scholarly journals Real-Time Rain Rate Evaluation via Satellite Downlink Signal Attenuation Measurement

Sensors ◽  
2017 ◽  
Vol 17 (8) ◽  
pp. 1864 ◽  
Author(s):  
Filippo Giannetti ◽  
Ruggero Reggiannini ◽  
Marco Moretti ◽  
Elisa Adirosi ◽  
Luca Baldini ◽  
...  
Keyword(s):  
Real Time ◽  
Rain Rate ◽  
Signal Attenuation ◽  
Attenuation Measurement ◽  
Downlink Signal ◽  
Rate Evaluation

scholarly journals PERSIANN Dynamic Infrared–Rain Rate (PDIR-Now): A Near-Real-Time, Quasi-Global Satellite Precipitation Dataset

2020 ◽  
Vol 21 (12) ◽  
pp. 2893-2906
Author(s):  
Phu Nguyen ◽  
Mohammed Ombadi ◽  
Vesta Afzali Gorooh ◽  
Eric J. Shearer ◽  
Mojtaba Sadeghi ◽  
...  
Keyword(s):  
The Netherlands ◽  
Real Time ◽  
Rain Rate ◽  
Web Based ◽  
Precipitation Patterns ◽  
Diurnal Cycles ◽  
Critical Success ◽  
Extensive Evaluation ◽  
Precipitation Estimation ◽  
Precipitation Estimates

AbstractThis study presents the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Dynamic Infrared Rain Rate (PDIR-Now) near-real-time precipitation dataset. This dataset provides hourly, quasi-global, infrared-based precipitation estimates at 0.04° × 0.04° spatial resolution with a short latency (15–60 min). It is intended to supersede the PERSIANN–Cloud Classification System (PERSIANN-CCS) dataset previously produced as the near-real-time product of the PERSIANN family. We first provide a brief description of the algorithm’s fundamentals and the input data used for deriving precipitation estimates. Second, we provide an extensive evaluation of the PDIR-Now dataset over annual, monthly, daily, and subdaily scales. Last, the article presents information on the dissemination of the dataset through the Center for Hydrometeorology and Remote Sensing (CHRS) web-based interfaces. The evaluation, conducted over the period 2017–18, demonstrates the utility of PDIR-Now and its improvement over PERSIANN-CCS at all temporal scales. Specifically, PDIR-Now improves the estimation of rain/no-rain days as demonstrated by a critical success index (CSI) of 0.53 compared to 0.47 of PERSIANN-CCS. In addition, PDIR-Now improves the estimation of seasonal and diurnal cycles of precipitation as well as regional precipitation patterns erroneously estimated by PERSIANN-CCS. Finally, an evaluation is carried out to examine the performance of PDIR-Now in capturing two extreme events, Hurricane Harvey and a cluster of summer thunderstorms that occurred over the Netherlands, where it is shown that PDIR-Now adequately represents spatial precipitation patterns as well as subdaily precipitation rates with a correlation coefficient (CORR) of 0.64 for Hurricane Harvey and 0.76 for the Netherlands thunderstorms.

scholarly journals Automated rain rate estimates using the Ka-band ARM zenith radar (KAZR)

2015 ◽  
Vol 8 (9) ◽  
pp. 3685-3699 ◽  
Author(s):  
A. Chandra ◽  
C. Zhang ◽  
P. Kollias ◽  
S. Matrosov ◽  
W. Szyrmer
Keyword(s):  
Rain Rate ◽  
Tropical Indian Ocean ◽  
Rain Gauge ◽  
Signal Attenuation ◽  
Ka Band ◽  
Automated Algorithm ◽  
Atmospheric Radiation Measurement ◽  
Microphysical Processes ◽  
The Tropics ◽  
Rain Rates

Abstract. The use of millimeter wavelength radars for probing precipitation has recently gained interest. However, estimation of precipitation variables is not straightforward due to strong signal attenuation, radar receiver saturation, antenna wet radome effects and natural microphysical variability. Here, an automated algorithm is developed for routinely retrieving rain rates from the profiling Ka-band (35-GHz) ARM (Atmospheric Radiation Measurement) zenith radars (KAZR). A 1-dimensional, simple, steady state microphysical model is used to estimate impacts of microphysical processes and attenuation on the profiles of radar observables at 35-GHz and thus provide criteria for identifying situations when attenuation or microphysical processes dominate KAZR observations. KAZR observations are also screened for signal saturation and wet radome effects. The algorithm is implemented in two steps: high rain rates are retrieved by using the amount of attenuation in rain layers, while low rain rates are retrieved from the reflectivity–rain rate (Ze–R) relation. Observations collected by the KAZR, rain gauge, disdrometer and scanning precipitating radars during the DYNAMO/AMIE field campaign at the Gan Island of the tropical Indian Ocean are used to validate the proposed approach. The differences in the rain accumulation from the proposed algorithm are quantified. The results indicate that the proposed algorithm has a potential for deriving continuous rain rate statistics in the tropics.

scholarly journals Rain‐rate estimation algorithm using signal attenuation of Ka‐band cloud radar

2019 ◽  
Vol 27 (1) ◽  
Author(s):  
Su‐Bin Oh ◽  
Pavlos Kollias ◽  
Jeong‐Soon Lee ◽  
Seung‐Woo Lee ◽  
Yong Hee Lee ◽  
...  
Keyword(s):  
Rain Rate ◽  
Estimation Algorithm ◽  
Signal Attenuation ◽  
Ka Band ◽  
Rate Estimation ◽  
Cloud Radar

Real Time Process Monitoring with Multiwave-Length Pyrometric Interferometry (Pi)

1993 ◽  
Vol 324 ◽  
Author(s):  
F.G. BÖbel ◽  
A. Wowchak ◽  
P.P. Chow ◽  
J. Van Hove ◽  
L.A. Chow
Keyword(s):  
Process Control ◽  
Real Time ◽  
Device Fabrication ◽  
Time Process ◽  
Quarter Wavelength ◽  
Surface Emitting Laser ◽  
In Situ Sensing ◽  
Temperature And Growth ◽  
Rate Evaluation ◽  
Two Parameters

AbstractPyrometry Interferometry (PI) is a powerful technique for in-situ sensing of the wafer temperature and growth rate. Evaluation of the two parameters would allow exact process control required for sophisticated device fabrication and material processing. The PI technique analyzes the interference patterns of the thermal radiation from the growing layer with a changing thickness d at growth temperature T. Since it is non-contact, applicable to all semiconductor materials and insensitive to wafer motion, the method is an ideal candidate for real time process control. We use a reflection assisted method to aid real time computation of these parameters. One could select the wavlength of interest to optimize the temperature and layer thickness resolution. We present data on MBE grown quarter wavelength stacks of GaAs and AlAs, and silicon oxidation to show P1 is extremely useful for growth of surface emitting laser and for silicon processing.

scholarly journals PERSIANN Dynamic Infrared–Rain Rate Model (PDIR) for High-Resolution, Real-Time Satellite Precipitation Estimation

2020 ◽  
Vol 101 (3) ◽  
pp. E286-E302 ◽  
Author(s):  
Phu Nguyen ◽  
Eric J. Shearer ◽  
Mohammed Ombadi ◽  
Vesta Afzali Gorooh ◽  
Kuolin Hsu ◽  
...  
Keyword(s):  
Real Time ◽  
Rain Rate ◽  
Extreme Rainfall ◽  
Rate Model ◽  
Spatiotemporal Resolution ◽  
Atmospheric Rivers ◽  
Estimation Algorithms ◽  
Quantitative Precipitation Estimation ◽  
Precipitation Estimation ◽  
Products Analysis

Abstract Precipitation measurements with high spatiotemporal resolution are a vital input for hydrometeorological and water resources studies; decision-making in disaster management; and weather, climate, and hydrological forecasting. Moreover, real-time precipitation estimation with high precision is pivotal for the monitoring and managing of catastrophic hydroclimate disasters such as flash floods, which frequently transpire after extreme rainfall. While algorithms that exclusively use satellite infrared data as input are attractive owing to their rich spatiotemporal resolution and near-instantaneous availability, their sole reliance on cloud-top brightness temperature (Tb) readings causes underestimates in wet regions and overestimates in dry regions—this is especially evident over the western contiguous United States (CONUS). We introduce an algorithm, the Precipitation Estimations from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN) Dynamic Infrared–Rain rate model (PDIR), which utilizes climatological data to construct a dynamic (i.e., laterally shifting) Tb–rain rate relationship that has several notable advantages over other quantitative precipitation-estimation algorithms and noteworthy skill over the western CONUS. Validation of PDIR over the western CONUS shows a promising degree of skill, notably at the annual scale, where it performs well in comparison to other satellite-based products. Analysis of two extreme landfalling atmospheric rivers show that solely IR-based PDIR performs reasonably well compared to other IR- and PMW-based satellite rainfall products, marking its potential to be effective in real-time monitoring of extreme storms. This research suggests that IR-based algorithms that contain the spatiotemporal richness and near-instantaneous availability needed for rapid natural hazards response may soon contain the skill needed for hydrologic and water resource applications.

scholarly journals Rainfall Estimation from Tropospheric Attenuation Affecting Satellite Links

Information ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 11
Author(s):  
Riccardo Angelo Giro ◽  
Lorenzo Luini ◽  
Carlo Giuseppe Riva
Keyword(s):  
Numerical Weather Prediction ◽  
Rain Rate ◽  
Weather Prediction ◽  
Inversion Algorithm ◽  
Model Accuracy ◽  
Satellite Links ◽  
Propagation Experiment ◽  
Rain Events ◽  
Downlink Signal ◽  
The One

A novel methodology for estimating rainfall rate from satellite signals is presented. The proposed inversion algorithm yields rain rate estimates by making opportunistic use of the downlink signal and exploiting local ancillary meteorological information (0 °C isotherm height and monthly convectivity index), which can be extracted on a Global basis from Numerical Weather Prediction (NWP) products. The methodology includes different expressions to take the different impact of stratiform and convective rain events on the link into due account. The model accuracy in predicting the rain rate is assessed (and compared to the one of other models), both on a statistical and on an instantaneous basis, by exploiting a full year of data collected in Milan, in the framework of the Alphasat Aldo Paraboni propagation experiment.

scholarly journals Smart Bluetooth beacon based on LoRa Positioning method and system research

2021 ◽  
Vol 2079 (1) ◽  
pp. 012032
Author(s):  
Rui Su ◽  
Yawen Dai
Keyword(s):  
Real Time ◽  
Low Cost ◽  
Real Life ◽  
Signal Attenuation ◽  
Positioning Accuracy ◽  
Positioning Method ◽  
New Type ◽  
Positioning Analysis ◽  
Transmission Speed ◽  
Indoor And Outdoor

Abstract In the era of the Internet of Everything, applications based on real-time location continue to appear in various industries. The indoor and outdoor positioning, analysis and management of personnel and objects can effectively improve the efficiency of production and management, which is of great significance to many industries. The use of Bluetooth beacon positioning has the advantages of low energy consumption, low cost, and fast data transmission speed. However, in real life, there are two obstacles to receiving signals, which are easily affected by the environment and the need for frequent on-site maintenance. This paper designs and implements a new type of LoRa-based smart Bluetooth beacon, which can be quickly connected to LoRa. Online monitoring and remote control can achieve better adaptation to the environment, and can fit a better signal attenuation model in the deployment environment in real time. Traditional signal strength positioning solutions have their own limitations. In order to improve the positioning accuracy of the Bluetooth beacon and the universality of the algorithm, in view of the low deployment density of beacons, the positioning accuracy is not good and the anchor circle has various situations, an optimized weighted centroid positioning scheme integrating greedy strategy is proposed.

Real-Time In-Situ Measurement of Slurry Concentration Distribution in Tanks

2002 ◽  
Author(s):  
Judith Ann Bamberger
Keyword(s):  
Real Time ◽  
Process Conditions ◽  
Signal Attenuation ◽  
Steady State Condition ◽  
Ultrasonic Signals ◽  
Slurry Concentration ◽  
Fluid Properties ◽  
Multiphase Fluid

Ultrasonic signals are well suited for characterization of multiphase fluid properties and flows. The signals are not degraded by noisy process conditions, can penetrate vessel and piping walls, and can be used to interrogate fluids and dense opaque suspensions. This paper presents a simple ultrasonic measurement application that shows the ability of ultrasonic sensors to measure slurry concentration during slurry mixing in tanks. Results from two experiments show the use of real-time measurements of ultrasonic signal attenuation to track the process of slurry mixing in situ and to track the ability to maintain a well mixed steady state condition. Comparison of means of the ultrasonic measurements with means obtained from discrete extractive measurements show that the distributions overlap and cannot be statistically distinguished.

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