scholarly journals A Prognostic Nested k-Nearest Approach for Microwave Precipitation Phase Detection over Snow Cover

2019 ◽  
Vol 20 (2) ◽  
pp. 251-274 ◽  
Author(s):  
Zeinab Takbiri ◽  
Ardeshir Ebtehaj ◽  
Efi Foufoula-Georgiou ◽  
Pierre-Emmanuel Kirstetter ◽  
F. Joseph Turk
Keyword(s):  
Snow Cover ◽  
Nearest Neighbor ◽  
Solid Phase ◽  
A Priori ◽  
Liquid Water Content ◽  
Phase Detection ◽  
Probability Of False Alarm ◽  
Using Data ◽  
Global Precipitation ◽  
Precipitation Phase

Abstract Monitoring changes of precipitation phase from space is important for understanding the mass balance of Earth’s cryosphere in a changing climate. This paper examines a Bayesian nearest neighbor approach for prognostic detection of precipitation and its phase using passive microwave observations from the Global Precipitation Measurement (GPM) satellite. The method uses the weighted Euclidean distance metric to search through an a priori database populated with coincident GPM radiometer and radar observations as well as ancillary snow-cover data. The algorithm performance is evaluated using data from GPM official precipitation products, ground-based radars, and high-fidelity simulations from the Weather Research and Forecasting Model. Using the presented approach, we demonstrate that the hit probability of terrestrial precipitation detection can reach to 0.80, while the probability of false alarm remains below 0.11. The algorithm demonstrates higher skill in detecting snowfall than rainfall, on average by 10%. In particular, the probability of precipitation detection and its solid phase increases by 11% and 8%, over dry snow cover, when compared to other surface types. The main reason is found to be related to the ability of the algorithm in capturing the signal of increased liquid water content in snowy clouds over radiometrically cold snow-covered surfaces.

Estimation of the Time-Dependent Profile of a Melting Front by Inverse Resolution

1997 ◽  
Vol 119 (3) ◽  
pp. 574-578 ◽  
Author(s):  
B. Guerrier ◽  
H. G. Liu ◽  
C. Be´nard
Keyword(s):  
Solid Phase ◽  
Transfer Problem ◽  
Numerical Study ◽  
A Priori ◽  
Inverse Operator ◽  
Melting Front ◽  
Inverse Heat Transfer ◽  
Ill Posed ◽  
Using Data ◽  
Inverse Resolution

The profile and time evolution of a solid/liquid interface in a phase change process is estimated by solving an inverse heat transfer problem, using data measurements in the solid phase only. One then faces the inverse resolution of a heat equation in a variable and a priori unknown 2D domain. This ill-posed problem is solved by a regularization approach: the unknown function (position of the melting front) is obtained by minimization of a two component criterion, consisting of a distance between the output of a simulation model and the measured data, to which a penalizing function is added in order to restore the continuity of the inverse operator. A numerical study is developed to analyze the validity domain of the identification method. From simulation tests, it is shown that the minimum signal/noise ratio that can be handled depends strongly on the position of the measurement sensors.

scholarly journals Quantitative Investigation of Radiometric Interactions between Snowfall, Snow Cover, and Cloud Liquid Water Over Land

2021 ◽  
Author(s):  
Zeinab Takbiri ◽  
Lisa Milani ◽  
Clement Guilloteau ◽  
Efi Foufoula-Georgiou
Keyword(s):  
Snow Cover ◽  
Water Content ◽  
Liquid Water ◽  
Liquid Water Content ◽  
Reanalysis Data ◽  
Liquid Water Path ◽  
Cloud Liquid Water ◽  
Cloud Liquid Water Path ◽  
Microwave Signatures ◽  
Global Precipitation

Falling snow alters its own microwave signatures when it begins to accumulate on the ground making retrieval of precipitation challenging. This paper investigates the effects of snow-cover depth and cloud liquid water content on microwave signatures of terrestrial snowfall using reanalysis data and multi-annual measurements by the Global Precipitation Measurement (GPM) core satellite with particular emphasis on the 89 and 166 GHz channels. It is found that over snow cover shallower than 10 cm and low values of cloud liquid water path (LWP ≤125gm−2), the scattering of light snowfall (<0.5mmh−1) is detectable only at frequency 166 GHz while for higher intensities the signal can be also detected at 89 GHz. However, when snow depth exceeds ∼20 cm and the LWP is greater than ∼125gm−2 , the emission from the increased liquid water content in snowing clouds becomes the only surrogate microwave signal of snowfall that is stronger at frequency 89 GHz than 166 GHz. The results also reveal that over high latitudes above 60∘ N where the snow cover is thicker than 20 cm and LWP is lower than 125 gm−2 the microwave snowfall signal could not be detected with GPM. Our results provide quantitative insights for improving retrieval of snowfall in particular over snow-covered terrain.

scholarly journals Quantitative Investigation of Radiometric Interactions between Snowfall, Snow Cover, and Cloud Liquid Water over Land

2021 ◽  
Vol 13 (13) ◽  
pp. 2641
Author(s):  
Zeinab Takbiri ◽  
Lisa Milani ◽  
Clement Guilloteau ◽  
Efi Foufoula-Georgiou
Keyword(s):  
Snow Cover ◽  
Water Content ◽  
Liquid Water ◽  
Snow Water Equivalent ◽  
A Priori ◽  
Liquid Water Content ◽  
Microwave Signal ◽  
Liquid Water Path ◽  
Cloud Liquid Water ◽  
Microwave Signatures

Falling snow alters its own microwave signatures when it begins to accumulate on the ground, making retrieval of snowfall challenging. This paper investigates the effects of snow-cover depth and cloud liquid water content on microwave signatures of terrestrial snowfall using reanalysis data and multi-annual observations by the Global Precipitation Measurement (GPM) core satellite with particular emphasis on the 89 and 166 GHz channels. It is found that over shallow snow cover (snow water equivalent (SWE) ≤100kg m−2) and low values of cloud liquid water path (LWP 100–150 g m−2), the scattering of light snowfall (intensities ≤0.5mm h−1) is detectable only at frequency 166 GHz, while for higher snowfall rates, the signal can also be detected at 89 GHz. However, when SWE exceeds 200 kg m−2 and the LWP is greater than 100–150 g m−2, the emission from the increased liquid water content in snowing clouds becomes the only surrogate microwave signal of snowfall that is stronger at frequency 89 than 166 GHz. The results also reveal that over high latitudes above 60°N where the SWE is greater than 200 kg m−2 and LWP is lower than 100–150 g m−2, the snowfall microwave signal could not be detected with GPM without considering a priori data about SWE and LWP. Our findings provide quantitative insights for improving retrieval of snowfall in particular over snow-covered terrain.

Perancangan Aplikasi Prediksi Kelulusan Tepat Waktu Bagi Mahasiswa Baru Dengan Teknik Data Mining (Studi Kasus: Data Akademik Mahasiswa STMIK Dipanegara Makassar)

2015 ◽  
Vol 1 (4) ◽  
pp. 270
Author(s):  
Muhammad Syukri Mustafa ◽  
I. Wayan Simpen
Keyword(s):  
Data Mining ◽  
Nearest Neighbor ◽  
Test Results ◽  
K Nearest Neighbor ◽  
Accuracy Rate ◽  
Sample Data ◽  
New Students ◽  
K Nearest Neighbor Algorithm ◽  
Using Data ◽  
Existing Data

Penelitian ini dimaksudkan untuk melakukan prediksi terhadap kemungkian mahasiswa baru dapat menyelesaikan studi tepat waktu dengan menggunakan analisis data mining untuk menggali tumpukan histori data dengan menggunakan algoritma K-Nearest Neighbor (KNN). Aplikasi yang dihasilkan pada penelitian ini akan menggunakan berbagai atribut yang klasifikasikan dalam suatu data mining antara lain nilai ujian nasional (UN), asal sekolah/ daerah, jenis kelamin, pekerjaan dan penghasilan orang tua, jumlah bersaudara, dan lain-lain sehingga dengan menerapkan analysis KNN dapat dilakukan suatu prediksi berdasarkan kedekatan histori data yang ada dengan data yang baru, apakah mahasiswa tersebut berpeluang untuk menyelesaikan studi tepat waktu atau tidak. Dari hasil pengujian dengan menerapkan algoritma KNN dan menggunakan data sampel alumni tahun wisuda 2004 s.d. 2010 untuk kasus lama dan data alumni tahun wisuda 2011 untuk kasus baru diperoleh tingkat akurasi sebesar 83,36%.This research is intended to predict the possibility of new students time to complete studies using data mining analysis to explore the history stack data using K-Nearest Neighbor algorithm (KNN). Applications generated in this study will use a variety of attributes in a data mining classified among other Ujian Nasional scores (UN), the origin of the school / area, gender, occupation and income of parents, number of siblings, and others that by applying the analysis KNN can do a prediction based on historical proximity of existing data with new data, whether the student is likely to complete the study on time or not. From the test results by applying the KNN algorithm and uses sample data alumnus graduation year 2004 s.d 2010 for the case of a long and alumni data graduation year 2011 for new cases obtained accuracy rate of 83.36%.

Recognizing Job Apathy Patterns of Iraqi Higher Education Employees Using Data Mining Techniques

2019 ◽  
Vol 54 (4) ◽  
Author(s):  
Mustafa S. Abd ◽  
Suhad Faisal Behadili
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Psychiatric Patients ◽  
Human Life ◽  
A Priori ◽  
Psychological Research ◽  
Attribute Selection ◽  
Specific Class ◽  
Data Mining Techniques ◽  
Using Data

Psychological research centers help indirectly contact professionals from the fields of human life, job environment, family life, and psychological infrastructure for psychiatric patients. This research aims to detect job apathy patterns from the behavior of employee groups in the University of Baghdad and the Iraqi Ministry of Higher Education and Scientific Research. This investigation presents an approach using data mining techniques to acquire new knowledge and differs from statistical studies in terms of supporting the researchers’ evolving needs. These techniques manipulate redundant or irrelevant attributes to discover interesting patterns. The principal issue identifies several important and affective questions taken from a questionnaire, and the psychiatric researchers recommend these questions. Useless questions are pruned using the attribute selection method. Moreover, pieces of information gained through these questions are measured according to a specific class and ranked accordingly. Association and a priori algorithms are used to detect the most influential and interrelated questions in the questionnaire. Consequently, the decisive parameters that may lead to job apathy are determined.

Non-porous sphérical polymér particlés for luminéscént solid phase detection reactors

1996 ◽  
Vol 320 (1) ◽  
pp. 87-97 ◽  
Author(s):  
Einar Pontén ◽  
Börje Glad ◽  
Malin Stigbrand ◽  
Anna Sjögren ◽  
Knut Irgum
Keyword(s):  
Solid Phase ◽  
Phase Detection ◽  
Polymer Particles

Combined Radar and Radiometer Analysis of Precipitation Profiles for a Parametric Retrieval Algorithm

2005 ◽  
Vol 22 (7) ◽  
pp. 909-929 ◽  
Author(s):  
Hirohiko Masunaga ◽  
Christian D. Kummerow
Keyword(s):  
A Priori ◽  
Three Dimensional ◽  
Tropical Rainfall Measuring Mission ◽  
Dimensional Structure ◽  
Retrieval Algorithm ◽  
Brightness Temperatures ◽  
Precipitation Measurement ◽  
Microwave Imager ◽  
Trmm Microwave Imager ◽  
Global Precipitation

Abstract A methodology to analyze precipitation profiles using the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and precipitation radar (PR) is proposed. Rainfall profiles are retrieved from PR measurements, defined as the best-fit solution selected from precalculated profiles by cloud-resolving models (CRMs), under explicitly defined assumptions of drop size distribution (DSD) and ice hydrometeor models. The PR path-integrated attenuation (PIA), where available, is further used to adjust DSD in a manner that is similar to the PR operational algorithm. Combined with the TMI-retrieved nonraining geophysical parameters, the three-dimensional structure of the geophysical parameters is obtained across the satellite-observed domains. Microwave brightness temperatures are then computed for a comparison with TMI observations to examine if the radar-retrieved rainfall is consistent in the radiometric measurement space. The inconsistency in microwave brightness temperatures is reduced by iterating the retrieval procedure with updated assumptions of the DSD and ice-density models. The proposed methodology is expected to refine the a priori rain profile database and error models for use by parametric passive microwave algorithms, aimed at the Global Precipitation Measurement (GPM) mission, as well as a future TRMM algorithms.

scholarly journals Automotive antenna diversity system for satellite radio with high phase accuracy in low SNR-scenarios

2018 ◽  
Vol 10 (5-6) ◽  
pp. 578-586 ◽  
Author(s):  
Simon Senega ◽  
Ali Nassar ◽  
Stefan Lindenmeier
Keyword(s):  
Signal To Noise Ratio ◽  
A Priori ◽  
Multipath Fading ◽  
Correction Method ◽  
Antenna System ◽  
Antenna Diversity ◽  
Phase Detection ◽  
Low Snr ◽  
Single Antenna ◽  
Noise Correction

AbstractFor a fast scan-phase satellite radio antenna diversity system a noise correction method is presented for a significant improvement of audio availability at low signal-to-noise ratio (SNR) conditions. An error analysis of the level and phase detection within the diversity system in the presence of noise leads to a correction method based on a priori knowledge of the system's noise floor. This method is described and applied in a hardware example of a satellite digital audio radio services antenna diversity circuit for fast fading conditions. Test drives, which have been performed in real fading scenarios, are described and results are analyzed statistically. Simulations of the scan-phase antenna diversity system show higher signal amplitudes and availabilities. Measurement results of dislocated antennas as well as of a diversity antenna set on a single mounting position are presented. A comparison of a diversity system with noise correction, the same system without noise correction, and a single antenna system with each other is performed. Using this new method in fast multipath fading driving scenarios underneath dense foliage with a low SNR of the antenna signals, a reduction in audio mute time by one order of magnitude compared with single antenna systems is achieved with the diversity system.

scholarly journals An outline of avalanches in the Tien Shan mountains

1992 ◽  
Vol 16 ◽  
pp. 7-10 ◽  
Author(s):  
Hu Ruji ◽  
Ma Hong ◽  
Wang Guo
Keyword(s):  
Snow Cover ◽  
Basal Layer ◽  
Liquid Water Content ◽  
Snow Accumulation ◽  
Tien Shan ◽  
Large Temperature ◽  
Snow Avalanches ◽  
Climatic Variations ◽  
Tien Shan Mountains ◽  
Seasonal Snow Cover

The seasonal snow cover in the Tien Shan mountains is characterized by low density, low liquid-water content and low temperature. It is known as typical dry snow. Large temperature gradients in the basal layer of the snow cover exist throughout the entire period of snow accumulation, and depth hoar is therefore extremely well-developed. Full-depth depth-hoar avalanches, however, seldom occur. Avalanches in the Tien Shan mountains are mostly loose snow avalanches. Although normally not large in size, they are the most dangerous type. The occurrence of hazardous avalanches shows cycles of about ten years because of periodic climatic variations.

Modeling Evolution of Microstructures Beneath Topographically Textured Surfaces Produced Using Shear Based Material Removal

2016 ◽  
Author(s):  
Saurabh Basu ◽  
Zhiyu Wang ◽  
Christopher Saldana
Keyword(s):  
Microstructure Evolution ◽  
Material Removal ◽  
A Priori ◽  
Orientation Imaging Microscopy ◽  
Machining Process ◽  
Textured Surfaces ◽  
Orientation Imaging ◽  
Using Data ◽  
Electron Back Scattered Diffraction ◽  
Tool Chatter

Tool chatter is envisaged as a technique to create undulations on fabricated biomedical components. Herein, a-priori designed topographies were fabricated using modulate assisted machining of oxygen free high conductivity copper. Subsequently, underpinnings of microstructure evolution in this machining process were characterized using electron back scattered diffraction based orientation imaging microscopy. These underpinnings were related to the unsteady mechanical states present during modulated assisted machining, this numerically modeled using data obtained from simpler machining configurations. In this manner, relationships between final microstructural states and the underlying mechanics were found. Finally, these results were discussed in the context of unsteady mechanics present during tool chatter, it was shown that statistically predictable microstructural outcomes result during tool chatter.

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