scholarly journals Development of Fog Detection Algorithm Using GK2A/AMI and Ground Data

2020 ◽  
Vol 12 (19) ◽  
pp. 3181
Author(s):  
Ji-Hye Han ◽  
Myoung-Seok Suh ◽  
Ha-Yeong Yu ◽  
Na-Young Roh
Keyword(s):  
South Korea ◽  
Geographic Location ◽  
Surface Characteristics ◽  
Detection Algorithm ◽  
Time Of Day ◽  
Probability Of Detection ◽  
Bias Ratio ◽  
Quantitative Validation ◽  
Fog Detection ◽  
Total Average

Fog affects transportation due to low visibility and also aggravates air pollutants. Thus, accurate detection and forecasting of fog are important for the safety of transportation. In this study, we developed a decision tree type fog detection algorithm (hereinafter GK2A_FDA) using the GK2A/AMI and auxiliary data. Because of the responses of the various channels depending on the time of day and the underlying surface characteristics, several versions of the algorithm were created to account for these differences according to the solar zenith angle (day/dawn/night) and location (land/sea/coast). Numerical model data were used to distinguish the fog from low clouds. To test the detection skill of GK2A_FDA, we selected 23 fog cases that occurred in South Korea and used them to determine the threshold values (12 cases) and validate GK2A_FDA (11 cases). Fog detection results were validated using the visibility data from 280 stations in South Korea. For quantitative validation, statistical indices, such as the probability of detection (POD), false alarm ratio (FAR), bias ratio (Bias), and equitable threat score (ETS), were used. The total average POD, FAR, Bias, and ETS for training cases (validation cases) were 0.80 (0.82), 0.37 (0.29), 1.28 (1.16), and 0.52 (0.59), respectively. In general, validation results showed that GK2A_FDA effectively detected the fog irrespective of time and geographic location, in terms of accuracy and stability. However, its detection skill and stability were slightly dependent on geographic location and time. In general, the detection skill and stability of GK2A_FDA were found to be better on land than on coast at all times, and at night than day at any location.

scholarly journals Arctic Fog Detection Using Infrared Spectral Measurements

2019 ◽  
Vol 36 (8) ◽  
pp. 1643-1656
Author(s):  
Li Yi ◽  
King-Fai Li ◽  
Xianyao Chen ◽  
Ka-Kit Tung
Keyword(s):  
Sea Ice ◽  
Chukchi Sea ◽  
Open Water ◽  
Detection Algorithm ◽  
Probability Of Detection ◽  
Satellite Observations ◽  
The Arctic ◽  
Water Surface Area ◽  
Infrared Spectral ◽  
Fog Detection

AbstractThe rapid increase in open-water surface area in the Arctic, resulting from sea ice melting during the summer likely as a result of global warming, may lead to an increase in fog [defined as a cloud with a base height below 1000 ft (~304 m)], which may imperil ships and small aircraft transportation in the region. There is a need for monitoring fog formation over the Arctic. Given that ground-based observations of fog over Arctic open water are very sparse, satellite observations may become the most effective way for Arctic fog monitoring. We developed a fog detection algorithm using the temperature difference between the cloud top and the surface, called ∂T in this work. A fog event is said to be detected if ∂T is greater than a threshold, which is typically between −6 and −12 K, depending on the time of the day (day or night) and the surface types (open water or sea ice). We applied this method to the coastal regions of Chukchi Sea and Beaufort Sea near Barrow, Alaska (now known as Utqiaġvik), during the months of March–October. Training with satellite observations between 2007 and 2014 over this region, the ∂T method can detect Arctic fog with an optimal probability of detection (POD) between 74% and 90% and false alarm rate (FAR) between 5% and 17%. These statistics are validated with data between 2015 and 2016 and are shown to be robust from one subperiod to another.

Using deep learning to nowcast the spatial coverage of convection from Himawari-8 satellite data

2021 ◽  
Author(s):  
Ryan Lagerquist ◽  
Jebb Q. Stewart ◽  
Imme Ebert-Uphoff ◽  
Christina Kumler
Keyword(s):  
Deep Learning ◽  
Satellite Data ◽  
Geographic Location ◽  
Ground Truth ◽  
Atmospheric Science ◽  
Skill Score ◽  
Time Of Day ◽  
Lead Times ◽  
Weather Radars ◽  
Spatial Coverage

AbstractPredicting the timing and location of thunderstorms (“convection”) allows for preventive actions that can save both lives and property. We have applied U-nets, a deep-learning-based type of neural network, to forecast convection on a grid at lead times up to 120 minutes. The goal is to make skillful forecasts with only present and past satellite data as predictors. Specifically, predictors are multispectral brightness-temperature images from the Himawari-8 satellite, while targets (ground truth) are provided by weather radars in Taiwan. U-nets are becoming popular in atmospheric science due to their advantages for gridded prediction. Furthermore, we use three novel approaches to advance U-nets in atmospheric science. First, we compare three architectures – vanilla, temporal, and U-net++ – and find that vanilla U-nets are best for this task. Second, we train U-nets with the fractions skill score, which is spatially aware, as the loss function. Third, because we do not have adequate ground truth over the full Himawari-8 domain, we train the U-nets with small radar-centered patches, then apply trained U-nets to the full domain. Also, we find that the best predictions are given by U-nets trained with satellite data from multiple lag times, not only the present. We evaluate U-nets in detail – by time of day, month, and geographic location – and compare to persistence models. The U-nets outperform persistence at lead times ≥ 60 minutes, and at all lead times the U-nets provide a more realistic climatology than persistence. Our code is available publicly.

scholarly journals Day-time identification of summer hailstorm cells from MSG data

2013 ◽  
Vol 1 (5) ◽  
pp. 5453-5498 ◽  
Author(s):  
A. Merino ◽  
L. López ◽  
J. L. Sánchez ◽  
E. García-Ortega ◽  
E. Cattani ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Deep Convection ◽  
Detection Algorithm ◽  
Probability Of Detection ◽  
Phase Method ◽  
Data Sets ◽  
Liquid Water Path ◽  
Two Phase ◽  
Logistic Regression Models ◽  
Ebro Valley

Abstract. Identifying deep convection is of paramount importance, as it may be associated with extreme weather that has significant impact on the environment, property and the population. A new method, the Hail Detection Tool (HDT), is described for identifying hail-bearing storms using multi-spectral Meteosat Second Generation (MSG) data. HDT was conceived as a two-phase method, in which the first step is the Convective Mask (CM) algorithm devised for detection of deep convection, and the second a Hail Detection algorithm (HD) for the identification of hail-bearing clouds among cumulonimbus systems detected by CM. Both CM and HD are based on logistic regression models trained with multi-spectral MSG data-sets comprised of summer convective events in the middle Ebro Valley between 2006–2010, and detected by the RGB visualization technique (CM) or C-band weather radar system of the University of León. By means of the logistic regression approach, the probability of identifying a cumulonimbus event with CM or a hail event with HD are computed by exploiting a proper selection of MSG wavelengths or their combination. A number of cloud physical properties (liquid water path, optical thickness and effective cloud drop radius) were used to physically interpret results of statistical models from a meteorological perspective, using a method based on these "ingredients." Finally, the HDT was applied to a new validation sample consisting of events during summer 2011. The overall Probability of Detection (POD) was 76.9% and False Alarm Ratio 16.7%.

scholarly journals A cloud detection algorithm using the downwelling infrared radiance measured by an infrared pyrometer of the ground-based microwave radiometer

2015 ◽  
Vol 8 (2) ◽  
pp. 553-566 ◽  
Author(s):  
M.-H. Ahn ◽  
D. Han ◽  
H. Y. Won ◽  
V. Morris
Keyword(s):  
Water Vapor Pressure ◽  
Microwave Radiometer ◽  
Surface Air Temperature ◽  
Detection Algorithm ◽  
Probability Of Detection ◽  
Cloud Base ◽  
Cloud Detection ◽  
Clear Sky ◽  
Spectral Test ◽  
Free Data

Abstract. For better utilization of the ground-based microwave radiometer, it is important to detect the cloud presence in the measured data. Here, we introduce a simple and fast cloud detection algorithm by using the optical characteristics of the clouds in the infrared atmospheric window region. The new algorithm utilizes the brightness temperature (Tb) measured by an infrared radiometer installed on top of a microwave radiometer. The two-step algorithm consists of a spectral test followed by a temporal test. The measured Tb is first compared with a predicted clear-sky Tb obtained by an empirical formula as a function of surface air temperature and water vapor pressure. For the temporal test, the temporal variability of the measured Tb during one minute compares with a dynamic threshold value, representing the variability of clear-sky conditions. It is designated as cloud-free data only when both the spectral and temporal tests confirm cloud-free data. Overall, most of the thick and uniform clouds are successfully detected by the spectral test, while the broken and fast-varying clouds are detected by the temporal test. The algorithm is validated by comparison with the collocated ceilometer data for six months, from January to June 2013. The overall proportion of correctness is about 88.3% and the probability of detection is 90.8%, which are comparable with or better than those of previous similar approaches. Two thirds of discrepancies occur when the new algorithm detects clouds while the ceilometer does not, resulting in different values of the probability of detection with different cloud-base altitude, 93.8, 90.3, and 82.8% for low, mid, and high clouds, respectively. Finally, due to the characteristics of the spectral range, the new algorithm is found to be insensitive to the presence of inversion layers.

scholarly journals Long-term evaluation of air sensor technology under ambient conditions in Denver, Colorado

2018 ◽  
Vol 11 (8) ◽  
pp. 4605-4615 ◽  
Author(s):  
Stephen Feinberg ◽  
Ron Williams ◽  
Gayle S. W. Hagler ◽  
Joshua Rickard ◽  
Ryan Brown ◽  
...  
Keyword(s):  
Air Pollution ◽  
Wind Direction ◽  
Low Cost ◽  
Geographic Location ◽  
Field Evaluation ◽  
Time Of Day ◽  
Sensor Technology ◽  
Ambient Conditions ◽  
Data Completeness ◽  
Sampling Campaign

Abstract. Air pollution sensors are quickly proliferating for use in a wide variety of applications, with a low price point that supports use in high-density networks, citizen science, and individual consumer use. This emerging technology motivates the assessment under real-world conditions, including varying pollution levels and environmental conditions. A seven-month, systematic field evaluation of low-cost air pollution sensors was performed in Denver, Colorado, over 2015–2016; the location was chosen to evaluate the sensors in a high-altitude, cool, and dry climate. A suite of particulate matter (PM), ozone (O3), and nitrogen dioxide (NO2) sensors were deployed in triplicate and were collocated with federal equivalent method (FEM) monitors at an urban regulatory site. Sensors were evaluated for their data completeness, correlation with reference monitors, and ability to reproduce trends in pollution data, such as daily concentration values and wind-direction patterns. Most sensors showed high data completeness when data loggers were functioning properly. The sensors displayed a range of correlations with reference instruments, from poor to very high (e.g., hourly-average PM Pearson correlations with reference measurements varied from 0.01 to 0.86). Some sensors showed a change in response to laboratory audits/testing from before the sampling campaign to afterwards, such as Aeroqual, where the O3 response slope changed from about 1.2 to 0.6. Some PM sensors measured wind-direction and time-of-day trends similar to those measured by reference monitors, while others did not. This study showed different results for sensor performance than previous studies performed by the U.S. EPA and others, which could be due to different geographic location, meteorology, and aerosol properties. These results imply that continued field testing is necessary to understand emerging air sensing technology.

INFRARED SMALL TARGET DETECTION ALGORITHM BASED ON FEATURE SALIENCE AND MULTI-FEATURES FUSION

2011 ◽  
Vol 25 (02) ◽  
pp. 299-308 ◽  
Author(s):  
ZHEN-XUE CHEN ◽  
CHENG-YUN LIU ◽  
FA-LIANG CHANG
Keyword(s):  
Target Detection ◽  
Detection Algorithm ◽  
Probability Of Detection ◽  
Absolute Difference ◽  
Small Target ◽  
Probability Of Error ◽  
Small Target Detection ◽  
Low Snr ◽  
Complex Background ◽  
Small Targets

It is an important and challenging problem to detect small targets in clutter scene and low SNR (Signal Noise Ratio) in infrared (IR) images. In order to solve this problem, a method based on feature salience is proposed for automatic detection of targets in complex background. Firstly, in this paper, the method utilizes the average absolute difference maximum (AADM) as the dissimilarity measurement between targets and background region to enhance targets. Secondly, minimum probability of error was used to build the model of feature salience. Finally, by computing the realistic degree of features, this method solves the problem of multi-feather fusion. Experimental results show that the algorithm proposed shows better performance with respect to the probability of detection. It is an effective and valuable small target detection algorithm under a complex background.

scholarly journals Validation of GMI Snowfall Observations by Using a Combination of Weather Radar and Surface Measurements

2018 ◽  
Vol 57 (4) ◽  
pp. 797-820 ◽  
Author(s):  
Annakaisa von Lerber ◽  
Dmitri Moisseev ◽  
David A. Marks ◽  
Walter Petersen ◽  
Ari-Matti Harri ◽  
...  
Keyword(s):  
Large Scale ◽  
Quantitative Estimation ◽  
Weather Radar ◽  
Detection Algorithm ◽  
Probability Of Detection ◽  
Research Station ◽  
Radar Observations ◽  
Microphysical Properties ◽  
Ground Validation ◽  
Microwave Imager

AbstractCurrently, there are several spaceborne microwave instruments suitable for the detection and quantitative estimation of snowfall. To test and improve retrieval snowfall algorithms, ground validation datasets that combine detailed characterization of snowfall microphysics and spatial precipitation measurements are required. To this endpoint, measurements of snow microphysics are combined with large-scale weather radar observations to generate such a dataset. The quantitative snowfall estimates are computed by applying event-specific relations between the equivalent reflectivity factor and snowfall rate to weather radar observations. The relations are derived using retrieved ice particle microphysical properties from observations that were carried out at the University of Helsinki research station in Hyytiälä, Finland, which is about 64 km east of the radar. For each event, the uncertainties of the estimate are also determined. The feasibility of using this type of data to validate spaceborne snowfall measurements and algorithms is demonstrated with the NASA GPM Microwave Imager (GMI) snowfall product. The detection skill and retrieved surface snowfall precipitation of the GPROF detection algorithm, versions V04A and V05A, are assessed over southern Finland. On the basis of the 26 studied overpasses, probability of detection (POD) is 0.90 for version V04A and 0.84 for version V05A, and corresponding false-alarm rates are 0.09 and 0.10, respectively. A clear dependence of detection skill on cloud echo top height is shown: POD increased from 0.8 to 0.99 (V04A) and from 0.61 to 0.94 (V05A) as the cloud echo top altitude increased from 2 to 5 km. Both versions underestimate the snowfall rate by factors of 6 (V04A) and 3 (V05A).

Urine specific gravity in exercisers prior to physical training

2006 ◽  
Vol 31 (3) ◽  
pp. 320-327 ◽  
Author(s):  
Elizabeth A Stover ◽  
Heather J Petrie ◽  
Dennis Passe ◽  
Craig A Horswill ◽  
Bob Murray ◽  
...  
Keyword(s):  
Los Angeles ◽  
Specific Gravity ◽  
Geographic Location ◽  
The United States ◽  
Time Of Day ◽  
Fluid Replacement ◽  
Urine Specific Gravity ◽  
Hydration Status ◽  
The Mean ◽  
And Gender

Urine specific gravity (USG) is used as an index of hydration status. Many studies have used USG to estimate pre-exercise hydration in athletes. However, very little is known about the pre-exercise hydration status of recreational exercisers. The purpose of the present study was to measure the pre-exercise USG in a large sample of recreational exercisers who attended 2 different fitness centers in the United States. In addition, we wanted to determine if factors such as time of day, geographic location, and gender influenced USG. We tested 166 subjects in Chicago and 163 subjects in Los Angeles. Subjects completed a survey on their typical training regimen and fluid-replacement habits, and thereafter voided and delivered a urine sample to the investigators prior to beginning exercise. Samples were measured on site for USG using a hand-held refractometer. The mean (SD) USG was 1.018 (± 0.007) for all subjects. Males had a higher average USG (1.020 ± 0.007) when compared with females (1.017 ± 0.008; p = 0.001). Despite differences in climate, no difference in mean USG occurred based on location or time of day. Based on standards used for athletes (USG >= 1.020), 46% of the exercisers were likely to be dehydrated.Key words: dehydration, exercise, hydration.

Damage Detectability Model of Pitch-Catch Configuration in Composite Plates

2017 ◽  
Vol 754 ◽  
pp. 387-390 ◽  
Author(s):  
Nan Yue ◽  
Zahra Sharif Khodaei ◽  
Ferri M.H. Aliabadi
Keyword(s):  
Wave Propagation ◽  
Damage Detection ◽  
Health Monitoring ◽  
Lamb Waves ◽  
Composite Plates ◽  
Detection Algorithm ◽  
Probability Of Detection ◽  
Operational Conditions ◽  
Proposed Model ◽  
Lamb Wave Propagation

Detectability of damage using Lamb waves depends on many factors such as size and severity of damage, attenuation of the wave and distance to the transducers. This paper presents a detectability model for pitch-catch sensors configuration for structural health monitoring (SHM) applications. The proposed model considers the physical properties of lamb wave propagation and is independent of damage detection algorithm, which provides a generic solution for probability of detection. The applicability of the model in different environmental and operational conditions is also discussed.

scholarly journals Pest Animal's Detection, and Habitat Identification in Low-resolution Airborne Thermal Imagery

2020 ◽  
Author(s):  
Anwaar Ulhaq
Keyword(s):  
Invasive Species ◽  
High Resolution ◽  
Object Detection ◽  
Visual Analysis ◽  
Detection System ◽  
Detection Algorithm ◽  
Probability Of Detection ◽  
Small Object ◽  
Pest Species ◽  
Thermal Imagery

Invasive species are significant threats to global agriculture and food security being the major causes of crop loss. An operative biosecurity policy requires full automation of detection and habitat identification of the potential pests and pathogens. Unmanned Aerial Vehicles (UAVs) mounted thermal imaging cameras can observe and detect pest animals and their habitats, and estimate their population size around the clock. However, their effectiveness becomes limited due to manual detection of cryptic species in hours of captured flight videos, failure in habitat disclosure and the requirement of expensive high-resolution cameras. Therefore, the cost and efficiency trade-off often restricts the use of these systems. In this paper, we present an invasive animal species detection system that uses cost-effectiveness of consumer-level cameras while harnessing the power of transfer learning and an optimised small object detection algorithm. Our proposed optimised object detection algorithm named Optimised YOLO (OYOLO) enhances YOLO (You Only Look Once) by improving its training and structure for remote detection of elusive targets. Our system, trained on the massive data collected from New South Wales and Western Australia, can detect invasive species (rabbits, Kangaroos and pigs) in real-time with a higher probability of detection (85–100 %), compared to the manual detection. This work will enhance the visual analysis of pest species while performing well on low, medium and high-resolution thermal imagery, and equally accessible to all stakeholders and end-users in Australia via a public cloud.

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