scholarly journals A Daytime Complement to the Reverse Absorption Technique for Improved Automated Detection of Volcanic Ash

2006 ◽  
Vol 23 (11) ◽  
pp. 1422-1444 ◽  
Author(s):  
Michael J. Pavolonis ◽  
Wayne F. Feltz ◽  
Andrew K. Heidinger ◽  
Gregory M. Gallina
Keyword(s):  
Brightness Temperature ◽  
Temperature Difference ◽  
Detection Algorithm ◽  
Detection Sensitivity ◽  
False Alarms ◽  
Cloud Detection ◽  
Cloud Optical Depth ◽  
Absorption Signal ◽  
Volcanic Clouds ◽  
Volcanic Cloud

Abstract An automated volcanic cloud detection algorithm that utilizes four spectral channels (0.65, 3.75, 11, and 12 μm) that are common among several satellite-based instruments is presented. The new algorithm is physically based and globally applicable and can provide quick information on the horizontal location of volcanic clouds that can be used to improve real-time ash hazard assessments. It can also provide needed input into volcanic cloud optical depth and particle size retrieval algorithms, the products of which can help improve ash dispersion forecasts. The results of this new four-channel algorithm for several scenes were compared to a threshold-based reverse absorption algorithm, where the reverse absorption algorithm is used to identify measurements with a negative 11–12-μm brightness temperature difference. The results indicate that the new four-channel algorithm is not only more sensitive to the presence of volcanic clouds but also generally less prone to false alarms than the standard reverse absorption algorithm. The greatest impact on detection sensitivity is seen in the Tropics, where water vapor can often mask the reverse absorption signal. The four-channel algorithm was able to detect volcanic clouds even when the 11–12-μm brightness temperature difference was greater than +2 K. In the higher latitudes, the greatest impact seen was the significant reduction in false alarms compared to the reverse absorption algorithm and the improved ability to detect optically thick volcanic clouds. Cloud water can also mask the reverse absorption signal. The four-channel algorithm was shown to be more sensitive to volcanic clouds that have a water (ice or liquid water) component than the reverse absorption algorithm.

scholarly journals DECISION TREE CLOUD DETECTION ALGORITHM BASED ON FY-4A SATELLITE DATA

2019 ◽  
Vol XLII-3/W9 ◽  
pp. 219-226
Author(s):  
Z. F. Yu ◽  
W. H. Ai ◽  
Z. H. Tan ◽  
W. Yan
Keyword(s):  
Decision Tree ◽  
Brightness Temperature ◽  
Temperature Difference ◽  
Satellite Data ◽  
Detection Algorithm ◽  
Processing Technology ◽  
Cloud Detection ◽  
Detection Model ◽  
Meteorological Satellite

Abstract. In order to study the on-board processing technology of meteorological satellites, a decision tree cloud detection algorithm is proposed by taking FY-4A satellite data as an example. According to the channel setting of the Advanced Geosynchronous Radiation Imager (AGRI) on FY-4A satellite, the 0.65 μm, 1.375 μm, 3.75 μm, and 10.7 μm bands are selected as the cloud detection channels, and the reflectance, brightness temperature or bright temperature difference of the four channels are used as the cloud detection indicators, the thresholds of the four cloud detection indicators are obtained through statistics. On this basis, the decision tree cloud detection model is constructed and validated using FY-4A satellite data. The results show that the algorithm is simple, convenient and efficient, and the overall effect of cloud detection is good. It is an effective way for meteorological satellite cloud detection on-board processing technology.

scholarly journals Development of CO2 Band-Based Cloud Emission and Scattering Indices and Their Applications to FY-3D Hyperspectral Infrared Atmospheric Sounder

2020 ◽  
Vol 12 (24) ◽  
pp. 4171
Author(s):  
Xinlu Xia ◽  
Xiaolei Zou
Keyword(s):  
Brightness Temperature ◽  
Optical Depth ◽  
Co2 Absorption ◽  
Cloud Detection ◽  
Detection Scheme ◽  
Cloud Optical Depth ◽  
Ice Cloud ◽  
Clear Sky ◽  
Temperature Observations ◽  
Cloud Top Pressure

The Hyperspectral Infrared Atmospheric Sounder (HIRAS) onboard the Feng Yun-3D (FY-3D) satellite is the first Chinese hyperspectral infrared instrument. In this study, an improved cloud detection scheme using brightness temperature observations from paired HIRAS long-wave infrared (LWIR) and short-wave infrared (SWIR) channels at CO2 absorption bands (15-μm and 4.3-μm) is developed. The weighting function broadness and a set of height-dependent thresholds of cloud-sensitive-level differences are incorporated into pairing LWIR and SWIR channels. HIRAS brightness temperature observations made under clear-sky conditions during a training period are used to develop a set of linear regression equations between paired LWIR and SWIR channels. Moderate-resolution Imaging Spectroradiometer (MODIS) cloud mask data are used for selecting HIRAS clear-sky observations. Cloud Emission and Scattering Indices (CESIs) are defined as the differences in SWIR channels between HIRAS observations and regression simulations from LWIR observations. The cloud retrieval products of ice cloud optical depth and cloud-top pressure from the Atmospheric Infrared Sounder (AIRS) are used to illustrate the effectiveness of the proposed cloud detection scheme for FY-3D HIRAS observations. Results show that the distributions of modified CESIs at different altitudes can capture features in the distributions of AIRS-retrieved ice cloud optical depth and cloud-top pressure better than the CESIs obtained by the original method.

scholarly journals Passive Earth Observations of Volcanic Clouds in the Atmosphere

Atmosphere ◽  
2019 ◽  
Vol 10 (4) ◽  
pp. 199 ◽  
Author(s):  
Fred Prata ◽  
Mervyn Lynch
Keyword(s):  
Spatial Information ◽  
Physical Models ◽  
Optical Parameters ◽  
Cloud Detection ◽  
Volcanic Emissions ◽  
Temporal Behaviour ◽  
Statistical Measures ◽  
Volcanic Clouds ◽  
Infrared Measurements ◽  
Volcanic Cloud

Current Earth Observation (EO) satellites provide excellent spatial, temporal and spectral coverage for passive measurements of atmospheric volcanic emissions. Of particular value for ash detection and quantification are the geostationary satellites that now carry multispectral imagers. These instruments have multiple spectral channels spanning the visible to infrared (IR) wavelengths and provide 1 × 1 km2 to 4 × 4 km2 resolution data every 5–15 min, continuously. For ash detection, two channels situated near 11 and 12 μ m are needed; for ash quantification a third or fourth channel also in the infrared is useful for constraining the height of the ash cloud. This work describes passive EO infrared measurements and techniques to determine volcanic cloud properties and includes examples using current methods with an emphasis on the main difficulties and ways to overcome them. A challenging aspect of using satellite data is to design algorithms that make use of the spectral, temporal (especially for geostationary sensors) and spatial information. The hyperspectral sensor AIRS is used to identify specific molecules from their spectral signatures (e.g., for SO2) and retrievals are demonstrated as global, regional and hemispheric maps of AIRS column SO2. This kind of information is not available on all sensors, but by combining temporal, spatial and broadband multi-spectral information from polar and geo sensors (e.g., MODIS and SEVIRI) useful insights can be made. For example, repeat coverage of a particular area using geostationary data can reveal temporal behaviour of broadband channels indicative of eruptive activity. In many instances, identifying the nature of a pixel (clear, cloud, ash etc.) is the major challenge. Sophisticated cloud detection schemes have been developed that utilise statistical measures, physical models and temporal variation to classify pixels. The state of the art on cloud detection is good, but improvements are always needed. An IR-based multispectral cloud identification scheme is described and some examples shown. The scheme is physically based but has deficiencies that can be improved during the daytime by including information from the visible channels. Physical retrieval schemes applied to ash detected pixels suffer from a lack of knowledge of some basic microphysical and optical parameters needed to run the retrieval models. In particular, there is a lack of accurate spectral refractive index information for ash particles. The size distribution of fine ash (1–63 μ m, diameter) is poorly constrained and more measurements are needed, particularly for ash that is airborne. Height measurements are also lacking and a satellite-based stereoscopic height retrieval is used to illustrate the value of this information for aviation. The importance of water in volcanic clouds is discussed here and the separation of ice-rich and ash-rich portions of volcanic clouds is analysed for the first time. More work is required in trying to identify ice-coated ash particles, and it is suggested that a class of ice-rich volcanic cloud be recognized and termed a ‘volcanic ice’ cloud. Such clouds are frequently observed in tropical eruptions of great vertical extent (e.g., 8 km or higher) and are often not identified correctly by traditional IR methods (e.g., reverse absorption). Finally, the global, hemispheric and regional sampling of EO satellites is demonstrated for a few eruptions where the ash and SO 2 dispersed over large distances (1000s km).

Advancing Maritime Transparent Cirrus Detection Using the Advanced Baseline Imager “Cirrus” Band

2021 ◽  
Author(s):  
Theodore M. McHardy ◽  
James R. Campbell ◽  
David A. Peterson ◽  
Simone Lolli ◽  
Richard L. Bankert ◽  
...  
Keyword(s):  
Precipitable Water ◽  
Detection Algorithm ◽  
Machine Learning Techniques ◽  
Orthogonal Polarization ◽  
Cirrus Cloud ◽  
Cloud Detection ◽  
Cloud Optical Depth ◽  
Clear Sky ◽  
Learning Techniques ◽  
Microphysical Property

AbstractWe describe a quantitative evaluation of maritime transparent cirrus cloud detection, which is based on Geostationary Operational Environmental Satellite – 16 (GOES-16) and developed with collocated Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) profiling. The detection algorithm is developed using one month of collocated GOES-16 Advanced Baseline Imager (ABI) Channel 4 (1.378 μm) radiance and CALIOP 0.532 μm column-integrated cloud optical depth (COD). First, the relationships between the clear-sky 1.378 μm radiance, viewing/solar geometry, and precipitable water vapor (PWV) are characterized. Using machine learning techniques, it is shown that the total atmospheric pathlength, proxied by airmass factor (AMF), is a suitable replacement for viewing zenith and solar zenith angles alone, and that PWV is not a significant problem over ocean. Detection thresholds are computed using the Ch. 4 radiance as a function of AMF. The algorithm detects nearly 50% of sub-visual cirrus (COD < 0.03), 80% of transparent cirrus (0.03 < COD < 0.3), and 90% of opaque cirrus (COD > 0.3). Using a conservative radiance threshold results in 84% of cloudy pixels being correctly identified and 4% of clear-sky pixels being misidentified as cirrus. A semi-quantitative COD retrieval is developed for GOES ABI based on the observed relationship between CALIOP COD and 1.378 μm radiance. This study lays the groundwork for a more complex, operational GOES transparent cirrus detection algorithm. Future expansion includes an over-land algorithm, a more robust COD retrieval that is suitable for assimilation purposes, and downstream GOES products such as cirrus cloud microphysical property retrieval based on ABI infrared channels.

scholarly journals Variability of cirrus cloud properties using a Polly<sup>XT</sup> Raman Lidar over high and tropical latitudes

2019 ◽  
Author(s):  
Kalliopi Artemis Voudouri ◽  
Elina Giannakaki ◽  
Mika Komppula ◽  
Dimitris Balis
Keyword(s):  
Optical Properties ◽  
Optical Depth ◽  
Middle Latitude ◽  
Detection Algorithm ◽  
Cirrus Clouds ◽  
Cirrus Cloud ◽  
Raman Lidar ◽  
Cloud Detection ◽  
Cloud Optical Depth ◽  
Lidar Ratio

Abstract. Measurements of cirrus clouds geometrical and optical properties, performed with a multi-wavelength PollyXT Raman Lidar, during the period 2008 to 2016 are analysed. The measurements were performed with the same instrument, during sequential periods, in three places at different latitudes, Gual Pahari (28.43° N, 77.15° E, 243 m a.s.l) in India, Elandsfontein (26.25° S, 29.43° E, 1745 m a.s.l) in South Africa and Kuopio (62.74° N, 27.54° E, 190 m a.s.l) in Finland. The lidar dataset has been processed by an automatic cirrus cloud detection algorithm. In the following, we present a statistical analysis of the lidar derived geometrical characteristics (cloud boundaries, geometrical thickness) and optical properties of cirrus clouds (cloud optical depth, lidar ratio, ice crystal depolarization ratio) measured in different latitudes that correspond to subtropical and subarctic regions as well as their seasonal variability. The effect of multiple-scattering from ice particles to the derived optical products is also considered and corrected in this study. Our results show that, over the subtropical stations, cirrus layers, which have a noticeable monthly variability, were observed between 7 to 13 km, with mid-cloud temperatures ranging from −60 °C to −21 °C and a mean thickness of 1295 ± 489 m and 1383 ± 735 m for Gual Pahari and Elandsfontein respectively. The corresponding overall mean cirrus optical depth at 355 nm is calculated to be 0.59 ± 0.39 and 0.40 ± 0.33, with lidar ratio values at 355 nm of 26 ± 12 sr and 25 ± 6 sr, respectively. A more extended dataset was acquired for the subarctic area of Kuopio Finland, between 2012 and 2016. The estimated average geometrical thickness of the cirrus clouds over Kuopio is 1200 ± 585 m and the temperature values vary from −71 °C to −21 °C, while the mean cirrus optical depth at 355 nm is 0.25 ± 0.2, with an estimated mean lidar ratio of 33 ± 7 sr, similar to the idar ratio values observed over middle latitude stations. The kind of information presented here can be rather useful in the cirrus parameterizations required as input to radiative transfer models, and can be a complementary tool to satellite products that cannot provide cloud vertical structure. In addition, a ground-based statistics of the cirrus properties could be useful in the validation and improvement of the corresponding derived products from satellite retrievals.

A Machine Learning-based Cloud Detection Algorithm for the Himawari-8 Spectral Image

2021 ◽  
Author(s):  
Chao Liu ◽  
Shu Yang ◽  
Di Di ◽  
Yuanjian Yang ◽  
Chen Zhou ◽  
...  
Keyword(s):  
Machine Learning ◽  
Detection Algorithm ◽  
Cloud Detection ◽  
Spectral Image

The 2015 Calbuco Volcanic Cloud Detection Using GNSS Radio Occultation and Satellite Lidar

2020 ◽  
Author(s):  
Pierre-Yves Tournigand ◽  
Valeria Cigala ◽  
Alfredo J. Prata ◽  
Andrea K. Steiner ◽  
Gottfried Kirchengast ◽  
...  
Keyword(s):  
Radio Occultation ◽  
Cloud Detection ◽  
Volcanic Cloud

Algorithms for detecting broadband signals with a constant level of false alarms

2021 ◽  
Author(s):  
I.F. Lozovskiy
Keyword(s):  
Detection Threshold ◽  
Signal Amplitude ◽  
Rayleigh Distribution ◽  
Training Sample ◽  
Detection Algorithm ◽  
Constant Level ◽  
False Alarms ◽  
Interference Power ◽  
Number Of False Alarms ◽  
Distribution Type

The use of broadband souding signals in radars, which has become real in recent years, leads to a significant reduction in the size of resolution elements in range and, accordingly, in the size of the window in which the training sample is formed, which is used to adapt the detection threshold in signal detection algorithms with a constant level of false alarms. In existing radars, such a window would lead to huge losses. The purpose of the work was to study the most rational options for constructing detectors with a constant level of false alarms in radars with broadband sounding signals. The problem was solved for the Rayleigh distribution of the envelope of the noise and a number of non-Rayleigh laws — Weibull and the lognormal, the appearance of which is associated with a decrease in the number of reflecting elements in the resolution volume. For Rayleigh interference, an algorithm is proposed with a multi-channel in range incoherent signal amplitude storage and normalization to the larger of the two estimates of the interference power in the range segments. The detection threshold in it adapts not only to the interference power, but also to the magnitude of the «power jump» in range, which allows reducing the number of false alarms during sudden changes in the interference power – the increase in the probability of false alarms did not exceed one order of magnitude. In this algorithm, there is a certain increase in losses associated with incoherent accumulation of signals reflected from target elements, and losses can be reduced by certain increasing the size of the distance segments that make up the window. Algorithms for detecting broadband signals against interference with non-Rayleigh laws of distribution of the envelope – Weibull and lognormal, based on the addition of the algorithm for detecting signals by non-linear transformation of sample counts into counts with a Rayleigh distribution, are studied. The structure of the detection algorithm remains unchanged in practice. The options for detectors of narrowband and broadband signals are considered. It was found that, in contrast to algorithms designed for the Rayleigh distribution, these algorithms provide a stable level of false alarms regardless of the values of the parameters of non-Rayleigh interference. To reduce losses due to interference with the distribution of amplitudes according to the Rayleigh law, detectors consisting of two channels are used, in which one of the channels is tuned for interference with the Rayleigh distribution, and the other for lognormal or Weibull interference. Channels are switched according to special distribution type recognition algorithms. In such detectors, however, there is a certain increase in the probability of false alarms in a rather narrow range of non-Rayleigh interference parameters, where their distribution approaches the Rayleigh distribution. It is shown that when using broadband signals, there is a noticeable decrease in detection losses in non-Rayleigh noise due to lower detection thresholds for in range signal amplitudes incoherent storage.

scholarly journals An automated cloud detection method based on the green channel of total-sky visible images

2015 ◽  
Vol 8 (11) ◽  
pp. 4671-4679 ◽  
Author(s):  
J. Yang ◽  
Q. Min ◽  
W. Lu ◽  
W. Yao ◽  
Y. Ma ◽  
...  
Keyword(s):  
Detection Algorithm ◽  
Mie Scattering ◽  
Brightness Distribution ◽  
Adaptive Threshold ◽  
Cloud Detection ◽  
Blue Band ◽  
Green Channel ◽  
The Past ◽  
Visible Images ◽  
Cloud Mask

Abstract. Obtaining an accurate cloud-cover state is a challenging task. In the past, traditional two-dimensional red-to-blue band methods have been widely used for cloud detection in total-sky images. By analyzing the imaging principle of cameras, the green channel has been selected to replace the 2-D red-to-blue band for detecting cloud pixels from partly cloudy total-sky images in this study. The brightness distribution in a total-sky image is usually nonuniform, because of forward scattering and Mie scattering of aerosols, which results in increased detection errors in the circumsolar and near-horizon regions. This paper proposes an automatic cloud detection algorithm, "green channel background subtraction adaptive threshold" (GBSAT), which incorporates channel selection, background simulation, computation of solar mask and cloud mask, subtraction, an adaptive threshold, and binarization. Five experimental cases show that the GBSAT algorithm produces more accurate retrieval results for all these test total-sky images.

scholarly journals A Method for Calculating the Height of Overshooting Convective Cloud Tops Using Satellite-Based IR Imager and CloudSat Cloud Profiling Radar Observations

2016 ◽  
Vol 55 (2) ◽  
pp. 479-491 ◽  
Author(s):  
Sarah M. Griffin ◽  
Kristopher M. Bedka ◽  
Christopher S. Velden
Keyword(s):  
Brightness Temperature ◽  
Characteristic Temperature ◽  
Weather Prediction ◽  
Convective Cloud ◽  
Detection Algorithm ◽  
Lapse Rate ◽  
Temperature Lapse Rate ◽  
Cloud Tops ◽  
Height Assignment ◽  
Vertical Level

AbstractAssigning accurate heights to convective cloud tops that penetrate into the upper troposphere–lower stratosphere (UTLS) region using infrared (IR) satellite imagery has been an unresolved issue for the satellite research community. The height assignment for the tops of optically thick clouds is typically accomplished by matching the observed IR brightness temperature (BT) with a collocated rawinsonde or numerical weather prediction (NWP) profile. However, “overshooting tops” (OTs) are typically colder (in BT) than any vertical level in the associated profile, leaving the height of these tops undetermined using this standard approach. A new method is described here for calculating the heights of convectively driven OTs using the characteristic temperature lapse rate of the cloud top as it ascends into the UTLS region. Using 108 MODIS-identified OT events that are directly observed by the CloudSat Cloud Profiling Radar (CPR), the MODIS-derived brightness temperature difference (BTD) between the OT and anvil regions can be defined. This BTD is combined with the CPR- and NWP-derived height difference between these two regions to determine the mean lapse rate, −7.34 K km−1, for the 108 events. The anvil height is typically well known, and an automated OT detection algorithm is used to derive BTD, so the lapse rate allows a height to be calculated for any detected OT. An empirical fit between MODIS and geostationary imager IR BT for OTs and anvil regions was performed to enable application of this method to coarser-spatial-resolution geostationary data. Validation indicates that ~75% (65%) of MODIS (geostationary) OT heights are within ±500 m of the coincident CPR-estimated heights.

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