New Ocean Winds Satellite Mission to Probe Hurricanes and Tropical Convection

Abstract The Cyclone Global Navigation Satellite System (CYGNSS) is a new NASA earth science mission scheduled to be launched in 2016 that focuses on tropical cyclones (TCs) and tropical convection. The mission’s two primary objectives are the measurement of ocean surface wind speed with sufficient temporal resolution to resolve short-time-scale processes such as the rapid intensification phase of TC development and the ability of the surface measurements to penetrate through the extremely high precipitation rates typically encountered in the TC inner core. The mission’s goal is to support significant improvements in our ability to forecast TC track, intensity, and storm surge through better observations and, ultimately, better understanding of inner-core processes. CYGNSS meets its temporal sampling objective by deploying a constellation of eight satellites. Its ability to see through heavy precipitation is enabled by its operation as a bistatic radar using low-frequency GPS signals. The mission will deploy an eight-spacecraft constellation in a low-inclination (35°) circular orbit to maximize coverage and sampling in the tropics. Each CYGNSS spacecraft carries a four-channel radar receiver that measures GPS navigation signals scattered by the ocean surface. The mission will measure inner-core surface winds with high temporal resolution and spatial coverage, under all precipitating conditions, and over the full dynamic range of TC wind speeds.

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Spatial and Temporal Sampling of Polar Regions from Two-Satellite System on Molniya Orbit

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech-d-10-05013.1 ◽

2011 ◽

Vol 28 (8) ◽

pp. 977-992 ◽

Cited By ~ 15

Author(s):

Alexander P. Trishchenko ◽

Louis Garand

Keyword(s):

Temporal Resolution ◽

Arctic Region ◽

Satellite System ◽

Elliptical Orbit ◽

High Temporal Resolution ◽

Polar Regions ◽

Temporal Sampling ◽

Continuous Coverage ◽

Depth Analysis

Abstract There has been a significant increase of interest in the building of a comprehensive Arctic observing system in recent years to properly and timely track the environmental and climate processes in this vast region. In this regard, a satellite observing system on highly elliptical orbit (HEO) with 12-h period (Molniya type) is of particular interest, because it enables continuous coverage of the entire Arctic region (58°–90°N) from a constellation of two satellites. Canada is currently proposing to operate such a constellation by 2017. Extending the pioneering study of S. Q. Kidder and T. H. Vonder Haar, this paper presents in-depth analysis of spatiotemporal sampling properties of the imagery from this system. This paper also discusses challenges and advantages of this orbit for various applications that require high temporal resolution and angular sampling.

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Retrieval of Sea Surface Wind Fields Using Multi-Source Remote Sensing Data

Remote Sensing ◽

10.3390/rs12091482 ◽

2020 ◽

Vol 12 (9) ◽

pp. 1482 ◽

Cited By ~ 1

Author(s):

Tangao Hu ◽

Yue Li ◽

Yao Li ◽

Yiyue Wu ◽

Dengrong Zhang

Keyword(s):

Linear Model ◽

Temporal Resolution ◽

Science Studies ◽

Piecewise Linear ◽

Surface Wind ◽

Sea Surface ◽

Mean Bias Error ◽

High Temporal Resolution ◽

Sea Surface Wind ◽

Piecewise Linear Model

Timely and accurate sea surface wind field (SSWF) information plays an important role in marine environmental monitoring, weather forecasting, and other atmospheric science studies. In this study, a piecewise linear model is proposed to retrieve SSWF information based on the combination of two different satellite sensors (a microwave scatterometer and an infrared scanning radiometer). First, the time series wind speed dataset, extracted from the HY-2A satellite, and the brightness temperature dataset, extracted from the FY-2E satellite, were matched. The piecewise linear regression model with the highest R2 was then selected as the best model to retrieve SSWF information. Finally, experiments were conducted with the Usagi, Fitow, and Nari typhoons in 2013 to evaluate accuracy. The results show that: (1) the piecewise linear model is successfully established for all typhoons with high R2 (greater than 0.61); (2) for all three cases, the root mean square error () and mean bias error (MBE) are smaller than 2.2 m/s and 1.82 m/s, which indicates that it is suitable and reliable for SSWF information retrieval; and (3) it solves the problem of the low temporal resolution of HY-2A data (12 h), and inherits the high temporal resolution of the FY-2E data (0.5 h). It can provide reliable and high temporal SSWF products.

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Comparison of Landsat and Land-Based Phenology Camera Normalized Difference Vegetation Index (NDVI) for Dominant Plant Communities in the Great Basin

Sensors ◽

10.3390/s19051139 ◽

2019 ◽

Vol 19 (5) ◽

pp. 1139 ◽

Cited By ~ 4

Author(s):

Keirith Snyder ◽

Justin Huntington ◽

Bryce Wehan ◽

Charles Morton ◽

Tamzen Stringham

Keyword(s):

Great Basin ◽

Plant Communities ◽

Spatial Resolution ◽

Temporal Resolution ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Spatial Scales ◽

Plant Phenology ◽

High Temporal Resolution ◽

Spatial Coverage

Phenology of plants is important for ecological interactions. The timing and development of green leaves, plant maturity, and senescence affects biophysical interactions of plants with the environment. In this study we explored the agreement between land-based camera and satellite-based phenology metrics to quantify plant phenology and phenophases dates in five plant community types characteristic of the semi-arid cold desert region of the Great Basin. Three years of data were analyzed. We calculated the Normalized Difference Vegetation Index (NDVI) for both land-based cameras (i.e., phenocams) and Landsat imagery. NDVI from camera images was calculated by taking a standard RGB (red, green, and blue) image and then a near infrared (NIR) plus RGB image. Phenocam NDVI was calculated by extracting the red digital number (DN) and the NIR DN from images taken a few seconds apart. Landsat has a spatial resolution of 30 m2, while phenocam spatial resolution can be analyzed at the single pixel level at the scale of cm2 or area averaged regions can be analyzed with scales up to 1 km2. For this study, phenocam regions of interest were used that approximated the scale of at least one Landsat pixel. In the tall-statured pinyon and juniper woodland sites, there was a lack of agreement in NDVI between phenocam and Landsat NDVI, even after using National Agricultural Imagery Program (NAIP) imagery to account for fractional coverage of pinyon and juniper versus interspace in the phenocam data. Landsat NDVI appeared to be dominated by the signal from the interspace and was insensitive to subtle changes in the pinyon and juniper tree canopy. However, for short-statured sagebrush shrub and meadow communities, there was good agreement between the phenocam and Landsat NDVI as reflected in high Pearson’s correlation coefficients (r > 0.75). Due to greater temporal resolution of the phenocams with images taken daily, versus the 16-day return interval of Landsat, phenocam data provided more utility in determining important phenophase dates: start of season, peak of season, and end of season. More specific species-level information can be obtained with the high temporal resolution of phenocams, but only for a limited number of sites, while Landsat can provide the multi-decadal history and spatial coverage that is unmatched by other platforms. The agreement between Landsat and phenocam NDVI for short-statured plant communities of the Great Basin, shows promise for monitoring landscape and regional-level plant phenology across large areas and time periods, with phenocams providing a more comprehensive understanding of plant phenology at finer spatial scales, and Landsat extending the historical record of observations.

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Correcting Himawari-8 Advanced Himawari Imager Data for the Production of Vivid True-Color Imagery

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech-d-18-0060.1 ◽

2019 ◽

Vol 36 (3) ◽

pp. 427-442 ◽

Cited By ~ 1

Author(s):

Mark A. Broomhall ◽

Leon J. Majewski ◽

Vincent O. Villani ◽

Ian F. Grant ◽

Steven D. Miller

Keyword(s):

Temporal Resolution ◽

Rayleigh Scattering ◽

Lookup Table ◽

Asia Pacific ◽

High Temporal Resolution ◽

Surface Reflectance ◽

Aesthetic Appearance ◽

Spatial Coverage ◽

True Color ◽

Color Imagery

AbstractObservations of top-of-atmosphere radiances from the Advanced Himawari Imager (AHI) blue, green, and red spectral bands can be used to produce high-temporal-resolution, true-color imagery at 1-km spatial resolution over the Asia–Pacific region. To enhance interpretability and aesthetic appearance of these images, the top-of-atmosphere radiance data are processed to remove the Rayleigh-scattered atmospheric component, corrected for limb effects, blended with brightness temperature data from a thermal infrared window band at night, and the resultant imagery adjusted to optimize contrast. The contribution of Rayleigh scattering to the AHI observations is calculated by interpolating radiative transfer parameters from a preconstructed set of lookup tables, which are specifically created for the Himawari-8 AHI instrument. A surface reflectance value for each pixel is calculated after the Rayleigh contribution is removed. The spectrally dependent reflectance values produced from the lookup table differ from the exact calculation by up to 18% at the planetary limb, over 100% at the solar terminator, and by less than 0.5% at low to moderate solar and sensor zenith angles. The subsequent corrections applied for limb effects mitigate the areas with high interpolation error, which slightly reduces the spatial coverage, but provides Rayleigh-corrected surface reflectance products that have interpolation errors at or below 0.5%. Resolution sharpening increases the nominal pixel size from 1000 to 500 m while still producing sharp images. The resultant images are colorful, visually intuitive, high contrast, and of sufficient spatial and temporal resolution to provide a unique and complementary observational tool for use by weather forecasters and the general public alike.

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Estimating Tropical Cyclone Integrated Kinetic Energy with the CYGNSS Satellite Constellation

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-16-0176.1 ◽

2017 ◽

Vol 56 (1) ◽

pp. 235-245 ◽

Cited By ~ 7

Author(s):

Mary Morris ◽

Christopher S. Ruf

Keyword(s):

Tropical Cyclone ◽

Kinetic Energy ◽

Tropical Cyclones ◽

Measurement Errors ◽

Inner Core ◽

Surface Wind ◽

Surface Wind Speed ◽

Satellite System ◽

Insurance Companies ◽

High Temporal Resolution

AbstractThe Cyclone Global Navigation Satellite System (CYGNSS) constellation is designed to provide observations of surface wind speed in and near the inner core of tropical cyclones with high temporal resolution throughout the storm’s life cycle. A method is developed for estimating tropical cyclone integrated kinetic energy (IKE) using CYGNSS observations. IKE is calculated for each geographically based quadrant out to an estimate of the 34-kt (1 kt = 0.51 m s−1) wind radius. The CYGNSS-IKE estimator is tested and its performance is characterized using simulated CYGNSS observations with realistic measurement errors. CYGNSS-IKE performance improves for stronger, more organized storms and with increasing number of observations over the extent of the 34-kt radius. Known sampling information can be used for quality control. While CYGNSS-IKE is calculated for individual geographic quadrants, using a total-IKE—a sum over all quadrants—improves performance. CYGNSS-IKE should be of interest to operational and research meteorologists, insurance companies, and others interested in the destructive potential of tropical cyclones developing in data-sparse regions, which will now be covered by CYGNSS. The CYGNSS-IKE product will be available for the 2017 Atlantic Ocean hurricane season.

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Fog dissipation through ground warming monitored by satellite image : an approach to support regional forecasting

10.5194/ems2021-361 ◽

2021 ◽

Author(s):

Sylvain Cros ◽

Martial Haeffelin ◽

Felipe Toledo ◽

Dupont Jean-Charles ◽

Badosa Jordi

Keyword(s):

Temporal Resolution ◽

Satellite Image ◽

Ground Level ◽

High Temporal Resolution ◽

Short Time Scale ◽

Spatial Gradient ◽

Military Operations ◽

Air Quality Forecast ◽

Ground Warming ◽

Fog Dissipation

By reducing the atmospheric visibility, fog events have strong impacts on several humans activities. Transport security, military operations, air quality forecast and solar energy production are critical activities considering fog dissipation time as a high valuable information.Fog dissipation occurs through these two following processes. (1) An adiabatic cloud elevation converts the fog into a low stratus, increasing the visibility at ground level while keeping an overcast sky. (2) A radiative warming can break through a large continuous fog deck. Then, the cleared area increases progressively by heating the ground of the neighboured fog covered area.These two events are particularly difficult to forecast using NWP models as many non-linear local processes at short-time scale are involved. Moreover, current network of fog presence sensors is too scarce to analyse and/or anticipate the phenomena. Subsequent images of geostationary meteorological satellite offer a high temporal resolution that enables to monitor large fog decks and detect punctual clear areas that induce dissipation (case 2). However, fog detection using satellite images suffers from a lack of distinction between fog and very low stratus.In this work, we explored the potential of MSG SEVIRI radiometer through radiance observations and more advanced cloud products to analyse fog events effectively observed at the SIRTA atmospheric observatory (Palaiseau, France). We assumed that, during these events, pixels classified as &#8220;very low cloud&#8221; according to SAF-NWC algorithm were covered by fog. We monitored the evolution of these pixels using a cloud index derived from HRV channels, providing a more detailed spatial distribution of cloud cover during day time. We analysed the evolution of brightness temperature spatial gradient from the SEVIRI infrared window channel (IR 10.8&#181;m). We isolated cases where ground warming situation could anticipate an irreversible fog dissipation. Then we deduced some fog dissipation forecasting principles.This approach has the potential to provide to users information on morning fog sustainability with a higher accuracy and finer temporal resolution than NWP. Ongoing work focuses on characterizing favourable situations for accurate forecasts, while further predictors are investigated using recent products providing a smart distinction between fog and low stratus using SEVIRI images.

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HIGH TEMPORAL RESOLUTION PERMAFROST MONITORING USING A MULTIPLE STACK INSAR TECHNIQUE

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xl-7-w3-1171-2015 ◽

2015 ◽

Vol XL-7/W3 ◽

pp. 1171-1176 ◽

Cited By ~ 3

Author(s):

J. Eppler ◽

M. Kubanski ◽

J. Sharma ◽

J. Busler

Keyword(s):

Temporal Resolution ◽

Surface Deformation ◽

High Temporal Resolution ◽

Test Cases ◽

Surficial Geology ◽

Spatial Coverage ◽

Northern Regions ◽

Multi Temporal ◽

Deformation Patterns

The combined effect of climate change and accelerated economic development in Northern regions increases the threat of permafrost related surface deformation to buildings and transportation infrastructure. Satellite based InSAR provides a means for monitoring infrastructure that may be both remote and spatially extensive. However, permafrost poses challenges for InSAR monitoring due to the complex temporal deformation patterns caused by both seasonal active layer fluctuations and long-term changes in permafrost thickness. These dynamics suggest a need for increasing the temporal resolution of multi-temporal InSAR methods. To address this issue we have developed a method that combines and jointly processes two or more same side geometry InSAR stacks to provide a high-temporal resolution estimate of surface deformation. The method allows for combining stacks from more than a single SAR sensor and for a combination of frequency bands. Data for this work have been collected and analysed for an area near the community of Umiujaq, Quebec in Northern Canada and include scenes from RADARSAT-2, TerraSAR-X and COSMO-SkyMed. Multiple stack based surface deformation estimates are compared for several cases including results from the three sensors individually and for all sensors combined. The test cases show substantially similar surface deformation results which correlate well with surficial geology. The best spatial coverage of coherent targets was achieved when data from all sensors were combined. The proposed multiple stack method is demonstrated to improve the estimation of surface deformation in permafrost affected areas and shows potential for deriving InSAR based permafrost classification maps to aid in the monitoring of Northern infrastructure.

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In-Orbit Performance of the Constellation of CYGNSS Hurricane Satellites

Bulletin of the American Meteorological Society ◽

10.1175/bams-d-18-0337.1 ◽

2019 ◽

Vol 100 (10) ◽

pp. 2009-2023 ◽

Cited By ~ 19

Author(s):

Christopher Ruf ◽

Shakeel Asharaf ◽

Rajeswari Balasubramaniam ◽

Scott Gleason ◽

Timothy Lang ◽

...

Keyword(s):

Wind Speed ◽

Tropical Cyclones ◽

Dynamic Range ◽

Surface Wind ◽

Low Frequency ◽

Surface Wind Speed ◽

Satellite System ◽

Low Earth Orbit ◽

Current Status ◽

Temporal Sampling

AbstractThe NASA Cyclone Global Navigation Satellite System (CYGNSS) constellation of eight satellites was successfully launched into low Earth orbit on 15 December 2016. Each satellite carries a radar receiver that measures GPS signals scattered from the surface. Wind speed over the ocean is determined from distortions in the signal caused by wind-driven surface roughness. GPS operates at a sufficiently low frequency to allow for propagation through all precipitation, including the extreme rain rates present in the eyewall of tropical cyclones. The spacing and orbit of the satellites were chosen to optimize frequent sampling of tropical cyclones. In this study, we characterize the CYGNSS ocean surface wind speed measurements by their uncertainty, dynamic range, sensitivity to precipitation, spatial resolution, spatial and temporal sampling, and data latency. The current status of each of these properties is examined and potential future improvements are discussed. In addition, examples are given of current science investigations that make use of the data.

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AFOD: An Adaptable Framework for Object Detection in Event-based Vision

10.36227/techrxiv.14931603.v1 ◽

2021 ◽

Author(s):

Shixiong Zhang ◽

Wenmin Wang

Keyword(s):

Computer Vision ◽

Object Detection ◽

Temporal Resolution ◽

Dynamic Range ◽

High Dynamic Range ◽

Motion Blur ◽

High Temporal Resolution ◽

High Dynamic ◽

Event Based ◽

Conventional Computer

<div>Event-based vision is a novel bio-inspired vision that has attracted the interest of many researchers. As a neuromorphic vision, the sensor is different from the traditional frame-based cameras. It has such advantages that conventional frame-based cameras can’t match, e.g., high temporal resolution, high dynamic range(HDR), sparse and minimal motion blur. Recently, a lot of computer vision approaches have been proposed with demonstrated success. However, there is a lack of some general methods to expand the scope of the application of event-based vision. To be able to effectively bridge the gap between conventional computer vision and event-based vision, in this paper, we propose an adaptable framework for object detection in event-based vision.</div>

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High Temporal-Resolution Dynamic PET Image Reconstruction Using A New Spatiotemporal Kernel Method

10.1101/323394 ◽

2018 ◽

Author(s):

Guobao Wang

Keyword(s):

Image Reconstruction ◽

Temporal Resolution ◽

Kernel Method ◽

High Temporal Resolution ◽

Reconstruction Method ◽

Dynamic Pet ◽

Real Patient ◽

Temporal Sampling ◽

Image Prior ◽

Pet Image Reconstruction

AbstractCurrent clinical dynamic PET has an effective temporal resolution of 5-10 seconds, which can be adequate for traditional compartmental modeling but is inadequate for exploiting the benefit of more advanced tracer kinetic modeling. There is a need to improve dynamic PET to allow fine temporal sampling of 1-2 seconds. However, reconstruction of these shorttime frames from tomographic data is extremely challenging as the count level of each frame is very low and high noise presents in both spatial and temporal domains. Previously the kernel framework has been developed and demonstrated as a statistically efficient approach to utilizing image prior for low-count PET image reconstruction. Nevertheless, the existing kernel methods mainly explore spatial correlations in the data and only have a limited ability in suppressing temporal noise. In this paper, we propose a new kernel method which extends the previous spatial kernel method to the general spatiotemporal domain. The new kernelized model encodes both spatial and temporal correlations obtained from image prior information and is incorporated into the PET forward projection model to improve the maximum likelihood (ML) image reconstruction. Computer simulations and an application to real patient scan have shown that the proposed approach can achieve effective noise reduction in both spatial and temporal domains and outperform the spatial kernel method and conventional ML reconstruction method for improving high temporal-resolution dynamic PET imaging.

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