Validation of Polarimetric Hail Detection

2006 ◽  
Vol 21 (5) ◽  
pp. 839-850 ◽  
Author(s):  
Pamela L. Heinselman ◽  
Alexander V. Ryzhkov
Keyword(s):  
Confidence Level ◽  
Statistical Significance ◽  
Ground Truth ◽  
Heavy Rain ◽  
Detection Algorithm ◽  
Probability Of Detection ◽  
Superior Performance ◽  
False Alarms ◽  
Statistical Significance Testing ◽  
Light Rain

Abstract This study describes, illustrates, and validates hail detection by a simplified version of the National Severe Storms Laboratory’s fuzzy logic polarimetric hydrometeor classification algorithm (HCA). The HCA uses four radar variables: reflectivity, differential reflectivity, cross-correlation coefficient, and “reflectivity texture” to classify echoes as rain mixed with hail, ground clutter–anomalous propagation, biological scatterers (insects, birds, and bats), big drops, light rain, moderate rain, and heavy rain. Diagnostic capabilities of HCA, such as detection of hail, are illustrated for a variety of storm environments using polarimetric radar data collected mostly during the Joint Polarimetric Experiment (JPOLE; 28 April–13 June 2003). Hail classification with the HCA is validated using 47 rain and hail reports collected by storm-intercept teams during JPOLE. For comparison purposes, probability of hail output from the Next-Generation Weather Radar Hail Detection Algorithm (HDA) is validated using the same ground truth. The anticipated polarimetric upgrade of the Weather Surveillance Radar-1988 Doppler network drives this direct comparison of performance. For the four examined cases, contingency table statistics show that the HCA outperforms the HDA. The superior performance of the HCA results primary from the algorithm’s lack of false alarms compared to the HDA. Statistical significance testing via bootstrapping indicates that differences in the probability of detection and critical success index between the algorithms are statistically significant at the 95% confidence level, whereas differences in the false alarm rate and Heidke skill score are statistically significant at the 90% confidence level.

scholarly journals Bayesian characterization of uncertainty in species interaction strengths

2015 ◽  
Author(s):  
Christopher Wolf ◽  
Mark Novak ◽  
Alix I. Gitelman
Keyword(s):  
Species Interactions ◽  
Statistical Significance ◽  
Interaction Strength ◽  
Species Interaction ◽  
Superior Performance ◽  
Empirical Estimation ◽  
Statistical Significance Testing ◽  
Estimation Uncertainty ◽  
Point Estimates

Considerable effort has been devoted to the empirical estimation of species interaction strengths. This effort has focused primarily on statistical significance testing and on obtaining point estimates of parameters that contribute to interaction strength magnitude, leaving characterizations of estimation uncertainty and distinctions between the deterministic and stochastic contributions to variation largely unconsidered. Here we consider a means of quantifying interaction strength uncertainty by formulating an observational method for estimating per capita attack rates as a Bayesian statistical model. This formulation permits the explicit incorporation of multiple sources of uncertainty. In doing so we highlight the informative nature of several so-called non-informative prior choices in modeling the sparse data typical of predator feeding surveys and provide evidence for the superior performance of a new neutral prior choice. A case study application shows that while Bayesian point estimates may be made to correspond with those obtained by frequentist approaches, estimation uncertainty as described by the 95% intervals is more biologically realistic using the Bayesian method in that the lower bounds of the Bayesian posterior intervals for the attack rates do not include zero when the occurrence of a given predator-prey interaction is in fact observed. This contrasts with bootstrap confidence intervals that often do contain zero in such cases. The Bayesian approach provides a straightforward, probabilistic characterization of interaction strength uncertainty. In doing so it provides a framework for considering both the deterministic and stochastic drivers of species interactions and their impact on food web dynamics.

scholarly journals Nonreference Medical Image Edge Map Measure

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Karen Panetta ◽  
Chen Gao ◽  
Sos Agaian ◽  
Shahan Nercessian
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Medical Image ◽  
Visual Analysis ◽  
Medical Image Processing ◽  
Ground Truth ◽  
Detection Algorithm ◽  
Reconstructed Image ◽  
False Alarms ◽  
Original Image

Edge detection is a key step in medical image processing. It is widely used to extract features, perform segmentation, and further assist in diagnosis. A poor quality edge map can result in false alarms and misses in cancer detection algorithms. Therefore, it is necessary to have a reliable edge measure to assist in selecting the optimal edge map. Existing reference based edge measures require a ground truth edge map to evaluate the similarity between the generated edge map and the ground truth. However, the ground truth images are not available for medical images. Therefore, a nonreference edge measure is ideal for medical image processing applications. In this paper, a nonreference reconstruction based edge map evaluation (NREM) is proposed. The theoretical basis is that a good edge map keeps the structure and details of the original image thus would yield a good reconstructed image. The NREM is based on comparing the similarity between the reconstructed image with the original image using this concept. The edge measure is used for selecting the optimal edge detection algorithm and optimal parameters for the algorithm. Experimental results show that the quantitative evaluations given by the edge measure have good correlations with human visual analysis.

scholarly journals Overview of the New Version 3 NASA Micro-Pulse Lidar Network (MPLNET) Automatic Precipitation Detection Algorithm

2019 ◽  
Vol 12 (1) ◽  
pp. 71 ◽  
Author(s):  
Simone Lolli ◽  
Gemine Vivone ◽  
Jasper R. Lewis ◽  
Michaël Sicard ◽  
Ellsworth J. Welton ◽  
...  
Keyword(s):  
Climate Models ◽  
Hydrological Cycle ◽  
Global Climate ◽  
Heavy Rain ◽  
Detection Algorithm ◽  
Processing Technique ◽  
Global Climate Models ◽  
Mitigation Strategies ◽  
Image Processing Technique ◽  
Light Rain

Precipitation modifies atmospheric column thermodynamics through the process of evaporation and serves as a proxy for latent heat modulation. For this reason, a correct precipitation parameterization (especially for low-intensity precipitation) within global scale models is crucial. In addition to improving our modeling of the hydrological cycle, this will reduce the associated uncertainty of global climate models in correctly forecasting future scenarios, and will enable the application of mitigation strategies. In this manuscript we present a proof of concept algorithm to automatically detect precipitation from lidar measurements obtained from the National Aeronautics and Space Administration Micropulse lidar network (MPLNET). The algorithm, once tested and validated against other remote sensing instruments, will be operationally implemented into the network to deliver a near real time (latency <1.5 h) rain masking variable that will be publicly available on MPLNET website as part of the new Version 3 data products. The methodology, based on an image processing technique, detects only light precipitation events (defined by intensity and duration) such as light rain, drizzle, and virga. During heavy rain events, the lidar signal is completely extinguished after a few meters in the precipitation or it is unusable because of water accumulated on the receiver optics. Results from the algorithm, in addition to filling a gap in light rain, drizzle, and virga detection by radars, are of particular interest for the scientific community as they help to fully characterize the aerosol cycle, from emission to deposition, as precipitation is a crucial meteorological phenomenon accelerating atmospheric aerosol removal through the scavenging effect. Algorithm results will also help the understanding of long term aerosol–cloud interactions, exploiting the multi-year database from several MPLNET permanent observational sites across the globe. The algorithm is also applicable to other lidar and/or ceilometer network infrastructures in the framework of the Global Aerosol Watch (GAW) aerosol lidar observation network (GALION).

scholarly journals Evaluation of CMPA precipitation estimate in the evolution of typhoon-related storm rainfall in Guangdong, China

2016 ◽  
Vol 18 (6) ◽  
pp. 1055-1068 ◽  
Author(s):  
Dashan Wang ◽  
Xianwei Wang ◽  
Lin Liu ◽  
Dagang Wang ◽  
Huabing Huang ◽  
...  
Keyword(s):  
Correlation Coefficients ◽  
Extreme Rainfall ◽  
Heavy Rain ◽  
Rain Gauge ◽  
Probability Of Detection ◽  
Spatiotemporal Evolution ◽  
Light Rain ◽  
Storm Rainfall ◽  
Climate Prediction Center ◽  
Rain Gauge Network

The merged precipitation data of Climate Prediction Center Morphing Technique and gauge observations (CMPA) generated for continental China has relatively high spatial and temporal resolution (hourly and 0.1°), while few studies have applied it to investigate the typhoon-related extreme rainfall. This study evaluates the CMPA estimate in quantifying the typhoon-related extreme rainfall using a dense rain gauge network in Guangdong Province, China. The results show that the event-total precipitation from CMPA is generally in agreement with gauges by relative bias (RB) of 2.62, 10.74 and 0.63% and correlation coefficients (CCs) of 0.76, 0.86 and 0.91 for typhoon Utor, Usagi and Linfa events, respectively. At the hourly scale, CMPA underestimates the occurrence of light rain (&lt;1 mm/h) and heavy rain (&gt;16 mm/h), while overestimates the occurrence of moderate rain. CMPA shows high probability of detection (POD = 0.93), relatively large false alarm ratio (FAR = 0.22) and small missing ratio (0.07). CMPA captures the spatial patterns of typhoon-related rain depth, and is in agreement with the spatiotemporal evolution of hourly gauge observations by CC from 0.93 to 0.99. In addition, cautiousness should be taken when applying it in hydrologic modeling for flooding forecasting since CMPA underestimates heavy rain (&gt;16 mm/h).

scholarly journals Bayesian characterization of uncertainty in species interaction strengths

2015 ◽  
Author(s):  
Christopher Wolf ◽  
Mark Novak ◽  
Alix I. Gitelman
Keyword(s):  
Species Interactions ◽  
Statistical Significance ◽  
Interaction Strength ◽  
Species Interaction ◽  
Superior Performance ◽  
Empirical Estimation ◽  
Statistical Significance Testing ◽  
Estimation Uncertainty ◽  
Point Estimates

Considerable effort has been devoted to the empirical estimation of species interaction strengths. This effort has focused primarily on statistical significance testing and on obtaining point estimates of parameters that contribute to interaction strength magnitude, leaving characterizations of estimation uncertainty and distinctions between the deterministic and stochastic contributions to variation largely unconsidered. Here we consider a means of quantifying interaction strength uncertainty by formulating an observational method for estimating per capita attack rates as a Bayesian statistical model. This formulation permits the explicit incorporation of multiple sources of uncertainty. In doing so we highlight the informative nature of several so-called non-informative prior choices in modeling the sparse data typical of predator feeding surveys and provide evidence for the superior performance of a new neutral prior choice. A case study application shows that while Bayesian point estimates may be made to correspond with those obtained by frequentist approaches, estimation uncertainty as described by the 95% intervals is more biologically realistic using the Bayesian method in that the lower bounds of the Bayesian posterior intervals for the attack rates do not include zero when the occurrence of a given predator-prey interaction is in fact observed. This contrasts with bootstrap confidence intervals that often do contain zero in such cases. The Bayesian approach provides a straightforward, probabilistic characterization of interaction strength uncertainty. In doing so it provides a framework for considering both the deterministic and stochastic drivers of species interactions and their impact on food web dynamics.

scholarly journals Performance Assessment of Sub-Daily and Daily Precipitation Estimates Derived from GPM and GSMaP Products over an Arid Environment

2019 ◽  
Vol 11 (23) ◽  
pp. 2840 ◽  
Author(s):  
Mohamed Shawky ◽  
Adel Moussa ◽  
Quazi K Hassan ◽  
Naser El-Sheimy
Keyword(s):  
Flash Flood ◽  
Heavy Rain ◽  
Arid Environment ◽  
Probability Of Detection ◽  
Absolute Difference ◽  
Spatial And Temporal Patterns ◽  
Light Rain ◽  
Precipitation Estimates ◽  
Precipitation Records

Precipitation is a critical variable for comprehending various climate-related research, such as water resources management, flash flood monitoring and forecasting, climatic analyses, and hydrogeological studies, etc. Here, our objective was to evaluate the rainfall estimates obtained from Global Precipitation Mission (GPM), and Global Satellite Mapping of Precipitation (GSMaP) constellation over an arid environment like the Sultanate of Oman that is characterized by a complex topography and extremely variable rainfall patterns. Global Satellite-based Precipitation Estimates (GSPEs) can provide wide coverage and high spatial and temporal resolutions, but evaluating their accuracy is a mandatory step before involving them in different hydrological applications. In this paper, the reliability of the Integrated Multi-satellitE Retrievals for the GPM (IMERG) V04 and GSMaP V06 products were evaluated using the reference in-situ rain gauges at sub-daily (e.g., 6, 12, and 18 h) and daily time scales during the period of March 2014–December 2016. A set of continuous difference statistical indices (e.g., mean absolute difference, root mean square error, mean difference, and unconditional bias), and categorical metrics (e.g., probability of detection, critical success index, false alarm ratio, and frequency bias index) were used to evaluate recorded precipitation occurrences. The results showed that the five GSPEs could generally delineate the spatial and temporal patterns of rainfall while they might have over- and under-estimations of in-situ gauge measurements. The overall quality of the GSMaP runs was superior to the IMERG products; however, it also encountered an exaggeration in case of light rain and an underestimation for heavy rain. The effects of the gauge calibration algorithm (GCA) used in the final IMERG (IMERG-F) were investigated by comparison with early and late runs. The IMERG-F V04 product did not show a significant improvement over the early (i.e., after 4 h of rainfall observations) and late (i.e., after 12 h of rainfall observations) products. The results indicated that GCA could not reduce the missed precipitation records considerably.

Preliminary Assessment of GPM Satellite Rainfall over Myanmar

2018 ◽  
Vol 13 (1) ◽  
pp. 22-30 ◽  
Author(s):  
Muhammad Mohsan ◽  
Ralph Allen Acierto ◽  
Akiyuki Kawasaki ◽  
Win Win Zin ◽  
◽  
...  
Keyword(s):  
Daily Rainfall ◽  
Heavy Rain ◽  
Probability Of Detection ◽  
Light Rain ◽  
Precipitation Measurement ◽  
Observation Network ◽  
Satellite Rainfall ◽  
Global Precipitation Measurement ◽  
Global Precipitation ◽  
Statistical Metrics

Intensive and long-term rainfall in Myanmar causes floods and landslides that affect thousands of people every year. However, the rainfall observation network is still limited in number and extent, so satellite rainfall products have been shown to supplement observations over the ungauged areas. One example is the estimates from Global Precipitation Measurement (GPM) called Integrated Multi-satellite Retrievals for GPM (IMERG), which has high spatial (0.1 × 0.1 degree) and temporal (30 min) resolution. This has potential to be used for modeling streamflow, early warnings, and forecasting systems. This study investigates the utility of these GPM satellite estimates for representing the daily rainfall for 25 rain gauges over Myanmar. Statistical metrics were used to understand the characteristic performance of the GPM satellite estimates. Daily rainfall estimates from GPM show a range of 29.3% to 81.1% probability of detection (POD). The satellite estimates show a capability of detecting no-rain days between 61.4 and 93.5%. For different rainfall intensities, the satellite estimates have a 12.9 to 39.1% POD for light rain (1–10 mm/day), 11.1 to 49% POD for moderate rain (10–50 mm/day), a maximum of 36% for heavy rain (50–150 mm/day), and a maximum of 12.5% for extreme rain (=150 mm/day). However, the correlation coefficient (CC) only ranges from 0.064 to 0.581, which is considered low, and is not uniform for all the stations. The highest CC scores and POD scores tend to be located in the northern part and deltaic region extending to the southern coasts in Myanmar, indicating a dependency of the statistical metrics on rainfall magnitude. The high POD scores indicate the utility of the estimates without correction for early warning purposes, but the estimates have low reliability for rainfall intensity. The satellite estimates can be used for forecasting and modeling purposes in the region, but the estimates require bias-correction before application.

scholarly journals Combined Dust Detection Algorithm for Asian Dust Events Over East Asia Using GK2A/AMI: a Case Study in October 2019

2021 ◽  
Author(s):  
Jae-Cheol Jang ◽  
Soobong Lee ◽  
Eun-Ha Sohn ◽  
Yoo-Jeong Noh ◽  
Steven D. Miller
Keyword(s):  
Detection Algorithm ◽  
Low Earth Orbit ◽  
Probability Of Detection ◽  
Dust Storms ◽  
Detection Methods ◽  
False Alarms ◽  
Monitoring Methods ◽  
Dust Detection ◽  
Ground Based Lidar ◽  
Combined Algorithm

AbstractA combined algorithm comprising multiple dust detection methods was developed using infrared (IR) channels onboard the GEOstationary Korea Multi-Purpose SATellite 2A equipped with the Advanced Meteorological Imager (GK2A/AMI). Six cloud tests using brightness temperature difference (BTD) were utilized to reduce errors caused by clouds. For detecting dust storms, three standard BTD tests (i.e., $${BT}_{12.3}-{BT}_{10.5}$$, $${BT}_{8.7}-{BT}_{10.5}$$, and $${BT}_{11.2}-{BT}_{10.5}$$) were combined with the polarized optical depth index (PODI). The combined algorithm normalizes the indices for cloud and dust detection, and adopts weighted combinations of dust tests depending on the observation time (day/night) and surface type (land/sea). The dust detection results were produced as quantitative confidence factors and displayed as false color imagery, applying a dynamic enhancement background reduction algorithm (DEBRA). The combined dust detection algorithm was qualitatively assessed by comparing it with dust RGB imageries and ground-based lidar data. The combined algorithm especially improved the discontinuity in weak dust advection to the sea and considerably reduced false alarms as compared to previous dust monitoring methods. For quantitative validation, we used aerosol optical thickness (AOT) and fine mode fraction (FMF) derived from low Earth orbit (LEO) satellites in daytime. For both severe and weakened dust cases, the probability of detection (POD) ranged from 0.667 to 0.850 and it indicated that the combined algorithm detects more potential dust pixels than other satellites. In particular, the combined algorithm was advantageous in detecting weak dust storms passing over the warm and humid Yellow Sea with low dust height and small AOT.

scholarly journals A Comprehensive Evaluation of Latest GPM IMERG V06 Early, Late and Final Precipitation Products across China

2021 ◽  
Vol 13 (6) ◽  
pp. 1208
Author(s):  
Linfei Yu ◽  
Guoyong Leng ◽  
Andre Python ◽  
Jian Peng
Keyword(s):  
Comprehensive Evaluation ◽  
Probability Of Detection ◽  
Light Rain ◽  
Spatial And Temporal Scales ◽  
Rain Gauges ◽  
Precipitation Estimation ◽  
Major River ◽  
Low Levels ◽  
Rain Events ◽  
Early Late

This study evaluated the performance of the early, late and final runs of IMERG version 06 precipitation products at various spatial and temporal scales in China from 2008 to 2017, against observations from 696 rain gauges. The results suggest that the three IMERG products can well reproduce the spatial patterns of precipitation, but exhibit a gradual decrease in the accuracy from the southeast to the northwest of China. Overall, the three runs show better performances in the eastern humid basins than the western arid basins. Compared to the early and late runs, the final run shows an improvement in the performance of precipitation estimation in terms of correlation coefficient, Kling–Gupta Efficiency and root mean square error at both daily and monthly scales. The three runs show similar daily precipitation detection capability over China. The biases of the three runs show a significantly positive (p < 0.01) correlation with elevation, with higher accuracy observed with an increase in elevation. However, the categorical metrics exhibit low levels of dependency on elevation, except for the probability of detection. Over China and major river basins, the three products underestimate the frequency of no/tiny rain events (P < 0.1 mm/day) but overestimate the frequency of light rain events (0.1 ≤ P < 10 mm/day). The three products converge with ground-based observation with regard to the frequency of rainstorm (P ≥ 50 mm/day) in the southern part of China. The revealed uncertainties associated with the IMERG products suggests that sustaining efforts are needed to improve their retrieval algorithms in the future.

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