LMODEL: A satellite precipitation methodology using cloud development modeling. Part I: Algorithm construction and calibration

The Lagrangian Model (LMODEL) is a new multisensor satellite rainfall monitoring methodology based on the use of a conceptual cloud-development model that is driven by geostationary satellite imagery and is locally updated using microwave-based rainfall measurements from low earth-orbiting platforms. This paper describes the cloud development model and updating procedures; the companion paper presents model validation results. The model uses single-band thermal infrared geostationary satellite imagery to characterize cloud motion, growth, and dispersal at high spatial resolution (similar to 4 km). These inputs drive a simple, linear, semi-Lagrangian, conceptual cloud mass balance model, incorporating separate representations of convective and stratiform processes. The model is locally updated against microwave satellite data using a two-stage process that scales precipitable water fluxes into the model and then updates model states using a Kalman filter. Model calibration and updating employ an empirical rainfall collocation methodology designed to compensate for the effects of measurement time difference, geolocation error, cloud parallax, and rainfall shear

The impact of foreign trading information on emerging futures markets: a study of Taiwan's unique data set

Using a unique dataset from the Taiwan Futures Exchange, this paper investigates whether trading imbalances by foreign investors affect emerging Taiwan futures market in terms of returns and volatility. First, this evidence demonstrates a positive relation between contemporaneous futures returns and net purchases by foreign investors when other market factor effects are controlled. Second, this failure to detect price reversals is inconsistent with the price pressure hypothesis. Third, foreign investors do not exhibit positive feedback trading patterns. Fourth, a bi-directional Granger-causality relationship exists between futures volatility and foreign trading flows. As found for other stock or foreign exchange markets, our empirical results demonstrate that foreign trading flows do have impacts on the return and volatility of developing futures market, suggesting that trading by foreign investors may enhance the information flow of the local futures market.Foreign trading

Effective Cloud Detection and Segmentation using a Gradient-Based Algorithm for Satellite Imagery; Application to improve PERSIANN-CCS

Being able to effectively identify clouds and monitor their evolution is one important step toward more accurate quantitative precipitation estimation and forecast. In this study, a new gradient-based cloud-image segmentation technique is developed using tools from image processing techniques. This method integrates morphological image gradient magnitudes to separable cloud systems and patches boundaries. A varying scale-kernel is implemented to reduce the sensitivity of image segmentation to noise and capture objects with various finenesses of the edges in remote-sensing images. The proposed method is flexible and extendable from single- to multi-spectral imagery. Case studies were carried out to validate the algorithm by applying the proposed segmentation algorithm to synthetic radiances for channels of the Geostationary Operational Environmental Satellites (GOES-R) simulated by a high-resolution weather prediction model. The proposed method compares favorably with the existing cloud-patch-based segmentation technique implemented in the PERSIANN-CCS (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network - Cloud Classification System) rainfall retrieval algorithm. Evaluation of event-based images indicates that the proposed algorithm has potential to improve rain detection and estimation skills with an average of more than 45% gain comparing to the segmentation technique used in PERSIANN-CCS and identifying cloud regions as objects with accuracy rates up to 98%

Evaluation of satellite-based precipitation estimation over Iran

Precipitation in semi-arid countries such as Iran is one of the most important elements for all aspects of human life. In areas with sparse ground-based precipitation observation networks, the reliable high spatial and temporal resolution of satellite-based precipitation estimation might be the best source for meteorological and hydrological studies. In the present study, four different satellite rainfall estimates (CMORPH, PERSIANN, adjusted PERSIANN, and TRMM-3B42 V6) are evaluated using a relatively dense Islamic Republic of Iran's Meteorological Organization (IRIMO) rain-gauge network as reference. These evaluations were done at daily and monthly time scales with a spatial resolution of 0.25° × 0.25° latitude/longitude. The topography of Iran is complicated and includes different, very diverse climates. For example, there is an extremely wet (low-elevation) Caspian Sea coastal region in the north, an arid desert in the center, and high mountainous areas in the west and north. Different rainfall regimes vary between these extremes. In order to conduct an objective intercomparison of the various satellite products, the study was designed to minimize the level of uncertainties in the evaluation process. To reduce gauge uncertainties, only the 32 pixels, which include at least five rain gauges, are considered. Evaluation results vary by different areas. The satellite products had a Probability of Detection (POD) greater than 40% in the southern part of the country and the regions of the Zagros Mountains. However, all satellite products exhibited poor performance over the Caspian Sea coastal region, where they underestimated precipitation in this relatively wet and moderate climate region. Seasonal analysis shows that spring precipitations are detected more accurately than winter precipitation, especially for the mountainous areas all over the country. Comparisons of different satellite products show that adj-PERSIANN and TRMM-3B42 V6 have better performance, and CMORPH has poor estimation, especially over the Zagros Mountains. The comparison between PERSIANN and adj-PERSIANN shows that the bias adjustment improved the POD, which is a daily scale statistic

Identification and application of physical and chemical parameters to predict indicator bacterial concentration in a small Californian creek.

This study of Aliso Creek in California aimed to identify physical and chemical parameters that could be measured instantly to be used in a model to serve as surrogates for indicator bacterial concentrations during dry season flow. In this study, a new data smoothing technique and ranking/categorizing analysis was used to reduce variation to allow better delineation of the relationships between adopted variables and concentrations of indicator bacteria. The ranking/categorizing approach clarified overall trends between physico-chemical data and the indicators and suggested sources of the bacteria. This study also applied a principle component regression model to the data. Although the model was promising for predicting concentrations of total and fecal coliforms, it was somewhat weaker in predicting enteroccocci

Examining Stakeholder Perspectives: Process, Performance and Progress of the Age-Friendly Taiwan Program.

Since Taiwans age-friendly city (AFC) program was launched in 2012, the central government has provided various resources to the countrys 22 local authorities, including budgetary support, policy advocacy, and consultation from a team of experts. This study examines stakeholder perspectives on the process, performance, and outcome of the AFC program. A 53-item questionnaire was developed based on the World Health Organization (WHO) guideline, including mechanisms and processes (20 items), outcome evaluations (23 items), and resource integration (10 items). There was a great difference found among scores between facilitators and experts for inter-exchange experience with local and international cities (40%) and monitor and revise indicators (37%) in mechanisms and processes, evaluate performance of indicators and action plans (37%) in outcome evaluations, and interaction between government and community (46%) and interaction between civil organization and senior society (39%) in resource integration. Clearly, facilitators showed overly optimistic assessments in AFC mechanisms and processes, outcome evaluation, and resource integration. The results showed disconnect between experts expectations versus actual practice conducted by facilitators. Implications of these findings are to integrate top down expectations with the realities of bottom up practice to design more realistic evaluations; continue to educate stakeholders about design, implementation and evaluation; and further integrate resources from government, civil organizations, and community

Thermal rectification effects of multiple semiconductor quantum dot junctions

Based on the multiple energy level Anderson model, this study theoretically examines the thermoelectric effects of semiconductor quantum dots (QDs) in the nonlinear response regime. The charge and heat currents in the sequential tunneling process are calculated by using the Keldysh Green's function technique. Results show that the thermal rectification effect can be observed in a multiple QD junction system, whereas the tunneling rate, size fluctuation, and location distribution of QD significantly influence the rectification efficiency.Comment: 5 pages, 8figure

Bilateral Waveform Similarity Overlap-and-Add Based Packet Loss Concealment for Voice over IP

This paper invested a bilateral waveform similarity overlap-and-add algorithm for voice packet lost. Since Packet lost will cause the semantic misunderstanding, it has become one of the most essential problems in speech communication. This investment is based on waveform similarity measure using overlap-and-Add algorithm and provides the bilateral information to enhance the speech signal reconstruction. Traditionally, it has been improved that waveform similarity overlap-and-add (WSOLA) technique is an effective algorithm to deal with packet loss concealment (PLC) for real-time time communication. WSOLA algorithm is widely applied to deal with the length adaptation and packet loss concealment of speech signal. Time scale modification of audio signal is one of the most essential research topics in data communication, especially in voice of IP (VoIP). Herein, the proposed the bilateral WSOLA (BWSOLA) that is derived from WSOLA. Instead of only exploitation one direction speech data, the proposed method will reconstruct the lost voice data according to the preceding and cascading data. The related algorithms have been developed to achieve the optimal reconstructing estimation. The experimental results show that the quality of the reconstructed speech signal of the bilateral WSOLA is much better compared to the standard WSOLA and GWSOLA on different packet loss rate and length using the metrics PESQ and MOS. The significant improvement is obtained by bilateral information and proposed method. The proposed bilateral waveform similarity overlap-and-add (BWSOLA) outperforms the traditional approaches especially in the long duration data loss