Daytime precipitation estimation using bispectral cloud classification system

Two previously developed Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) algorithms that incorporate cloud classification system (PERSIANN-CCS) and multispectral analysis (PERSIANN-MSA) are integrated and employed to analyze the role of cloud albedo from Geostationary Operational Environmental Satellite-12 (GOES-12) visible (0.65 μm) channel in supplementing infrared (10.7 mm) data. The integrated technique derives finescale (0.04° × 0.04° latitudelongitude every 30 min) rain rate for each grid box through four major steps: 1) segmenting clouds into a number of cloud patches using infrared or albedo images; 2) classification of cloud patches into a number of cloud types using radiative, geometrical, and textural features for each individual cloud patch; 3) classification of each cloud type into a number of subclasses and assigning rain rates to each subclass using a multidimensional histogram matching method; and 4) associating satellite gridbox information to the appropriate corresponding cloud type and subclass to estimate rain rate in grid scale. The technique was applied over a study region that includes the U.S. landmass east of 115°W. One reference infrared-only and three different bis-pectral (visible and infrared) rain estimation scenarios were compared to investigate the technique's ability to address two major drawbacks of infrared-only methods: 1) underestimating warm rainfall and 2) the inability to screen out no-rain thin cirrus clouds. Radar estimates were used to evaluate the scenarios at a range of temporal (3 and 6 hourly) and spatial (0.04°, 0.08°, 0.12°, and 0.24° latitude-longitude) scales. Overall, the results using daytime data during June-August 2006 indicate that significant gain over infrared-only technique is obtained once albedo is used for cloud segmentation followed by bispectral cloud classification and rainfall estimation. At 3-h, 0.04° resolution, the observed improvement using bispectral information was about 66% for equitable threat score and 26% for the correlation coefficient. At coarser 0.24° resolution, the gains were 34% and 32% for the two performance measures, respectively. © 2010 American Meteorological Society

PERSIANN-MSA: A precipitation estimation method from satellite-based multispectral analysis

Visible and infrared data obtained from instruments onboard geostationary satellites have been extensively used for monitoring clouds and their evolution. The Advanced Baseline Imager (ABI) that will be launched onboard the Geostationary Operational Environmental Satellite-R (GOES-R) series in the near future will offer a larger range of spectral bands; hence, it will provide observations of cloud and rain systems at even finer spatial, temporal, and spectral resolutions than are possible with the current GOES. In this paper, a new method called Precipitation Estimation from Remotely Sensed information using Artificial Neural Networks-Multispectral Analysis (PERSIANN-MSA) is proposed to evaluate the effect of using multispectral imagery on precipitation estimation. The proposed approach uses a self-organizing feature map (SOFM) to classify multidimensional input information, extracted from each grid box and corresponding textural features of multispectral bands. In addition, principal component analysis (PCA) is used to reduce the dimensionality to a few independent input features while preserving most of the variations of all input information. The above method is applied to estimate rainfall using multiple channels of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard the Meteosat Second Generation (MSG) satellite. In comparison to the use of a single thermal infrared channel, the analysis shows that using multispectral data has the potential to improve rain detection and estimation skills with an average of more than 50% gain in equitable threat score for rain/no-rain detection, and more than 20% gain in correlation coefficient associated with rain-rate estimation. © 2009 American Meteorological Society

Effects of Reading Skills on Students' Performance in Science and Mathematics in Public and Private Secondary Schools

In the Philippine education system, reading, mathematics, and science formed part of the core areas of basic education curriculum. For the last decade, the quality of Philippine education was put into a big question due to poor performance of students in mathematics and science tests both local and abroad. The initial result of current efforts of the government by adopting K-12 curriculum didn't do much to change the status quo. The purpose of this study is to determine the reading predictors of students' performance in Mathematics and Science and identify its effects to such performance. A total of 660 freshmen students from public and private high schools in Cotabato City, Philippines were taken as sample. A validated and reliable 150-item test in reading comprehension skills, mathematics and science was used to get primary data to perform correlation and regression analysis. Findings showed that only making inference and getting main idea were predictors of mathematics performance of students in public school and private schools, respectively. Data analysis also revealed that two reading skills such as noting details and making inference had an influence on science performance of students in public school while skills in getting main idea and drawing conclusion influenced science performance of students in private schools. However, there was only one skill such as vocabulary in context which was predictor of overall science performance of all students. Moreover, separate effects of making inference, identifying main idea explained only 1.8 percent and 1.3 percent of students' math performance while their combined effects provided only .1 percent or nearly zero percent. Furthermore, the study found out that separate effects of noting details contributed 3.3 percent and its combined effects with making inference explained 4.2 percent of science performance of students in public schools. In terms of effects of reading to science performance in private schools, making inference provided 1.2 percent of separate effect; making inference and drawing conclusion influenced 2.8 percent of combined effect; understanding vocabulary in context has overall one percent of separate effect

Effects of Reading Skills on Students' Performance in Science and Mathematics in Public and Private Secondary Schools

In the Philippine education system, reading, mathematics, and science formed part of the core areas of basic education curriculum. For the last decade, the quality of Philippine education was put into a big question due to poor performance of students in mathematics and science tests both local and abroad. The initial result of current efforts of the government by adopting K-12 curriculum didn't do much to change the status quo. The purpose of this study is to determine the reading predictors of students' performance in Mathematics and Science and identify its effects to such performance. A total of 660 freshmen students from public and private high schools in Cotabato City, Philippines were taken as sample. A validated and reliable 150-item test in reading comprehension skills, mathematics and science was used to get primary data to perform correlation and regression analysis. Findings showed that only making inference and getting main idea were predictors of mathematics performance of students in public school and private schools, respectively. Data analysis also revealed that two reading skills such as noting details and making inference had an influence on science performance of students in public school while skills in getting main idea and drawing conclusion influenced science performance of students in private schools. However, there was only one skill such as vocabulary in context which was predictor of overall science performance of all students. Moreover, separate effects of making inference, identifying main idea explained only 1.8 percent and 1.3 percent of students' math performance while their combined effects provided only .1 percent or nearly zero percent. Furthermore, the study found out that separate effects of noting details contributed 3.3 percent and its combined effects with making inference explained 4.2 percent of science performance of students in public schools. In terms of effects of reading to science performance in private schools, making inference provided 1.2 percent of separate effect; making inference and drawing conclusion influenced 2.8 percent of combined effect; understanding vocabulary in context has overall one percent of separate effect

Model Penilaian Sebaya Untuk Meningkatkan Hasil Pembelajaran Menulis Di SMP

: Peer-Assessment Model to Improve the Writing Ability of Junior-High-School Students. This study is aimed at developing a peer-assessment (PA) model to improve the writing ability of junior-high-school students. This study employed RDR (research, development, and research) design as well as R2D2 (recursive, reflective design and development) design, going through three procedural stages: pre-development, development, and try-out. Data concerning product try-out were qualitative in nature, whereas data concerning product experimentation were quantitative. The results of this study include a PA package and the description of its effectiveness. The PA package contains lesson plans, PA guides, learning models, and guidelines for developing PA. The results of product experimentation show that PA has a number of strengths

Inertia and chiral edge modes of a skyrmion magnetic bubble

The dynamics of a vortex in a thin-film ferromagnet resembles the motion of a charged massless particle in a uniform magnetic field. Similar dynamics is expected for other magnetic textures with a nonzero skyrmion number. However, recent numerical simulations revealed that skyrmion magnetic bubbles show significant deviations from this model. We show that a skyrmion bubble possesses inertia and derive its mass from the standard theory of a thin-film ferromagnet. Besides center-of-mass motion, other low energy modes are waves on the edge of the bubble traveling with different speeds in opposite directions.Comment: updated simulation detail

Evaluating the utility of multispectral information in delineating the areal extent of precipitation

Data from geosynchronous Earth-orbiting (GEO) satellites equipped with visible (VIS) and infrared (IR) scanners are commonly used in rain retrieval algorithms. These algorithms benefit from the high spatial and temporal resolution of GEO observations, either in stand-alone mode or in combination with higher-quality but less frequent microwave observations from low Earth-orbiting (LEO) satellites. In this paper, a neural network-based framework is presented to evaluate the utility of multispectral information in improving rain/no-rain (R/NR) detection. The algorithm uses the powerful classification features of the self-organizing feature map (SOFM), along with probability matching techniques to map single- or multispectral input space into R/NR maps. The framework was tested and validated using the 31 possible combinations of the five Geostationary Operational Environmental Satellite 12 (GOES-12) channels. An algorithm training and validation study was conducted over the conterminous United States during June-August 2006. The results indicate that during daytime, the visible channel (0.65 μm) can yield significant improvements in R/NR detection capabilities, especially when combined with any of the other four GOES-12 channels. Similarly, for nighttime detection the combination of two IR channels - particularly channels 3 (6.5 μm) and 4 (10.7 μm)-resulted in significant performance gain over any single IR channel. In both cases, however, using more than two channels resulted only in marginal improvements over two-channel combinations. Detailed examination of event-based images indicate that the proposed algorithm is capable of extracting information useful to screen no-rain pixels associated with cold, thin clouds and identifying rain areas under warm but rainy clouds. Both cases have been problematic areas for IR-only algorithms. © 2009 American Meteorological Society