scholarly journals Solid hydrometeor classification and riming degree estimation from pictures collected with a Multi-Angle Snowflake Camera

2017 ◽  
Author(s):  
Christophe Praz ◽  
Yves-Alain Roulet ◽  
Alexis Berne
Keyword(s):  
High Performance ◽  
Multinomial Logistic Regression ◽  
Skill Score ◽  
Swiss Alps ◽  
Second Step ◽  
Probabilistic Information ◽  
Heidke Skill Score ◽  
Classification Tasks ◽  
Type Classification ◽  
Mlr Model

Abstract. A new method to automatically classify solid hydrometeors based on a Multi-Angle Snowflake Camera (MASC) images is presented. For each individual image, the method relies on the calculation of a set of geometric and texture-based descriptors to simultaneously identify the hydrometeor type (among six predefined classes), estimate the degree of riming and detect melting snow. The classification tasks are achieved by means of a regularized multinomial logistic regression (MLR) model trained over more than 3000 MASC images manually labeled by visual inspection. In a second step, the probabilistic information provided by the MLR is weighed on the three stereoscopic views of the MASC in order to assign a unique label to each hydrometeor. The accuracy and robustness of the proposed algorithm is evaluated on data collected in the Swiss Alps and in Antarctica. The algorithm achieves high performance, with a hydrometeor type classification accuracy and Heidke skill score of 95 % and 0.93, respectively. The degree of riming is evaluated by introducing a riming index ranging between zero (no riming) and one (graupel), and characterized by a probable error of 5.3 %. A validation study is conducted through a comparison with an existing classification method based on two-dimensional video disdrometer (2DVD) data and shows that the two methods are consistent.

scholarly journals Solid hydrometeor classification and riming degree estimation from pictures collected with a Multi-Angle Snowflake Camera

2017 ◽  
Vol 10 (4) ◽  
pp. 1335-1357 ◽  
Author(s):  
Christophe Praz ◽  
Yves-Alain Roulet ◽  
Alexis Berne
Keyword(s):  
High Performance ◽  
Multinomial Logistic Regression ◽  
Skill Score ◽  
Swiss Alps ◽  
Second Step ◽  
Probabilistic Information ◽  
Heidke Skill Score ◽  
Classification Tasks ◽  
Type Classification ◽  
Mlr Model

Abstract. A new method to automatically classify solid hydrometeors based on Multi-Angle Snowflake Camera (MASC) images is presented. For each individual image, the method relies on the calculation of a set of geometric and texture-based descriptors to simultaneously identify the hydrometeor type (among six predefined classes), estimate the degree of riming and detect melting snow. The classification tasks are achieved by means of a regularized multinomial logistic regression (MLR) model trained over more than 3000 MASC images manually labeled by visual inspection. In a second step, the probabilistic information provided by the MLR is weighed on the three stereoscopic views of the MASC in order to assign a unique label to each hydrometeor. The accuracy and robustness of the proposed algorithm is evaluated on data collected in the Swiss Alps and in Antarctica. The algorithm achieves high performance, with a hydrometeor-type classification accuracy and Heidke skill score of 95 % and 0.93, respectively. The degree of riming is evaluated by introducing a riming index ranging between zero (no riming) and one (graupel) and characterized by a probable error of 5.5 %. A validation study is conducted through a comparison with an existing classification method based on two-dimensional video disdrometer (2DVD) data and shows that the two methods are consistent.

scholarly journals Identification of blowing snow particles in images from a multi-angle snowflake camera

2018 ◽  
Author(s):  
Mathieu Schaer ◽  
Christophe Praz ◽  
Alexis Berne
Keyword(s):  
Austral Summer ◽  
Gaussian Mixture ◽  
Second Step ◽  
Blowing Snow ◽  
Kappa Score ◽  
Probabilistic Information ◽  
Complex Interactions ◽  
Field Campaigns ◽  
Number Of Particles ◽  
Image Frequency

Abstract. A new method to automatically discriminate between hydrometeors and blowing snow particles on Multi-Angle Snowflake Camera (MASC) images is introduced. The method uses four selected descriptors related to the image frequency, the number of particles detected per image as well as their size and geometry to classify each individual image. The classification task is achieved with a two components Gaussian Mixture Model fitted on a subset of representative images of each class from field campaigns in Antarctica and Davos, Switzerland. The performance is evaluated by labelling the subset of images on which the model was fitted. An overall accuracy and Cohen's Kappa score of 99.4 and 98.8 %, respectively, is achieved. In a second step, the probabilistic information is used to flag images composed of a mix of blowing snow particles and hydrometeors, which turns out to occur frequently. The percentage of images belonging to each class from an entire austral summer in Antartica and during a winter in Davos, respectively, are presented. The capability to distinguish precipitation, blowing snow and a mix of those in MASC images is highly relevant to disentangle the complex interactions between wind, snowflakes and snowpack close to the surface.

Livelihood Assets and Income Generating Activities: A Comparative Analysis in the Scheduled and Non-Scheduled Areas of Jharkhand

2021 ◽  
pp. 097370302110649
Author(s):  
Ashish Aman Sinha ◽  
Hari Charan Behera ◽  
Ajit Kumar Behura ◽  
Amiya Kumar Sahoo ◽  
Utpal Kumar De
Keyword(s):  
Latent Class ◽  
Multinomial Logistic Regression ◽  
Primary Source ◽  
Animal Husbandry ◽  
Livelihood Assets ◽  
Income Generating Activities ◽  
Daily Wage ◽  
Activity Cluster ◽  
Mlr Model

The main objective of the article is to identify different types of livelihood assets, income generating activities (IGAs) and choices of these activities by households across social groups in the Fifth and non-Fifth Scheduled areas of Jharkhand in eastern India. It is based on a primary survey of 785 households randomly selected across caste and Scheduled Tribe groups in Giridih and Latehar districts of Jharkhand. K-means clustering is applied for determination of latent class activity clusters and Multinomial Logistic Regression (MLR) model used for understanding the importance of livelihood assets in determining livelihood activity cluster (LC) for income generation. Further, discriminant analysis is applied to obtain probability of choice of individual households in determining livelihood generating activity. The analysis shows that forest-based activity remains a better livelihood support system in the Fifth Scheduled areas, which is less significant and further diminishing in the non-Fifth Scheduled areas. Rural households engaged in a diverse set of IGAs to obtain additional income to reduce risk and maintain a balanced consumption. Occupational transition is marked by the decline of agriculture and increasing reliance on daily-wage activities as the primary source of income. Other traditional livelihood activities such as animal husbandry and the collection of forest produce have less scope for income in the absence of institutional support.

Large-Sample Application of Radar Reflectivity Object-Based Verification to Evaluate HRRR Warm-Season Forecasts

2021 ◽  
Author(s):  
Jeffrey D. Duda ◽  
David D. Turner
Keyword(s):  
Warm Season ◽  
Skill Score ◽  
Central Plains ◽  
Attribute Weights ◽  
Object Based ◽  
Convective Scale ◽  
Heidke Skill Score ◽  
Sample Application ◽  
Storm Cells ◽  
The Impact

AbstractThe Method of Object-based Diagnostic Evaluation (MODE) is used to perform an object-based verification of approximately 1400 forecasts of composite reflectivity from the operational HRRR from April – September 2019. In this study, MODE is configured to prioritize deep, moist convective storm cells typical of those that produce severe weather across the central and eastern US during the warm season. In particular, attributes related to distance and size are given the greatest attribute weights for computing interest in MODE.HRRR tends to over-forecast all objects, but substantially over-forecasts both small objects at low reflectivity thresholds and large objects at high reflectivity thresholds. HRRR tends to either under-forecast objects in the southern and central Plains or has a correct frequency bias there, whereas it over-forecasts objects across the southern and eastern US. Attribute comparisons reveal the inability of the HRRR to fully resolve convective scale features and the impact of data assimilation and loss of skill during the initial hours of the forecasts.Scalar metrics are defined and computed based on MODE output, chiefly relying on the interest value. The object-based threat score (OTS), in particular, reveals similar performance of HRRR forecasts as does the Heidke Skill Score, but with differing magnitudes, suggesting value in adopting an object-based approach to forecast verification. The typical distance between centroids of objects is also analyzed and shows gradual degradation with increasing forecast length.

scholarly journals Improving Topic Models with Latent Feature Word Representations

2015 ◽  
Vol 3 ◽  
pp. 299-313 ◽  
Author(s):  
Dat Quoc Nguyen ◽  
Richard Billingsley ◽  
Lan Du ◽  
Mark Johnson
Keyword(s):  
High Performance ◽  
Feature Vector ◽  
Topic Models ◽  
Document Collections ◽  
Probabilistic Topic Models ◽  
New Models ◽  
Classification Tasks ◽  
Latent Topics ◽  
Vector Representations ◽  
Feature Word

Probabilistic topic models are widely used to discover latent topics in document collections, while latent feature vector representations of words have been used to obtain high performance in many NLP tasks. In this paper, we extend two different Dirichlet multinomial topic models by incorporating latent feature vector representations of words trained on very large corpora to improve the word-topic mapping learnt on a smaller corpus. Experimental results show that by using information from the external corpora, our new models produce significant improvements on topic coherence, document clustering and document classification tasks, especially on datasets with few or short documents.

Four Years of Tropical ERA-40 Vorticity Maxima Tracks. Part II: Differences between Developing and Nondeveloping Disturbances

2009 ◽  
Vol 137 (8) ◽  
pp. 2576-2591 ◽  
Author(s):  
Brandon Kerns ◽  
Edward Zipser
Keyword(s):  
Large Scale ◽  
Deep Convection ◽  
Vertical Wind Shear ◽  
Skill Score ◽  
Relative Vorticity ◽  
Prediction Skill ◽  
Tropical Cyclogenesis ◽  
Linear Discriminant ◽  
Predictive Skill ◽  
Heidke Skill Score

Abstract Using a subset of the relative vorticity maxima (VM) tracks described in Part I, large-scale environmental fields, cold cloud area, and rainfall area are used to discriminate between developing and nondeveloping tropical disturbances in the eastern North Pacific (EPAC) and Atlantic Oceans. By using a minimum cold cloud coverage requirement, the nondeveloping VM are limited to disturbances with enhanced low-level relative vorticity and widespread deep convection. Linear discriminant analysis is used to determine the overall discrimination and the relative importance of each predictor for each basin separately. It is important to distinguish the two basins because, for many predictors, the differences between the basins are greater than the differences between developing and nondeveloping VM in each basin. Using the parametric forecast method, there is greater discrimination and prediction skill in the EPAC than in the Atlantic. There are also significant differences between the two basins in terms of the degree of discrimination provided by each of the predictors. Surprisingly, the mean vertical wind shear magnitude is greater for EPAC developing VM than for EPAC nondeveloping VM. Incorporating the satellite-derived predictors marginally improves the potential forecast skill in the EPAC but not in the Atlantic. The prediction skill (Heidke skill score) of tropical cyclogenesis in the Atlantic is similar to what has been obtained in previous studies using cloud cluster tracks. There is greater predictive skill in the EPAC.

scholarly journals A Probabilistic Derivation of Heidke Skill Score

2014 ◽  
Vol 29 (1) ◽  
pp. 177-181 ◽  
Author(s):  
Otto Hyvärinen
Keyword(s):  
Skill Score ◽  
Base Rate ◽  
Harmonic Mean ◽  
Posterior Probabilities ◽  
Alternative Derivation ◽  
Prior Probabilities ◽  
Maximum Value ◽  
Heidke Skill Score ◽  
The Difference

Abstract An alternative derivation of Heidke skill score for 2 × 2 tables is presented, starting from the assumption that a categorical forecast is useful, if the probability of an occurrence of an event, given the forecast, is greater than the base rate of the event. A tentative measure of skill would then be the difference of these probabilities, normalized by the maximum value based on the base rate. For binary events, the Heidke skill score is then the harmonic mean of these differences for both the occurrence and the nonoccurrence of the event. This derivation differs from the usual derivation in that the concept of chance agreement is not used. It is Bayesian in nature with implied updating of prior probabilities to posterior probabilities.

scholarly journals Estimation of Seasonal Precipitation Tercile-Based Categorical Probabilities from Ensembles

2007 ◽  
Vol 20 (10) ◽  
pp. 2210-2228 ◽  
Author(s):  
Michael K. Tippett ◽  
Anthony G. Barnston ◽  
Andrew W. Robertson
Keyword(s):  
General Circulation ◽  
Good Description ◽  
Circulation Model ◽  
Skill Score ◽  
Seasonal Precipitation ◽  
Distribution Fitting ◽  
Probabilistic Information ◽  
Analytical Results ◽  
Weak Factor ◽  
Mean And Variance

Abstract Ensemble simulations and forecasts provide probabilistic information about the inherently uncertain climate system. Counting the number of ensemble members in a category is a simple nonparametric method of using an ensemble to assign categorical probabilities. Parametric methods of assigning quantile-based categorical probabilities include distribution fitting and generalized linear regression. Here the accuracy of counting and parametric estimates of tercile category probabilities is compared. The methods are first compared in an idealized setting where analytical results show how ensemble size and level of predictability control the accuracy of both methods. The authors also show how categorical probability estimate errors degrade the rank probability skill score. The analytical results provide a good description of the behavior of the methods applied to seasonal precipitation from a 53-yr, 79-member ensemble of general circulation model simulations. Parametric estimates of seasonal precipitation tercile category probabilities are generally more accurate than the counting estimate. In addition to determining the relative accuracies of the different methods, the analysis quantifies the relative importance of the ensemble mean and variance in determining tercile probabilities. Ensemble variance is shown to be a weak factor in determining seasonal precipitation probabilities, meaning that differences between the tercile probabilities and the equal-odds probabilities are due mainly to shifts of the forecast mean away from its climatological value.

Using NWP Simulations in Satellite Rainfall Estimation of Heavy Precipitation Events over Mountainous Areas

2013 ◽  
Vol 14 (6) ◽  
pp. 1844-1858 ◽  
Author(s):  
Xinxuan Zhang ◽  
Emmanouil N. Anagnostou ◽  
Maria Frediani ◽  
Stavros Solomos ◽  
George Kallos
Keyword(s):  
Heavy Precipitation ◽  
Skill Score ◽  
Mountainous Areas ◽  
Error Statistics ◽  
Radar Rainfall ◽  
Adjustment Procedure ◽  
Heidke Skill Score ◽  
Satellite Rainfall ◽  
Rain Rates ◽  
Precipitation Events

Abstract In this study, the authors investigate the use of high-resolution simulations from the Weather Research and Forecasting Model (WRF) for evaluating satellite rainfall biases of flood-inducing storms in mountainous areas. A probability matching approach is applied to evaluate a power-law relationship between satellite-retrieved and WRF-simulated rain rates over the storm domain. Satellite rainfall in this study is from the NOAA Climate Prediction Center morphing technique (CMORPH). Results are presented based on analyses of five heavy precipitation events that induced flash floods in northern Italy and southern France complex terrain basins. The WRF-based adjusted CMORPH rain rates exhibited improved error statistics against independent radar rainfall estimates. The authors show that the adjustment procedure reduces the underestimation of high rain rates, thus moderating the magnitude dependence of CMORPH rainfall bias. The Heidke skill score for the WRF-based adjusted CMORPH was consistently higher for a range of rain rate thresholds. This is an indication that the adjustment procedure ameliorates the satellite rain rates to provide a better estimation. Results also indicate that the low rain detection of CMORPH technique is also identifiable in the WRF–CMORPH comparison; however, the adjustment procedure herein does not incorporate this effect on the satellite rainfall bias adjustment.

scholarly journals Electrodeposition Combination with Hydrothermal Preparation of ZnO Films and Their Application in Dye-Sensitized Solar Cell

2014 ◽  
Vol 2014 ◽  
pp. 1-5
Author(s):  
Xiaoping Zou ◽  
Yan Liu ◽  
Cuiliu Wei ◽  
Zongbo Huang ◽  
Xiangmin Meng
Keyword(s):  
High Performance ◽  
Short Circuit ◽  
Short Circuit Current ◽  
Open Circuit ◽  
Suitable Method ◽  
Second Step ◽  
Zno Films ◽  
Hydrothermal Preparation ◽  
Dye Sensitized ◽  
Sensitized Solar Cells

A suitable method is necessary for the high performance of dyes-sensitized solar cells (DSSCs). In this paper, photoanodes of DSSCs have been fabricated through electrodeposition and combination with hydrothermal method. The results of mix method showed better performance than the single one. After the second step electrodeposition, the ZnO films formed flack finally. With the increase of hydrothermal time, ZnO films become thicker and bigger, which can offer large surface area to absorb much more dyes. The short-circuit current (2.4 mA/cm2) and open-circuit voltage (0.67 V) were greater than the single one, alternating current impedance indicating that electrodeposition and hydrothermal mix are a more suitable method for high performance DSSCs. We expected to obtain higher conversion efficiency of DSSCs by this method.

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