Classifying precipitation from GEO Satellite Observations: Diagnostic Model

10.1002/essoar.10505957.1 ◽

2021 ◽

Author(s):

Shruti Ashok Upadhyaya ◽

Pierre-Emmanuel Kirstetter ◽

Robert J. Kuligowski ◽

Maresa Searls

Keyword(s):

Satellite Observations ◽

Diagnostic Model ◽

Geo Satellite

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Machine Learning based Precipitation Types Classification from GEO Satellite Observations: Diagnostic Model

10.5194/egusphere-egu21-10366 ◽

2021 ◽

Author(s):

Shruti Upadhyaya ◽

Pierre-Emmanuel Kirstetter

Keyword(s):

Machine Learning ◽

Classification Accuracy ◽

Research Question ◽

Forecast Model ◽

Satellite Observations ◽

Convective Precipitation ◽

Precipitation Process ◽

Diagnostic Model ◽

Surface Precipitation ◽

Geo Satellites

<p>The high spatial, temporal, and spectral resolutions from the new generation of GEO satellites provide opportunities to map precipitation more accurately and enhance our understanding of precipitation processes. The research question addressed in this study is: Which predictors derived from satellite observations are significant in estimating the occurrence of a given precipitation process? Several indices from the Advanced Baseline Imager (ABI) sensor onboard the Geostationary Observing Environmental Satellite (GOES)-16 are derived and matched with surface precipitation types from the Ground Validation Multi-Radar/Multi-Sensor (GV-MRMS) system across the conterminous United States (CONUS). A machine learning (ML) based Random Forest (RF) classification is developed with several categories of predictors, such as ABI brightness temperatures (Tb) from five channels, spectral channel differences and textures, and environmental variables from the Rapid Refresh numerical forecast model (NWP).</p><p>The developed RF model displays overall classification accuracy of around 75%. Investigating the model shows that the absence of precipitation (no-precipitation) and convective types are better detected using GOES-16 derived predictors, while the detection of stratiform types is better with the NWP predictors. Simple Tbs detect no-precipitation and hail types correctly, whereas Tb textures contribute to the classification accuracy of warm stratiform and convective precipitation types. The accuracy of all precipitation types identification significantly improved with the addition of NWP predictors along with GOES-16 derived predictors. Overall, the analysis provided new insights on the monitoring of precipitation with GEO satellites and showed novel ways to diagnose ML models.</p>

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Ionospheric variations following the geomagnetic storm from BeiDou GEO satellite observations: A case study

2014 XXXIth URSI General Assembly and Scientific Symposium (URSI GASS) ◽

10.1109/ursigass.2014.6929805 ◽

2014 ◽

Author(s):

Rui Jin ◽

Shuanggen Jin ◽

Xuilin Tao

Keyword(s):

Geomagnetic Storm ◽

Satellite Observations ◽

Geo Satellite

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Classifying precipitation from GEO Satellite Observations: Prognostic Model

Quarterly Journal of the Royal Meteorological Society ◽

10.1002/qj.4134 ◽

2021 ◽

Author(s):

Shruti A. Upadhyaya ◽

Pierre‐Emmanuel Kirstetter ◽

Robert J. Kuligowski ◽

Jonathan J. Gourley ◽

Heather Grams

Keyword(s):

Prognostic Model ◽

Satellite Observations ◽

Geo Satellite

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Results of Latitude Observations with the Floating Zenith Telescope at Mizusawa: Comparisons between the Results Obtained from the Astronomical and Doppler Satellite Observations

International Astronomical Union Colloquium ◽

10.1017/s0252921100051241 ◽

1975 ◽

Vol 26 ◽

pp. 461-468

Author(s):

S. Takagi

Keyword(s):

Photographic Plate ◽

Satellite Observations ◽

Pole Motion

In this article, we intended to see whether we can obtain the same pole motion from two kinds of telescopes: the floating zenith telescope (PZT) and the ILS zenith telescope (VZT). The observations with the PZT have been pursued since 1967.0 with a star list whose star places are taken from the PK4 and its supplement. We revised the method of reduction of the observations with the PZT by adopting a variable scale value for the photographic plate (Takagi et al., 1974).

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THE METHODS OF DEVELOPING COMPUTATIONAL THINKING OF STUDENTS WHILE STUDYING THE COURSE "NUMERICAL METHODS" BASED ON BLENDED LEARNING

Informatics and Education ◽

10.32517/0234-0453-2019-34-6-34-41 ◽

2019 ◽

pp. 34-41

Author(s):

M. M. Klunnikova

Keyword(s):

Numerical Methods ◽

Blended Learning ◽

Computational Thinking ◽

Individual Characteristics ◽

Diagnostic Model ◽

Mathematics Students ◽

Professional Activities ◽

Cognitive Component ◽

Professional Field ◽

The Individual

The work is devoted to the consideration of improving the quality of teaching students the discipline “Numerical methods” through the development of the cognitive component of computational thinking based on blended learning. The article presents a methodology for the formation of computational thinking of mathematics students, based on the visualization of algorithmic design schemes and the activation of the cognitive independence of students. The characteristic of computational thinking is given, the content and structure of computational thinking are shown. It is argued that a student with such a mind is able to manifest himself in his professional field in the best possible way. The results of the application of the technique are described. To determine the level of development of the cognitive component of computational thinking, a diagnostic model has been developed based on measuring the content, operational and motivational components. It is shown that the proposed method of developing computational thinking of students, taking into account the individual characteristics of students’ thinking, meaningfully based on the theoretical and practical aspects of studying the discipline, increases the effectiveness of learning the course “Numerical methods”. The materials of the article are of practical value for teachers of mathematical disciplines who use information and telecommunication technologies in their professional activities.

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