF model. The ASRVN takes several steps to ensure high quality of results: 1) cloud mask algorithm filters opaque clouds; 2) an aerosol filter has been developed to filter residual semi-transparent and sub-pixel clouds, as well as cases with high inhomogeneity of aerosols in the processing area; 3) imposing requirement of consistency of the new solution with previously retrieved BRF and albedo; 4) rapid adjustment of the 16-day retrieval to the surface changes using the last day of measurements; and 5) development of seasonal back-up spectral BRF database to increase data coverage. The ASRVN provides a gapless or near-gapless coverage for the processing area. The gaps, caused by clouds, are filled most naturally with the latest solution for a given pixels. The ASRVN products include three parameters of LSRT model (k(sup L), k(sup G), k(sup V)), surface albedo, NBRF (a normalized BRF computed for a standard viewing geometry, VZA=0 deg., SZA=45 deg.), and IBRF (instantaneous, or one angle, BRF value derived from the last day of MODIS measurement for specific viewing geometry) for MODIS 500m bands 1-7. The results are produced daily at resolution of 1 km in gridded format. We also provide cloud mask, quality flag and a browse bitmap image. The new dataset can be used for a wide range of applications including validation analysis and science research.

Automation of Endmember Pixel Selection in SEBAL/METRIC Model

NASA Astrophysics Data System (ADS)

Bhattarai, N.; Quackenbush, L. J.; Im, J.; Shaw, S. B.

2015-12-01

The commonly applied surface energy balance for land (SEBAL) and its variant, mapping evapotranspiration (ET) at high resolution with internalized calibration (METRIC) models require manual selection of endmember (i.e. hot and cold) pixels to calibrate sensible heat flux. Current approaches for automating this process are based on statistical methods and do not appear to be robust under varying climate conditions and seasons. In this paper, we introduce a new approach based on simple machine learning tools and search algorithms that provides an automatic and time efficient way of identifying endmember pixels for use in these models. The fully automated models were applied on over 100 cloud-free Landsat images with each image covering several eddy covariance flux sites in Florida and Oklahoma. Observed land surface temperatures at automatically identified hot and cold pixels were within 0.5% of those from pixels manually identified by an experienced operator (coefficient of determination, R2, ≥ 0.92, Nash-Sutcliffe efficiency, NSE, ≥ 0.92, and root mean squared error, RMSE, ≤ 1.67 K). Daily ET estimates derived from the automated SEBAL and METRIC models were in good agreement with their manual counterparts (e.g., NSE ≥ 0.91 and RMSE ≤ 0.35 mm day-1). Automated and manual pixel selection resulted in similar estimates of observed ET across all sites. The proposed approach should reduce time demands for applying SEBAL/METRIC models and allow for their more widespread and frequent use. This automation can also reduce potential bias that could be introduced by an inexperienced operator and extend the domain of the models to new users.

Effect of ship-stack effluents on cloud reflectivity

NASA Technical Reports Server (NTRS)

Coakley, James A., Jr.; Bernstein, Robert L.; Durkee, Philip A.

1987-01-01

Under stable meteorological conditions the effect of ship-stack exhaust on overlying clouds was detected in daytime satellite images as an enhancement in cloud reflectivity at 3.7 micrometers. The exhaust is a source of cloud-condensation nuclei that increases the number of cloud droplets while reducing droplet size. This reduction in droplet size causes the reflectivity at 3.7 micrometers to be greater than the levels for nearby noncontaminated clouds of similar physical characteristics. The increase in droplet number causes the reflectivity at 0.63 micrometer to be significantly higher for the contaminated clouds despite the likelihood that the exhaust is a source of particles that absorb at visible wavelengths. The effect of aerosols on cloud reflectivity is expected to have a larger influence on the earth's albedo than that due to the direct scattering and absorption of sunlight by the aerosols alone.

Quantifying the Climate-Scale Accuracy of Satellite Cloud Retrievals

NASA Astrophysics Data System (ADS)

Roberts, Y.; Wielicki, B. A.; Sun-Mack, S.; Minnis, P.; Liang, L.; Di Girolamo, L.

2014-12-01

Instrument calibration and cloud retrieval algorithms have been developed to minimize retrieval errors on small scales. However, measurement uncertainties and assumptions within retrieval algorithms at the pixel level may alias into decadal-scale trends of cloud properties. We first, therefore, quantify how instrument calibration changes could alias into cloud property trends. For a perfect observing system the climate trend accuracy is limited only by the natural variability of the climate variable. Alternatively, for an actual observing system, the climate trend accuracy is additionally limited by the measurement uncertainty. Drifts in calibration over time may therefore be disguised as a true climate trend. We impose absolute calibration changes to MODIS spectral reflectance used as input to the CERES Cloud Property Retrieval System (CPRS) and run the modified MODIS reflectance through the CPRS to determine the sensitivity of cloud properties to calibration changes. We then use these changes to determine the impact of instrument calibration changes on trend uncertainty in reflected solar cloud properties. Secondly, we quantify how much cloud retrieval algorithm assumptions alias into cloud optical retrieval trends by starting with the largest of these biases: the plane-parallel assumption in cloud optical thickness (τC) retrievals. First, we collect liquid water cloud fields obtained from Multi-angle Imaging Spectroradiometer (MISR) measurements to construct realistic probability distribution functions (PDFs) of 3D cloud anisotropy (a measure of the degree to which clouds depart from plane-parallel) for different ISCCP cloud types. Next, we will conduct a theoretical study with dynamically simulated cloud fields and a 3D radiative transfer model to determine the relationship between 3D cloud anisotropy and 3D τC bias for each cloud type. Combining these results provides distributions of 3D τC bias by cloud type. Finally, we will estimate the change in frequency of occurrence of cloud types between two decades and will have the information needed to calculate the total change in 3D optical thickness bias between two decades. If we uncover aliases in this study, the results will motivate the development and rigorous testing of climate specific cloud retrieval algorithms.

Neptune Clouds Showing Vertical Relief

NASA Image and Video Library

1996-01-29

NASA's Voyager 2 high resolution color image, taken 2 hours before closest approach, provides obvious evidence of vertical relief in Neptune's bright cloud streaks. These clouds were observed at a latitude of 29 degrees north near Neptune's east terminator. The linear cloud forms are stretched approximately along lines of constant latitude and the sun is toward the lower left. The bright sides of the clouds which face the sun are brighter than the surrounding cloud deck because they are more directly exposed to the sun. Shadows can be seen on the side opposite the sun. These shadows are less distinct at short wavelengths (violet filter) and more distinct at long wavelengths (orange filter). This can be understood if the underlying cloud deck on which the shadow is cast is at a relatively great depth, in which case scattering by molecules in the overlying atmosphere will diffuse light into the shadow. Because molecules scatter blue light much more efficiently than red light, the shadows will be darkest at the longest (reddest) wavelengths, and will appear blue under white light illumination. The resolution of this image is 11 kilometers (6.8 miles per pixel) and the range is only 157,000 kilometers (98,000 miles). The width of the cloud streaks range from 50 to 200 kilometers (31 to 124 miles), and their shadow widths range from 30 to 50 kilometers (18 to 31 miles). Cloud heights appear to be of the order of 50 kilometers (31 miles). This corresponds to 2 scale heights. http://photojournal.jpl.nasa.gov/catalog/PIA00058

Neptune Clouds Showing Vertical Relief

NASA Technical Reports Server (NTRS)

1989-01-01

This Voyager 2 high resolution color image, taken 2 hours before closest approach, provides obvious evidence of vertical relief in Neptune's bright cloud streaks. These clouds were observed at a latitude of 29 degrees north near Neptune's east terminator. The linear cloud forms are stretched approximately along lines of constant latitude and the sun is toward the lower left. The bright sides of the clouds which face the sun are brighter than the surrounding cloud deck because they are more directly exposed to the sun. Shadows can be seen on the side opposite the sun. These shadows are less distinct at short wavelengths (violet filter) and more distinct at long wavelengths (orange filter). This can be understood if the underlying cloud deck on which the shadow is cast is at a relatively great depth, in which case scattering by molecules in the overlying atmosphere will diffuse light into the shadow. Because molecules scatter blue light much more efficiently than red light, the shadows will be darkest at the longest (reddest) wavelengths, and will appear blue under white light illumination. The resolution of this image is 11 kilometers (6.8 miles per pixel) and the range is only 157,000 kilometers (98,000 miles). The width of the cloud streaks range from 50 to 200 kilometers (31 to 124 miles), and their shadow widths range from 30 to 50 kilometers (18 to 31 miles). Cloud heights appear to be of the order of 50 kilometers (31 miles). This corresponds to 2 scale heights. The Voyager Mission is conducted by JPL for NASA's Office of Space Science and Applications.

Transport of contaminants in the planetary boundary layer

NASA Technical Reports Server (NTRS)

Lee, I. Y.; Swan, P. R.

1978-01-01

A planetary boundary layer model is described and used to simulate PBL phenomena including cloud formation and pollution transport in the San Francisco Bay Area. The effect of events in the PBL on air pollution is considered, and governing equations for the average momentum, potential temperature, water vapor mixing ratio, and air contaminants are presented. These equations are derived by integrating the basic equations vertically through the mixed layer. Characteristics of the day selected for simulation are reported, and the results suggest that the diurnally cyclic features of the mesoscale motion, including clouds and air pollution, can be simulated in a readily interpretable way with the model.

a Low-Cost Panoramic Camera for the 3d Documentation of Contaminated Crime Scenes

NASA Astrophysics Data System (ADS)

Abate, D.; Toschi, I.; Sturdy-Colls, C.; Remondino, F.

2017-11-01

Crime scene documentation is a fundamental task which has to be undertaken in a fast, accurate and reliable way, highlighting evidence which can be further used for ensuring justice for victims and for guaranteeing the successful prosecution of perpetrators. The main focus of this paper is on the documentation of a typical crime scene and on the rapid recording of any possible contamination that could have influenced its original appearance. A 3D reconstruction of the environment is first generated by processing panoramas acquired with the low-cost Ricoh Theta 360 camera, and further analysed to highlight potentials and limits of this emerging and consumer-grade technology. Then, a methodology is proposed for the rapid recording of changes occurring between the original and the contaminated crime scene. The approach is based on an automatic 3D feature-based data registration, followed by a cloud-to-cloud distance computation, given as input the 3D point clouds generated before and after e.g. the misplacement of evidence. All the algorithms adopted for panoramas pre-processing, photogrammetric 3D reconstruction, 3D geometry registration and analysis, are presented and currently available in open-source or low-cost software solutions.

Automatic Extraction of Road Markings from Mobile Laser-Point Cloud Using Intensity Data

NASA Astrophysics Data System (ADS)

Yao, L.; Chen, Q.; Qin, C.; Wu, H.; Zhang, S.

2018-04-01

With the development of intelligent transportation, road's high precision information data has been widely applied in many fields. This paper proposes a concise and practical way to extract road marking information from point cloud data collected by mobile mapping system (MMS). The method contains three steps. Firstly, road surface is segmented through edge detection from scan lines. Then the intensity image is generated by inverse distance weighted (IDW) interpolation and the road marking is extracted by using adaptive threshold segmentation based on integral image without intensity calibration. Moreover, the noise is reduced by removing a small number of plaque pixels from binary image. Finally, point cloud mapped from binary image is clustered into marking objects according to Euclidean distance, and using a series of algorithms including template matching and feature attribute filtering for the classification of linear markings, arrow markings and guidelines. Through processing the point cloud data collected by RIEGL VUX-1 in case area, the results show that the F-score of marking extraction is 0.83, and the average classification rate is 0.9.

Sloped Terrain Segmentation for Autonomous Drive Using Sparse 3D Point Cloud

PubMed Central

Cho, Seoungjae; Kim, Jonghyun; Ikram, Warda; Cho, Kyungeun; Sim, Sungdae

2014-01-01

A ubiquitous environment for road travel that uses wireless networks requires the minimization of data exchange between vehicles. An algorithm that can segment the ground in real time is necessary to obtain location data between vehicles simultaneously executing autonomous drive. This paper proposes a framework for segmenting the ground in real time using a sparse three-dimensional (3D) point cloud acquired from undulating terrain. A sparse 3D point cloud can be acquired by scanning the geography using light detection and ranging (LiDAR) sensors. For efficient ground segmentation, 3D point clouds are quantized in units of volume pixels (voxels) and overlapping data is eliminated. We reduce nonoverlapping voxels to two dimensions by implementing a lowermost heightmap. The ground area is determined on the basis of the number of voxels in each voxel group. We execute ground segmentation in real time by proposing an approach to minimize the comparison between neighboring voxels. Furthermore, we experimentally verify that ground segmentation can be executed at about 19.31 ms per frame. PMID:25093204

Real-Time Cloud, Radiation, and Aircraft Icing Parameters from GOES over the USA

NASA Technical Reports Server (NTRS)

Minnis, Patrick; Nguyen, Louis; Smith, William, Jr.; Young, David; Khaiyer, Mandana; Palikonda, Rabindra; Spangenberg, Douglas; Doelling, Dave; Phan, Dung; Nowicki, Greg

2004-01-01

A preliminary new, physically based method for realtime estimation of the probability of icing conditions has been demonstrated using merged GOES-10 and 12 data over the continental United States and southern Canada. The algorithm produces pixel-level cloud and radiation properties as well as an estimate of icing probability with an associated intensity rating Because icing depends on so many different variables, such as aircraft size or air speed, it is not possible to achieve 100% success with this or any other type of approach. This initial algorithm, however, shows great promise for diagnosing aircraft icing and putting it at the correct altitude within 0.5 km most of the time. Much additional research must be completed before it can serve as a reliable input for the operational CIP. The delineation of the icing layer vertical boundaries will need to be improved using either the RUC or balloon soundings or ceilometer data to adjust the cloud base height, a change that would require adjustment of the cloud-top altitude also.

Cloud field classification based on textural features

NASA Technical Reports Server (NTRS)

Sengupta, Sailes Kumar

1989-01-01

An essential component in global climate research is accurate cloud cover and type determination. Of the two approaches to texture-based classification (statistical and textural), only the former is effective in the classification of natural scenes such as land, ocean, and atmosphere. In the statistical approach that was adopted, parameters characterizing the stochastic properties of the spatial distribution of grey levels in an image are estimated and then used as features for cloud classification. Two types of textural measures were used. One is based on the distribution of the grey level difference vector (GLDV), and the other on a set of textural features derived from the MaxMin cooccurrence matrix (MMCM). The GLDV method looks at the difference D of grey levels at pixels separated by a horizontal distance d and computes several statistics based on this distribution. These are then used as features in subsequent classification. The MaxMin tectural features on the other hand are based on the MMCM, a matrix whose (I,J)th entry give the relative frequency of occurrences of the grey level pair (I,J) that are consecutive and thresholded local extremes separated by a given pixel distance d. Textural measures are then computed based on this matrix in much the same manner as is done in texture computation using the grey level cooccurrence matrix. The database consists of 37 cloud field scenes from LANDSAT imagery using a near IR visible channel. The classification algorithm used is the well known Stepwise Discriminant Analysis. The overall accuracy was estimated by the percentage or correct classifications in each case. It turns out that both types of classifiers, at their best combination of features, and at any given spatial resolution give approximately the same classification accuracy. A neural network based classifier with a feed forward architecture and a back propagation training algorithm is used to increase the classification accuracy, using these two classes of features. Preliminary results based on the GLDV textural features alone look promising.

Global, Persistent, Real-time Multi-sensor Automated Satellite Image Analysis and Crop Forecasting in Commercial Cloud

NASA Astrophysics Data System (ADS)

Brumby, S. P.; Warren, M. S.; Keisler, R.; Chartrand, R.; Skillman, S.; Franco, E.; Kontgis, C.; Moody, D.; Kelton, T.; Mathis, M.

2016-12-01

Cloud computing, combined with recent advances in machine learning for computer vision, is enabling understanding of the world at a scale and at a level of space and time granularity never before feasible. Multi-decadal Earth remote sensing datasets at the petabyte scale (8×10^15 bits) are now available in commercial cloud, and new satellite constellations will generate daily global coverage at a few meters per pixel. Public and commercial satellite observations now provide a wide range of sensor modalities, from traditional visible/infrared to dual-polarity synthetic aperture radar (SAR). This provides the opportunity to build a continuously updated map of the world supporting the academic community and decision-makers in government, finanace and industry. We report on work demonstrating country-scale agricultural forecasting, and global-scale land cover/land, use mapping using a range of public and commercial satellite imagery. We describe processing over a petabyte of compressed raw data from 2.8 quadrillion pixels (2.8 petapixels) acquired by the US Landsat and MODIS programs over the past 40 years. Using commodity cloud computing resources, we convert the imagery to a calibrated, georeferenced, multiresolution tiled format suited for machine-learning analysis. We believe ours is the first application to process, in less than a day, on generally available resources, over a petabyte of scientific image data. We report on work combining this imagery with time-series SAR collected by ESA Sentinel 1. We report on work using this reprocessed dataset for experiments demonstrating country-scale food production monitoring, an indicator for famine early warning. We apply remote sensing science and machine learning algorithms to detect and classify agricultural crops and then estimate crop yields and detect threats to food security (e.g., flooding, drought). The software platform and analysis methodology also support monitoring water resources, forests and other general indicators of environmental health, and can detect growth and changes in cities that are displacing historical agricultural zones.

The Use of the Deep Convective Cloud Technique (DCCT) to Monitor On-Orbit Performance of the Geostationary Lightning Mapper (GLM): Use of Lightning Imaging Sensor (LIS) Data as Proxy

NASA Technical Reports Server (NTRS)

Buechler, Dennis E.; Christian, H. J.; Koshak, William J.; Goodman, Steve J.

2013-01-01

The Geostationary Lightning Mapper (GLM) on the next generation Geostationary Operational Environmental Satellite-R (GOES-R) will not have onboard calibration capability to monitor its performance. The Lightning Imaging Sensor (LIS) onboard the Tropical Rainfall Measuring Mission (TRMM) satellite has been providing observations of total lightning over the Earth's Tropics since 1997. The GLM design is based on LIS heritage, making it a good proxy dataset. This study examines the performance of LIS throughout its time in orbit. This was accomplished through application of the Deep Convective Cloud Technique (DCCT) (Doelling et al., 2004) to LIS background pixel radiance data. The DCCT identifies deep convective clouds by their cold Infrared (IR) brightness temperatures and using them as invariant targets in the solar reflective portion of the solar spectrum. The GLM and LIS operate in the near-IR at a wavelength of 777.4 nm. In the present study the IR data is obtained from the Visible Infrared Sensor (VIRS) which is collocated with LIS onboard the Tropical Rainfall Measuring Mission (TRMM) satellite. The DCCT is applied to LIS observations for July and August of each year from 1998-2010. The resulting distributions of LIS background DCC pixel radiance for each July August are very similar, indicating stable performance. The mean radiance of the DCCT analysis does not show a long term trend and the maximum deviation of the July August mean radiance for each year is within 0.7% of the overall mean. These results demonstrate that there has been no discernible change in LIS performance throughout its lifetime. A similar approach will used for monitoring the performance of GLM, with cold clouds identified using IR data from the Advanced Baseline Imager (ABI) which will also be located on GOES-R. Since GLM is based on LIS design heritage, the LIS results indicate that GLM should also experience stable performance over its lifetime.

Frequency and causes of failed MODIS cloud property retrievals for liquid phase clouds over global oceans.

PubMed

Cho, Hyoun-Myoung; Zhang, Zhibo; Meyer, Kerry; Lebsock, Matthew; Platnick, Steven; Ackerman, Andrew S; Di Girolamo, Larry; C-Labonnote, Laurent; Cornet, Céline; Riedi, Jerome; Holz, Robert E

2015-05-16

Moderate Resolution Imaging Spectroradiometer (MODIS) retrieves cloud droplet effective radius ( r e ) and optical thickness ( τ ) by projecting observed cloud reflectances onto a precomputed look-up table (LUT). When observations fall outside of the LUT, the retrieval is considered "failed" because no combination of τ and r e within the LUT can explain the observed cloud reflectances. In this study, the frequency and potential causes of failed MODIS retrievals for marine liquid phase (MLP) clouds are analyzed based on 1 year of Aqua MODIS Collection 6 products and collocated CALIOP and CloudSat observations. The retrieval based on the 0.86 µm and 2.1 µm MODIS channel combination has an overall failure rate of about 16% (10% for the 0.86 µm and 3.7 µm combination). The failure rates are lower over stratocumulus regimes and higher over the broken trade wind cumulus regimes. The leading type of failure is the " r e too large" failure accounting for 60%-85% of all failed retrievals. The rest is mostly due to the " r e too small" or τ retrieval failures. Enhanced retrieval failure rates are found when MLP cloud pixels are partially cloudy or have high subpixel inhomogeneity, are located at special Sun-satellite viewing geometries such as sunglint, large viewing or solar zenith angles, or cloudbow and glory angles, or are subject to cloud masking, cloud overlapping, and/or cloud phase retrieval issues. The majority (more than 84%) of failed retrievals along the CALIPSO track can be attributed to at least one or more of these potential reasons. The collocated CloudSat radar reflectivity observations reveal that the remaining failed retrievals are often precipitating. It remains an open question whether the extremely large r e values observed in these clouds are the consequence of true cloud microphysics or still due to artifacts not included in this study.

Frequency and causes of failed MODIS cloud property retrievals for liquid phase clouds over global oceans

PubMed Central

Cho, Hyoun‐Myoung; Meyer, Kerry; Lebsock, Matthew; Platnick, Steven; Ackerman, Andrew S.; Di Girolamo, Larry; C.‐Labonnote, Laurent; Cornet, Céline; Riedi, Jerome; Holz, Robert E.

2015-01-01

Abstract Moderate Resolution Imaging Spectroradiometer (MODIS) retrieves cloud droplet effective radius (r e) and optical thickness (τ) by projecting observed cloud reflectances onto a precomputed look‐up table (LUT). When observations fall outside of the LUT, the retrieval is considered “failed” because no combination of τ and r e within the LUT can explain the observed cloud reflectances. In this study, the frequency and potential causes of failed MODIS retrievals for marine liquid phase (MLP) clouds are analyzed based on 1 year of Aqua MODIS Collection 6 products and collocated CALIOP and CloudSat observations. The retrieval based on the 0.86 µm and 2.1 µm MODIS channel combination has an overall failure rate of about 16% (10% for the 0.86 µm and 3.7 µm combination). The failure rates are lower over stratocumulus regimes and higher over the broken trade wind cumulus regimes. The leading type of failure is the “r e too large” failure accounting for 60%–85% of all failed retrievals. The rest is mostly due to the “r e too small” or τ retrieval failures. Enhanced retrieval failure rates are found when MLP cloud pixels are partially cloudy or have high subpixel inhomogeneity, are located at special Sun‐satellite viewing geometries such as sunglint, large viewing or solar zenith angles, or cloudbow and glory angles, or are subject to cloud masking, cloud overlapping, and/or cloud phase retrieval issues. The majority (more than 84%) of failed retrievals along the CALIPSO track can be attributed to at least one or more of these potential reasons. The collocated CloudSat radar reflectivity observations reveal that the remaining failed retrievals are often precipitating. It remains an open question whether the extremely large r e values observed in these clouds are the consequence of true cloud microphysics or still due to artifacts not included in this study. PMID:27656330

Examination about the Impact of Thin Clouds in IFOV of GOSAT TANSO-FTS on Estimates of XCO2 in the Amazon

NASA Astrophysics Data System (ADS)

Oishi, Y.; Kamei, A.; Murakami, K.; Dupuy, E.; Yokota, Y.; Hiraki, K.; Ninomiya, K.; Saito, M.; Yoshida, Y.; Morino, I.; Nakajima, T. Y.; Yokota, T.; Matsunaga, T.

2013-12-01

Greenhouse gases Observing SATellite (GOSAT) was launched in 2009 to measure the global atmospheric CO2 and CH4 concentrations. GOSAT is equipped with two sensors: the Thermal And Near-infrared Sensor for carbon Observation-Fourier Transform Spectrometer (TANSO-FTS) and the Cloud and Aerosol Imager (TANSO-CAI). The presence of clouds in the instantaneous field-of-view (IFOV) of the FTS leads to incorrect estimates of the CO2 or CH4 concentration. To deal with this problem, the FTS data which are suspected to be cloud-contaminated must be identified and rejected. As a result, there are very few remaining FTS data in the region of tropical rainforest such as the Amazon. In the meanwhile the feasibility studies of GOSAT-2 started for more precise monitoring of atmospheric greenhouse gases than GOSAT in 2011. To improve the accuracy of estimates of the column-averaged dry air mole fraction of atmospheric CO2 (XCO2), we need to understand the present situation about cloud screening in the rain forest regions and to examine the cloud-contaminated data whose processing might be possible with improvement of instruments or algorithms. In this study we evaluated the impact of thin clouds on estimates of the XCO2 using an atmospheric radiative transfer code, which can simulate the spectrum at the top of the atmosphere under thin cloud conditions. First, we decided the input parameters, among which relative position of the sun and satellite to observation point, surface reflectance using cloud-free GOSAT data products in the Amazon, FTS L1B data products (radiance spectral data), FTS L2 data products (CO2 column abundance data), and CAI L3 data products (clear-sky reflectance data). The evaluation was performed by comparing depths of the CO2 absorption lines in output radiation spectra with varying CO2 concentrations and cloud conditions, cloud type, cloud optical depth, and cloud top altitude. We will present our latest results.

New NASA Infrared Image of Irma Shows an Angry Eye

NASA Image and Video Library

2017-09-05

Hurricane Irma is the strongest hurricane ever recorded outside the Caribbean Sea and Gulf of Mexico. These two images from the Atmospheric Infrared Sounder (AIRS) instrument aboard NASA's Aqua satellite show what Hurricane Irma looked like when Aqua passed overhead just before 1 p.m. local time (10 a.m. PDT) on Sept. 5, 2017. Forecasts at the National Hurricane Center have Irma passing near the major islands to its west before turning northward near Florida this weekend. The first image (top) is an infrared snapshot from AIRS (see Figure 1 for larger image). In orange and red areas, the ocean surface shines through, while blue and purple areas represent cold, high clouds that obscure what lies below. Typical of well-developed hurricanes, Irma is nearly circular with a well-defined eye at its center. The eye is about 25 miles (40 kilometers) in diameter. Careful scrutiny shows a red pixel in the center of the eye, which means that AIRS achieved a bulls-eye with one of its "looks" and was able to see to the ocean between the dense clouds in the eye wall. The second image (bottom) shows the view through AIRS' microwave-colored "lenses" (see Figure 2 for larger image). Here the ocean surface looks yellow, while green represents various degrees of cloudiness. Blue shows areas where it is raining heavily. The eye is not apparent in this image because the "pixel size" of the microwave sounder, about 30 miles (50 kilometers), is larger than the eye and therefore cannot "thread the needle." The infrared sounder, on the other hand, has a pixel size of only 10 miles (16.5 kilometers) and can distinguish the small eye. https://photojournal.jpl.nasa.gov/catalog/PIA21941

Cloud and aerosol studies using combined CPL and MAS data

NASA Astrophysics Data System (ADS)

Vaughan, Mark A.; Rodier, Sharon; Hu, Yongxiang; McGill, Matthew J.; Holz, Robert E.

2004-11-01

Current uncertainties in the role of aerosols and clouds in the Earth's climate system limit our abilities to model the climate system and predict climate change. These limitations are due primarily to difficulties of adequately measuring aerosols and clouds on a global scale. The A-train satellites (Aqua, CALIPSO, CloudSat, PARASOL, and Aura) will provide an unprecedented opportunity to address these uncertainties. The various active and passive sensors of the A-train will use a variety of measurement techniques to provide comprehensive observations of the multi-dimensional properties of clouds and aerosols. However, to fully achieve the potential of this ensemble requires a robust data analysis framework to optimally and efficiently map these individual measurements into a comprehensive set of cloud and aerosol physical properties. In this work we introduce the Multi-Instrument Data Analysis and Synthesis (MIDAS) project, whose goal is to develop a suite of physically sound and computationally efficient algorithms that will combine active and passive remote sensing data in order to produce improved assessments of aerosol and cloud radiative and microphysical properties. These algorithms include (a) the development of an intelligent feature detection algorithm that combines inputs from both active and passive sensors, and (b) identifying recognizable multi-instrument signatures related to aerosol and cloud type derived from clusters of image pixels and the associated vertical profile information. Classification of these signatures will lead to the automated identification of aerosol and cloud types. Testing of these new algorithms is done using currently existing and readily available active and passive measurements from the Cloud Physics Lidar and the MODIS Airborne Simulator, which simulate, respectively, the CALIPSO and MODIS A-train instruments.

Remote Sensing of Clouds for Solar Forecasting Applications

NASA Astrophysics Data System (ADS)

Mejia, Felipe

A method for retrieving cloud optical depth (tauc) using a UCSD developed ground- based Sky Imager (USI) is presented. The Radiance Red-Blue Ratio (RRBR) method is motivated from the analysis of simulated images of various tauc produced by a Radiative Transfer Model (RTM). From these images the basic parameters affecting the radiance and RBR of a pixel are identified as the solar zenith angle (SZA), tau c , solar pixel an- gle/scattering angle (SPA), and pixel zenith angle/view angle (PZA). The effects of these parameters are described and the functions for radiance, Ilambda (tau c ,SZA,SPA,PZA) , and the red-blue ratio, RBR(tauc ,SZA,SPA,PZA) , are retrieved from the RTM results. RBR, which is commonly used for cloud detection in sky images, provides non-unique solutions for tau c , where RBR increases with tauc up to about tauc = 1 (depending on other parameters) and then decreases. Therefore, the RRBR algorithm uses the measured Imeaslambda (SPA,PZA) , in addition to RBRmeas (SPA,PZA ) to obtain a unique solution for tauc . The RRBR method is applied to images of liquid water clouds taken by a USI at the Oklahoma Atmospheric Radiation Measurement program (ARM) site over the course of 220 days and compared against measurements from a microwave radiometer (MWR) and output from the Min [ MH96a ] method for overcast skies. tau c values ranged from 0-80 with values over 80 being capped and registered as 80. A tauc RMSE of 2.5 between the Min method [ MH96b ] and the USI are observed. The MWR and USI have an RMSE of 2.2 which is well within the uncertainty of the MWR. The procedure developed here provides a foundation to test and develop other cloud detection algorithms. Using the RRBR tauc estimate as an input we then explore the potential of using tomographic techniques for 3-D cloud reconstruction. The Algebraic Reconstruction Technique (ART) is applied to optical depth maps from sky images to reconstruct 3-D cloud extinction coefficients. Reconstruction accuracy is explored for different products, including surface irradiance, extinction coefficients and Liquid Water Path, as a function of the number of available sky imagers (SIs) and setup distance. Increasing the number of cameras improves the accuracy of the 3-D reconstruction: For surface irradiance, the error decreases significantly up to four imagers at which point the improvements become marginal while k error continues to decrease with more cameras. The ideal distance between imagers was also explored: For a cloud height of 1 km, increasing distance up to 3 km (the domain length) improved the 3-D reconstruction for surface irradiance, while k error continued to decrease with increasing decrease. An iterative reconstruction technique was also used to improve the results of the ART by minimizing the error between input images and reconstructed simulations. For the best case of a nine imager deployment, the ART and iterative method resulted in 53.4% and 33.6% mean average error (MAE) for the extinction coefficients, respectively. The tomographic methods were then tested on real world test cases in the Uni- versity of California San Diego's (UCSD) solar testbed. Five UCSD sky imagers (USI) were installed across the testbed based on the best performing distances in simulations. Topographic obstruction is explored as a source of error by analyzing the increased error with obstruction in the field of view of the horizon. As more of the horizon is obstructed the error increases. If at least a field of view of 70° is available for the camera the accuracy is within 2% of the full field of view. Errors caused by stray light are also explored by removing the circumsolar region from images and comparing the cloud reconstruction to a full image. Removing less than 30% of the circumsolar region image and GHI errors were within 0.2% of the full image while errors in k increased 1%. Removing more than 30° around the sun resulted in inaccurate cloud reconstruction. Using four of the five USI a 3D cloud is reconstructed and compared to the fifth camera. The image of the fifth camera (excluded from the reconstruction) was then simulated and found to have a 22.9% error compared to the ground truth.

Estimating optically-thin cirrus cloud induced cold bias on infrared radiometric satellite sea surface temperature retrieval in the tropics

NASA Astrophysics Data System (ADS)

Marquis, Jared Wayne

Passive longwave infrared radiometric satellite-based retrievals of sea surface temperature (SST) at instrument nadir are investigated for cold bias caused by unscreened optically-thin cirrus (OTC) clouds (cloud optical depth ≤ 0.3; COD). Level 2 split-window SST retrievals over tropical oceans (30° S - 30° N) from Moderate Resolution Imaging Spectroradiometer (MODIS) radiances collected aboard the NASA Aqua satellite (Aqua-MODIS) are collocated with cloud profiles from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument, mounted on the independent NASA CALIPSO satellite. OTC are present in approximately 25% of tropical quality-assured (QA) Aqua-MODIS Level-2 data, representing over 99% of all contaminating cirrus found. This results in cold-biased SST retrievals using either split- (MODIS, AVHRR and VIIRS) or triple-window (AVHRR and VIIRS only) retrieval methods. SST retrievals are modeled based on operational algorithms using radiative transfer model simulations conducted with a hypothetical 1.5 km thick OTC cloud placed incrementally from 10.0 - 18.0 km above mean sea level for cloud optical depths (COD) between 0.0 - 0.3. Corresponding cold bias estimates for each sensor are estimated using relative Aqua-MODIS cloud contamination frequencies as a function of cloud top height and COD (assuming them consistent across each platform) integrated within each corresponding modeled cold bias matrix. Split-window relative OTC cold biases, for any single observation, range from 0.40° - 0.49° C for the three sensors, with an absolute (bulk mean) bias between 0.10° - 0.13° C. Triple-window retrievals are more resilient, ranging from 0.03° - 0.04° C relative and 0.11° - 0.16° C absolute. Cold biases are constant across the Pacific and Indian Ocean domains. Absolute bias is smaller over the Atlantic, but relative bias is larger due to different cloud properties indicating that this issue persists globally.

Particle tracking with a Timepix based triple GEM detector

NASA Astrophysics Data System (ADS)

George, S. P.; Murtas, F.; Alozy, J.; Curioni, A.; Rosenfeld, A. B.; Silari, M.

2015-11-01

This paper details the response of a triple GEM detector with a 55 μmetre pitch pixelated ASIC for readout. The detector is operated as a micro TPC with 9.5 cm3 sensitive volume and characterized with a mixed beam of 120 GeV protons and positive pions. A process for reconstruction of incident particle tracks from individual ionization clusters is described and scans of the gain and drift fields are performed. The angular resolution of the measured tracks is characterized. Also, the readout was operated in a mixed mode where some pixels measure drift time and others charge. This was used to measure the energy deposition in the detector and the charge cloud size as a function of interaction depth. The future uses of the device, including in microdosimetry are discussed.

Impulsive noise suppression in color images based on the geodesic digital paths

NASA Astrophysics Data System (ADS)

Smolka, Bogdan; Cyganek, Boguslaw

2015-02-01

In the paper a novel filtering design based on the concept of exploration of the pixel neighborhood by digital paths is presented. The paths start from the boundary of a filtering window and reach its center. The cost of transitions between adjacent pixels is defined in the hybrid spatial-color space. Then, an optimal path of minimum total cost, leading from pixels of the window's boundary to its center is determined. The cost of an optimal path serves as a degree of similarity of the central pixel to the samples from the local processing window. If a pixel is an outlier, then all the paths starting from the window's boundary will have high costs and the minimum one will also be high. The filter output is calculated as a weighted mean of the central pixel and an estimate constructed using the information on the minimum cost assigned to each image pixel. So, first the costs of optimal paths are used to build a smoothed image and in the second step the minimum cost of the central pixel is utilized for construction of the weights of a soft-switching scheme. The experiments performed on a set of standard color images, revealed that the efficiency of the proposed algorithm is superior to the state-of-the-art filtering techniques in terms of the objective restoration quality measures, especially for high noise contamination ratios. The proposed filter, due to its low computational complexity, can be applied for real time image denoising and also for the enhancement of video streams.

A robust object-based shadow detection method for cloud-free high resolution satellite images over urban areas and water bodies

NASA Astrophysics Data System (ADS)

Tatar, Nurollah; Saadatseresht, Mohammad; Arefi, Hossein; Hadavand, Ahmad

2018-06-01

Unwanted contrast in high resolution satellite images such as shadow areas directly affects the result of further processing in urban remote sensing images. Detecting and finding the precise position of shadows is critical in different remote sensing processing chains such as change detection, image classification and digital elevation model generation from stereo images. The spectral similarity between shadow areas, water bodies, and some dark asphalt roads makes the development of robust shadow detection algorithms challenging. In addition, most of the existing methods work on pixel-level and neglect the contextual information contained in neighboring pixels. In this paper, a new object-based shadow detection framework is introduced. In the proposed method a pixel-level shadow mask is built by extending established thresholding methods with a new C4 index which enables to solve the ambiguity of shadow and water bodies. Then the pixel-based results are further processed in an object-based majority analysis to detect the final shadow objects. Four different high resolution satellite images are used to validate this new approach. The result shows the superiority of the proposed method over some state-of-the-art shadow detection method with an average of 96% in F-measure.

The Nature of Turbulence in the LITTLE THINGS Dwarf Irregular Galaxies

DOE Office of Scientific and Technical Information (OSTI.GOV)

Maier, Erin; Chien, Li-Hsin; Hollyday, Gigja

We present probability density functions and higher order (skewness and kurtosis) analyses of the galaxy-wide and spatially resolved distributions of H i column density in the LITTLE THINGS sample of dwarf irregular galaxies. This analysis follows that of Burkhart et al. for the Small Magellanic Cloud (SMC). About 60% of our sample have galaxy-wide values of kurtosis that are similar to that found for the SMC, with a range up to much higher values, and kurtosis increases with integrated star formation rate. Kurtosis and skewness were calculated for radial annuli and for a grid of 32 pixel × 32 pixel kernels acrossmore » each galaxy. For most galaxies, kurtosis correlates with skewness. For about half of the galaxies, there is a trend of increasing kurtosis with radius. The range of kurtosis and skewness values is modeled by small variations in the Mach number close to the sonic limit and by conversion of H i to molecules at high column density. The maximum H i column densities decrease with increasing radius in a way that suggests molecules are forming in the weak-field limit, where H{sub 2} formation balances photodissociation in optically thin gas at the edges of clouds.« less

Comparisons of GLM and LMA Observations

NASA Astrophysics Data System (ADS)

Thomas, R. J.; Krehbiel, P. R.; Rison, W.; Stanley, M. A.; Attanasio, A.

2017-12-01

Observations from 3-dimensional VHF lightning mapping arrays (LMAs) provide a valuable basis for evaluating the spatial accuracy and detection efficiencies of observations from the recently launched, optical-based Geosynchronous Lightning Mapper (GLM). In this presentation, we describe results of comparing the LMA and GLM observations. First, the observations are compared spatially and temporally at the individual event (pixel) level for sets of individual discharges. For LMA networks in Florida, Colorado, and Oklahoma, the GLM observations are well correlated time-wise with LMA observations but are systematically offset by one- to two pixels ( 10 to 15 or 20 km) in a southwesterly direction from the actual lightning activity. The graphical comparisons show a similar location uncertainty depending on the altitude at which the scattered light is emitted from the parent cloud, due to being observed at slant ranges. Detection efficiencies (DEs) can be accurately determined graphically for intervals where individual flashes in a storm are resolved time-wise, and DEs and false alarm rates can be automated using flash sorting algorithms for overall and/or larger storms. This can be done as a function of flash size and duration, and generally shows high detection rates for larger flashes. Preliminary results during the May 1 2017 ER-2 overflight of Colorado storms indicate decreased detection efficiency if the storm is obscured by an overlying cloud layer.

Extensive Floods in United Kingdom

NASA Technical Reports Server (NTRS)

2002-01-01

Rain continues to fall in the United Kingdom, bringing more water to an already water-logged landscape. Some rivers there are experiencing their worst flooding in more than 50 years. Of particular note, Britain's River Ouse reached its highest levels on record since 1625. Thousands of people have been evacuated from their homes since October 30, when a large low-pressure system brought torrential rains and hurricane-force winds, placing regions around more than 40 rivers across the country on flood alert. Since then, the rains have persisted, keeping water levels high and causing additional rivers to overrun their banks. In all, at least 12 people have been killed and more than 5,000 properties flooded. Some officials estimate damages could reach 500 million pounds (roughly $715 million). These Landsat 7 scenes show a comparison of the region surrounding Exeter, England, before and after the floods. The top image was acquired September 28 and the bottom image was acquired October 30, 2000. Note the extensive flooding along the River Exe in the bottom image (blue pixels). The light bluish-white pixels in the top image are clouds, and the black splotches on the landscape are the clouds' shadows. The reddish-brown shapes are agricultural fields. Image by Robert Simmon and Brian Montgomery, NASA GSFC. Data provided by Ron Beck, USGS EROS Data Center.

Spring Dust Storm Smothers Beijing

NASA Technical Reports Server (NTRS)

2002-01-01

A few days earlier than usual, a large, dense plume of dust blew southward and eastward from the desert plains of Mongolia-quite smothering to the residents of Beijing. Citizens of northeastern China call this annual event the 'shachenbao,' or 'dust cloud tempest.' However, the tempest normally occurs during the spring time. The dust storm hit Beijing on Friday night, March 15, and began coating everything with a fine, pale brown layer of grit. The region is quite dry; a problem some believe has been exacerbated by decades of deforestation. According to Chinese government estimates, roughly 1 million tons of desert dust and sand blow into Beijing each year. This true-color image was made using two adjacent swaths (click to see the full image) of data from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS), flying aboard the OrbView-2 satellite, on March 17, 2002. The massive dust storm (brownish pixels) can easily be distinguished from clouds (bright white pixels) as it blows across northern Japan and eastward toward the open Pacific Ocean. The black regions are gaps between SeaWiFS' viewing swaths and represent areas where no data were collected. Image courtesy the SeaWiFS Project, NASA/Goddard Space Flight Center, and ORBIMAGE

All-sky photogrammetry techniques to georeference a cloud field

NASA Astrophysics Data System (ADS)

Crispel, Pierre; Roberts, Gregory

2018-01-01

In this study, we present a novel method of identifying and geolocalizing cloud field elements from a portable all-sky camera stereo network based on the ground and oriented towards zenith. The methodology is mainly based on stereophotogrammetry which is a 3-D reconstruction technique based on triangulation from corresponding stereo pixels in rectified images. In cases where clouds are horizontally separated, identifying individual positions is performed with segmentation techniques based on hue filtering and contour detection algorithms. Macroscopic cloud field characteristics such as cloud layer base heights and velocity fields are also deduced. In addition, the methodology is fitted to the context of measurement campaigns which impose simplicity of implementation, auto-calibration, and portability. Camera internal geometry models are achieved a priori in the laboratory and validated to ensure a certain accuracy in the peripheral parts of the all-sky image. Then, stereophotogrammetry with dense 3-D reconstruction is applied with cameras spaced 150 m apart for two validation cases. The first validation case is carried out with cumulus clouds having a cloud base height at 1500 m a.g.l. The second validation case is carried out with two cloud layers: a cumulus fractus layer with a base height at 1000 m a.g.l. and an altocumulus stratiformis layer with a base height of 2300 m a.g.l. Velocity fields at cloud base are computed by tracking image rectangular patterns through successive shots. The height uncertainty is estimated by comparison with a Vaisala CL31 ceilometer located on the site. The uncertainty on the horizontal coordinates and on the velocity field are theoretically quantified by using the experimental uncertainties of the cloud base height and camera orientation. In the first cumulus case, segmentation of the image is performed to identify individuals clouds in the cloud field and determine the horizontal positions of the cloud centers.

Low-level water vapor fields from the VAS split-window channels at 11 and 12 microns

NASA Technical Reports Server (NTRS)

Chesters, D.; Uccellini, L. W.; Robinson, W.

1983-01-01

Originally, the VAS split window channels were designed to use the differential water vapor absorption between 11 and 12 microns to estimate sea surface temperature by correcting for the radiometric losses caused by atmospheric moisture. It is shown that it is possible to reverse the procedure in order to estimate the vertically integrated low level moisture content with the background surface (skin) temperature removed, even over the bright, complex background of the land. Because the lower troposphere's water vapor content is an important factor in convective instability, the derived fields are of considerable value to mesoscale meteorology. Moisture patterns are available as quantitative fields (centimeters of precipitable water) at full VAS resolution (as fine as 7 kilometers horizontal resolution every 15 minutes), and are readily converted to image format for false color movies. The technique, demonstrated with GOES-5, uses a sequence of split window radiances taken once every 3 hours from dawn to dusk over the Eastern and Central United States. The algorithm is calibrated with the morning radiosonde sites embedded within the first VAS radiance field; then, entire moisture fields are calculated at all five observation times. Cloud contamination is removed by rejecting any pixel having a radiance less than the atmospheric brightness determined at the radiosonde sites.

Component separation for cosmic microwave background radiation

NASA Astrophysics Data System (ADS)

Fernández-Cobos, R.; Vielva, P.; Barreiro, R. B.; Martínez-González, E.

2011-11-01

Cosmic microwave background (CMB) radiation data obtained by different experiments contains, besides the desired signal, a superposition of microwave sky contributions mainly due to, on the one hand, synchrotron radiation, free-free emission and re-emission of dust clouds in our galaxy; and, on the other hand, extragalactic sources. We present an analytical method, using a wavelet decomposition on the sphere, to recover the CMB signal from microwave maps. Being applied to both temperature and polarization data, it is shown as a significant powerful tool when it is used in particularly polluted regions of the sky. The applied wavelet has the advantages of requiring little computering time in its calculations being adapted to the HEALPix pixelization scheme (which is the format that the community uses to report the CMB data) and offering the possibility of multi-resolution analysis. The decomposition is implemented as part of a template fitting method, minimizing the variance of the resulting map. The method was tested with simulations of WMAP data and results have been positive, with improvements up to 12% in the variance of the resulting full sky map and about 3% in low contaminate regions. Finally, we also present some preliminary results with WMAP data in the form of an angular cross power spectrum C_ℓ^{TE}, consistent with the spectrum offered by WMAP team.

Dual Microwave Radiometer Experiment Field Campaign Report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Marchand, Roger

Passive microwave radiometers (MWRs) are the most commonly used and accurate instruments the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Research Facility has to retrieve cloud liquid water path (LWP). The MWR measurements (microwave radiances or brightness temperatures) are often used to derive LWP using climatological constraints, but are frequently also combined with measurements from radar and other instruments for cloud microphysical retrievals. Nominally this latter approach improves the retrieval of LWP and other cloud microphysical quantities (such as effective radius or number concentration), but this also means that when MWR data are poor, other cloud microphysical quantitiesmore » are also negatively affected. Unfortunately, current MWR data is often contaminated by water on the MWR radome. This water makes a substantial contribution to the measured radiance and typically results in retrievals of cloud liquid water and column water vapor that are biased high. While it is obvious when the contamination by standing water is large (and retrieval biases are large), much of the time it is difficult to know with confidence that there is no contamination. At present there is no attempt to estimate or correct for this source of error, and identification of problems is largely left to users. Typically users are advised to simply throw out all data when the MWR “wet-window” resistance-based sensor indicates water is present, but this sensor is adjusted by hand and is known to be temperamental. In order to address this problem, a pair of ARM microwave radiometers was deployed to the University of Washington (UW) in Seattle, Washington, USA. The radiometers were operated such that one radiometer was scanned under a cover that (nominally) prevents this radiometer radome from gathering water and permits measurements away from zenith; while the other radiometer is operated normally – open or uncovered - with the radome exposed to the sky. The idea is that (1) the covered radiometer data can provide LWP (and water vapor) along the off-zenith slant path and (2) the two sets of measurements can be compared to identify when wet-radome contamination is occurring.« less

Convective Cloud Towers and Precipitation Initiation, Frequency and Intensity

NASA Astrophysics Data System (ADS)

Vant-hull, B.; Mahani, S. E.; Autones, F.; Rabin, R.; Mecikalski, J. R.; Khanbilvardi, R.

2012-12-01

: Geosynchronous satellite retrieval of precipitation is desirable because it would provide continuous observation throughout most of the globe in regions where radar data is not available. In the current work the distribution of precipitation rates is examined as a function of cloud tower area and cloud top temperature. A thunderstorm tracking algorithm developed at Meteo-France is used to track cumulus towers that are matched up with radar data at 5 minute 1 km resolution. It is found that roughly half of the precipitation occurs in the cloud mass that surrounds the towers, and when a tower is first detected the precipitation is already in progress 50% of the time. The average density of precipitation per area is greater as the towers become smaller and colder, yet the averaged shape of the precipitation intensity distribution is remarkably constant in all convective situations with cloud tops warmer than 220 K. This suggests that on average all convective precipitation events look the same, unaffected by the higher frequency of occurrence per area inside the convective towers. Only once the cloud tops are colder than 220 K does the precipitation intensity distribution become weighted towards higher instantaneous intensities. Radar precipitation shown in shades of green to blue, lightning in orange; black diamonds are coldest points in each tower. Ratio of number of pixels of given precipitation inside versus outside the convective towers, for various average cloud top temperatures. A flat plot indicates the distribution of rainfall inside and outside the towers has the same shape.

Cloud detection algorithm comparison and validation for operational Landsat data products

USGS Publications Warehouse

Foga, Steven Curtis; Scaramuzza, Pat; Guo, Song; Zhu, Zhe; Dilley, Ronald; Beckmann, Tim; Schmidt, Gail L.; Dwyer, John L.; Hughes, MJ; Laue, Brady

2017-01-01

Clouds are a pervasive and unavoidable issue in satellite-borne optical imagery. Accurate, well-documented, and automated cloud detection algorithms are necessary to effectively leverage large collections of remotely sensed data. The Landsat project is uniquely suited for comparative validation of cloud assessment algorithms because the modular architecture of the Landsat ground system allows for quick evaluation of new code, and because Landsat has the most comprehensive manual truth masks of any current satellite data archive. Currently, the Landsat Level-1 Product Generation System (LPGS) uses separate algorithms for determining clouds, cirrus clouds, and snow and/or ice probability on a per-pixel basis. With more bands onboard the Landsat 8 Operational Land Imager (OLI)/Thermal Infrared Sensor (TIRS) satellite, and a greater number of cloud masking algorithms, the U.S. Geological Survey (USGS) is replacing the current cloud masking workflow with a more robust algorithm that is capable of working across multiple Landsat sensors with minimal modification. Because of the inherent error from stray light and intermittent data availability of TIRS, these algorithms need to operate both with and without thermal data. In this study, we created a workflow to evaluate cloud and cloud shadow masking algorithms using cloud validation masks manually derived from both Landsat 7 Enhanced Thematic Mapper Plus (ETM +) and Landsat 8 OLI/TIRS data. We created a new validation dataset consisting of 96 Landsat 8 scenes, representing different biomes and proportions of cloud cover. We evaluated algorithm performance by overall accuracy, omission error, and commission error for both cloud and cloud shadow. We found that CFMask, C code based on the Function of Mask (Fmask) algorithm, and its confidence bands have the best overall accuracy among the many algorithms tested using our validation data. The Artificial Thermal-Automated Cloud Cover Algorithm (AT-ACCA) is the most accurate nonthermal-based algorithm. We give preference to CFMask for operational cloud and cloud shadow detection, as it is derived from a priori knowledge of physical phenomena and is operable without geographic restriction, making it useful for current and future land imaging missions without having to be retrained in a machine-learning environment.

Infrared Cloud Imager Development for Atmospheric Optical Communication Characterization, and Measurements at the JPL Table Mountain Facility

NASA Astrophysics Data System (ADS)

Nugent, P. W.; Shaw, J. A.; Piazzolla, S.

2013-02-01

The continuous demand for high data return in deep space and near-Earth satellite missions has led NASA and international institutions to consider alternative technologies for high-data-rate communications. One solution is the establishment of wide-bandwidth Earth-space optical communication links, which require (among other things) a nearly obstruction-free atmospheric path. Considering the atmospheric channel, the most common and most apparent impairments on Earth-space optical communication paths arise from clouds. Therefore, the characterization of the statistical behavior of cloud coverage for optical communication ground station candidate sites is of vital importance. In this article, we describe the development and deployment of a ground-based, long-wavelength infrared cloud imaging system able to monitor and characterize the cloud coverage. This system is based on a commercially available camera with a 62-deg diagonal field of view. A novel internal-shutter-based calibration technique allows radiometric calibration of the camera, which operates without a thermoelectric cooler. This cloud imaging system provides continuous day-night cloud detection with constant sensitivity. The cloud imaging system also includes data-processing algorithms that calculate and remove atmospheric emission to isolate cloud signatures, and enable classification of clouds according to their optical attenuation. Measurements of long-wavelength infrared cloud radiance are used to retrieve the optical attenuation (cloud optical depth due to absorption and scattering) in the wavelength range of interest from visible to near-infrared, where the cloud attenuation is quite constant. This article addresses the specifics of the operation, calibration, and data processing of the imaging system that was deployed at the NASA/JPL Table Mountain Facility (TMF) in California. Data are reported from July 2008 to July 2010. These data describe seasonal variability in cloud cover at the TMF site, with cloud amount (percentage of cloudy pixels) peaking at just over 51 percent during February, of which more than 60 percent had optical attenuation exceeding 12 dB at wavelengths in the range from the visible to the near-infrared. The lowest cloud amount was found during August, averaging 19.6 percent, and these clouds were mostly optically thin, with low attenuation.

Advancing the quantification of humid tropical forest cover loss with multi-resolution optical remote sensing data: Sampling & wall-to-wall mapping

NASA Astrophysics Data System (ADS)

Broich, Mark

Humid tropical forest cover loss is threatening the sustainability of ecosystem goods and services as vast forest areas are rapidly cleared for industrial scale agriculture and tree plantations. Despite the importance of humid tropical forest in the provision of ecosystem services and economic development opportunities, the spatial and temporal distribution of forest cover loss across large areas is not well quantified. Here I improve the quantification of humid tropical forest cover loss using two remote sensing-based methods: sampling and wall-to-wall mapping. In all of the presented studies, the integration of coarse spatial, high temporal resolution data with moderate spatial, low temporal resolution data enable advances in quantifying forest cover loss in the humid tropics. Imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS) are used as the source of coarse spatial resolution, high temporal resolution data and imagery from the Landsat Enhanced Thematic Mapper Plus (ETM+) sensor are used as the source of moderate spatial, low temporal resolution data. In a first study, I compare the precision of different sampling designs for the Brazilian Amazon using the annual deforestation maps derived by the Brazilian Space Agency for reference. I show that sampling designs can provide reliable deforestation estimates; furthermore, sampling designs guided by MODIS data can provide more efficient estimates than the systematic design used for the United Nations Food and Agricultural Organization Forest Resource Assessment 2010. Sampling approaches, such as the one demonstrated, are viable in regions where data limitations, such as cloud contamination, limit exhaustive mapping methods. Cloud-contaminated regions experiencing high rates of change include Insular Southeast Asia, specifically Indonesia and Malaysia. Due to persistent cloud cover, forest cover loss in Indonesia has only been mapped at a 5-10 year interval using photo interpretation of single best Landsat images. Such an approach does not provide timely results, and cloud cover reduces the utility of map outputs. In a second study, I develop a method to exhaustively mine the recently opened Landsat archive for cloud-free observations and automatically map forest cover loss for Sumatra and Kalimantan for the 2000-2005 interval. In a comparison with a reference dataset consisting of 64 Landsat sample blocks, I show that my method, using per pixel time-series, provides more accurate forest cover loss maps for multiyear intervals than approaches using image composites. In a third study, I disaggregate Landsat-mapped forest cover loss, mapped over a multiyear interval, by year using annual forest cover loss maps generated from coarse spatial, high temporal resolution MODIS imagery. I further disaggregate and analyze forest cover loss by forest land use, and provinces. Forest cover loss trends show high spatial and temporal variability. These results underline the importance of annual mapping for the quantification of forest cover loss in Indonesia, specifically in the light of the developing Reducing Emissions from Deforestation and Forest Degradation in Developing Countries policy framework (REDD). All three studies highlight the advances in quantifying forest cover loss in the humid tropics made by integrating coarse spatial, high temporal resolution data with moderate spatial, low temporal resolution data. The three methods presented can be combined into an integrated monitoring strategy.

Optimizing selection of training and auxiliary data for operational land cover classification for the LCMAP initiative

NASA Astrophysics Data System (ADS)

Zhu, Zhe; Gallant, Alisa L.; Woodcock, Curtis E.; Pengra, Bruce; Olofsson, Pontus; Loveland, Thomas R.; Jin, Suming; Dahal, Devendra; Yang, Limin; Auch, Roger F.

2016-12-01

The U.S. Geological Survey's Land Change Monitoring, Assessment, and Projection (LCMAP) initiative is a new end-to-end capability to continuously track and characterize changes in land cover, use, and condition to better support research and applications relevant to resource management and environmental change. Among the LCMAP product suite are annual land cover maps that will be available to the public. This paper describes an approach to optimize the selection of training and auxiliary data for deriving the thematic land cover maps based on all available clear observations from Landsats 4-8. Training data were selected from map products of the U.S. Geological Survey's Land Cover Trends project. The Random Forest classifier was applied for different classification scenarios based on the Continuous Change Detection and Classification (CCDC) algorithm. We found that extracting training data proportionally to the occurrence of land cover classes was superior to an equal distribution of training data per class, and suggest using a total of 20,000 training pixels to classify an area about the size of a Landsat scene. The problem of unbalanced training data was alleviated by extracting a minimum of 600 training pixels and a maximum of 8000 training pixels per class. We additionally explored removing outliers contained within the training data based on their spectral and spatial criteria, but observed no significant improvement in classification results. We also tested the importance of different types of auxiliary data that were available for the conterminous United States, including: (a) five variables used by the National Land Cover Database, (b) three variables from the cloud screening "Function of mask" (Fmask) statistics, and (c) two variables from the change detection results of CCDC. We found that auxiliary variables such as a Digital Elevation Model and its derivatives (aspect, position index, and slope), potential wetland index, water probability, snow probability, and cloud probability improved the accuracy of land cover classification. Compared to the original strategy of the CCDC algorithm (500 pixels per class), the use of the optimal strategy improved the classification accuracies substantially (15-percentage point increase in overall accuracy and 4-percentage point increase in minimum accuracy).

Cloud Front

NASA Technical Reports Server (NTRS)

2006-01-01

[figure removed for brevity, see original site] Context image for PIA02171 Cloud Front

These clouds formed in the south polar region. The faintness of the cloud system likely indicates that these are mainly ice clouds, with relatively little dust content.

Image information: VIS instrument. Latitude -86.7N, Longitude 212.3E. 17 meter/pixel resolution.

Note: this THEMIS visual image has not been radiometrically nor geometrically calibrated for this preliminary release. An empirical correction has been performed to remove instrumental effects. A linear shift has been applied in the cross-track and down-track direction to approximate spacecraft and planetary motion. Fully calibrated and geometrically projected images will be released through the Planetary Data System in accordance with Project policies at a later time.

NASA's Jet Propulsion Laboratory manages the 2001 Mars Odyssey mission for NASA's Office of Space Science, Washington, D.C. The Thermal Emission Imaging System (THEMIS) was developed by Arizona State University, Tempe, in collaboration with Raytheon Santa Barbara Remote Sensing. The THEMIS investigation is led by Dr. Philip Christensen at Arizona State University. Lockheed Martin Astronautics, Denver, is the prime contractor for the Odyssey project, and developed and built the orbiter. Mission operations are conducted jointly from Lockheed Martin and from JPL, a division of the California Institute of Technology in Pasadena.

Detection of ground fog in mountainous areas from MODIS (Collection 051) daytime data using a statistical approach

NASA Astrophysics Data System (ADS)

Schulz, Hans Martin; Thies, Boris; Chang, Shih-Chieh; Bendix, Jörg

2016-03-01

The mountain cloud forest of Taiwan can be delimited from other forest types using a map of the ground fog frequency. In order to create such a frequency map from remotely sensed data, an algorithm able to detect ground fog is necessary. Common techniques for ground fog detection based on weather satellite data cannot be applied to fog occurrences in Taiwan as they rely on several assumptions regarding cloud properties. Therefore a new statistical method for the detection of ground fog in mountainous terrain from MODIS Collection 051 data is presented. Due to the sharpening of input data using MODIS bands 1 and 2, the method provides fog masks in a resolution of 250 m per pixel. The new technique is based on negative correlations between optical thickness and terrain height that can be observed if a cloud that is relatively plane-parallel is truncated by the terrain. A validation of the new technique using camera data has shown that the quality of fog detection is comparable to that of another modern fog detection scheme developed and validated for the temperate zones. The method is particularly applicable to optically thinner water clouds. Beyond a cloud optical thickness of ≈ 40, classification errors significantly increase.

An Improved Cloud Classification Algorithm for China’s FY-2C Multi-Channel Images Using Artificial Neural Network

PubMed Central

Liu, Yu; Xia, Jun; Shi, Chun-Xiang; Hong, Yang

2009-01-01

The crowning objective of this research was to identify a better cloud classification method to upgrade the current window-based clustering algorithm used operationally for China’s first operational geostationary meteorological satellite FengYun-2C (FY-2C) data. First, the capabilities of six widely-used Artificial Neural Network (ANN) methods are analyzed, together with the comparison of two other methods: Principal Component Analysis (PCA) and a Support Vector Machine (SVM), using 2864 cloud samples manually collected by meteorologists in June, July, and August in 2007 from three FY-2C channel (IR1, 10.3–11.3 μm; IR2, 11.5–12.5 μm and WV 6.3–7.6 μm) imagery. The result shows that: (1) ANN approaches, in general, outperformed the PCA and the SVM given sufficient training samples and (2) among the six ANN networks, higher cloud classification accuracy was obtained with the Self-Organizing Map (SOM) and Probabilistic Neural Network (PNN). Second, to compare the ANN methods to the present FY-2C operational algorithm, this study implemented SOM, one of the best ANN network identified from this study, as an automated cloud classification system for the FY-2C multi-channel data. It shows that SOM method has improved the results greatly not only in pixel-level accuracy but also in cloud patch-level classification by more accurately identifying cloud types such as cumulonimbus, cirrus and clouds in high latitude. Findings of this study suggest that the ANN-based classifiers, in particular the SOM, can be potentially used as an improved Automated Cloud Classification Algorithm to upgrade the current window-based clustering method for the FY-2C operational products. PMID:22346714

An Improved Cloud Classification Algorithm for China's FY-2C Multi-Channel Images Using Artificial Neural Network.

PubMed

Liu, Yu; Xia, Jun; Shi, Chun-Xiang; Hong, Yang

2009-01-01

The crowning objective of this research was to identify a better cloud classification method to upgrade the current window-based clustering algorithm used operationally for China's first operational geostationary meteorological satellite FengYun-2C (FY-2C) data. First, the capabilities of six widely-used Artificial Neural Network (ANN) methods are analyzed, together with the comparison of two other methods: Principal Component Analysis (PCA) and a Support Vector Machine (SVM), using 2864 cloud samples manually collected by meteorologists in June, July, and August in 2007 from three FY-2C channel (IR1, 10.3-11.3 μm; IR2, 11.5-12.5 μm and WV 6.3-7.6 μm) imagery. The result shows that: (1) ANN approaches, in general, outperformed the PCA and the SVM given sufficient training samples and (2) among the six ANN networks, higher cloud classification accuracy was obtained with the Self-Organizing Map (SOM) and Probabilistic Neural Network (PNN). Second, to compare the ANN methods to the present FY-2C operational algorithm, this study implemented SOM, one of the best ANN network identified from this study, as an automated cloud classification system for the FY-2C multi-channel data. It shows that SOM method has improved the results greatly not only in pixel-level accuracy but also in cloud patch-level classification by more accurately identifying cloud types such as cumulonimbus, cirrus and clouds in high latitude. Findings of this study suggest that the ANN-based classifiers, in particular the SOM, can be potentially used as an improved Automated Cloud Classification Algorithm to upgrade the current window-based clustering method for the FY-2C operational products.

Cloud photogrammetry with dense stereo for fisheye cameras

NASA Astrophysics Data System (ADS)

Beekmans, Christoph; Schneider, Johannes; Läbe, Thomas; Lennefer, Martin; Stachniss, Cyrill; Simmer, Clemens

2016-11-01

We present a novel approach for dense 3-D cloud reconstruction above an area of 10 × 10 km2 using two hemispheric sky imagers with fisheye lenses in a stereo setup. We examine an epipolar rectification model designed for fisheye cameras, which allows the use of efficient out-of-the-box dense matching algorithms designed for classical pinhole-type cameras to search for correspondence information at every pixel. The resulting dense point cloud allows to recover a detailed and more complete cloud morphology compared to previous approaches that employed sparse feature-based stereo or assumed geometric constraints on the cloud field. Our approach is very efficient and can be fully automated. From the obtained 3-D shapes, cloud dynamics, size, motion, type and spacing can be derived, and used for radiation closure under cloudy conditions, for example. Fisheye lenses follow a different projection function than classical pinhole-type cameras and provide a large field of view with a single image. However, the computation of dense 3-D information is more complicated and standard implementations for dense 3-D stereo reconstruction cannot be easily applied. Together with an appropriate camera calibration, which includes internal camera geometry, global position and orientation of the stereo camera pair, we use the correspondence information from the stereo matching for dense 3-D stereo reconstruction of clouds located around the cameras. We implement and evaluate the proposed approach using real world data and present two case studies. In the first case, we validate the quality and accuracy of the method by comparing the stereo reconstruction of a stratocumulus layer with reflectivity observations measured by a cloud radar and the cloud-base height estimated from a Lidar-ceilometer. The second case analyzes a rapid cumulus evolution in the presence of strong wind shear.

Radiative Susceptibility of Cloudy Atmospheres to Droplet Number Perturbations: 1. Theoretical Analysis and Examples from MODIS

NASA Technical Reports Server (NTRS)

Platnick, Steven; Oreopoulos, Lazaros

2008-01-01

Theoretical and satellite-based assessments of the sensitivity of broadband shortwave radiative fluxes in cloudy atmospheres to small perturbations in the cloud droplet number concentration (N) of liquid water clouds under constant water conditions are performed. Two approaches to study this sensitivity are adopted: absolute increases in N, for which the radiative response is referred to as absolute cloud susceptibility, and relative increases in N or relative cloud susceptibility. Estimating the former is more challenging as it requires an assumed value for either cloud liquid water content or geometrical thickness; both susceptibilities require an assumed relationship between the droplet volume and effective radius. Expanding upon previous susceptibility studies, present radiative calculations include the effect of AN perturbations on droplet asymmetry parameter and single-scattering albedo, in addition to extinction. Absolute cloud susceptibility has a strong nonlinear dependence on the droplet effective radius as expected, while relative cloud susceptibility is primarily dependent on optical thickness. Molecular absorption and reflecting surfaces both reduce the relative contribution of the cloud to the top-of-atmosphere (TOA) flux and therefore also reduce the TOA albedo susceptibility. Transmittance susceptibilities are negative with absolute values similar to albedo susceptibility, while atmospheric absorptance susceptibilities are about an order of magnitude smaller than albedo susceptibilities and can be either positive or negative. Observation-based susceptibility calculations are derived from MODIS pixel-level retrievals of liquid water cloud optical thickness, effective radius, and cloud top temperature; two data granule examples are shown. Susceptibility quantifies the aerosol indirect effect sensitivity in a way that can be easily computed from model fields. As such, susceptibilities derived from MODIS observations provide a higher-order test of model cloud properties used for indirect effect studies. MODIS-derived global distributions of cloud susceptibility and radiative forcing calculations are presented in a companion paper.

Coupled Retrieval of Liquid Water Cloud and Above-Cloud Aerosol Properties Using the Airborne Multiangle SpectroPolarimetric Imager (AirMSPI)

NASA Astrophysics Data System (ADS)

Xu, Feng; van Harten, Gerard; Diner, David J.; Davis, Anthony B.; Seidel, Felix C.; Rheingans, Brian; Tosca, Mika; Alexandrov, Mikhail D.; Cairns, Brian; Ferrare, Richard A.; Burton, Sharon P.; Fenn, Marta A.; Hostetler, Chris A.; Wood, Robert; Redemann, Jens

2018-03-01

An optimization algorithm is developed to retrieve liquid water cloud properties including cloud optical depth (COD), droplet size distribution and cloud top height (CTH), and above-cloud aerosol properties including aerosol optical depth (AOD), single-scattering albedo, and microphysical properties from sweep-mode observations by Jet Propulsion Laboratory's Airborne Multiangle SpectroPolarimetric Imager (AirMSPI) instrument. The retrieval is composed of three major steps: (1) initial estimate of the mean droplet size distribution across the entire image of 80-100 km along track by 10-25 km across track from polarimetric cloudbow observations, (2) coupled retrieval of image-scale cloud and above-cloud aerosol properties by fitting the polarimetric data at all observation angles, and (3) iterative retrieval of 1-D radiative transfer-based COD and droplet size distribution at pixel scale (25 m) by establishing relationships between COD and droplet size and fitting the total radiance measurements. Our retrieval is tested using 134 AirMSPI data sets acquired during the National Aeronautics and Space Administration (NASA) field campaign ObseRvations of Aerosols above CLouds and their intEractionS. The retrieved above-cloud AOD and CTH are compared to coincident HSRL-2 (HSRL-2, NASA Langley Research Center) data, and COD and droplet size distribution parameters (effective radius reff and effective variance veff) are compared to coincident Research Scanning Polarimeter (RSP) (NASA Goddard Institute for Space Studies) data. Mean absolute differences between AirMSPI and HSRL-2 retrievals of above-cloud AOD at 532 nm and CTH are 0.03 and <0.5 km, respectively. At RSP's footprint scale ( 323 m), mean absolute differences between RSP and AirMSPI retrievals of COD, reff, and veff in the cloudbow area are 2.33, 0.69 μm, and 0.020, respectively. Neglect of smoke aerosols above cloud leads to an underestimate of image-averaged COD by 15%.

Evaluation of Satellite-Based Upper Troposphere Cloud Top Height Retrievals in Multilayer Cloud Conditions During TC4

NASA Technical Reports Server (NTRS)

Chang, Fu-Lung; Minnis, Patrick; Ayers, J. Kirk; McGill, Matthew J.; Palikonda, Rabindra; Spangenberg, Douglas A.; Smith, William L., Jr.; Yost, Christopher R.

2010-01-01

Upper troposphere cloud top heights (CTHs), restricted to cloud top pressures (CTPs) less than 500 hPa, inferred using four satellite retrieval methods applied to Twelfth Geostationary Operational Environmental Satellite (GOES-12) data are evaluated using measurements during the July August 2007 Tropical Composition, Cloud and Climate Coupling Experiment (TC4). The four methods are the single-layer CO2-absorption technique (SCO2AT), a modified CO2-absorption technique (MCO2AT) developed for improving both single-layered and multilayered cloud retrievals, a standard version of the Visible Infrared Solar-infrared Split-window Technique (old VISST), and a new version of VISST (new VISST) recently developed to improve cloud property retrievals. They are evaluated by comparing with ER-2 aircraft-based Cloud Physics Lidar (CPL) data taken during 9 days having extensive upper troposphere cirrus, anvil, and convective clouds. Compared to the 89% coverage by upper tropospheric clouds detected by the CPL, the SCO2AT, MCO2AT, old VISST, and new VISST retrieved CTPs less than 500 hPa in 76, 76, 69, and 74% of the matched pixels, respectively. Most of the differences are due to subvisible and optically thin cirrus clouds occurring near the tropopause that were detected only by the CPL. The mean upper tropospheric CTHs for the 9 days are 14.2 (+/- 2.1) km from the CPL and 10.7 (+/- 2.1), 12.1 (+/- 1.6), 9.7 (+/- 2.9), and 11.4 (+/- 2.8) km from the SCO2AT, MCO2AT, old VISST, and new VISST, respectively. Compared to the CPL, the MCO2AT CTHs had the smallest mean biases for semitransparent high clouds in both single-layered and multilayered situations whereas the new VISST CTHs had the smallest mean biases when upper clouds were opaque and optically thick. The biases for all techniques increased with increasing numbers of cloud layers. The transparency of the upper layer clouds tends to increase with the numbers of cloud layers.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Davis, A.B.; Clothiaux, E.

Because of Earth`s gravitational field, its atmosphere is strongly anisotropic with respect to the vertical; the effect of the Earth`s rotation on synoptic wind patterns also causes a more subtle form of anisotropy in the horizontal plane. The authors survey various approaches to statistically robust anisotropy from a wavelet perspective and present a new one adapted to strongly non-isotropic fields that are sampled on a rectangular grid with a large aspect ratio. This novel technique uses an anisotropic version of Multi-Resolution Analysis (MRA) in image analysis; the authors form a tensor product of the standard dyadic Haar basis, where themore » dividing ratio is {lambda}{sub z} = 2, and a nonstandard triadic counterpart, where the dividing ratio is {lambda}{sub x} = 3. The natural support of the field is therefore 2{sup n} pixels (vertically) by 3{sup n} pixels (horizontally) where n is the number of levels in the MRA. The natural triadic basis includes the French top-hat wavelet which resonates with bumps in the field whereas the Haar wavelet responds to ramps or steps. The complete 2D basis has one scaling function and five wavelets. The resulting anisotropic MRA is designed for application to the liquid water content (LWC) field in boundary-layer clouds, as the prevailing wind advects them by a vertically pointing mm-radar system. Spatial correlations are notoriously long-range in cloud structure and the authors use the wavelet coefficients from the new MRA to characterize these correlations in a multifractal analysis scheme. In the present study, the MRA is used (in synthesis mode) to generate fields that mimic cloud structure quite realistically although only a few parameters are used to control the randomness of the LWC`s wavelet coefficients.« less

Cirrus Horizontal Heterogeneity Effects on Cloud Optical Properties Retrieved from MODIS VNIR to TIR Channels as a Function of the Spatial Resolution

NASA Astrophysics Data System (ADS)

Fauchez, T.; Platnick, S. E.; Sourdeval, O.; Wang, C.; Meyer, K.; Cornet, C.; Szczap, F.

2017-12-01

Cirrus are an important part of the Earth radiation budget but an assessment of their role yet remains highly uncertain. Cirrus optical properties such as Cloud Optical Thickness (COT) and ice crystal effective particle size (Re) are often retrieved with a combination of Visible/Near InfraRed (VNIR) and ShortWave-InfraRed (SWIR) reflectance channels. Alternatively, Thermal InfraRed (TIR) techniques, such as the Split Window Technique (SWT), have demonstrated better sensitivity to thin cirrus. However, current satellite operational products for both retrieval methods assume that cloudy pixels are horizontally homogeneous (Plane Parallel and Homogeneous Approximation (PPHA)) and independent (Independent Pixel Approximation (IPA)). The impact of these approximations on cirrus retrievals needs to be understood and, as far as possible, corrected. Horizontal heterogeneity effects can be more easily estimated and corrected in the TIR range because they are mainly dominated by the PPA bias, which primarily depends on the COT subpixel heterogeneity. For solar reflectance channels, in addition to the PPHA bias, the IPA can lead to significant retrieval errors if there is large photon transport between cloudy columns in addition to brightening and shadowing effects that are more difficult to quantify.The effects of cirrus horizontal heterogeneity are here studied on COT and Re retrievals obtained using simulated MODIS reflectances at 0.86 and 2.11 μm and radiances at 8.5, 11.0 and 12.0 μm, for spatial resolutions ranging from 50 m to 10 km. For each spatial resolution, simulated TOA reflectances and radiances are combined for cloud optical property retrievals with a research-level optimal estimation retrieval method (OEM). The impact of horizontal heterogeneity on the retrieved products is assessed for different solar geometries and various combinations of the five channels.

Use of MODIS Cloud Top Pressure to Improve Assimilation Yields of AIRS Radiances in GSI

NASA Technical Reports Server (NTRS)

Zavodsky, Bradley; Srikishen, Jayanthi

2014-01-01

Radiances from hyperspectral sounders such as the Atmospheric Infrared Sounder (AIRS) are routinely assimilated both globally and regionally in operational numerical weather prediction (NWP) systems using the Gridpoint Statistical Interpolation (GSI) data assimilation system. However, only thinned, cloud-free radiances from a 281-channel subset are used, so the overall percentage of these observations that are assimilated is somewhere on the order of 5%. Cloud checks are performed within GSI to determine which channels peak above cloud top; inaccuracies may lead to less assimilated radiances or introduction of biases from cloud-contaminated radiances.Relatively large footprint from AIRS may not optimally represent small-scale cloud features that might be better resolved by higher-resolution imagers like the Moderate Resolution Imaging Spectroradiometer (MODIS). Objective of this project is to "swap" the MODIS-derived cloud top pressure (CTP) for that designated by the AIRS-only quality control within GSI to test the hypothesis that better representation of cloud features will result in higher assimilated radiance yields and improved forecasts.

MISR CMVs and Multiangular Views of Tropical Cyclone Inner-Core Dynamics

NASA Technical Reports Server (NTRS)

Wu, Dong L.; Diner, David J.; Garay, Michael J; Jovanovic, Veljko M.; Lee, Jae N.; Moroney, Catherine M.; Mueller, Kevin J.; Nelson, David L.

2010-01-01

Multi-camera stereo imaging of cloud features from the MISR (Multiangle Imaging SpectroRadiometer) instrument on NASA's Terra satellite provides accurate and precise measurements of cloud top heights (CTH) and cloud motion vector (CMV) winds. MISR observes each cloudy scene from nine viewing angles (Nadir, +/-26(sup o), +/-46(sup o), +/-60(sup o), +/-70(sup o)) with approximatel 275-m pixel resolution. This paper provides an update on MISR CMV and CTH algorithm improvements, and explores a high-resolution retrieval of tangential winds inside the eyewall of tropical cyclones (TC). The MISR CMV and CTH retrievals from the updated algorithm are significantly improved in terms of spatial coverage and systematic errors. A new product, the 1.1-km cross-track wind, provides high accuracy and precision in measuring convective outflows. Preliminary results obtained from the 1.1-km tangential wind retrieval inside the TC eyewall show that the inner-core rotation is often faster near the eyewall, and this faster rotation appears to be related linearly to cyclone intensity.

A Platform for Scalable Satellite and Geospatial Data Analysis

NASA Astrophysics Data System (ADS)

Beneke, C. M.; Skillman, S.; Warren, M. S.; Kelton, T.; Brumby, S. P.; Chartrand, R.; Mathis, M.

2017-12-01

At Descartes Labs, we use the commercial cloud to run global-scale machine learning applications over satellite imagery. We have processed over 5 Petabytes of public and commercial satellite imagery, including the full Landsat and Sentinel archives. By combining open-source tools with a FUSE-based filesystem for cloud storage, we have enabled a scalable compute platform that has demonstrated reading over 200 GB/s of satellite imagery into cloud compute nodes. In one application, we generated global 15m Landsat-8, 20m Sentinel-1, and 10m Sentinel-2 composites from 15 trillion pixels, using over 10,000 CPUs. We recently created a public open-source Python client library that can be used to query and access preprocessed public satellite imagery from within our platform, and made this platform available to researchers for non-commercial projects. In this session, we will describe how you can use the Descartes Labs Platform for rapid prototyping and scaling of geospatial analyses and demonstrate examples in land cover classification.

Observations and simulations of three-dimensional radiative interactions between Arctic boundary layer clouds and ice floes

NASA Astrophysics Data System (ADS)

Schäfer, M.; Bierwirth, E.; Ehrlich, A.; Jäkel, E.; Wendisch, M.

2015-01-01

Based on airborne spectral imaging observations three-dimensional (3-D) radiative effects between Arctic boundary layer clouds and ice floes have been identified and quantified. A method is presented to discriminate sea ice and open water in case of clouds from imaging radiance measurements. This separation simultaneously reveals that in case of clouds the transition of radiance between open water and sea ice is not instantaneously but horizontally smoothed. In general, clouds reduce the nadir radiance above bright surfaces in the vicinity of sea ice - open water boundaries, while the nadir radiance above dark surfaces is enhanced compared to situations with clouds located above horizontal homogeneous surfaces. With help of the observations and 3-D radiative transfer simulations, this effect was quantified to range between 0 and 2200 m distance to the sea ice edge. This affected distance Δ L was found to depend on both, cloud and sea ice properties. For a ground overlaying cloud in 0-200 m altitude, increasing the cloud optical thickness from τ = 1 to τ = 10 decreases Δ L from 600 to 250 m, while increasing cloud base altitude or cloud geometrical thickness can increase Δ L; Δ L(τ = 1/10) = 2200 m/1250 m for 500-1000 m cloud altitude. To quantify the effect for different shapes and sizes of the ice floes, various albedo fields (infinite straight ice edge, circles, squares, realistic ice floe field) were modelled. Simulations show that Δ L increases by the radius of the ice floe and for sizes larger than 6 km (500-1000 m cloud altitude) asymptotically reaches maximum values, which corresponds to an infinite straight ice edge. Furthermore, the impact of these 3-D-radiative effects on retrieval of cloud optical properties was investigated. The enhanced brightness of a dark pixel next to an ice edge results in uncertainties of up to 90 and 30% in retrievals of cloud optical thickness and effective radius reff, respectively. With help of Δ L quantified here, an estimate of the distance to the ice edge for which the retrieval errors are negligible is given.

Early Spring Dust over the Mediterranean Sea

NASA Technical Reports Server (NTRS)

2002-01-01

The Sea-viewing Wide Field-of-view Sensor (SeaWiFS) observed this large cloud of dust (brownish pixels) blowing from northern Africa across the Mediterranean Sea on March 4, 2002. The dust can be seen clearly blowing across Southern Italy, Albania, Greece, and Turkey-all along the Mediterranean's northeastern shoreline. Notice that there also appears to be human-made aerosol pollution (greyish pixels) pooling in the air just south of the Italian Alps and blowing southeastward over the Adriatic Sea. The Alps can be easily identified as the crescent-shaped, snow-capped mountain range in the top center of this true-color scene. There also appears to be a similar haze over Austria, Hungary, and Yugoslavia to the north and east of Italy. Image courtesy the SeaWiFS Project, NASA/Goddard Space Flight Center, and ORBIMAGE

A new airborne sampler for interstitial particles in ice and liquid clouds

NASA Astrophysics Data System (ADS)

Moharreri, A.; Craig, L.; Rogers, D. C.; Brown, M.; Dhaniyala, S.

2011-12-01

In-situ measurements of cloud droplets and aerosols using aircraft platforms are required for understanding aerosol-cloud processes and aiding development of improved aerosol-cloud models. A variety of clouds with different temperature ranges and cloud particle sizes/phases must be studied for comprehensive knowledge about the role of aerosols in the formation and evolution of cloud systems under different atmospheric conditions. While representative aerosol measurements are regularly made from aircrafts under clear air conditions, aerosol measurements in clouds are often contaminated by the generation of secondary particles from the high speed impaction of ice particles and liquid droplets on the surfaces of the aircraft probes/inlets. A new interstitial particle sampler, called the blunt-body aerosol sampler (BASE) has been designed and used for aerosol sampling during two recent airborne campaigns using NCAR/NSF C-130 aircraft: PLOWS (2009-2010) and ICE-T (2011). Central to the design of the new interstitial inlet is an upstream blunt body housing that acts to shield/deflect large cloud droplets and ice particles from an aft sampling region. The blunt-body design also ensures that small shatter particles created from the impaction of cloud-droplets on the blunt-body are not present in the aft region where the interstitial inlet is located. Computational fluid dynamics (CFD) simulations along with particle transport modeling and wind tunnel studies have been utilized in different stages of design and development of this inlet. The initial flights tests during the PLOWS campaign showed that the inlet had satisfactory performance only in warm clouds and when large precipitation droplets were absent. In the presence of large droplets and ice, the inlet samples were contaminated with significant shatter artifacts. These initial results were reanalyzed in conjunction with a computational droplet shatter model and the numerical results were used to arrive at an improved sampler design. Analysis of the data from the recent ICE-T campaign with the improved sampler design shows that the modified version of BASE can provide shatter-artifact free sampling of aerosol particles in the presence of ice particles and significantly reduced shatter artifacts in warm clouds. Detailed design and modeling aspects of the sampler will be discussed and the sampler performance in warm and cold clouds will be presented and compared with measurements made using other aerosol inlets flown on the NCAR/NSF C-130 aircraft.

The Radiative Consistency of Atmospheric Infrared Sounder and Moderate Resolution Imaging Spectroradiometer Cloud Retrievals

NASA Technical Reports Server (NTRS)

Kahn, Brian H.; Fishbein, Evan; Nasiri, Shaima L.; Eldering, Annmarie; Fetzer, Eric J.; Garay, Michael J.; Lee, Sung-Yung

2007-01-01

The consistency of cloud top temperature (Tc) and effective cloud fraction (f) retrieved by the Atmospheric Infrared Sounder (AIRS)/Advanced Microwave Sounding Unit (AMSU) observation suite and the Moderate Resolution Imaging Spectroradiometer (MODIS) on the EOS-Aqua platform are investigated. Collocated AIRS and MODIS TC and f are compared via an 'effective scene brightness temperature' (Tb,e). Tb,e is calculated with partial field of view (FOV) contributions from TC and surface temperature (TS), weighted by f and 1-f, respectively. AIRS reports up to two cloud layers while MODIS reports up to one. However, MODIS reports TC, TS, and f at a higher spatial resolution than AIRS. As a result, pixel-scale comparisons of TC and f are difficult to interpret, demonstrating the need for alternatives such as Tb,e. AIRS-MODIS Tb,e differences ((Delta)Tb,e) for identical observing scenes are useful as a diagnostic for cloud quantity comparisons. The smallest values of DTb,e are for high and opaque clouds, with increasing scatter in (Delta)Tb,e for clouds of smaller opacity and lower altitude. A persistent positive bias in DTb,e is observed in warmer and low-latitude scenes, characterized by a mixture of MODIS CO2 slicing and 11-mm window retrievals. These scenes contain heterogeneous cloud cover, including mixtures of multilayered cloudiness and misplaced MODIS cloud top pressure. The spatial patterns of (Delta)Tb,e are systematic and do not correlate well with collocated AIRS-MODIS radiance differences, which are more random in nature and smaller in magnitude than (Delta)Tb,e. This suggests that the observed inconsistencies in AIRS and MODIS cloud fields are dominated by retrieval algorithm differences, instead of differences in the observed radiances. The results presented here have implications for the validation of cloudy satellite retrieval algorithms, and use of cloud products in quantitative analyses.

Sub-pixel mapping of hyperspectral imagery using super-resolution

NASA Astrophysics Data System (ADS)

Sharma, Shreya; Sharma, Shakti; Buddhiraju, Krishna M.

2016-04-01

With the development of remote sensing technologies, it has become possible to obtain an overview of landscape elements which helps in studying the changes on earth's surface due to climate, geological, geomorphological and human activities. Remote sensing measures the electromagnetic radiations from the earth's surface and match the spectral similarity between the observed signature and the known standard signatures of the various targets. However, problem lies when image classification techniques assume pixels to be pure. In hyperspectral imagery, images have high spectral resolution but poor spatial resolution. Therefore, the spectra obtained is often contaminated due to the presence of mixed pixels and causes misclassification. To utilise this high spectral information, spatial resolution has to be enhanced. Many factors make the spatial resolution one of the most expensive and hardest to improve in imaging systems. To solve this problem, post-processing of hyperspectral images is done to retrieve more information from the already acquired images. The algorithm to enhance spatial resolution of the images by dividing them into sub-pixels is known as super-resolution and several researches have been done in this domain.In this paper, we propose a new method for super-resolution based on ant colony optimization and review the popular methods of sub-pixel mapping of hyperspectral images along with their comparative analysis.

Distributed MRI reconstruction using Gadgetron-based cloud computing.

PubMed

Xue, Hui; Inati, Souheil; Sørensen, Thomas Sangild; Kellman, Peter; Hansen, Michael S

2015-03-01

To expand the open source Gadgetron reconstruction framework to support distributed computing and to demonstrate that a multinode version of the Gadgetron can be used to provide nonlinear reconstruction with clinically acceptable latency. The Gadgetron framework was extended with new software components that enable an arbitrary number of Gadgetron instances to collaborate on a reconstruction task. This cloud-enabled version of the Gadgetron was deployed on three different distributed computing platforms ranging from a heterogeneous collection of commodity computers to the commercial Amazon Elastic Compute Cloud. The Gadgetron cloud was used to provide nonlinear, compressed sensing reconstruction on a clinical scanner with low reconstruction latency (eg, cardiac and neuroimaging applications). The proposed setup was able to handle acquisition and 11 -SPIRiT reconstruction of nine high temporal resolution real-time, cardiac short axis cine acquisitions, covering the ventricles for functional evaluation, in under 1 min. A three-dimensional high-resolution brain acquisition with 1 mm(3) isotropic pixel size was acquired and reconstructed with nonlinear reconstruction in less than 5 min. A distributed computing enabled Gadgetron provides a scalable way to improve reconstruction performance using commodity cluster computing. Nonlinear, compressed sensing reconstruction can be deployed clinically with low image reconstruction latency. © 2014 Wiley Periodicals, Inc.

VizieR Online Data Catalog: OGLE Magellanic Clouds anomalous Cepheids (Soszynski+, 2015)

NASA Astrophysics Data System (ADS)

Soszynski, I.; Udalski, A.; Szymanski, M. K.; Pietrzynski, G.; Wyrzykowski, L.; Ulaczyk, K.; Poleski, R.; Pietrukowicz, P.; Kozlowski, S.; Skowron, J.; Mroz, P.; Pawlak, M.

2016-06-01

Time-series I and V-band photometry of the Magellanic Clouds was obtained in the years 2010-2015 using the 32-chip mosaic CCD camera mounted at the focus of the 1.3-m Warsaw Telescope located at Las Campanas Observatory in Chile. The observatory is operated by the Carnegie Institution for Science. The OGLE- IV camera has a total field of view of 1.4 square degrees and pixel scale of 0.26". The OGLE-IV fields cover approximately 650 square degrees in both Clouds and a region between both galaxies, the so-called Magellanic Bridge. For each field we obtained from 90 (in sparse regions far from the centers of the Magellanic Clouds) to over 750 observing points (in the densest fields) in the Cousins I-band and from several to over 260 points in the Johnson V-band. Data reduction of the OGLE images was performed using the Difference Image Analysis technique (Alard and Lupton 1998ApJ...503..325A, Wozniak 2000). Detailed descriptions of the instrumentation, photometric reductions and astrometric calibrations of the OGLE-IV data are provided by Udalski et al. (2015, Cat. J/AcA/50/421). (8 data files).

Satellite-Based Assessment of Possible Dust Aerosols Semi-Direct Effect on Cloud Water Path over East Asia

NASA Technical Reports Server (NTRS)

Huang, Jianping; Lin, Bing; Minnis, Patrick; Wang, Tainhe; Wang, Xin; Hu, Yongxiang; Yi, Yuhong; Ayers, J. Kirk

2006-01-01

The semi-direct effects of dust aerosols are analyzed over eastern Asia using 2 years (June 2002 to June 2004) of data from the Clouds and the Earth s Radiant Energy System (CERES) scanning radiometer and MODerate Resolution Imaging Spectroradiometer (MODIS) on the Aqua satellite, and 18 years (1984 to 2001) of International Satellite Cloud Climatology Project (ISCCP) data. The results show that the water path of dust-contaminated clouds is considerably smaller than that of dust-free clouds. The mean ice water path (IWP) and liquid water path (LWP) of dusty clouds are less than their dust-free counterparts by 23.7% and 49.8%, respectively. The long-term statistical relationship derived from ISCCP also confirms that there is significant negative correlation between dust storm index and ISCCP cloud water path. These results suggest that dust aerosols warm clouds, increase the evaporation of cloud droplets and further reduce cloud water path, the so-called semi-direct effect. The semi-direct effect may play a role in cloud development over arid and semi-arid areas of East Asia and contribute to the reduction of precipitation.

Seasonal and Interannual Variations of Top-of-Atmosphere Irradiance and Cloud Cover over Polar Regions Derived from the CERES Data Set

NASA Technical Reports Server (NTRS)

Kato, Seiji; Loeb, Norman G.; Minnis, Patrick; Francis, Jennifer A.; Charlock, Thomas P.; Rutan, David A.; Clothiaux, Eugene E.; Sun-Mack, Szedung

2006-01-01

The semi-direct effects of dust aerosols are analyzed over eastern Asia using 2 years (June 2002 to June 2004) of data from the Clouds and the Earth s Radiant Energy System (CERES) scanning radiometer and MODerate Resolution Imaging Spectroradiometer (MODIS) on the Aqua satellite, and 18 years (1984 to 2001) of International Satellite Cloud Climatology Project (ISCCP) data. The results show that the water path of dust-contaminated clouds is considerably smaller than that of dust-free clouds. The mean ice water path (IWP) and liquid water path (LWP) of dusty clouds are less than their dust-free counterparts by 23.7% and 49.8%, respectively. The long-term statistical relationship derived from ISCCP also confirms that there is significant negative correlation between dust storm index and ISCCP cloud water path. These results suggest that dust aerosols warm clouds, increase the evaporation of cloud droplets and further reduce cloud water path, the so-called semi-direct effect. The semi-direct effect may play a role in cloud development over arid and semi-arid areas of East Asia and contribute to the reduction of precipitation.

Comparison View of Mars Cloud Cover

NASA Technical Reports Server (NTRS)

1997-01-01

These color and black and white pictures of Mars were taken by NASA's Hubble Space Telescope just two weeks after Earth made its closest approach to the Red Planet during the 1997 opposition. When the Hubble pictures were taken Mars was at a distance of 62 million miles (100 million kilometers) and the resolution at the center of the disk is 13.5 miles/pixel (22 kilometers/pixel). Both images were made with the Wide Field and Planetary Camera 2. The color composite (left image) is constructed from three images taken in red (673 nanometers), green (502 nm) and blue (410 nm) light. The right image, in blue light only, brings out details in the cloud structure and is remarkably similar to weather satellite pictures taken of Earth. A planetary-scale wave curls around the north pole, similar in behavior to high latitude cold fronts which descend over North America and Europe during springtime.

The picture was taken when Mars was near aphelion, its farthest point from the Sun. The faint sunlight results in cold atmospheric conditions which stimulate the formation of water ice clouds. The clouds themselves further reduce atmospheric temperatures. Atmospheric heating, resulting when sunlight is absorbed by the dust, is reduced when ice forms around the dust particles and causes the dust to gravitationally settle to the ground.

These images of Mars are centered at approximately 94 degrees longitude and 23 degrees N latitude (oriented with north up). The four largest Tharsis Montes (massive extinct volcanoes) are visible as dark spots extending through the clouds. The vast canyon system, Valles Marineris, stretches across the eastern (lower right) half of the image; the Pathfinder landing site is near the eastern edge of the image. It is early summer in the northern hemisphere, and the North polar cap has retreated to about 80 degrees N latitude; the 'residual' summer cap, which is composed of water ice, is about one-third the size of the 'seasonal' winter cap, which consists mostly of carbon-dioxide frost (dry ice) condensed on the surface. The polar cap is surrounded by a 'sand sea' made up of dark sand dunes. A distinct belt of water-ice clouds extends over much of this hemisphere.

This image and other images and data received from the Hubble Space Telescope are posted on the World Wide Web on the Space Telescope Science Institute home page at URL http://oposite.stsci.edu/pubinfo/

COMPARISON VIEW OF MARS CLOUD COVER

NASA Technical Reports Server (NTRS)

2002-01-01

These color and black and white pictures of Mars were taken by NASA's Hubble Space Telescope just two weeks after Earth made its closest approach to the Red Planet during the 1997 opposition. When the Hubble pictures were taken Mars was at a distance of 62 million miles (100 million kilometers) and the resolution at the center of the disk is 13.5 miles/pixel (22 kilometers/pixel). Both images were made with the Wide Field and Planetary Camera 2. The color composite (left image) is constructed from three images taken in red (673 nanometers), green (502 nm) and blue (410 nm) light. The right image, in blue light only, brings out details in the cloud structure and is remarkably similar to weather satellite pictures taken of Earth. A planetary-scale wave curls around the north pole, similar in behavior to high latitude cold fronts which descend over North America and Europe during springtime. The picture was taken when Mars was near aphelion, its farthest point from the Sun. The faint sunlight results in cold atmospheric conditions which stimulate the formation of water ice clouds. The clouds themselves further reduce atmospheric temperatures. Atmospheric heating, resulting when sunlight is absorbed by the dust, is reduced when ice forms around the dust particles and causes the dust to gravitationally settle to the ground. These images of Mars are centered at approximately 94 degrees longitude and 23 degrees N latitude (oriented with north up). The four largest Tharsis Montes (massive extinct volcanoes) are visible as dark spots extending through the clouds. The vast canyon system, Valles Marineris, stretches across the eastern (lower right) half of the image; the Pathfinder landing site is near the eastern edge of the image. It is early summer in the northern hemisphere, and the North polar cap has retreated to about 80 degrees N latitude; the 'residual' summer cap, which is composed of water ice, is about one-third the size of the 'seasonal' winter cap, which consists mostly of carbon-dioxide frost (dry ice) condensed on the surface. The polar cap is surrounded by a 'sand sea' made up of dark sand dunes. A distinct belt of water-ice clouds extends over much of this hemisphere. Credit: Phil James (Univ. Toledo), Todd Clancy (Space Science Inst., Boulder, CO), Steve Lee (Univ. Colorado), and NASA Image files in GIF and JPEG format and captions may be accessed on the Internet via anonymous ftp from oposite.stsci.edu in /pubinfo.

Electron Cloud Measurements in Fermilab Main Injector and Recycler

DOE Office of Scientific and Technical Information (OSTI.GOV)

Eldred, Jeffrey Scott; Backfish, M.; Tan, C. Y.

This conference paper presents a series of electron cloud measurements in the Fermilab Main Injector and Recycler. A new instability was observed in the Recycler in July 2014 that generates a fast transverse excitation in the first high intensity batch to be injected. Microwave measurements of electron cloud in the Recycler show a corresponding depen- dence on the batch injection pattern. These electron cloud measurements are compared to those made with a retard- ing field analyzer (RFA) installed in a field-free region of the Recycler in November. RFAs are also used in the Main Injector to evaluate the performance ofmore » beampipe coatings for the mitigation of electron cloud. Contamination from an unexpected vacuum leak revealed a potential vulnerability in the amorphous carbon beampipe coating. The diamond-like carbon coating, in contrast, reduced the electron cloud signal to 1% of that measured in uncoated stainless steel beampipe.« less

New optical package and algorithms for accurate estimation and interactive recording of the cloud cover information over land and sea

NASA Astrophysics Data System (ADS)

Krinitskiy, Mikhail; Sinitsyn, Alexey; Gulev, Sergey

2014-05-01

Cloud fraction is a critical parameter for the accurate estimation of short-wave and long-wave radiation - one of the most important surface fluxes over sea and land. Massive estimates of the total cloud cover as well as cloud amount for different layers of clouds are available from visual observations, satellite measurements and reanalyses. However, these data are subject of different uncertainties and need continuous validation against highly accurate in-situ measurements. Sky imaging with high resolution fish eye camera provides an excellent opportunity for collecting cloud cover data supplemented with additional characteristics hardly available from routine visual observations (e.g. structure of cloud cover under broken cloud conditions, parameters of distribution of cloud dimensions). We present operational automatic observational package which is based on fish eye camera taking sky images with high resolution (up to 1Hz) in time and a spatial resolution of 968x648px. This spatial resolution has been justified as an optimal by several sensitivity experiments. For the use of the package at research vessel when the horizontal positioning becomes critical, a special extension of the hardware and software to the package has been developed. These modules provide the explicit detection of the optimal moment for shooting. For the post processing of sky images we developed a software realizing the algorithm of the filtering of sunburn effect in case of small and moderate could cover and broken cloud conditions. The same algorithm accurately quantifies the cloud fraction by analyzing color mixture for each point and introducing the so-called "grayness rate index" for every pixel. The accuracy of the algorithm has been tested using the data collected during several campaigns in 2005-2011 in the North Atlantic Ocean. The collection of images included more than 3000 images for different cloud conditions supplied with observations of standard parameters. The system is fully autonomous and has a block for digital data collection at the hard disk. The system has been tested for a wide range of open ocean cloud conditions and we will demonstrate some pilot results of data processing and physical interpretation of fractional cloud cover estimation.

Spectral analysis of Uranus' 2014 bright storm with VLT/SINFONI

NASA Astrophysics Data System (ADS)

Irwin, P. G. J.; Fletcher, L. N.; Read, P. L.; Tice, D.; de Pater, I.; Orton, G. S.; Teanby, N. A.; Davis, G. R.

2016-01-01

An extremely bright storm system observed in Uranus' atmosphere by amateur observers in September 2014 triggered an international campaign to observe this feature with many telescopes across the world. Observations of the storm system in the near infrared were acquired in October and November 2014 with SINFONI on ESO's Very Large Telescope (VLT) in Chile. SINFONI is an Integral Field Unit spectrometer returning 64 × 64 pixel images with 2048 wavelengths and uses adaptive optics. Image cubes in the H-band (1.43-1.87 μm) were obtained at spatial resolutions of ∼ 0.1″ per pixel. The observations show that the centre of the storm feature shifts markedly with increasing altitude, moving in the retrograde direction and slightly poleward with increasing altitude. We also see a faint 'tail' of more reflective material to the immediate south of the storm, which again trails in the retrograde direction. The observed spectra were analysed with the radiative transfer and retrieval code, NEMESIS (Irwin et al. [2008]. J. Quant. Spec. Radiat. Transfer, 109, 1136-1150). We find that the storm is well-modelled using either two main cloud layers of a 5-layer aerosol model based on Sromovsky et al. (Sromovsky et al. [2011]. Icarus, 215, 292-312) or by the simpler two-cloud-layer model of Tice et al. (Tice et al. [2013]. Icarus, 223, 684-698). The deep component appears to be due to a brightening (i.e. an increase in reflectivity) and increase in altitude of the main tropospheric cloud deck at 2-3 bars for both models, while the upper component of the feature was modelled as being due to either a thickening of the tropospheric haze of the 2-layer model or a vertical extension of the upper tropospheric cloud of the 5-layer model, assumed to be composed of methane ice and based at the methane condensation level of our assumed vertical temperature and abundance profile at 1.23 bar. We also found this methane ice cloud to be responsible for the faint 'tail' seen to the feature's south and the brighter polar 'hood' seen in all observations polewards of ∼45°N for the 5-layer model. During the twelve days between our sets of observations the higher-altitude component of the feature was observed to have brightened significantly and extended to even higher altitudes, while the deeper component faded.

Making limb and nadir measurements comparable: A common volume study of PMC brightness observed by Odin OSIRIS and AIM CIPS

NASA Astrophysics Data System (ADS)

Benze, Susanne; Gumbel, Jörg; Randall, Cora E.; Karlsson, Bodil; Hultgren, Kristoffer; Lumpe, Jerry D.; Baumgarten, Gerd

2018-01-01

Combining limb and nadir satellite observations of Polar Mesospheric Clouds (PMCs) has long been recognized as problematic due to differences in observation geometry, scattering conditions, and retrieval approaches. This study offers a method of comparing PMC brightness observations from the nadir-viewing Aeronomy of Ice in the Mesosphere (AIM) Cloud Imaging and Particle Size (CIPS) instrument and the limb-viewing Odin Optical Spectrograph and InfraRed Imaging System (OSIRIS). OSIRIS and CIPS measurements are made comparable by defining a common volume for overlapping OSIRIS and CIPS observations for two northern hemisphere (NH) PMC seasons: NH08 and NH09. We define a scattering intensity quantity that is suitable for either nadir or limb observations and for different scattering conditions. A known CIPS bias is applied, differences in instrument sensitivity are analyzed and taken into account, and effects of cloud inhomogeneity and common volume definition on the comparison are discussed. Not accounting for instrument sensitivity differences or inhomogeneities in the PMC field, the mean relative difference in cloud brightness (CIPS - OSIRIS) is -102 ± 55%. The differences are largest for coincidences with very inhomogeneous clouds that are dominated by pixels that CIPS reports as non-cloud points. Removing these coincidences, the mean relative difference in cloud brightness reduces to -6 ± 14%. The correlation coefficient between the CIPS and OSIRIS measurements of PMC brightness variations in space and time is remarkably high, at 0.94. Overall, the comparison shows excellent agreement despite different retrieval approaches and observation geometries.

MODIS comparisons with northeastern Pacific in situ stratocumulus microphysics

NASA Astrophysics Data System (ADS)

Noble, Stephen R.; Hudson, James G.

2015-08-01

Vertical sounding measurements within stratocumuli during two aircraft field campaigns, Marine Stratus/stratocumulus Experiment (MASE) and Physics of Stratocumulus Top (POST), are used to validate Moderate Resolution Imaging Spectroradiometer (MODIS) cloud optical thickness (COT), cloud liquid water path (LWP), and cloud effective radius (re). In situ COT, LWP, and re were calculated using 5 m vertically averaged droplet probe measurements of complete vertical cloud penetrations. MODIS COT, LWP, and re 1 km pixels were averaged along these penetrations. COT comparisons in POST showed strong correlations and a near 1:1 relationship. In MASE, comparisons showed strong correlations; however, MODIS COT exceeded in situ COT, likely due to larger temporal differences between MODIS and in situ measurements. LWP comparisons between two cloud probes show good agreement for POST but not MASE, giving confidence to POST data. Both projects provided strong LWP correlations but MODIS exceeded in situ by 14-36%. MODIS in situ re correlations were strong, but MODIS 2.1 µm re exceeded in situ re, which contributed to LWP bias; in POST, MODIS re was 20-30% greater than in situ re. Maximum in situ re near cloud top showed comparisons nearer 1:1. Other MODIS re bands (3.7 µm and 1.6 µm) showed similar comparisons. Temporal differences between MODIS and in situ measurements, airplane speed differences, and cloud probe artifacts were likely causes of weaker MASE correlations. POST COT comparison was best for temporal differences under 20 min. POST data validate MODIS COT but it also implies a positive MODIS re bias that propagates to LWP while still capturing variability.

Study on the cloud detection of GOCI by using the simulated surface reflectance from BRDF-model for the land application and meteorological utilization

NASA Astrophysics Data System (ADS)

Kim, Hye-Won; Yeom, Jong-Min; Woo, Sun-Hee; Chae, Tae-Byeong

2016-04-01

COMS (Communication, Ocean, and Meteorological Satellite) was launched at French Guiana Kourou space center on 27 June 2010. Geostationary Ocean Color Imager (GOCI), which is the first ocean color geostationary satellite in the world for observing the ocean phenomena, is able to obtain the scientific data per an hour from 00UTC to 07UTC. Moreover, the spectral channels of GOCI would enable not only monitoring for the ocean, but for extracting the information of the land surface over the Korean Peninsula, Japan, and Eastern China. Since it is extremely important to utilize GOCI data accurately for the land application, cloud pixels over the surface have to be removed. Unfortunately, infra-red (IR) channels that can easily detect the water vapor with the cloud top temperature, are not included in the GOCI sensor. In this paper, the advanced cloud masking algorithm will be proposed with visible and near-IR (NIR) bands that are within GOCI bands. The main obstacle of cloud masking with GOCI is how to handle the high variable surface reflectance, which is mainly depending on the solar zenith angle. In this study, we use semi-empirical BRDF model to simulate the surface reflectance by using 16 day composite cloudy free image. When estimating the simulated surface reflectance, same geometry for GOCI observation was applied. The simulated surface reflectance is used to discriminate cloud areas especially for the thin cloud and shows more reasonable result than original threshold methods.

Cirrus Cloud Optical Thickness and Effective Diameter Retrieved by MODIS: Impacts of Single Habit Assumption, 3-D Radiative Effects, and Cloud Inhomogeneity

NASA Astrophysics Data System (ADS)

Zhou, Yongbo; Sun, Xuejin; Mielonen, Tero; Li, Haoran; Zhang, Riwei; Li, Yan; Zhang, Chuanliang

2018-01-01

For inhomogeneous cirrus clouds, cloud optical thickness (COT) and effective diameter (De) provided by the Moderate Resolution Imaging Spectrometer (MODIS) Collection 6 cloud products are associated with errors due to the single habit assumption (SHA), independent pixel assumption (IPA), photon absorption effect (PAE), and plane-parallel assumption (PPA). SHA means that every cirrus cloud is assumed to have the same shape habit of ice crystals. IPA errors are caused by three-dimensional (3D) radiative effects. PPA and PAE errors are caused by cloud inhomogeneity. We proposed a method to single out these different errors. These errors were examined using the Spherical Harmonics Discrete Ordinate Method simulations done for the MODIS 0.86 μm and 2.13 μm bands. Four midlatitude and tropical cirrus cases were studied. For the COT retrieval, the impacts of SHA and IPA were especially large for optically thick cirrus cases. SHA errors in COT varied distinctly with scattering angles. For the De retrieval, SHA decreased De under most circumstances. PAE decreased De for optically thick cirrus cases. For the COT and De retrievals, the dominant error source was SHA for overhead sun whereas for oblique sun, it could be any of SHA, IPA, and PAE, varying with cirrus cases and sun-satellite viewing geometries. On the domain average, the SHA errors in COT (De) were within -16.1%-42.6% (-38.7%-2.0%), whereas the 3-D radiative effects- and cloud inhomogeneity-induced errors in COT (De) were within -5.6%-19.6% (-2.9%-8.0%) and -2.6%-0% (-3.7%-9.8%), respectively.

Calibration View of Earth and the Moon by Mars Color Imager

NASA Technical Reports Server (NTRS)

2005-01-01

Three days after the Mars Reconnaissance Orbiter's Aug. 12, 2005, launch, the spacecraft was pointed toward Earth and the Mars Color Imager camera was powered up to acquire a suite of images of Earth and the Moon. When it gets to Mars, the Mars Color Imager's main objective will be to obtain daily global color and ultraviolet images of the planet to observe martian meteorology by documenting the occurrence of dust storms, clouds, and ozone. This camera will also observe how the martian surface changes over time, including changes in frost patterns and surface brightness caused by dust storms and dust devils.

The purpose of acquiring an image of Earth and the Moon just three days after launch was to help the Mars Color Imager science team obtain a measure, in space, of the instrument's sensitivity, as well as to check that no contamination occurred on the camera during launch. Prior to launch, the team determined that, three days out from Earth, the planet would only be about 4.77 pixels across, and the Moon would be less than one pixel in size, as seen from the Mars Color Imager's wide-angle perspective. If the team waited any longer than three days to test the camera's performance in space, Earth would be too small to obtain meaningful results.

The Earth and Moon images were acquired by turning Mars Reconnaissance Orbiter toward Earth, then slewing the spacecraft so that the Earth and Moon would pass before each of the five color and two ultraviolet filters of the Mars Color Imager. The distance to the Moon was about 1,440,000 kilometers (about 895,000 miles); the range to Earth was about 1,170,000 kilometers (about 727,000 miles).

This view combines a sequence of frames showing the passage of Earth and the Moon across the field of view of a single color band of the Mars Color Imager. As the spacecraft slewed to view the two objects, they passed through the camera's field of view. Earth has been saturated white in this image so that both Earth and the Moon can be seen in the same frame. The Sun was coming from the left, so Earth and the Moon are seen in a quarter phase. Earth is on the left. The Moon appears briefly on the right. The Moon fades in and out; the Moon is only one pixel in size, and its fading is an artifact of the size and configuration of the light-sensitive pixels of the camera's charge-coupled device (CCD) detector.

A cloud detection scheme for the Chinese Carbon Dioxide Observation Satellite (TANSAT)

NASA Astrophysics Data System (ADS)

Wang, Xi; Guo, Zheng; Huang, Yipeng; Fan, Hongjie; Li, Wanbiao

2017-01-01

Cloud detection is an essential preprocessing step for retrieving carbon dioxide from satellite observations of reflected sunlight. During the pre-launch study of the Chinese Carbon Dioxide Observation Satellite (TANSAT), a cloud-screening scheme was presented for the Cloud and Aerosol Polarization Imager (CAPI), which only performs measurements in five channels located in the visible to near-infrared regions of the spectrum. The scheme for CAPI, based on previous cloudscreening algorithms, defines a method to regroup individual threshold tests for each pixel in a scene according to the derived clear confidence level. This scheme is proven to be more effective for sensors with few channels. The work relies upon the radiance data from the Visible and Infrared Radiometer (VIRR) onboard the Chinese FengYun-3A Polar-orbiting Meteorological Satellite (FY-3A), which uses four wavebands similar to that of CAPI and can serve as a proxy for its measurements. The scheme has been applied to a number of the VIRR scenes over four target areas (desert, snow, ocean, forest) for all seasons. To assess the screening results, comparisons against the cloud-screening product from MODIS are made. The evaluation suggests that the proposed scheme inherits the advantages of schemes described in previous publications and shows improved cloud-screening results. A seasonal analysis reveals that this scheme provides better performance during warmer seasons, except for observations over oceans, where results are much better in colder seasons.

Monte Carlo Radiative Transfer Modeling of Lightning Observed in Galileo Images of Jupiter

NASA Technical Reports Server (NTRS)

Dyudine, U. A.; Ingersoll, Andrew P.

2002-01-01

We study lightning on Jupiter and the clouds illuminated by the lightning using images taken by the Galileo orbiter. The Galileo images have a resolution of 25 km/pixel and axe able to resolve the shape of the single lightning spots in the images, which have full widths at half the maximum intensity in the range of 90-160 km. We compare the measured lightning flash images with simulated images produced by our ED Monte Carlo light-scattering model. The model calculates Monte Carlo scattering of photons in a ED opacity distribution. During each scattering event, light is partially absorbed. The new direction of the photon after scattering is chosen according to a Henyey-Greenstein phase function. An image from each direction is produced by accumulating photons emerging from the cloud in a small range (bins) of emission angles. Lightning bolts are modeled either as points or vertical lines. Our results suggest that some of the observed scattering patterns axe produced in a 3-D cloud rather than in a plane-parallel cloud layer. Lightning is estimated to occur at least as deep as the bottom of the expected water cloud. For the six cases studied, we find that the clouds above the lightning are optically thick (tau > 5). Jovian flashes are more regular and circular than the largest terrestrial flashes observed from space. On Jupiter there is nothing equivalent to the 30-40-km horizontal flashes which axe seen on Earth.

Cloud Optimized Image Format and Compression

NASA Astrophysics Data System (ADS)

Becker, P.; Plesea, L.; Maurer, T.

2015-04-01

Cloud based image storage and processing requires revaluation of formats and processing methods. For the true value of the massive volumes of earth observation data to be realized, the image data needs to be accessible from the cloud. Traditional file formats such as TIF and NITF were developed in the hay day of the desktop and assumed fast low latency file access. Other formats such as JPEG2000 provide for streaming protocols for pixel data, but still require a server to have file access. These concepts no longer truly hold in cloud based elastic storage and computation environments. This paper will provide details of a newly evolving image storage format (MRF) and compression that is optimized for cloud environments. Although the cost of storage continues to fall for large data volumes, there is still significant value in compression. For imagery data to be used in analysis and exploit the extended dynamic range of the new sensors, lossless or controlled lossy compression is of high value. Compression decreases the data volumes stored and reduces the data transferred, but the reduced data size must be balanced with the CPU required to decompress. The paper also outlines a new compression algorithm (LERC) for imagery and elevation data that optimizes this balance. Advantages of the compression include its simple to implement algorithm that enables it to be efficiently accessed using JavaScript. Combing this new cloud based image storage format and compression will help resolve some of the challenges of big image data on the internet.

Remote Sensing of Cloud, Aerosol, and Land Properties from MODIS: Applications to the East Asia Region

NASA Technical Reports Server (NTRS)

King, Michael D.; Platnick, Steven; Moody, Eric G.

2002-01-01

MODIS is an earth-viewing cross-track scanning spectroradiometer launched on the Terra satellite in December 1999 and the Aqua satellite in May 2002. MODIS scans a swath width sufficient to provide nearly complete global coverage every two days from a polar-orbiting, sun-synchronous, platform at an altitude of 705 km, and provides images in 36 spectral bands between 0.415 and 14.235 microns with spatial resolutions of 250 m (2 bands), 500 m (5 bands) and 1000 m (29 bands). These bands have been carefully selected to enable advanced studies of land, ocean, and atmospheric processes. In this paper we will describe the various methods being used for the remote sensing of cloud, aerosol, and surface properties using MODIS data, focusing primarily on (i) the MODIS cloud mask used to distinguish clouds, clear sky, heavy aerosol, and shadows on the ground, (ii) cloud optical properties, especially cloud optical thickness and effective radius of water drops and ice crystals, (iii) aerosol optical thickness and size characteristics both over land and ocean, and (iv) ecosystem classification and surface spectral reflectance. The physical principles behind the determination of each of these products will be described, together with an example of their application using MODIS observations to the east Asian region. All products are archived into two categories: pixel-level retrievals (referred to as Level-2 products) and global gridded products at a latitude and longitude resolution of 1 min (Level-3 products).

Applying Contamination Modelling to Spacecraft Propulsion Systems Designs and Operations

NASA Technical Reports Server (NTRS)

Chen, Philip T.; Thomson, Shaun; Woronowicz, Michael S.

2000-01-01

Molecular and particulate contaminants generated from the operations of a propulsion system may impinge on spacecraft critical surfaces. Plume depositions or clouds may hinder the spacecraft and instruments from performing normal operations. Firing thrusters will generate both molecular and particulate contaminants. How to minimize the contamination impact from the plume becomes very critical for a successful mission. The resulting effect from either molecular or particulate contamination of the thruster firing is very distinct. This paper will discuss the interconnection between the functions of spacecraft contamination modeling and propulsion system implementation. The paper will address an innovative contamination engineering approach implemented from the spacecraft concept design, manufacturing, integration and test (I&T), launch, to on- orbit operations. This paper will also summarize the implementation on several successful missions. Despite other contamination sources, only molecular contamination will be considered here.

Report on the search for atmospheric holes using airs image data

NASA Technical Reports Server (NTRS)

Reinleitner, Lee A.

1991-01-01

Frank et al (1986) presented a very controversial hypothesis which states that the Earth is being bombarded by water-vapor clouds resulting from the disruption and vaporization of small comets. This hypothesis was based on single-pixel intensity decreases in the images of the earth's dayglow emissions at vacuum-ultraviolet (VUV) wavelengths using the DE-1 imager. These dark spots, or atmospheric holes, are hypothesized to be the result of VUV absorption by a water-vapor cloud between the imager and the dayglow-emitting region. Examined here is the VUV data set from the Auroral Ionospheric Remote Sensor (AIRS) instrument that was flown on the Polar BEAR satellite. AIRS was uniquely situated to test this hypothesis. Due to the altitude of the sensor, the holes should show multi-pixel intensity decreases in a scan line. A statistical estimate indicated that sufficient 130.4-nm data from AIRS existed to detect eight to nine such holes, but none was detected. The probability of this occurring is less than 1.0 x 10(exp -4). A statistical estimate indicated that sufficient 135.6-nm data from AIRS existed to detect approx. 2 holes, and two ambiguous cases are shown. In spite of the two ambiguous cases, the 135.6-nm data did not show clear support for the small-comet hypothesis. The 130.4-nm data clearly do not support the small-comet hypothesis.

Understanding star formation in molecular clouds. I. Effects of line-of-sight contamination on the column density structure

NASA Astrophysics Data System (ADS)

Schneider, N.; Ossenkopf, V.; Csengeri, T.; Klessen, R. S.; Federrath, C.; Tremblin, P.; Girichidis, P.; Bontemps, S.; André, Ph.

2015-03-01

Column-density maps of molecular clouds are one of the most important observables in the context of molecular cloud- and star-formation (SF) studies. With the Herschel satellite it is now possible to precisely determine the column density from dust emission, which is the best tracer of the bulk of material in molecular clouds. However, line-of-sight (LOS) contamination from fore- or background clouds can lead to overestimating the dust emission of molecular clouds, in particular for distant clouds. This implies values that are too high for column density and mass, which can potentially lead to an incorrect physical interpretation of the column density probability distribution function (PDF). In this paper, we use observations and simulations to demonstrate how LOS contamination affects the PDF. We apply a first-order approximation (removing a constant level) to the molecular clouds of Auriga and Maddalena (low-mass star-forming), and Carina and NGC 3603 (both high-mass SF regions). In perfect agreement with the simulations, we find that the PDFs become broader, the peak shifts to lower column densities, and the power-law tail of the PDF for higher column densities flattens after correction. All corrected PDFs have a lognormal part for low column densities with a peak at Av ~ 2 mag, a deviation point (DP) from the lognormal at Av(DP) ~ 4-5 mag, and a power-law tail for higher column densities. Assuming an equivalent spherical density distribution ρ ∝ r- α, the slopes of the power-law tails correspond to αPDF = 1.8, 1.75, and 2.5 for Auriga, Carina, and NGC 3603. These numbers agree within the uncertainties with the values of α ≈ 1.5,1.8, and 2.5 determined from the slope γ (with α = 1-γ) obtained from the radial column density profiles (N ∝ rγ). While α ~ 1.5-2 is consistent with a structure dominated by collapse (local free-fall collapse of individual cores and clumps and global collapse), the higher value of α > 2 for NGC 3603 requires a physical process that leads to additional compression (e.g., expanding ionization fronts). From the small sample of our study, we find that clouds forming only low-mass stars and those also forming high-mass stars have slightly different values for their average column density (1.8 × 1021 cm-2 vs. 3.0 × 1021 cm-2), and they display differences in the overall column density structure. Massive clouds assemble more gas in smaller cloud volumes than low-mass SF ones. However, for both cloud types, the transition of the PDF from lognormal shape into power-law tail is found at the same column density (at Av ~ 4-5 mag). Low-mass and high-mass SF clouds then have the same low column density distribution, most likely dominated by supersonic turbulence. At higher column densities, collapse and external pressure can form the power-law tail. The relative importance of the twoprocesses can vary between clouds and thus lead to the observed differences in PDF and column density structure. Appendices are available in electronic form at http://www.aanda.orgHerschel maps as FITS files are only available at the CDS via anonymous ftp to http://cdsarc.u-strasbg.fr (ftp://130.79.128.5) or via http://cdsarc.u-strasbg.fr/viz-bin/qcat?J/A+A/575/A79

Cirrus cloud retrieval from MSG/SEVIRI during day and night using artificial neural networks

NASA Astrophysics Data System (ADS)

Strandgren, Johan; Bugliaro, Luca

2017-04-01

By covering a large part of the Earth, cirrus clouds play an important role in climate as they reflect incoming solar radiation and absorb outgoing thermal radiation. Nevertheless, the cirrus clouds remain one of the largest uncertainties in atmospheric research and the understanding of the physical processes that govern their life cycle is still poorly understood, as is their representation in climate models. To monitor and better understand the properties and physical processes of cirrus clouds, it's essential that those tenuous clouds can be observed from geostationary spaceborne imagers like SEVIRI (Spinning Enhanced Visible and InfraRed Imager), that possess a high temporal resolution together with a large field of view and play an important role besides in-situ observations for the investigation of cirrus cloud processes. CiPS (Cirrus Properties from Seviri) is a new algorithm targeting thin cirrus clouds. CiPS is an artificial neural network trained with coincident SEVIRI and CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) observations in order to retrieve a cirrus cloud mask along with the cloud top height (CTH), ice optical thickness (IOT) and ice water path (IWP) from SEVIRI. By utilizing only the thermal/IR channels of SEVIRI, CiPS can be used during day and night making it a powerful tool for the cirrus life cycle analysis. Despite the great challenge of detecting thin cirrus clouds and retrieving their properties from a geostationary imager using only the thermal/IR wavelengths, CiPS performs well. Among the cirrus clouds detected by CALIOP, CiPS detects 70 and 95 % of the clouds with an optical thickness of 0.1 and 1.0 respectively. Among the cirrus free pixels, CiPS classify 96 % correctly. For the CTH retrieval, CiPS has a mean absolute percentage error of 10 % or less with respect to CALIOP for cirrus clouds with a CTH greater than 8 km. For the IOT retrieval, CiPS has a mean absolute percentage error of 100 % or less with respect to CALIOP for cirrus clouds with an optical thickness down to 0.07. For such thin cirrus clouds an error of 100 % should be regarded as low from a geostationary imager like SEVIRI. The IWP retrieved by CiPS shows a similar performance, but has larger deviations for the thinner cirrus clouds.

Coupled retrieval of water cloud and above-cloud aerosol properties using the Airborne Multiangle SpectroPolarimetric Imager (AirMSPI)

NASA Astrophysics Data System (ADS)

Xu, F.; van Harten, G.; Diner, D. J.; Rheingans, B. E.; Tosca, M.; Seidel, F. C.; Bull, M. A.; Tkatcheva, I. N.; McDuffie, J. L.; Garay, M. J.; Davis, A. B.; Jovanovic, V. M.; Brian, C.; Alexandrov, M. D.; Hostetler, C. A.; Ferrare, R. A.; Burton, S. P.

2017-12-01

The Airborne Multiangle SpectroPolarimetric Imager (AirMSPI) has been flying aboard the NASA ER-2 high altitude aircraft since October 2010. AirMSPI acquires radiance and polarization data in bands centered at 355, 380, 445, 470*, 555, 660*, 865*, and 935 nm (*denotes polarimetric bands). In sweep mode, georectified images cover an area of 80-100 km (along track) by 10-25 km (across track) between ±66° off nadir, with a map-projected spatial resolution of 25 meters. An efficient and flexible retrieval algorithm has been developed using AirMSPI polarimetric bands for simultaneous retrieval of cloud and above-cloud aerosol microphysical properties. We design a three-step retrieval approach, namely 1) estimating effective droplet size distribution using polarimetric cloudbow observations and using it as initial guess for Step 2; 2) combining water cloud and aerosol above cloud retrieval by fitting polarimetric signals at all scattering angles (e.g. from 80° to 180°); and 3) constructing a lookup table of radiance for a set of cloud optical depth grids using aerosol and cloud information retrieved from Step 2 and then estimating pixel-scale cloud optical depth based on 1D radiative transfer (RT) theory by fitting the AirMSPI radiance. Retrieval uncertainty is formulated by accounting for instrumental errors and constraints imposed on spectral variations of aerosol and cloud droplet optical properties. As the forward RT model, a hybrid approach is developed to combine the computational strengths of Markov-chain and adding-doubling methods to model polarized RT in a coupled aerosol, Rayleigh and cloud system. Our retrieval approach is tested using 134 AirMSPI datasets acquired during NASA ORACLES field campaign in 09/2016, with low to high aerosol loadings. For validation, the retrieved aerosol optical depths and cloud-top heights are compared to coincident High Spectral Resolution Lidar-2 (HSRL-2) data, and the droplet size parameters including effective radius and effective variance and cloud optical thickness are compared to coincident Research Scanning Polarimeter (RSP) data.

A Local Index of Cloud Immersion in Tropical Forests Using Time-Lapse Photography

NASA Astrophysics Data System (ADS)

Bassiouni, M.; Scholl, M. A.

2015-12-01

Data on the frequency, duration and elevation of cloud immersion is essential to improve estimates of cloud water deposition in water budgets in cloud forests. Here, we present a methodology to detect local cloud immersion in remote tropical forests using time-lapse photography. A simple approach is developed to detect cloudy conditions in photographs within the canopy where image depth during clear conditions may be less than 10 meters and moving leaves and branches and changes in lighting are unpredictable. A primary innovation of this study is that cloudiness is determined from images without using a reference clear image and without minimal threshold value determination or human judgment for calibration. Five sites ranging from 600 to 1000 meters elevation along a ridge in the Luquillo Critical Zone Observatory, Puerto Rico were each equipped with a trail camera programmed to take an image every 30 minutes since March 2014. Images were classified using four selected cloud-sensitive image characteristics (SCICs) computed for small image regions: contrast, the coefficient of variation and the entropy of the luminance of each image pixel, and image colorfulness. K-means clustering provided reasonable results to discriminate cloudy from clear conditions. Preliminary results indicate that 79-94% (daytime) and 85-93% (nighttime) of validation images were classified accurately at one open and two closed canopy sites. The euclidian distances between SCICs vectors of images during cloudy conditions and the SCICs vector of the centroid of the cluster of clear images show potential to quantify cloud density in addition to immersion. The classification method will be applied to determine spatial and temporal patterns of cloud immersion in the study area. The presented approach offers promising applications to increase observations of low-lying clouds at remote mountain sites where standard instruments to measure visibility and cloud base may not be practical.

Global Free-tropospheric NO2 Abundances Derived Using a Cloud Slicing Technique from AURA OMI

NASA Technical Reports Server (NTRS)

Choi, S.; Joiner, J.; Choi, Y.; Duncan, B.N.; Vasilkov, A.; Krotkov, N.; Bucsela, E.J.

2014-01-01

We derive free-tropospheric NO2 volume mixing ratios (VMRs) by applying a cloud-slicing technique to data from the Ozone Monitoring Instrument (OMI) on the Aura satellite. In the cloud-slicing approach, the slope of the above-cloud NO2 column versus the cloud scene pressure is proportional to the NO2 VMR. In this work, we use a sample of nearby OMI pixel data from a single orbit for the linear fit. The OMI data include cloud scene pressures from the rotational-Raman algorithm and above-cloud NO2 vertical column density (VCD) (defined as the NO2 column from the cloud scene pressure to the top of the atmosphere) from a differential optical absorption spectroscopy (DOAS) algorithm. We compare OMI-derived NO2 VMRs with in situ aircraft profiles measured during the NASA Intercontinental Chemical Transport Experiment Phase B (INTEX-B) campaign in 2006. The agreement is generally within the estimated uncertainties when appropriate data screening is applied. We then derive a global seasonal climatology of free-tropospheric NO2 VMR in cloudy conditions. Enhanced NO2 in the free troposphere commonly appears near polluted urban locations where NO2 produced in the boundary layer may be transported vertically out of the boundary layer and then horizontally away from the source. Signatures of lightning NO2 are also shown throughout low and middle latitude regions in summer months. A profile analysis of our cloud-slicing data indicates signatures of lightning-generated NO2 in the upper troposphere. Comparison of the climatology with simulations from the global modeling initiative (GMI) for cloudy conditions (cloud optical depth less than10) shows similarities in the spatial patterns of continental pollution outflow. However, there are also some differences in the seasonal variation of free-tropospheric NO2 VMRs near highly populated regions and in areas affected by lightning-generated NOx.

Revisiting the Phase Curves of WASP-43b: Confronting Re-analyzed Spitzer Data with Cloudy Atmospheres

NASA Astrophysics Data System (ADS)

Mendonça, João M.; Malik, Matej; Demory, Brice-Olivier; Heng, Kevin

2018-04-01

Recently acquired Hubble and Spitzer phase curves of the short-period hot Jupiter WASP-43b make it an ideal target for confronting theory with data. On the observational front, we re-analyze the 3.6 and 4.5 μm Spitzer phase curves and demonstrate that our improved analysis better removes residual red noise due to intra-pixel sensitivity, which leads to greater fluxes emanating from the nightside of WASP-43b, thus reducing the tension between theory and data. On the theoretical front, we construct cloud-free and cloudy atmospheres of WASP-43b using our Global Circulation Model (GCM), THOR, which solves the non-hydrostatic Euler equations (compared to GCMs that typically solve the hydrostatic primitive equations). The cloud-free atmosphere produces a reasonable fit to the dayside emission spectrum. The multi-phase emission spectra constrain the cloud deck to be confined to the nightside and have a finite cloud-top pressure. The multi-wavelength phase curves are naturally consistent with our cloudy atmospheres, except for the 4.5 μm phase curve, which requires the presence of enhanced carbon dioxide in the atmosphere of WASP-43b. Multi-phase emission spectra at higher spectral resolution, as may be obtained using the James Webb Space Telescope, and a reflected-light phase curve at visible wavelengths would further constrain the properties of clouds in WASP-43b.

Investigations in thunderstorm energetics using satellite instrumentation and Monte Carlo simulations

NASA Astrophysics Data System (ADS)

Brunner, K. N.; Bitzer, P. M.

2017-12-01

The electrical energy dissipated by lightning is a fundamental question in lightning physics and may be used in severe weather applications. However, the electrical energy, flash area/extent and spectral energy density (radiance) are all influenced by the geometry of the lightning channel. We present details of a Monte Carlo based model simulating the optical emission from lightning and compare with observations. Using time-of-arrival techniques and the electric field change measurements from the Huntsville Alabama Marx Meter Array (HAMMA), the 4D lightning channel is reconstructed. The located sources and lightning channel emit optical emission, calibrated by the ground based electric field, that scatters until absorbed or a cloud boundary is reached within the model. At cloud top, the simulation is gridded as LIS pixels (events) and contiguous events (groups). The radiance is related via the LIS calibration and the estimated lightning electrical energy is calculated at the LIS/GLM time resolution. Previous Monte Carlo simulations have relied on a simplified lightning channel and scattering medium. This work considers the cloud a stratified medium of graupel/ice and inhomogeneous at flash scale. The impact of cloud inhomogeneity on the scattered optical emission at cloud top and at the time resolution of LIS and GLM are also considered. The simulation results and energy metrics provide an estimation of the electrical energy using GLM and LIS on the International Space Station (ISS-LIS).

Saturn Methane Image

NASA Image and Video Library

2004-03-05

NASA's Cassini narrow angle camera took this image of Saturn on Feb. 16, 2004, from a distance of 66.1 million kilometers (41.1 million miles) in a special filter which reveals clouds and haze high in the atmosphere. The image scale is 397 kilometers (247 miles) per pixel. The MT2 spectral filter samples a near-infrared region of the electromagnetic spectrum where methane gas absorbs light at a wavelength of 727 nanometers. In the image, methane gas is uniformly mixed with hydrogen, the main gas in Saturn's atmosphere. Dark locales are places of strong methane absorption, relatively free of high clouds; the bright areas are places with high, thick clouds which shield the methane below. Image details reveal a high, thick equatorial cloud and a relatively deep or thin haze encircling the pole, as well as several distinct latitude bands with different cloud height attributes. It also shows a high atmospheric disturbance, just south of the equator, which has persisted throughout the 1990s in images returned by NASA's Hubble Space Telescope. Four of Saturn's moons are visible (clockwise from above right): Enceladus (499 kilometers, or 310 miles across); Mimas (396 kilometers, or 245 miles across); Tethys (1,060 kilometers, or 659 miles across); and Rhea (1,528 kilometers, or 949 miles across). The imaging team enhanced the brightness of Mimas and Enceladus by a factor of three. http://photojournal.jpl.nasa.gov/catalog/PIA05381

Intercomparison of OMI NO2 and HCHO air mass factor calculations: recommendations and best practices for retrievals

NASA Astrophysics Data System (ADS)

Lorente Delgado, Alba; Klaas Boersma, Folkert; Hilboll, Andreas; Richter, Andreas; Yu, Huan; van Roozendael, Michel; Dörner, Steffen; Wagner, Thomas; Barkley, Michael; Lamsal, Lok; Lin, Jintai; Liu, Mengyao

2016-04-01

We present a detailed comparison of the air mass factor (AMF) calculation process used by various research groups for OMI satellite retrievals of NO2 and HCHO. Although satellite retrievals have strongly improved over the last decades, there is still a need to better understand and reduce the uncertainties associated with every retrieval step of satellite data products, such as the AMF calculation. Here we compare and evaluate the different approaches used to calculate AMFs by several scientific groups (KNMI (WUR), IASB-BIRA, IUP-UNI. BREMEN, MPI-C, NASA GSFC, LEICESTER UNI. and PEKING UNI.). Each group calculated altitude dependent (box-) AMFs and clear sky and total tropospheric AMFs for several OMI orbits. First, European groups computed AMFs for one OMI orbit using common settings for the choice of surface albedo data, terrain height, cloud treatment and a priori vertical profile. Second, every group computed AMFs for two complete days in different seasons using preferred settings for the ancillary data and cloud treatment as a part of a Round Robin exercise. Box-AMFs comparison showed good consistency and underlined the importance of a correct treatment of the physical processes affecting the effective light path and the vertical discretization of the atmosphere. Using common settings, tropospheric NO2 AMFs in polluted pixels on average agreed within 4.7% whereas in remote pixels agreed within 3.5%. Using preferred settings relative differences between AMFs increase up to 15-30%. This increase is traced back to the different choices and assumptions made throughout the AMF calculation, which affect the final AMF values and thus the uncertainty in the AMF calculation. Differences between state of the art cloud treatment approaches highlight the importance of an accurate cloud correction: total and clear sky AMFs in polluted conditions differ by up to 40% depending on the retrieval scenario. Based on the comparison results, specific recommendations on best practices are given and they will be used in QA4ECV community effort retrieval algorithm to be applied in past and future UV/Vis instrumentation for generating quality assured multi decadal NO2 and HCHO records.

Measurements of the light-absorbing material inside cloud droplets and its effect on cloud albedo

NASA Technical Reports Server (NTRS)

Twohy, C. H.; Clarke, A. D.; Warren, Stephen G.; Radke, L. F.; Charleson, R. J.

1990-01-01

Most of the measurements of light-absorbing aerosol particles made previously have been in non-cloudy air and therefore provide no insight into aerosol effects on cloud properties. Here, researchers describe an experiment designed to measure light absorption exclusively due to substances inside cloud droplets, compare the results to related light absorption measurements, and evaluate possible effects on the albedo of clouds. The results of this study validate those of Twomey and Cocks and show that the measured levels of light-absorbing material are negligible for the radiative properties of realistic clouds. For the measured clouds, which appear to have been moderately polluted, the amount of elemental carbon (EC) present was insufficient to affect albedo. Much higher contaminant levels or much larger droplets than those measured would be necessary to significantly alter the radiative properties. The effect of the concentrations of EC actually measured on the albedo of snow, however, would be much more pronounced since, in contrast to clouds, snowpacks are usually optically semi-infinite and have large particle sizes.

Selection and quality assessment of Landsat data for the North American forest dynamics forest history maps of the US

USGS Publications Warehouse

Schleeweis, Karen; Goward, Samuel N.; Huang, Chengquan; Dwyer, John L.; Dungan, Jennifer L.; Lindsey, Mary A.; Michaelis, Andrew; Rishmawi, Khaldoun; Masek, Jeffery G.

2016-01-01

Using the NASA Earth Exchange platform, the North American Forest Dynamics (NAFD) project mapped forest history wall-to-wall, annually for the contiguous US (1986–2010) using the Vegetation Change Tracker algorithm. As with any effort to identify real changes in remotely sensed time-series, data gaps, shifts in seasonality, misregistration, inconsistent radiometry and cloud contamination can be sources of error. We discuss the NAFD image selection and processing stream (NISPS) that was designed to minimize these sources of error. The NISPS image quality assessments highlighted issues with the Landsat archive and metadata including inadequate georegistration, unreliability of the pre-2009 L5 cloud cover assessments algorithm, missing growing-season imagery and paucity of clear views. Assessment maps of Landsat 5–7 image quantities and qualities are presented that offer novel perspectives on the growing-season archive considered for this study. Over 150,000+ Landsat images were considered for the NAFD project. Optimally, one high quality cloud-free image in each year or a total of 12,152 images would be used. However, to accommodate data gaps and cloud/shadow contamination 23,338 images were needed. In 220 specific path-row image years no acceptable images were found resulting in data gaps in the annual national map products.

Raman-spectroscopy-based chemical contaminant detection in milk powder

NASA Astrophysics Data System (ADS)

Dhakal, Sagar; Chao, Kuanglin; Qin, Jianwei; Kim, Moon S.

2015-05-01

Addition of edible and inedible chemical contaminants in food powders for purposes of economic benefit has become a recurring trend. In recent years, severe health issues have been reported due to consumption of food powders contaminated with chemical substances. This study examines the effect of spatial resolution used during spectral collection to select the optimal spatial resolution for detecting melamine in milk powder. Sample depth of 2mm, laser intensity of 200mw, and exposure time of 0.1s were previously determined as optimal experimental parameters for Raman imaging. Spatial resolution of 0.25mm was determined as the optimal resolution for acquiring spectral signal of melamine particles from a milk-melamine mixture sample. Using the optimal resolution of 0.25mm, sample depth of 2mm and laser intensity of 200mw obtained from previous study, spectral signal from 5 different concentration of milk-melamine mixture (1%, 0.5%, 0.1%, 0.05%, and 0.025%) were acquired to study the relationship between number of detected melamine pixels and corresponding sample concentration. The result shows that melamine concentration has a linear relation with detected number of melamine pixels with correlation coefficient of 0.99. It can be concluded that the quantitative analysis of powder mixture is dependent on many factors including physical characteristics of mixture, experimental parameters, and sample depth. The results obtained in this study are promising. We plan to apply the result obtained from this study to develop quantitative detection model for rapid screening of melamine in milk powder. This methodology can also be used for detection of other chemical contaminants in milk powders.

LIDAR pulse coding for high resolution range imaging at improved refresh rate.

PubMed

Kim, Gunzung; Park, Yongwan

2016-10-17

In this study, a light detection and ranging system (LIDAR) was designed that codes pixel location information in its laser pulses using the direct- sequence optical code division multiple access (DS-OCDMA) method in conjunction with a scanning-based microelectromechanical system (MEMS) mirror. This LIDAR can constantly measure the distance without idle listening time for the return of reflected waves because its laser pulses include pixel location information encoded by applying the DS-OCDMA. Therefore, this emits in each bearing direction without waiting for the reflected wave to return. The MEMS mirror is used to deflect and steer the coded laser pulses in the desired bearing direction. The receiver digitizes the received reflected pulses using a low-temperature-grown (LTG) indium gallium arsenide (InGaAs) based photoconductive antenna (PCA) and the time-to-digital converter (TDC) and demodulates them using the DS-OCDMA. When all of the reflected waves corresponding to the pixels forming a range image are received, the proposed LIDAR generates a point cloud based on the time-of-flight (ToF) of each reflected wave. The results of simulations performed on the proposed LIDAR are compared with simulations of existing LIDARs.

Micrometer-resolution imaging using MÖNCH: towards G2-less grating interferometry

PubMed Central

Cartier, Sebastian; Kagias, Matias; Bergamaschi, Anna; Wang, Zhentian; Dinapoli, Roberto; Mozzanica, Aldo; Ramilli, Marco; Schmitt, Bernd; Brückner, Martin; Fröjdh, Erik; Greiffenberg, Dominic; Mayilyan, Davit; Mezza, Davide; Redford, Sophie; Ruder, Christian; Schädler, Lukas; Shi, Xintian; Thattil, Dhanya; Tinti, Gemma; Zhang, Jiaguo; Stampanoni, Marco

2016-01-01

MÖNCH is a 25 µm-pitch charge-integrating detector aimed at exploring the limits of current hybrid silicon detector technology. The small pixel size makes it ideal for high-resolution imaging. With an electronic noise of about 110 eV r.m.s., it opens new perspectives for many synchrotron applications where currently the detector is the limiting factor, e.g. inelastic X-ray scattering, Laue diffraction and soft X-ray or high-resolution color imaging. Due to the small pixel pitch, the charge cloud generated by absorbed X-rays is shared between neighboring pixels for most of the photons. Therefore, at low photon fluxes, interpolation algorithms can be applied to determine the absorption position of each photon with a resolution of the order of 1 µm. In this work, the characterization results of one of the MÖNCH prototypes are presented under low-flux conditions. A custom interpolation algorithm is described and applied to the data to obtain high-resolution images. Images obtained in grating interferometry experiments without the use of the absorption grating G2 are shown and discussed. Perspectives for the future developments of the MÖNCH detector are also presented. PMID:27787252

Development of a remote sensing algorithm to retrieve atmospheric aerosol properties using multiwavelength and multipixel information

NASA Astrophysics Data System (ADS)

Hashimoto, Makiko; Nakajima, Teruyuki

2017-06-01

We developed a satellite remote sensing algorithm to retrieve the aerosol optical properties using satellite-received radiances for multiple wavelengths and pixels. Our algorithm utilizes spatial inhomogeneity of surface reflectance to retrieve aerosol properties, and the main target is urban aerosols. This algorithm can simultaneously retrieve aerosol optical thicknesses (AOT) for fine- and coarse-mode aerosols, soot volume fraction in fine-mode aerosols (SF), and surface reflectance over heterogeneous surfaces such as urban areas that are difficult to obtain by conventional pixel-by-pixel methods. We applied this algorithm to radiances measured by the Greenhouse Gases Observing Satellite/Thermal and Near Infrared Sensor for Carbon Observations-Cloud and Aerosol Image (GOSAT/TANSO-CAI) at four wavelengths and were able to retrieve the aerosol parameters in several urban regions and other surface types. A comparison of the retrieved AOTs with those from the Aerosol Robotic Network (AERONET) indicated retrieval accuracy within ±0.077 on average. It was also found that the column-averaged SF and the aerosol single scattering albedo (SSA) underwent seasonal changes as consistent with the ground surface measurements of SSA and black carbon at Beijing, China.

Saturnian Hexagon Collage

NASA Image and Video Library

2016-12-06

This collage of images from NASA's Cassini spacecraft shows Saturn's northern hemisphere and rings as viewed with four different spectral filters. Each filter is sensitive to different wavelengths of light and reveals clouds and hazes at different altitudes. Clockwise from top left, the filters used are sensitive to violet (420 nanometers), red (648 nanometers), near-infrared (728 nanometers) and infrared (939 nanometers) light. The image was taken with the Cassini spacecraft wide-angle camera on Dec. 2, 2016, at a distance of about 400,000 miles (640,000 kilometers) from Saturn. Image scale is 95 miles (153 kilometers) per pixel. The images have been enlarged by a factor of two. The original versions of these images, as sent by the spacecraft, have a size of 256 pixels by 256 pixels. Cassini's images are sometimes planned to be compressed to smaller sizes due to data storage limitations on the spacecraft, or to allow a larger number of images to be taken than would otherwise be possible. These images were obtained about two days before its first close pass by the outer edges of Saturn's main rings during its penultimate mission phase. http://photojournal.jpl.nasa.gov/catalog/PIA21053

Investigation of TM Band-to-band Registration Using the JSC Registration Processor

NASA Technical Reports Server (NTRS)

Yao, S. S.; Amis, M. L.

1984-01-01

The JSC registration processor performs scene-to-scene (or band-to-band) correlation based on edge images. The edge images are derived from a percentage of the edge pixels calculated from the raw scene data, excluding clouds and other extraneous data in the scene. Correlations are performed on patches (blocks) of the edge images, and the correlation peak location in each patch is estimated iteratively to fractional pixel location accuracy. Peak offset locations from all patches over the scene are then considered together, and a variety of tests are made to weed out outliers and other inconsistencies before a distortion model is assumed. Thus, the correlation peak offset locations in each patch indicate quantitatively how well the two TM bands register to each other over that patch of scene data. The average of these offsets indicate the overall accuracies of the band-to-band registration. The registration processor was also used to register one acquisition to another acquisition of multitemporal TM data acquired over the same ground track. Band 4 images from both acquisitions were correlated and an rms error of a fraction of a pixel was routinely obtained.

Investigation of scene identification algorithms for radiation budget measurements

NASA Technical Reports Server (NTRS)

Diekmann, F. J.

1986-01-01

The computation of Earth radiation budget from satellite measurements requires the identification of the scene in order to select spectral factors and bidirectional models. A scene identification procedure is developed for AVHRR SW and LW data by using two radiative transfer models. These AVHRR GAC pixels are then attached to corresponding ERBE pixels and the results are sorted into scene identification probability matrices. These scene intercomparisons show that there generally is a higher tendency for underestimation of cloudiness over ocean at high cloud amounts, e.g., mostly cloudy instead of overcast, partly cloudy instead of mostly cloudy, for the ERBE relative to the AVHRR results. Reasons for this are explained. Preliminary estimates of the errors of exitances due to scene misidentification demonstrates the high dependency on the probability matrices. While the longwave error can generally be neglected the shortwave deviations have reached maximum values of more than 12% of the respective exitances.

Jupiter's Stunning Southern Hemisphere

NASA Image and Video Library

2017-11-09

See Jupiter's southern hemisphere in beautiful detail in this new image taken by NASA's Juno spacecraft. The color-enhanced view captures one of the white ovals in the "String of Pearls," one of eight massive rotating storms at 40 degrees south latitude on the gas giant planet. The image was taken on Oct. 24, 2017 at 11:11 a.m. PDT (2:11 p.m. EDT), as Juno performed its ninth close flyby of Jupiter. At the time the image was taken, the spacecraft was 20,577 miles (33,115 kilometers) from the tops of the clouds of the planet at a latitude of minus 52.96 degrees. The spatial scale in this image is 13.86 miles/pixel (22.3 kilometers/pixel). Citizen scientists Gerald Eichstädt and Seán Doran processed this image using data from the JunoCam imager. https://photojournal.jpl.nasa.gov/catalog/PIA21970

3D reconstruction of microminiature objects based on contour line

NASA Astrophysics Data System (ADS)

Li, Cailin; Wang, Qiang; Guo, Baoyun

2009-10-01

A new 3D automatic reconstruction method of micro solid of revolution is presented in this paper. In the implementation procedure of this method, image sequence of the solid of revolution of 360° is obtained, which rotation speed is controlled by motor precisely, in the rotate photographic mode of back light. Firstly, we need calibrate the height of turntable, the size of pixel and rotation axis of turntable. Then according to the calibration result of rotation axis, the height of turntable, rotation angle and the pixel size, the contour points of each image can be transformed into 3D points in the reference coordinate system to generate the point cloud model. Finally, the surface geometrical model of solid of revolution is obtained by using the relationship of two adjacent contours. Experimental results on real images are presented, which demonstrate the effectiveness of the Approach.

A Color-locus Method for Mapping R V Using Ensembles of Stars

NASA Astrophysics Data System (ADS)

Lee, Albert; Green, Gregory M.; Schlafly, Edward F.; Finkbeiner, Douglas P.; Burgett, William; Chambers, Ken; Flewelling, Heather; Hodapp, Klaus; Kaiser, Nick; Kudritzki, Rolf-Peter; Magnier, Eugene; Metcalfe, Nigel; Wainscoat, Richard; Waters, Christopher

2018-02-01

We present a simple but effective technique for measuring angular variation in R V across the sky. We divide stars from the Pan-STARRS1 catalog into Healpix pixels and determine the posterior distribution of reddening and R V for each pixel using two independent Monte Carlo methods. We find the two methods to be self-consistent in the limits where they are expected to perform similarly. We also find some agreement with high-precision photometric studies of R V in Perseus and Ophiuchus, as well as with a map of reddening near the Galactic plane based on stellar spectra from APOGEE. While current studies of R V are mostly limited to isolated clouds, we have developed a systematic method for comparing R V values for the majority of observable dust. This is a proof of concept for a more rigorous Galactic reddening map.

Measurement Comparisons Towards Improving the Understanding of Aerosol-Cloud Processing

NASA Astrophysics Data System (ADS)

Noble, Stephen R.

Cloud processing of aerosol is an aerosol-cloud interaction that is not heavily researched but could have implications on climate. The three types of cloud processing are chemical processing, collision and coalescence processing, and Brownian capture of interstitial particles. All types improve cloud condensation nuclei (CCN) in size or hygroscopicity (kappa). These improved CCN affect subsequent clouds. This dissertation focuses on measurement comparisons to improve our observations and understanding of aerosol-cloud processing. Particle size distributions measured at the continental Southern Great Plains (SGP) site were compared with ground based measurements of cloud fraction (CF) and cloud base altitude (CBA). Particle size distributions were described by a new objective shape parameter to define bimodality rather than an old subjective one. Cloudy conditions at SGP were found to be correlated with lagged shape parameter. Horizontal wind speed and regional CF explained 42%+ of this lag time. Many of these surface particle size distributions were influenced by aerosol-cloud processing. Thus, cloud processing may be more widespread with more implications than previously thought. Particle size distributions measured during two aircraft field campaigns (MArine Stratus/stratocumulus Experiment; MASE; and Ice in Cloud Experiment-Tropical; ICE-T) were compared to CCN distributions. Tuning particle size to critical supersaturation revealed hygroscopicity expressed as ? when the distributions were overlain. Distributions near cumulus clouds (ICE-T) had a higher frequency of the same ?s (48% in ICE-T to 42% in MASE) between the accumulation (processed) and Aitken (unprocessed) modes. This suggested physical processing domination in ICE-T. More MASE (stratus cloud) kappa differences between modes pointed to chemical cloud processing. Chemistry measurements made in MASE showed increases in sulfates and nitrates with distributions that were more processed. This supported chemical cloud processing in MASE. This new method to determine kappa provides the needed information without interrupting ambient measurements. MODIS derived cloud optical thickness (COT), cloud liquid water path (LWP), and cloud effective radius (re) were compared to the same in situ derived variables from cloud probe measurements of two stratus/stratocumulus cloud campaigns (MASE and Physics Of Stratocumulus Tops; POST). In situ data were from complete vertical cloud penetrations, while MODIS data were from pixels along the aircraft penetration path. Comparisons were well correlated except that MODIS LWP (14-36%) and re (20-30%) were biased high. The LWP bias was from re bias and was not improved by using the vertically stratified assumption. MODIS re bias was almost removed when compared to cloud top maximum in situ re, but, that does not describe re for the full depth of the cloud. COT is validated by in situ COT. High correlations suggest that MODIS variables are useful in self-comparisons such as gradient changes in stratus cloud re during aerosol-cloud processing.

Climatology analysis of cirrus cloud in ARM site: South Great Plain

NASA Astrophysics Data System (ADS)

Olayinka, K.

2017-12-01

Cirrus cloud play an important role in the atmospheric energy balance and hence in the earth's climate system. The properties of optically thin clouds can be determined from measurements of transmission of the direct solar beam. The accuracy of cloud optical properties determined in this way is compromised by contamination of the direct transmission by light that is scattered into the sensors field of view. With the forward scattering correction method developed by Min et al., (2004), the accuracy of thin cloud retrievals from MFRSR has been improved. Our result shows over 30% of cirrus cloud present in the atmosphere are within optical depth between (1-2). In this study, we do statistics studies on cirrus clouds properties based on multi-years cirrus cloud measurements from MFRSR at ARM site from the South Great Plain (SGP) site due to its relatively easy accessibility, wide variability of climate cloud types and surface flux properties, large seasonal variation in temperature and specific humidity. Through the statistic studies, temporal and spatial variations of cirrus clouds are investigated. Since the presence of cirrus cloud increases the effect of greenhouse gases, we will retrieve the aerosol optical depth in all the cirrus cloud regions using a radiative transfer model for atmospheric correction. Calculate thin clouds optical depth (COD), and aerosol optical depth (AOD) using a radiative transfer model algorithm, e.g.: MODTRAN (MODerate resolution atmospheric TRANsmission)

Short-term solar irradiance forecasting via satellite/model coupling

DOE PAGES

Miller, Steven D.; Rogers, Matthew A.; Haynes, John M.; ...

2017-12-01

The short-term (0-3 h) prediction of solar insolation for renewable energy production is a problem well-suited to satellite-based techniques. The spatial, spectral, temporal and radiometric resolution of instrumentation hosted on the geostationary platform allows these satellites to describe the current cloud spatial distribution and optical properties. These properties relate directly to the transient properties of the downwelling solar irradiance at the surface, which come in the form of 'ramps' that pose a central challenge to energy load balancing in a spatially distributed network of solar farms. The short-term evolution of the cloud field may be approximated to first order simplymore » as translational, but care must be taken in how the advection is handled and where the impacts are assigned. In this research, we describe how geostationary satellite observations are used with operational cloud masking and retrieval algorithms, wind field data from Numerical Weather Prediction (NWP), and radiative transfer calculations to produce short-term forecasts of solar insolation for applications in solar power generation. The scheme utilizes retrieved cloud properties to group pixels into contiguous cloud objects whose future positions are predicted using four-dimensional (space + time) model wind fields, selecting steering levels corresponding to the cloud height properties of each cloud group. The shadows associated with these clouds are adjusted for sensor viewing parallax displacement and combined with solar geometry and terrain height to determine the actual location of cloud shadows. For mid/high-level clouds at mid-latitudes and high solar zenith angles, the combined displacements from these geometric considerations are non-negligible. The cloud information is used to initialize a radiative transfer model that computes the direct and diffuse-sky solar insolation at both shadow locations and intervening clear-sky regions. Here, we describe the formulation of the algorithm and validate its performance against Surface Radiation (SURFRAD; Augustine et al., 2000, 2005) network observations. Typical errors range from 8.5% to 17.2% depending on the complexity of cloud regimes, and an operational demonstration outperformed persistence-based forecasting of Global Horizontal Irradiance (GHI) under all conditions by ~10 W/m2.« less

Short-term solar irradiance forecasting via satellite/model coupling

DOE Office of Scientific and Technical Information (OSTI.GOV)

Miller, Steven D.; Rogers, Matthew A.; Haynes, John M.

The short-term (0-3 h) prediction of solar insolation for renewable energy production is a problem well-suited to satellite-based techniques. The spatial, spectral, temporal and radiometric resolution of instrumentation hosted on the geostationary platform allows these satellites to describe the current cloud spatial distribution and optical properties. These properties relate directly to the transient properties of the downwelling solar irradiance at the surface, which come in the form of 'ramps' that pose a central challenge to energy load balancing in a spatially distributed network of solar farms. The short-term evolution of the cloud field may be approximated to first order simplymore » as translational, but care must be taken in how the advection is handled and where the impacts are assigned. In this research, we describe how geostationary satellite observations are used with operational cloud masking and retrieval algorithms, wind field data from Numerical Weather Prediction (NWP), and radiative transfer calculations to produce short-term forecasts of solar insolation for applications in solar power generation. The scheme utilizes retrieved cloud properties to group pixels into contiguous cloud objects whose future positions are predicted using four-dimensional (space + time) model wind fields, selecting steering levels corresponding to the cloud height properties of each cloud group. The shadows associated with these clouds are adjusted for sensor viewing parallax displacement and combined with solar geometry and terrain height to determine the actual location of cloud shadows. For mid/high-level clouds at mid-latitudes and high solar zenith angles, the combined displacements from these geometric considerations are non-negligible. The cloud information is used to initialize a radiative transfer model that computes the direct and diffuse-sky solar insolation at both shadow locations and intervening clear-sky regions. Here, we describe the formulation of the algorithm and validate its performance against Surface Radiation (SURFRAD; Augustine et al., 2000, 2005) network observations. Typical errors range from 8.5% to 17.2% depending on the complexity of cloud regimes, and an operational demonstration outperformed persistence-based forecasting of Global Horizontal Irradiance (GHI) under all conditions by ~10 W/m2.« less

The Effect of Asian Dust Aerosols on Cloud Properties and Radiative Forcing from MODIS and CERES

NASA Technical Reports Server (NTRS)

Huang, Jianping; Minnis, Patrick; Lin, Bing; Wang, Tianhe; Yi, Yuhong; Hu, Yongxiang; Sun-Mack, Sunny; Ayers, Kirk

2005-01-01

The effects of dust storms on cloud properties and radiative forcing are analyzed over northwestern China from April 2001 to June 2004 using data collected by the Moderate Resolution Imaging Spectroradiometer (MODIS) and Clouds and the Earth's Radiant Energy System (CERES) instruments on the Aqua and Terra satellites. On average, ice cloud effective particle diameter, optical depth and ice water path of the cirrus clouds under dust polluted conditions are 11%, 32.8%, and 42% less, respectively, than those derived from ice clouds in dust-free atmospheric environments. The humidity differences are larger in the dusty region than in the dust-free region, and may be caused by removal of moisture by wet dust precipitation. Due to changes in cloud microphysics, the instantaneous net radiative forcing is reduced from -71.2 W/m2 for dust contaminated clouds to -182.7 W/m2 for dust-free clouds. The reduced cooling effects of dusts may lead to a net warming of 1 W/m2, which, if confirmed, would be the strongest aerosol forcing during later winter and early spring dust storm seasons over the studied region.

Separating Real and Apparent Effects of Cloud, Humidity, and Dynamics on Aerosol Optical Thickness near Cloud Edges

NASA Technical Reports Server (NTRS)

Jeong, Myeong-Jae; Li, Zhanqing

2010-01-01

Aerosol optical thickness (AOT) is one of aerosol parameters that can be measured on a routine basis with reasonable accuracy from Sun-photometric observations at the surface. However, AOT-derived near clouds is fraught with various real effects and artifacts, posing a big challenge for studying aerosol and cloud interactions. Recently, several studies have reported correlations between AOT and cloud cover, pointing to potential cloud contamination and the aerosol humidification effect; however, not many quantitative assessments have been made. In this study, various potential causes of apparent correlations are investigated in order to separate the real effects from the artifacts, using well-maintained observations from the Aerosol Robotic Network, Total Sky Imager, airborne nephelometer, etc., over the Southern Great Plains site operated by the U.S. Department of Energy's Atmospheric Radiation Measurement Program. It was found that aerosol humidification effects can explain about one fourth of the correlation between the cloud cover and AOT. New particle genesis, cloud-processed particles, atmospheric dynamics, and aerosol indirect effects are likely to be contributing to as much as the remaining three fourth of the relationship between cloud cover and AOT.

Study of wind retrieval from space-borne infrared coherent lidar in cloudy atmosphere.

NASA Astrophysics Data System (ADS)

Baron, Philippe; Ishii, Shoken; Mizutani, Kohei; Okamoto, Kozo; Ochiai, Satoshi

2015-04-01

Future spaceborne tropospheric wind missions using infrared coherent lidar are currently being studied in Japan and in the United States [1,2]. The line-of-sight wind velocity is retrieved from the Doppler shift frequency of the signal returned by aerosol particles. However a large percentage (70-80%) of the measured single-shot intensity profiles are expected to be contaminated by clouds [3]. A large number of cloud contaminated profiles (>40%) will be characterized by a cloud-top signal intensity stronger than the aerosol signal by a factor of one order of magnitude, and by a strong attenuation of the signal backscattered from below the clouds. Profiles including more than one cloud layer are also expected. This work is a simulation study dealing with the impacts of clouds on wind retrieval. We focus on the three following points: 1) definition of an algorithm for optimizing the wind retrieval from the cloud-top signal, 2) assessment of the clouds impact on the measurement performance and, 3) definition of a method for averaging the measurements before the retrieval. The retrieval simulations are conducted considering the instrumental characteristics selected for the Japanese study: wavelength at 2 µm, PRF of 30 Hz, pulse power of 0.125 mJ and platform altitude between 200-400 km. Liquid and ice clouds are considered. The analysis uses data from atmospheric models and statistics of cloud effects derived from CALIPSO measurements such as in [3]. A special focus is put on the average method of the measurements before retrieval. Good retrievals in the mid-upper troposphere implie the average of measured single-range power spectra over large horizontal (100 km) and vertical (1 km) ranges. Large differences of signal intensities due to the presence of clouds and the clouds non-uniform distribution have to be taken into account when averaging the data to optimize the measurement performances. References: [1] S. Ishii, T. Iwasaki, M. Sato, R. Oki, K. Okamoto, T. Ishibashi, P. Baron, and T. Nishizawa: Future Doppler lidar wind measurement from space in Japan, Proc. of SPIE Vol. 8529, 2012 [2] D. Wu, J. Tang, Z. Liu, and Y. Hu: Simulation of coherent doppler wind lidar measurement from space based on CALIPSO lidar global aerosol observations. Journal of Quantitative Spectroscopy and Radiative Transfer, 122(0), 79-86, 2013 [3] G.D Emmitt: CFLOS and cloud statistics from satellite and their impact on future space-based Doppler Wind Lidar development. Symposium on Recent Developments in Atmospheric Applications of Radar and Lidar, 2008

ISCCP CX observations during the FIRE/SRB Wisconsin Experiment from October 14 through November 2, 1986

NASA Technical Reports Server (NTRS)

Whitlock, Charles H.; Lecroy, Stuart R.; Rossow, William B.; Bell, Ken L.

1989-01-01

Maps and tables are presented which show 45 satellite derived physical, radiation, or cloud parameters from ISCCP CX tapes during the FIRE/SRB Wisconsin experiment region from October 14 through November 2, 1986. Pixel locations selected for presentation are for an area which coincided with a 100 x 100 km array of evenly spaced ground truth sites. Area-averaged parameters derived from the ISSCP data should be consistent with area averages from the groundtruth stations.

Mid-Infrared Spectroscopy of Carbon Stars in the Small Magellanic Cloud

DTIC Science & Technology

2006-07-10

nod. Before extracting spectra from fit a variety of spectral feature shapes using MgS considerably the images, we used the imclean software package...mined from neighboring pixels. In addition to the dust features , the IRS wavelength range also To extract spectra from the cleaned and differenced...Example of the extraction of the molecular bands and the SiC dust 24 jIm, and they avoid any potential problems at the joint be- feature from the spectrum

Combined Dust Detection Algorithm by Using MODIS Infrared Channels over East Asia

NASA Technical Reports Server (NTRS)

Park, Sang Seo; Kim, Jhoon; Lee, Jaehwa; Lee, Sukjo; Kim, Jeong Soo; Chang, Lim Seok; Ou, Steve

2014-01-01

A new dust detection algorithm is developed by combining the results of multiple dust detectionmethods using IR channels onboard the MODerate resolution Imaging Spectroradiometer (MODIS). Brightness Temperature Difference (BTD) between two wavelength channels has been used widely in previous dust detection methods. However, BTDmethods have limitations in identifying the offset values of the BTDto discriminate clear-sky areas. The current algorithm overcomes the disadvantages of previous dust detection methods by considering the Brightness Temperature Ratio (BTR) values of the dual wavelength channels with 30-day composite, the optical properties of the dust particles, the variability of surface properties, and the cloud contamination. Therefore, the current algorithm shows improvements in detecting the dust loaded region over land during daytime. Finally, the confidence index of the current dust algorithm is shown in 10 × 10 pixels of the MODIS observations. From January to June, 2006, the results of the current algorithm are within 64 to 81% of those found using the fine mode fraction (FMF) and aerosol index (AI) from the MODIS and Ozone Monitoring Instrument (OMI). The agreement between the results of the current algorithm and the OMI AI over the non-polluted land also ranges from 60 to 67% to avoid errors due to the anthropogenic aerosol. In addition, the developed algorithm shows statistically significant results at four AErosol RObotic NETwork (AERONET) sites in East Asia.

Generation of Classical DInSAR and PSI Ground Motion Maps on a Cloud Thematic Platform

NASA Astrophysics Data System (ADS)

Mora, Oscar; Ordoqui, Patrick; Romero, Laia

2016-08-01

This paper presents the experience of ALTAMIRA INFORMATION uploading InSAR (Synthetic Aperture Radar Interferometry) services in the Geohazard Exploitation Platform (GEP), supported by ESA. Two different processing chains are presented jointly with ground motion maps obtained from the cloud computing, one being DIAPASON for classical DInSAR and SPN (Stable Point Network) for PSI (Persistent Scatterer Interferometry) processing. The product obtained from DIAPASON is the interferometric phase related to ground motion (phase fringes from a SAR pair). SPN provides motion data (mean velocity and time series) on high-quality pixels from a stack of SAR images. DIAPASON is already implemented, and SPN is under development to be exploited with historical data coming from ERS-1/2 and ENVISAT satellites, and current acquisitions of SENTINEL-1 in SLC and TOPSAR modes.

Sensitivity of thermal inertia calculations to variations in environmental factors. [in mapping of Earth's surface by remote sensing

NASA Technical Reports Server (NTRS)

Kahle, A. B.; Alley, R. E.; Schieldge, J. P.

1984-01-01

The sensitivity of thermal inertia (TI) calculations to errors in the measurement or parameterization of a number of environmental factors is considered here. The factors include effects of radiative transfer in the atmosphere, surface albedo and emissivity, variations in surface turbulent heat flux density, cloud cover, vegetative cover, and topography. The error analysis is based upon data from the Heat Capacity Mapping Mission (HCMM) satellite for July 1978 at three separate test sites in the deserts of the western United States. Results show that typical errors in atmospheric radiative transfer, cloud cover, and vegetative cover can individually cause root-mean-square (RMS) errors of about 10 percent (with atmospheric effects sometimes as large as 30-40 percent) in HCMM-derived thermal inertia images of 20,000-200,000 pixels.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Noble, Stephen R.; Hudson, James G.

Here, vertical sounding measurements within stratocumuli during two aircraft field campaigns, Marine Stratus/stratocumulus Experiment (MASE) and Physics of Stratocumulus Top (POST), are used to validate Moderate Resolution Imaging Spectroradiometer (MODIS) cloud optical thickness (COT), cloud liquid water path (LWP), and cloud effective radius (r e). In situ COT, LWP, and r e were calculated using 5 m vertically averaged droplet probe measurements of complete vertical cloud penetrations. MODIS COT, LWP, and r e 1 km pixels were averaged along these penetrations. COT comparisons in POST showed strong correlations and a near 1:1 relationship. In MASE, comparisons showed strong correlations; however,more » MODIS COT exceeded in situ COT, likely due to larger temporal differences between MODIS and in situ measurements. LWP comparisons between two cloud probes show good agreement for POST but not MASE, giving confidence to POST data. Both projects provided strong LWP correlations but MODIS exceeded in situ by 14–36%. MODIS in situ r e correlations were strong, but MODIS 2.1 µm r e exceeded in situ r e, which contributed to LWP bias; in POST, MODIS r e was 20–30% greater than in situ r e. Maximum in situ r e near cloud top showed comparisons nearer 1:1. Other MODIS r e bands (3.7 µm and 1.6 µm) showed similar comparisons. Temporal differences between MODIS and in situ measurements, airplane speed differences, and cloud probe artifacts were likely causes of weaker MASE correlations. POST COT comparison was best for temporal differences under 20 min. POST data validate MODIS COT but it also implies a positive MODIS r e bias that propagates to LWP while still capturing variability.« less

Reducing the Impact of Sampling Bias in NASA MODIS and VIIRS Level 3 Satellite Derived IR SST Observations over the Arctic

NASA Astrophysics Data System (ADS)

Minnett, P. J.; Liu, Y.; Kilpatrick, K. A.

2016-12-01

Sea-surface temperature (SST) measurements by satellites in the northern hemisphere high latitudes confront several difficulties. Year-round prevalent clouds, effects near ice edges, and the relative small difference between SST and low-level cloud temperatures lead to a significant loss of infrared observations regardless of the more frequent polar satellite overpasses. Recent research (Liu and Minnett, 2016) identified sampling issues in the Level 3 NASA MODIS SST products when 4km observations are aggregated into global grids at different time and space scales, particularly in the Arctic, where a binary decision cloud mask designed for global data is often overly conservative at high latitudes and results in many gaps and missing data. This under sampling of some Arctic regions results in a warm bias in Level 3 products, likely a result of warmer surface temperature, more distant from the ice edge, being identified more frequently as cloud free. Here we present an improved method for cloud detection in the Arctic using a majority vote from an ensemble of four classifiers trained based on an Alternative Decision Tree (ADT) algorithm (Freund and Mason 1999, Pfahringer et. al. 2001). This new cloud classifier increases sampling of clear pixel by 50% in several regions and generally produces cooler monthly average SST fields in the ice-free Arctic, while still retaining the same error characteristics at 1km resolution relative to in situ observations. SST time series of 12 years of MODIS (Aqua and Terra) and more recently VIIRS sensors are compared and the improvements in errors and uncertainties resulting from better cloud screening for Level 3 gridded products are assessed and summarized.

MODIS comparisons with northeastern Pacific in situ stratocumulus microphysics

PubMed Central

Noble, Stephen R.

2015-01-01

Abstract Vertical sounding measurements within stratocumuli during two aircraft field campaigns, Marine Stratus/stratocumulus Experiment (MASE) and Physics of Stratocumulus Top (POST), are used to validate Moderate Resolution Imaging Spectroradiometer (MODIS) cloud optical thickness (COT), cloud liquid water path (LWP), and cloud effective radius (r e). In situ COT, LWP, and r e were calculated using 5 m vertically averaged droplet probe measurements of complete vertical cloud penetrations. MODIS COT, LWP, and r e 1 km pixels were averaged along these penetrations. COT comparisons in POST showed strong correlations and a near 1:1 relationship. In MASE, comparisons showed strong correlations; however, MODIS COT exceeded in situ COT, likely due to larger temporal differences between MODIS and in situ measurements. LWP comparisons between two cloud probes show good agreement for POST but not MASE, giving confidence to POST data. Both projects provided strong LWP correlations but MODIS exceeded in situ by 14–36%. MODIS in situ r e correlations were strong, but MODIS 2.1 µm r e exceeded in situ r e, which contributed to LWP bias; in POST, MODIS r e was 20–30% greater than in situ r e. Maximum in situ r e near cloud top showed comparisons nearer 1:1. Other MODIS r e bands (3.7 µm and 1.6 µm) showed similar comparisons. Temporal differences between MODIS and in situ measurements, airplane speed differences, and cloud probe artifacts were likely causes of weaker MASE correlations. POST COT comparison was best for temporal differences under 20 min. POST data validate MODIS COT but it also implies a positive MODIS r e bias that propagates to LWP while still capturing variability. PMID:27708990

The EarthCARE satellite payload

NASA Astrophysics Data System (ADS)

Wallace, Kotska; Perez-Albinana, Abelardo; Lemanczyk, Jerzy; Heliere, Arnaud; Wehr, Tobias; Eisinger, Michael; Lefebvre, Alain; Nakatsuka, Hirotaka; Tomita, Eiichi

2014-10-01

EarthCARE is ESA's third Earth Explorer Core Mission, with JAXA providing one instrument. The mission facilitates unique data product synergies, to improve understanding of atmospheric cloud-aerosol interactions and Earth radiative balance, towards enhancing climate and numerical weather prediction models. This paper will describe the payload, consisting of two active instruments: an ATmospheric LIDar (ATLID) and a Cloud Profiling Radar (CPR), and two passive instruments: a Multi Spectral Imager (MSI) and a Broad Band Radiometer (BBR). ATLID is a UV lidar providing atmospheric echoes, with a vertical resolution of 100 m, up to 40 km altitude. Using very high spectral resolution filtering the relative contributions of particle (aerosols) and Rayleigh (molecular) back scattering will be resolved, allowing cloud and aerosol optical depth to be deduced. Particle scatter co- and cross-polarisation measurements will provide information about the cloud and aerosol particles' physical characteristics. JAXA's 94.05 GHz Cloud Profiling Radar operates with a pulse width of 3.3 μm and repetition frequency 6100 to 7500 Hz. The 2.5 m aperture radar will retrieve data on clouds and precipitation. Doppler shift measurements in the backscatter signal will furthermore allow inference of the vertical motion of particles to an accuracy of about 1 m/s. MSI's 500 m pixel data will provide cloud and aerosol information and give context to the active instrument measurements for 3-D scene construction. Four solar channels and three thermal infrared channels cover 35 km on one side to 115 km on the other side of the other instrument's observations. BBR measures reflected solar and emitted thermal radiation from the scene. To reduce uncertainty in the radiance to flux conversion, three independent view angles are observed for each scene. The combined data allows more accurate flux calculations, which can be further improved using MSI data.

MODIS comparisons with northeastern Pacific in situ stratocumulus microphysics

DOE PAGES

Noble, Stephen R.; Hudson, James G.

2015-07-22

Here, vertical sounding measurements within stratocumuli during two aircraft field campaigns, Marine Stratus/stratocumulus Experiment (MASE) and Physics of Stratocumulus Top (POST), are used to validate Moderate Resolution Imaging Spectroradiometer (MODIS) cloud optical thickness (COT), cloud liquid water path (LWP), and cloud effective radius (r e). In situ COT, LWP, and r e were calculated using 5 m vertically averaged droplet probe measurements of complete vertical cloud penetrations. MODIS COT, LWP, and r e 1 km pixels were averaged along these penetrations. COT comparisons in POST showed strong correlations and a near 1:1 relationship. In MASE, comparisons showed strong correlations; however,more » MODIS COT exceeded in situ COT, likely due to larger temporal differences between MODIS and in situ measurements. LWP comparisons between two cloud probes show good agreement for POST but not MASE, giving confidence to POST data. Both projects provided strong LWP correlations but MODIS exceeded in situ by 14–36%. MODIS in situ r e correlations were strong, but MODIS 2.1 µm r e exceeded in situ r e, which contributed to LWP bias; in POST, MODIS r e was 20–30% greater than in situ r e. Maximum in situ r e near cloud top showed comparisons nearer 1:1. Other MODIS r e bands (3.7 µm and 1.6 µm) showed similar comparisons. Temporal differences between MODIS and in situ measurements, airplane speed differences, and cloud probe artifacts were likely causes of weaker MASE correlations. POST COT comparison was best for temporal differences under 20 min. POST data validate MODIS COT but it also implies a positive MODIS r e bias that propagates to LWP while still capturing variability.« less

An Automatic Cloud Mask Algorithm Based on Time Series of MODIS Measurements

NASA Technical Reports Server (NTRS)

Lyapustin, Alexei; Wang, Yujie; Frey, R.

2008-01-01

Quality of aerosol retrievals and atmospheric correction depends strongly on accuracy of the cloud mask (CM) algorithm. The heritage CM algorithms developed for AVHRR and MODIS use the latest sensor measurements of spectral reflectance and brightness temperature and perform processing at the pixel level. The algorithms are threshold-based and empirically tuned. They don't explicitly address the classical problem of cloud search, wherein the baseline clear-skies scene is defined for comparison. Here, we report on a new CM algorithm which explicitly builds and maintains a reference clear-skies image of the surface (refcm) using a time series of MODIS measurements. The new algorithm, developed as part of the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm for MODIS, relies on fact that clear-skies images of the same surface area have a common textural pattern, defined by the surface topography, boundaries of rivers and lakes, distribution of soils and vegetation etc. This pattern changes slowly given the daily rate of global Earth observations, whereas clouds introduce high-frequency random disturbances. Under clear skies, consecutive gridded images of the same surface area have a high covariance, whereas in presence of clouds covariance is usually low. This idea is central to initialization of refcm which is used to derive cloud mask in combination with spectral and brightness temperature tests. The refcm is continuously updated with the latest clear-skies MODIS measurements, thus adapting to seasonal and rapid surface changes. The algorithm is enhanced by an internal dynamic land-water-snow classification coupled with a surface change mask. An initial comparison shows that the new algorithm offers the potential to perform better than the MODIS MOD35 cloud mask in situations where the land surface is changing rapidly, and over Earth regions covered by snow and ice.

Military Knowledge Handbook for Commanders (Selected Section)

DTIC Science & Technology

1988-03-28

8217 Accession For NTIS GRA&I DTIC TAB 0l Unannoun ed 0] ju,.it . " t ation Distri’muti on/ Availabilit %Codes Dist cSpeial GRAPHICS DISCLAIMER...cloud layers, the energy of ray radiation on the ground will increase due to reflection effect of the cloud layers. Water surface, ice, accumulated... water flow can direct the radioactive fallout into defense works and water sources to worsen contaminations; radioactive fallout can easily be retained in

Fusion of light-field and photogrammetric surface form data

NASA Astrophysics Data System (ADS)

Sims-Waterhouse, Danny; Piano, Samanta; Leach, Richard K.

2017-08-01

Photogrammetry based systems are able to produce 3D reconstructions of an object given a set of images taken from different orientations. In this paper, we implement a light-field camera within a photogrammetry system in order to capture additional depth information, as well as the photogrammetric point cloud. Compared to a traditional camera that only captures the intensity of the incident light, a light-field camera also provides angular information for each pixel. In principle, this additional information allows 2D images to be reconstructed at a given focal plane, and hence a depth map can be computed. Through the fusion of light-field and photogrammetric data, we show that it is possible to improve the measurement uncertainty of a millimetre scale 3D object, compared to that from the individual systems. By imaging a series of test artefacts from various positions, individual point clouds were produced from depth-map information and triangulation of corresponding features between images. Using both measurements, data fusion methods were implemented in order to provide a single point cloud with reduced measurement uncertainty.

Extinction map of Chamaeleon I molecular cloud with DENIS star counts.

NASA Astrophysics Data System (ADS)

Cambresy, L.; Epchtein, N.; Copet, E.; de Batz, B.; Kimeswenger, S.; Le Bertre, T.; Rouan, D.; Tiphene, D.

1997-08-01

Massive, large scale star counts in the J (1.25μm) band provided by the Deep Near Infrared Survey of the Southern Sky (DENIS) are used for the first time to draw out an extinction map of the Chamaeleon I dark cloud. We derived a 2' resolution map of the cloud from J star counts within an area of 1.5°x3° around the centre of the cloud using an adaptive grid method and applying a wavelet decomposition. Possible contaminating young stellar objects within the cloud are removed, although they are shown to have a negligible effect on the counts. A comparison of our extinction map with the cold contribution of the IRAS 100μm emission shows an almost perfect matching. It is shown that J star counts supersede optical counts on Schmidt plate where A_V_>4.

Automated Sargassum Detection for Landsat Imagery

NASA Astrophysics Data System (ADS)

McCarthy, S.; Gallegos, S. C.; Armstrong, D.

2016-02-01

We implemented a system to automatically detect Sargassum, a floating seaweed, in 30-meter LANDSAT-8 Operational Land Imager (OLI) imagery. Our algorithm for Sargassum detection is an extended form of Hu's approach to derive a floating algae index (FAI) [1]. Hu's algorithm was developed for Moderate Resolution Imaging Spectroradiometer (MODIS) data, but we extended it for use with the OLI bands centered at 655, 865, and 1609 nm, which are comparable to the MODIS bands located at 645, 859, and 1640 nm. We also developed a high resolution true color product to mask cloud pixels in the OLI scene by applying a threshold to top of the atmosphere (TOA) radiances in the red (655 nm), green (561 nm), and blue (443 nm) wavelengths, as well as a method for removing false positive identifications of Sargassum in the imagery. Hu's algorithm derives a FAI for each Sargassum identified pixel. Our algorithm is currently set to only flag the presence of Sargassum in an OLI pixel by classifying any pixel with a FAI > 0.0 as Sargassum. Additionally, our system geo-locates the flagged Sargassum pixels identified in the OLI imagery into the U.S. Navy Global HYCOM model grid. One element of the model grid covers an area 0.125 degrees of latitude by 0.125 degrees of longitude. To resolve the differences in spatial coverage between Landsat and HYCOM, a scheme was developed to calculate the percentage of pixels flagged within the grid element and if above a threshold, it will be flagged as Sargassum. This work is a part of a larger system, sponsored by NASA/Applied Science and Technology Project at J.C. Stennis Space Center, to forecast when and where Sargassum will land on shore. The focus area of this work is currently the Texas coast. Plans call for extending our efforts into the Caribbean. References: [1] Hu, Chuanmin. A novel ocean color index to detect floating algae in the global oceans. Remote Sensing of Environment 113 (2009) 2118-2129.

Sensing, Spectra and Scaling: What's in Store for Land Observations

NASA Technical Reports Server (NTRS)

Goetz, Alexander F. H.

2001-01-01

Bill Pecora's 1960's vision of the future, using spacecraft-based sensors for mapping the environment and exploring for resources, is being implemented today. New technology has produced better sensors in space such as the Landsat Thematic Mapper (TM) and SPOT, and creative researchers are continuing to find new applications. However, with existing sensors, and those intended for launch in this century, the potential for extracting information from the land surface is far from being exploited. The most recent technology development is imaging spectrometry, the acquisition of images in hundreds of contiguous spectral bands, such that for any pixel a complete reflectance spectrum can be acquired. Experience with Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) has shown that, with proper attention paid to absolute calibration, it is possible to acquire apparent surface reflectance to 5% accuracy without any ground-based measurement. The data reduction incorporates in educated guess of the aerosol scattering, development of a precipitable water vapor map from the data and mapping of cirrus clouds in the 1.38 micrometer band. This is not possible with TM. The pixel size in images of the earth plays and important role in the type and quality of information that can be derived. Less understood is the coupling between spatial and spectral resolution in a sensor. Recent work has shown that in processing the data to derive the relative abundance of materials in a pixel, also known is unmixing, the pixel size is an important parameter. A variance in the relative abundance of materials among the pixels is necessary to be able to derive the endmembers or pure material constituent spectra. In most cases, the 1 km pixel size for the Earth Observing System Moderate Resolution Imaging Spectroradiometer (MODIS) instrument is too large to meet the variance criterion. A pointable high spatial and spectral resolution imaging spectrometer in orbit will be necessary to make the major next step in our understanding of the solid earth surface and its changing face.

Automatic cloud tracking applied to GOES and Meteosat observations

NASA Technical Reports Server (NTRS)

Endlich, R. M.; Wolf, D. E.

1981-01-01

An improved automatic processing method for the tracking of cloud motions as revealed by satellite imagery is presented and applications of the method to GOES observations of Hurricane Eloise and Meteosat water vapor and infrared data are presented. The method is shown to involve steps of picture smoothing, target selection and the calculation of cloud motion vectors by the matching of a group at a given time with its best likeness at a later time, or by a cross-correlation computation. Cloud motion computations can be made in as many as four separate layers simultaneously. For data of 4 and 8 km resolution in the eye of Hurricane Eloise, the automatic system is found to provide results comparable in accuracy and coverage to those obtained by NASA analysts using the Atmospheric and Oceanographic Information Processing System, with results obtained by the pattern recognition and cross correlation computations differing by only fractions of a pixel. For Meteosat water vapor data from the tropics and midlatitudes, the automatic motion computations are found to be reliable only in areas where the water vapor fields contained small-scale structure, although excellent results are obtained using Meteosat IR data in the same regions. The automatic method thus appears to be competitive in accuracy and coverage with motion determination by human analysts.

Southern Clouds

NASA Technical Reports Server (NTRS)

2005-01-01

[figure removed for brevity, see original site] Context image for PIA03026 Southern Clouds

This image shows a system of clouds just off the margin of the South Polar cap. Taken during the summer season, these clouds contain both water-ice and dust.

Image information: VIS instrument. Latitude 80.2S, Longitude 57.6E. 17 meter/pixel resolution.

Note: this THEMIS visual image has not been radiometrically nor geometrically calibrated for this preliminary release. An empirical correction has been performed to remove instrumental effects. A linear shift has been applied in the cross-track and down-track direction to approximate spacecraft and planetary motion. Fully calibrated and geometrically projected images will be released through the Planetary Data System in accordance with Project policies at a later time.

NASA's Jet Propulsion Laboratory manages the 2001 Mars Odyssey mission for NASA's Office of Space Science, Washington, D.C. The Thermal Emission Imaging System (THEMIS) was developed by Arizona State University, Tempe, in collaboration with Raytheon Santa Barbara Remote Sensing. The THEMIS investigation is led by Dr. Philip Christensen at Arizona State University. Lockheed Martin Astronautics, Denver, is the prime contractor for the Odyssey project, and developed and built the orbiter. Mission operations are conducted jointly from Lockheed Martin and from JPL, a division of the California Institute of Technology in Pasadena.

Linear Clouds

NASA Technical Reports Server (NTRS)

2006-01-01

[figure removed for brevity, see original site] Context image for PIA03667 Linear Clouds

These clouds are located near the edge of the south polar region. The cloud tops are the puffy white features in the bottom half of the image.

Image information: VIS instrument. Latitude -80.1N, Longitude 52.1E. 17 meter/pixel resolution.

Note: this THEMIS visual image has not been radiometrically nor geometrically calibrated for this preliminary release. An empirical correction has been performed to remove instrumental effects. A linear shift has been applied in the cross-track and down-track direction to approximate spacecraft and planetary motion. Fully calibrated and geometrically projected images will be released through the Planetary Data System in accordance with Project policies at a later time.

NASA's Jet Propulsion Laboratory manages the 2001 Mars Odyssey mission for NASA's Office of Space Science, Washington, D.C. The Thermal Emission Imaging System (THEMIS) was developed by Arizona State University, Tempe, in collaboration with Raytheon Santa Barbara Remote Sensing. The THEMIS investigation is led by Dr. Philip Christensen at Arizona State University. Lockheed Martin Astronautics, Denver, is the prime contractor for the Odyssey project, and developed and built the orbiter. Mission operations are conducted jointly from Lockheed Martin and from JPL, a division of the California Institute of Technology in Pasadena.

The Citizen CATE Experiment: High altitude cirrus cloud removal from eclipse data through use of Kalman filters.

NASA Astrophysics Data System (ADS)

Jensen, Logan; Citizen CATE Experiment 2017 Team

2018-01-01

The Citizen Continental America Telescopic Eclipse (CATE) Experiment was designed to fill in the current data gap for the solar corona from approximately 1 to 2.5 solar radii. Using the total solar eclipse, the project took advantage of the unique opportunity to study this region of the corona from 68 identical sites across the United States. Before the 2017 eclipse, image reduction pipelines and advanced processing techniques were researched and implemented using data that had been collected from the 2016 Indonesian eclipse as a test set. This would speed up the turnaround from data to science after the 2017 eclipse.When processing the 2016 eclipse data, cirrus clouds became apparent moving across the field of view. These would interfere with future processing goals for the data such as coronal filament tracing and polar plume measurements. As the clouds moved across the field they did not completely obscure any part of the image, instead they produced variable, moving absorption across the CATE field of view. This had the effect of creating a noisy signal for each pixel. A noise reduction procedure based on a Kalman filter was developed to effectively remove the clouds from the data. Initial results from the 2016 eclipse data are presented.

Registration of Vehicle-Borne Point Clouds and Panoramic Images Based on Sensor Constellations.

PubMed

Yao, Lianbi; Wu, Hangbin; Li, Yayun; Meng, Bin; Qian, Jinfei; Liu, Chun; Fan, Hongchao

2017-04-11

A mobile mapping system (MMS) is usually utilized to collect environmental data on and around urban roads. Laser scanners and panoramic cameras are the main sensors of an MMS. This paper presents a new method for the registration of the point clouds and panoramic images based on sensor constellation. After the sensor constellation was analyzed, a feature point, the intersection of the connecting line between the global positioning system (GPS) antenna and the panoramic camera with a horizontal plane, was utilized to separate the point clouds into blocks. The blocks for the central and sideward laser scanners were extracted with the segmentation feature points. Then, the point clouds located in the blocks were separated from the original point clouds. Each point in the blocks was used to find the accurate corresponding pixel in the relative panoramic images via a collinear function, and the position and orientation relationship amongst different sensors. A search strategy is proposed for the correspondence of laser scanners and lenses of panoramic cameras to reduce calculation complexity and improve efficiency. Four cases of different urban road types were selected to verify the efficiency and accuracy of the proposed method. Results indicate that most of the point clouds (with an average of 99.7%) were successfully registered with the panoramic images with great efficiency. Geometric evaluation results indicate that horizontal accuracy was approximately 0.10-0.20 m, and vertical accuracy was approximately 0.01-0.02 m for all cases. Finally, the main factors that affect registration accuracy, including time synchronization amongst different sensors, system positioning and vehicle speed, are discussed.

Earth cloud, aerosol, and radiation explorer optical payload development status

NASA Astrophysics Data System (ADS)

Hélière, A.; Wallace, K.; Pereira do Carmo, J.; Lefebvre, A.

2017-09-01

The European Space Agency (ESA) and the Japan Aerospace Exploration Agency (JAXA) are co-operating to develop as part of ESA's Living Planet Programme, the third Earth Explorer Core Mission, EarthCARE, with the ojective of improving the understanding of the processes involving clouds, aerosols and radiation in the Earth's atmosphere. EarthCARE payload consists of two active and two passive instruments: an ATmospheric LIDar (ATLID), a Cloud Profiling Radar (CPR), a Multi-Spectral Imager (MSI) and a Broad-Band Radiometer (BBR). The four instruments data are processed individually and in a synergetic manner to produce a large range of products, which include vertical profiles of aerosols, liquid water and ice, observations of cloud distribution and vertical motion within clouds, and will allow the retrieval of profiles of atmospheric radiative heating and cooling. MSI is a compact instrument with a 150 km swath providing 500 m pixel data in seven channels, whose retrieved data will give context to the active instrument measurements, as well as providing cloud and aerosol information. BBR measures reflected solar and emitted thermal radiation from the scene. Operating in the UV range at 355 nm, ATLID provides atmospheric echoes from ground to an altitude of 40 km. Thanks to a high spectral resolution filtering, the lidar is able to separate the relative contribution of aerosol and molecular scattering, which gives access to aerosol optical depth. Co-polarised and cross-polarised components of the Mie scattering contribution are measured on dedicated channels. This paper will provide a description of the optical payload implementation, the design and characterisation of the instruments.

Reconstruction of Missing Pixels in Satellite Images Using the Data Interpolating Empirical Orthogonal Function (DINEOF)

NASA Astrophysics Data System (ADS)

Liu, X.; Wang, M.

2016-02-01

For coastal and inland waters, complete (in spatial) and frequent satellite measurements are important in order to monitor and understand coastal biological and ecological processes and phenomena, such as diurnal variations. High-frequency images of the water diffuse attenuation coefficient at the wavelength of 490 nm (Kd(490)) derived from the Korean Geostationary Ocean Color Imager (GOCI) provide a unique opportunity to study diurnal variation of the water turbidity in coastal regions of the Bohai Sea, Yellow Sea, and East China Sea. However, there are lots of missing pixels in the original GOCI-derived Kd(490) images due to clouds and various other reasons. Data Interpolating Empirical Orthogonal Function (DINEOF) is a method to reconstruct missing data in geophysical datasets based on Empirical Orthogonal Function (EOF). In this study, the DINEOF is applied to GOCI-derived Kd(490) data in the Yangtze River mouth and the Yellow River mouth regions, the DINEOF reconstructed Kd(490) data are used to fill in the missing pixels, and the spatial patterns and temporal functions of the first three EOF modes are also used to investigate the sub-diurnal variation due to the tidal forcing. In addition, DINEOF method is also applied to the Visible Infrared Imaging Radiometer Suite (VIIRS) on board the Suomi National Polar-orbiting Partnership (SNPP) satellite to reconstruct missing pixels in the daily Kd(490) and chlorophyll-a concentration images, and some application examples in the Chesapeake Bay and the Gulf of Mexico will be presented.

Mapping Irrigated Areas in the Tunisian Semi-Arid Context with Landsat Thermal and VNIR Data Imagery

NASA Astrophysics Data System (ADS)

Rivalland, Vincent; Drissi, Hsan; Simonneaux, Vincent; Tardy, Benjamin; Boulet, Gilles

2016-04-01

Our study area is the Merguellil semi-arid irrigated plain in Tunisia, where the water resource management is an important stake for governmental institutions, farmer communities and more generally for the environment. Indeed, groundwater abstraction for irrigation is the primary cause of aquifer depletion. Moreover, unregistered pumping practices are widespread and very difficult to survey by authorities. Thus, the identification of areas actually irrigated in the whole plain is of major interest. In order to map the irrigated areas, we tried out a methodology based on the use of Landsat 7 and 8 Land Surface Temperature (LST) data issued from atmospherically corrected thermal band using the LANDARTs Tool jointly with the NDVI vegetation indices obtained from visible ane near infrared (VNIR) bands. For each Landsat acquisition during the years 2012 to 2014, we computed a probability of irrigation based on the location of the pixel in the NDVI - LST space. Basically for a given NDVI value, the cooler the pixel the higher its probability to be irrigated is. For each date, pixels were classified in seven bins of irrigation probability ranges. Pixel probabilities for each date were then summed over the study period resulting in a probability map of irrigation. Comparison with ground data shows a consistent identification of irrigated plots and supports the potential operational interest of the method. However, results were hampered by the low Landsat LST data availability due to clouds and the inadequate revisit frequency of the sensor.

Spatio-energetic cross-talk in photon counting detectors: Numerical detector model (PcTK) and workflow for CT image quality assessment.

PubMed

Taguchi, Katsuyuki; Stierstorfer, Karl; Polster, Christoph; Lee, Okkyun; Kappler, Steffen

2018-05-01

The interpixel cross-talk of energy-sensitive photon counting x-ray detectors (PCDs) has been studied and an analytical model (version 2.1) has been developed for double-counting between neighboring pixels due to charge sharing and K-shell fluorescence x-ray emission followed by its reabsorption (Taguchi K, et al., Medical Physics 2016;43(12):6386-6404). While the model version 2.1 simulated the spectral degradation well, it had the following problems that has been found to be significant recently: (1) The spectrum is inaccurate with smaller pixel sizes; (2) the charge cloud size must be smaller than the pixel size; (3) the model underestimates the spectrum/counts for 10-40 keV; and (4) the model version 2.1 cannot handlen-tuple-counting withn > 2 (i.e., triple-counting or higher). These problems are inherent to the design of the model version 2.1; therefore, we developed a new model and addressed these problems in this study. We propose a new PCD cross-talk model (version 3.2; Pc TK for "photon counting toolkit") that is based on a completely different design concept from the previous version. It uses a numerical approach and starts with a 2-D model of charge sharing (as opposed to an analytical approach and a 1-D model with version 2.1) and addresses all of the four problems. The model takes the following factors into account: (1) shift-variant electron density of the charge cloud (Gaussian-distributed), (2) detection efficiency, (3) interactions between photons and PCDs via photoelectric effect, and (4) electronic noise. Correlated noisy PCD data can be generated using either a multivariate normal random number generator or a Poisson random number generator. The effect of the two parameters, the effective charge cloud diameter (d 0 ) and pixel size (d pix ), was studied and results were compared with Monte Carlo simulations and the previous model version 2.1. Finally, a script for the workflow for CT image quality assessment has been developed, which started with a few material density images, generated material-specific sinogram (line integrals) data, noisy PCD data with spectral distortion using the model version 3.2, and reconstructed PCD- CT images for four energy windows. The model version 3.2 addressed all of the four problems listed above. The spectra withd pix  = 56-113 μm agreed with that of Medipix3 detector withd pix  = 55-110 μm without charge summing mode qualitatively. The counts for 10-40 keV were larger than the previous model (version 2.1) and agreed with MC simulations very well (root-mean-square difference values with model version 3.2 were decreased to 16%-67% of the values with version 2.1). There were many non-zero off-diagonal elements withn-tuple-counting withn > 2 in the normalized covariance matrix of 3 × 3 neighboring pixels. Reconstructed images showed biases and artifacts attributed to the spectral distortion due to the charge sharing and fluorescence x rays. We have developed a new PCD model for spatio-energetic cross-talk and correlation between PCD pixels. The workflow demonstrated the utility of the model for general or task-specific image quality assessments for the PCD- CT.Note: The program (Pc TK) and the workflow scripts have been made available to academic researchers. Interested readers should visit the website (pctk.jhu.edu) or contact the corresponding author. © 2018 American Association of Physicists in Medicine.

Snow cover retrieval over Rhone and Po river basins from MODIS optical satellite data (2000-2009).

NASA Astrophysics Data System (ADS)

Dedieu, Jean-Pierre, ,, Dr.; Boos, Alain; Kiage, Wiliam; Pellegrini, Matteo

2010-05-01

Estimation of the Snow Covered Area (SCA) is an important issue for meteorological application and hydrological modeling of runoff. With spectral bands in the visible, near and middle infrared, the MODIS optical satellite sensor can be used to detect snow cover because of large differences between reflectance from snow covered and snow free surfaces. At the same time, it allows separation between snow and clouds. Moreover, the sensor provides a daily coverage of large areas (2,500 km range). However, as the pixel size is 500m x 500m, a MODIS pixel may be partially covered by snow, particularly in Alpine areas, where snow may not be present in valleys lying at lower altitudes. Also, variation of reflectance due to differential sunlit effects as a function of slope and aspect, as well as bidirectional effects may be present in images. Nevertheless, it is possible to estimate snow cover at the Sub-Pixel level with a relatively good accuracy and with very good results if the sub-pixel estimations are integrated for a few pixels relative to an entire watershed. Integrated into the EU-FP7 ACQWA Project (www.acqwa.ch), this approach was first applied over Alpine area of Rhone river basin upper Geneva Lake: Canton du Valais, Switzerland (5 375 km²). In a second step over Alps, rolling hills and plain areas in Po catchment for Val d'Aosta and Piemonte regions, Italy (37 190 km²). Watershed boundaries were provided respectively by GRID (Ch) and ARPA (It) partners. The complete satellite images database was extracted from the U.S. MODIS/NASA website (http://modis.gsfc.nasa.gov/) for MOD09_B1 Reflectance images, and from the MODIS/NSIDC website (http://nsidc.org/index.html) for MOD10_A2 snow cover images. Only the Terra platform was used because images are acquired in the morning and are therefore better correlated with dry snow surface, avoiding cloud coverage of the afternoon (Aqua Platform). The MOD9 Image reflectance and MOD10_A2 products were respectively analyzed to retrieve (i) Fractional Snow cover at sub-pixel scale, and (ii) maximum snow cover. All products were retrieved at 8-days over a complete time period of 10 years (2000-2009), giving 500 images for each river basin. Digital Model Elevation was given by NASA/SRTM database at 90-m resolution and used (i) for illumination versus topography correction on snow cover, (ii) geometric rectification of images. Geographic projection is WGS84, UTM 32. Fractional Snow cover mapping was derived from the NDSI linear regression method (Salomonson et al., 2004). Cloud mask was given by MODIS-NASA library (radiometric threshold) and completed by inverse slope regression to avoid lowlands fog confusing with thin snow cover (Po river basin). Maximum Snow Cover mapping was retrieved from the NSIDC database classification (Hall et al., 2001). Validation step was processed using comparison between MODIS Snow maps outputs and meteorological data provided by network of 87 meteorological stations: temperature, precipitation, snow depth measurement. A 0.92 correlation was observed for snow/non snow cover and can be considered as quite satisfactory, given the radiometric problems encountered in mountainous areas, particularly in snowmelt season. The 10-years time period results indicates a main difference between (i) regular snow accumulation and depletion in Rhone and (ii) the high temporal and spatial variability of snow cover for Po. Then, a high sensitivity to low variation of air temperature, often close to 1° C was observed. This is the case in particular for the beginning and the end of the winter season. The regional snow cover depletion is both influenced by thermal positives anomalies (e.g. 2000 and 2006), and the general trend of rising atmospheric temperatures since the late 1980s, particularly for Po river basin. Results will be combined with two hydrological models: Topkapi and Fest.

Raman spectroscopy-based detection of chemical contaminants in food powders

NASA Astrophysics Data System (ADS)

Chao, Kuanglin; Dhakal, Sagar; Qin, Jianwei; Kim, Moon; Bae, Abigail

2016-05-01

Raman spectroscopy technique has proven to be a reliable method for qualitative detection of chemical contaminants in food ingredients and products. For quantitative imaging-based detection, each contaminant particle in a food sample must be detected and it is important to determine the necessary spatial resolution needed to effectively detect the contaminant particles. This study examined the effective spatial resolution required for detection of maleic acid in tapioca starch and benzoyl peroxide in wheat flour. Each chemical contaminant was mixed into its corresponding food powder at a concentration of 1% (w/w). Raman spectral images were collected for each sample, leveled across a 45 mm x 45 mm area, using different spatial resolutions. Based on analysis of these images, a spatial resolution of 0.5mm was selected as effective spatial resolution for detection of maleic acid in starch and benzoyl peroxide in flour. An experiment was then conducted using the 0.5mm spatial resolution to demonstrate Raman imaging-based quantitative detection of these contaminants for samples prepared at 0.1%, 0.3%, and 0.5% (w/w) concentrations. The results showed a linear correlation between the detected numbers of contaminant pixels and the actual concentrations of contaminant.

Cloud Base Height Measurements at Manila Observatory: Initial Results from Constructed Paired Sky Imaging Cameras

NASA Astrophysics Data System (ADS)

Lagrosas, N.; Tan, F.; Antioquia, C. T.

2014-12-01

Fabricated all sky imagers are efficient and cost effective instruments for cloud detection and classification. Continuous operation of this instrument can result in the determination of cloud occurrence and cloud base heights for the paired system. In this study, a fabricated paired sky imaging system - consisting two commercial digital cameras (Canon Powershot A2300) enclosed in weatherproof containers - is developed in Manila Observatory for the purpose of determining cloud base heights at the Manila Observatory area. One of the cameras is placed on the rooftop of Manila Observatory and the other is placed on the rooftop of the university dormitory, 489m from the first camera. The cameras are programmed to simultaneously gather pictures every 5 min. Continuous operation of these cameras were implemented since the end of May of 2014 but data collection started end of October 2013. The data were processed following the algorithm proposed by Kassianov et al (2005). The processing involves the calculation of the merit function that determines the area of overlap of the two pictures. When two pictures are overlapped, the minimum of the merit function corresponds to the pixel column positions where the pictures have the best overlap. In this study, pictures of overcast sky prove to be difficult to process for cloud base height and were excluded from processing. The figure below shows the initial results of the hourly average of cloud base heights from data collected from November 2013 to July 2014. Measured cloud base heights ranged from 250m to 1.5km. These are the heights of cumulus and nimbus clouds that are dominant in this part of the world. Cloud base heights are low in the early hours of the day indicating low convection process during these times. However, the increase in the convection process in the atmosphere can be deduced from higher cloud base heights in the afternoon. The decrease of cloud base heights after 15:00 follows the trend of decreasing solar energy in the atmosphere after this time. The results show the potential of these instruments to determine cloud base heights on prolonged time intervals. The continuous operation of these instruments is implemented to gather seasonal variation of cloud base heights in this part of the world and to add to the much-needed dataset for future climate studies in Manila Observatory.

Cirrus cloud retrieval with MSG/SEVIRI using artificial neural networks

NASA Astrophysics Data System (ADS)

Strandgren, Johan; Bugliaro, Luca; Sehnke, Frank; Schröder, Leon

2017-09-01

Cirrus clouds play an important role in climate as they tend to warm the Earth-atmosphere system. Nevertheless their physical properties remain one of the largest sources of uncertainty in atmospheric research. To better understand the physical processes of cirrus clouds and their climate impact, enhanced satellite observations are necessary. In this paper we present a new algorithm, CiPS (Cirrus Properties from SEVIRI), that detects cirrus clouds and retrieves the corresponding cloud top height, ice optical thickness and ice water path using the SEVIRI imager aboard the geostationary Meteosat Second Generation satellites. CiPS utilises a set of artificial neural networks trained with SEVIRI thermal observations, CALIOP backscatter products, the ECMWF surface temperature and auxiliary data. CiPS detects 71 and 95 % of all cirrus clouds with an optical thickness of 0.1 and 1.0, respectively, that are retrieved by CALIOP. Among the cirrus-free pixels, CiPS classifies 96 % correctly. With respect to CALIOP, the cloud top height retrieved by CiPS has a mean absolute percentage error of 10 % or less for cirrus clouds with a top height greater than 8 km. For the ice optical thickness, CiPS has a mean absolute percentage error of 50 % or less for cirrus clouds with an optical thickness between 0.35 and 1.8 and of 100 % or less for cirrus clouds with an optical thickness down to 0.07 with respect to the optical thickness retrieved by CALIOP. The ice water path retrieved by CiPS shows a similar performance, with mean absolute percentage errors of 100 % or less for cirrus clouds with an ice water path down to 1.7 g m-2. Since the training reference data from CALIOP only include ice water path and optical thickness for comparably thin clouds, CiPS also retrieves an opacity flag, which tells us whether a retrieved cirrus is likely to be too thick for CiPS to accurately derive the ice water path and optical thickness. By retrieving CALIOP-like cirrus properties with the large spatial coverage and high temporal resolution of SEVIRI during both day and night, CiPS is a powerful tool for analysing the temporal evolution of cirrus clouds including their optical and physical properties. To demonstrate this, the life cycle of a thin cirrus cloud is analysed.