Development and Evaluation of a New Method for AMSU-A Cloud Detection over Land

Satellite data are the main source of information for operational data assimilation systems, and Advanced Microwave Sounding Unit-A (AMSU-A) data are one of the types of satellite data that contribute most to the reduction of numerical forecast errors. However, the assimilation of AMSU-A data over land lags behind that over the ocean. In this respect, the accuracy of cloud detection over land is one of the factors affecting the assimilation of AMSU-A data, especially for the window and low-peaking channel (23–53.59 GHz and 89 GHz) data. Strong surface emissivity and high spatial and temporal variability make it difficult to distinguish between the radiative contributions of clouds and the atmosphere. Based on the differences in the response characteristics of different channels to clouds, five AMSU-A window and low-peaking channels (channels 1–4 and 15) were selected to develop a new index for cloud detection over land. Case studies showed that the AMSU-A cloud index can detect most of the convective clouds; additionally, by further matching the MHS (Microwave Humidity Sounder) cloud detection index, we can effectively distinguish between cloudy and clear-sky observations. Batch test results also verified the accuracy and stability of the new cloud detection method. By referring to the MODIS (Moderate Resolution Imaging Spectroradiometer) cloud product, the POD (probability of detection) of the cloud fields of view with the new method was nearly 84%. By using the new cloud detection method to remove the cloudy data, the bias and standard deviation of the observation-minus-simulated brightness temperature (O−B) were significantly reduced, with the bias of O−B for channels 2–4 being below 1.0 K and the standard deviation of channels 5 and 6 being nearly 1.0 K.

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Daytime Cloud Detection Method Using the All-Sky Imager over PERMATApintar Observatory

Universe ◽

10.3390/universe7020041 ◽

2021 ◽

Vol 7 (2) ◽

pp. 41

Author(s):

Mohammad Afiq Dzuan Mohd Azhar ◽

Nurul Shazana Abdul Hamid ◽

Wan Mohd Aimran Wan Mohd Kamil ◽

Nor Sakinah Mohamad

Keyword(s):

Detection Method ◽

Color Difference ◽

Database System ◽

New Method ◽

Cloud Detection ◽

Cloud Coverage ◽

B Method ◽

Difference Methods ◽

Sky Imager ◽

Sky Conditions

In this study, we explored a new method of cloud detection called the Blue-Green (B-G) Color Difference, which is adapted from the widely used Red-Blue (R-B) Color Difference. The objective of this study was to test the effectiveness of these two methods in detecting daytime clouds. Three all-sky images were selected from a database system at PERMATApintar Observatory. Each selected all-sky image represented different sky conditions, namely clear, partially cloudy and overcast. Both methods were applied to all three images and compared in terms of cloud coverage detection. Our analysis revealed that both color difference methods were able to detect a thick cloud efficiently. However, the B-G was able to detect thin clouds better compared to the R-B method, resulting in a higher and more accurate cloud coverage detection.

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On differentiating ground clutter and insect echoes from Doppler weather radars using archived data

Atmospheric Measurement Techniques Discussions ◽

10.5194/amtd-3-1843-2010 ◽

2010 ◽

Vol 3 (2) ◽

pp. 1843-1860 ◽

Cited By ~ 4

Author(s):

S. J. Rennie ◽

A. J. Illingworth ◽

S. L. Dance

Keyword(s):

Standard Deviation ◽

Detection Method ◽

Probability Of Detection ◽

Alternative Methods ◽

Radar Target ◽

Velocity Data ◽

Weather Radars ◽

Ground Clutter ◽

Fine Weather ◽

Wind Measurements

Abstract. Normally wind measurements from Doppler radars rely on the presence of rain. During fine weather, insects become a potential radar target for wind measurement. However, it is difficult to separate ground clutter and insect echoes when spectral or polarimetric methods are not available. Archived reflectivity and velocity data from repeated scans provide alternative methods. The probability of detection (POD) method, which maps areas with a persistent signal as ground clutter, is ineffective when most scans also contain persistent insect echoes. We developed a clutter detection method which maps the standard deviation of velocity (SDV) over a large number of scans, and can differentiate insects and ground clutter close to the radar. Beyond the range of persistent insect echoes, the POD method more thoroughly removes ground clutter. A new, pseudo-probability clutter map was created by combining the POD and SDV maps. The new map optimised ground clutter detection without removing insect echoes.

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Using MSG-SEVIRI Cloud Physical Properties and Weather Radar Observations for the Detection of Cb/TCu Clouds

Journal of Applied Meteorology and Climatology ◽

10.1175/2011jamc2601.1 ◽

2011 ◽

Vol 50 (7) ◽

pp. 1587-1600 ◽

Cited By ~ 17

Author(s):

Cintia Carbajal Henken ◽

Maurice J. Schmeits ◽

Hartwig Deneke ◽

Rob A. Roebeling

Keyword(s):

Physical Properties ◽

Detection Method ◽

Convective Cloud ◽

Weather Radar ◽

Probability Of Detection ◽

Radar Reflectivity ◽

Cloud Detection ◽

Radar Method ◽

Cloud Mask ◽

Group Memberships

AbstractA new automated daytime cumulonimbus/towering cumulus (Cb/TCu) cloud detection method for the months of May–September is presented that combines information on cloud physical properties retrieved from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board Meteosat Second Generation (MSG) satellites and weather radar reflectivity factors. First, a pixel-based convective cloud mask (CCM) is constructed on the basis of cloud physical properties [cloud-top temperature, cloud optical thickness (COT), effective radius, and cloud phase] derived from SEVIRI. Second, a logistic regression model is applied to determine the probability of Cb/TCu clouds for the collection of pixels that pass the CCM. In this model, MSG-SEVIRI cloud physical properties and weather radar reflectivity factors are used as potential predictor sources. The predictand is derived from aviation routine weather reports (METAR) made by human observers at Amsterdam Airport Schiphol for 2004–07. Results show that the CCM filters out >70% of the “no” events (no Cb/TCu cloud) and that >93% of the “yes” events (Cb/TCu cloud) are retained. Most skillful predictors are derived from radar reflectivity factors and the COT of high resolution. The derived probabilities from the combined MSG and radar method clearly show skill over sample climatology. Probability thresholds are used to convert derived probabilities into derived group memberships (i.e., yes/no Cb/TCu clouds). When comparing verification scores between the combined MSG and radar method and either the radar-only method or the MSG-only method, the combined MSG and radar method shows slightly better performance. When comparing the combined MSG and radar method with the current Royal Netherlands Meteorological Institute (KNMI) radar-based Cb/TCu cloud detection method, the two methods show comparable probability of detection, but the former shows a false-alarm ratio that is about 8% lower. Moreover, a big advantage of the newly developed method is that it provides probabilities, in contrast to the current KNMI method.

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A SIMPLE CLOUD DETECTION METHOD FOR GF-1 WFV IMAGERY

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

10.5194/isprs-archives-xlii-2-w7-1197-2017 ◽

2017 ◽

Vol XLII-2/W7 ◽

pp. 1197-1201

Author(s):

L. L. Jia ◽

X. Q. Wang

Keyword(s):

Detection Method ◽

Texture Feature ◽

New Method ◽

Detection Methods ◽

Cloud Detection ◽

Segmentation Method ◽

Optical Images ◽

Routine Work ◽

Cloud Mask ◽

Better Than

Identification of clouds in optical images is often a necessary step toward their use. However, aimed at the cloud detection methods used on GF-1 is relatively less. In order to meet the requirement of accurate cloud detection in GF-1 WFV imagery, a new method based on the combination of band operation and spatial texture feature (BOTF) is proposed in this paper. First of all, the BOTF algorithm minimize interference of highlight surface and cloud regions by the band operation, and then distinguish between cloud area and non-cloud area with spatial texture feature. Finally, the cloud mask can be acquired by threshold segmentation method. The method was validated using scenes. The results indicate that the BOTF performs well under normal conditions, and the average overall accuracy of BOTF cloud detection is better than 90&thinsp;%. The proposed method can meet the needs of routine work.

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Seasonal cycle of cloud cover analyzed using Meteosat images

Annales Geophysicae ◽

10.1007/s00585-998-0331-3 ◽

1998 ◽

Vol 16 (3) ◽

pp. 331-341 ◽

Cited By ~ 5

Author(s):

J. Massons ◽

D. Domingo ◽

J. Lorente

Keyword(s):

Spatial Resolution ◽

Seasonal Cycle ◽

Cloud Cover ◽

High Spatial Resolution ◽

Detection Method ◽

Cloud Amount ◽

Cloud Detection ◽

Total Cloud Amount ◽

Cloud Parameters ◽

Cloud Properties

Abstract. A cloud-detection method was used to retrieve cloudy pixels from Meteosat images. High spatial resolution (one pixel), monthly averaged cloud-cover distribution was obtained for a 1-year period. The seasonal cycle of cloud amount was analyzed. Cloud parameters obtained include the total cloud amount and the percentage of occurrence of clouds at three altitudes. Hourly variations of cloud cover are also analyzed. Cloud properties determined are coherent with those obtained in previous studies.Key words. Cloud cover · Meteosat

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Cloud Detection Method Based on Spectral Area Ratios in MODIS Data

Canadian Journal of Remote Sensing ◽

10.1080/07038992.2015.1112729 ◽

2015 ◽

Vol 41 (6) ◽

pp. 561-576

Author(s):

Feng Guo ◽

Xiaohua Shen ◽

Lejun Zou ◽

Yupeng Ren ◽

Yi Qin ◽

...

Keyword(s):

Detection Method ◽

Cloud Detection ◽

Modis Data ◽

Spectral Area

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A method for shock train leading edge detection based on differential pressure

Proceedings of the Institution of Mechanical Engineers Part G Journal of Aerospace Engineering ◽

10.1177/0954410016673669 ◽

2016 ◽

Vol 231 (1) ◽

pp. 61-71 ◽

Cited By ~ 6

Author(s):

B Xiong ◽

Z-G Wang ◽

X-Q Fan ◽

Y Wang

Keyword(s):

Standard Deviation ◽

Edge Detection ◽

Detection Method ◽

Pressure Ratio ◽

Leading Edge ◽

Differential Pressure ◽

Shock Train ◽

Pressure Method ◽

Operational Application ◽

Deviation Method

In order to make the shock train leading edge detection method more possible for operational application, a new detection method based on differential pressure signals is introduced in this paper. Firstly, three previous detection methods, including the pressure ratio method, the pressure increase method, and the standard deviation method, have been examined whether they are also applicable for shock train moving at different speeds. Accordingly, three experimental cases of back-pressure changing at different rates were conducted in this paper. The results show that the pressure ratio and the pressure increase method both have acceptable detection accuracy for shock train moving rapidly and slowly, and the standard deviation method is not applicable for rapid shock train movement due to its running time window. Considering the operational application, the differential pressure method is raised and tested in this paper. This detection method has sufficient temporal resolution for rapidly and slowly shock train moving, and can make a real-time detection. In the end, the improvements brought by the differential pressure method have been discussed.

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A Bilateral Texture Filtering Based Cloud Detection Method for VHR Satellite Images

10.1109/iciea51954.2021.9516083 ◽

2021 ◽

Author(s):

Yongzheng Zhou ◽

Xu Li ◽

Baoguo Wei ◽

Lixin Li

Keyword(s):

Satellite Images ◽

Detection Method ◽

Cloud Detection ◽

Texture Filtering

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Noninvasive Electromagnetic Evaluation of Bjo¨rk-Shiley Heart Valves: Lifetime Prediction

Volume 2: Automotive Systems; Bioengineering and Biomedical Technology; Computational Mechanics; Controls; Dynamical Systems ◽

10.1115/esda2008-59347 ◽

2008 ◽

Author(s):

Raimond Grimberg ◽

Adriana Savin ◽

Shiu C. Chan ◽

Rozina Steigmann ◽

Lalita Udpa ◽

...

Keyword(s):

Eddy Current ◽

Heart Valves ◽

Probability Of Detection ◽

New Method ◽

Lifetime Prediction ◽

Prosthetic Heart Valves ◽

In Vitro Experiments ◽

Current Transducer ◽

Eddy Current Transducer

Prosthetic heart valves of the Bjork-Shiley Convexo-Concave (BSCC) type have long been used extensively in implants; however, there have been reports of cases where one component of the valves failed, leading to the demise of the patient. This paper presents a new method for noninvasive electromagnetic evaluation for this type of valve, using an eddy current transducer with orthogonal coils. In vitro experiments have shown that discontinuities of outlet strut with depths equal or larger than 0.4mm can be detected with a probability of detection (POD) of 86.4%, and in the case of discontinuities with depth equal or larger than 0.6mm with POD of 97%.

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