scholarly journals Statistical and Hydrological Evaluations of Multi-Satellite Precipitation Products over Fujiang River Basin in Humid Southeast China

2018 ◽  
Vol 10 (12) ◽  
pp. 1898 ◽  
Author(s):  
Weiwei Sun ◽  
Jun Ma ◽  
Gang Yang ◽  
Weiyue Li
Keyword(s):  
Real Time ◽  
River Basin ◽  
Local Community ◽  
Pearson Correlation ◽  
Warm Season ◽  
Correlation Coefficients ◽  
Cold Season ◽  
Optimal Interpolation ◽  
Distributed Model ◽  
Satellite Precipitation

The purpose of the paper is to evaluate the quality and hydrological utility of four popular satellite precipitation products, including the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) product (3B42V7), near real-time product (3B42RT), and the Climate Prediction Center (CPC) MORPHing technique (CMORPH) satellite–gauge merged product (CMORPH BLD) and bias-corrected product (CMORPH CRT) over Fujiang River basin, China. First, we provided a statistical assessment of the four precipitation products at multiple spatiotemporal scales. The results show that: (1) all the products except 3B42RT capture the spatial pattern of annual precipitation fairly well; (2) in general, CMORPH BLD benefits from the application of the probability density function-optimal interpolation (PDF-OI) gauge adjustment algorithm and performs best among all the products with Pearson correlation coefficients (CC) of 0.84 and 0.94, equitable threat score (ETS) of 0.56 and 0.63 in grid and basin scales, respectively, followed by 3B42V7 and CMORPH CRT; whereas 3B42RT performs worst across all the metrics; (3) according to the occurrence frequencies of rainfall, satellite estimates mainly fall into the bin of 0–1 mm/day and tend to underestimate light precipitation. In addition, the performance of all the products in warm season is much better than in cold season in both grid and basin scales. Subsequently, a physically based distributed model is established to further evaluate the hydrological utility of different precipitation products. The results reveal that: (1) the errors in precipitation products mainly propagate into hydrological simulations, resulting in the best hydrological performance in CMORPH BLD in both daily and monthly scales after recalibrating the model, while 3B42RT shows limited skills in reproducing the daily observed hydrograph; (2) after recalibrating the model with the respective satellite data, significant improvements are observed for all the products; (3) CMORPH BLD no longer shows its superiority during near-real-time monitoring of floods. There is still a great challenge for the application of current satellite-based estimates into local flood monitoring. This study could be used as guidance for choosing alternative satellite precipitation products for hydrological applications in a local community, particularly in basins in which rainfall gauges are scarce.

scholarly journals Temperature and Relative Humidity Profile Retrieval from Fengyun-3D/HIRAS in the Arctic Region

2021 ◽  
Vol 13 (10) ◽  
pp. 1884
Author(s):  
Jingjing Hu ◽  
Yansong Bao ◽  
Jian Liu ◽  
Hui Liu ◽  
George P. Petropoulos ◽  
...  
Keyword(s):  
Relative Humidity ◽  
Real Time ◽  
Wide Spectrum ◽  
Warm Season ◽  
Arctic Region ◽  
Cold Season ◽  
The Arctic ◽  
Practical Applications ◽  
A New Technique ◽  
The Arctic Region

The acquisition of real-time temperature and relative humidity (RH) profiles in the Arctic is of great significance for the study of the Arctic’s climate and Arctic scientific research. However, the operational algorithm of Fengyun-3D only takes into account areas within 60°N, the innovation of this work is that a new technique based on Neural Network (NN) algorithm was proposed, which can retrieve these parameters in real time from the Fengyun-3D Hyperspectral Infrared Radiation Atmospheric Sounding (HIRAS) observations in the Arctic region. Considering the difficulty of obtaining a large amount of actual observation (such as radiosonde) in the Arctic region, collocated ERA5 data from European Centre for Medium-Range Weather Forecasts (ECMWF) and HIRAS observations were used to train the neural networks (NNs). Brightness temperature and training targets were classified using two variables: season (warm season and cold season) and surface type (ocean and land). NNs-based retrievals were compared with ERA5 data and radiosonde observations (RAOBs) independent of the NN training sets. Results showed that (1) the NNs retrievals accuracy is generally higher on warm season and ocean; (2) the root-mean-square error (RMSE) of retrieved profiles is generally slightly higher in the RAOB comparisons than in the ERA5 comparisons, but the variation trend of errors with height is consistent; (3) the retrieved profiles by the NN method are closer to ERA5, comparing with the AIRS products. All the results demonstrated the potential value in time and space of NN algorithm in retrieving temperature and relative humidity profiles of the Arctic region from HIRAS observations under clear-sky conditions. As such, the proposed NN algorithm provides a valuable pathway for retrieving reliably temperature and RH profiles from HIRAS observations in the Arctic region, providing information of practical value in a wide spectrum of practical applications and research investigations alike.All in all, our work has important implications in broadening Fengyun-3D’s operational implementation range from within 60°N to the Arctic region.

Machine learning approaches for improving near-real-time IMERG rainfall estimates by integrating Cloud Properties from NOAA CDR PATMOSX

2021 ◽  
Author(s):  
Zhi Zhang ◽  
Dagang Wang ◽  
Jianxiu Qiu ◽  
Jinxin Zhu ◽  
Tingli Wang
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
River Basin ◽  
Central China ◽  
Support Vector ◽  
Learning Approaches ◽  
Satellite Precipitation ◽  
Cloud Properties ◽  
Spatial Coverage ◽  
Extreme Gradient Boosting

AbstractThe Global Precipitation Measurement (GPM) mission provides satellite precipitation products with an unprecedented spatio-temporal resolution and spatial coverage. However, its near-real-time (NRT) product still suffers from low accuracy. This study aims to improve the early run of the Integrated Multi-satellitE Retrievals for GPM (IMERG) by using four machine learning approaches, i.e., support vector machine (SVM), random forest (RF), artificial neural network (ANN), and Extreme Gradient Boosting (XGB). The cloud properties are selected as the predictors in addition to the original IMERG in these approaches. All the four approaches show similar improvement, with 53%-60% reduction of root-mean-square error (RMSE) compared with the original IMERG in a humid area, i.e., the Dongjiang River Basin (DJR) in southeastern China. The improvements are even greater in a semi-arid area, i.e., the Fenhe River Basin (FHR) in central China, the RMSE reduction ranges from 63%-66%. The products generated by the machine learning methods performs similarly to or even outperform than the final run of IMERG. Feature importance analysis, a technique to evaluate input features based on how useful they are in predicting a target variable, indicates that the cloud height and the brightness temperature are the most useful information in improving satellite precipitation products, followed by the atmospheric reflectivity and the surface temperature. This study shows that a more accurate NRT precipitation product can be produced by combining machine learning approaches and cloud information, which is of importance for hydrological applications that requires NRT precipitation information including flood monitoring.

scholarly journals Evaluation and Analysis of Grid Precipitation Fusion Products in Jinsha River Basin Based on China Meteorological Assimilation Datasets for the SWAT Model

Water ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 253 ◽  
Author(s):  
Dandan Guo ◽  
Hantao Wang ◽  
Xiaoxiao Zhang ◽  
Guodong Liu
Keyword(s):  
Statistical Analysis ◽  
River Basin ◽  
Assessment Tool ◽  
Swat Model ◽  
Correlation Coefficients ◽  
High Accuracy ◽  
Jinsha River ◽  
Hydrological Simulation ◽  
Large Spatial Scale ◽  
Satellite Precipitation

Highly accurate and high-quality precipitation products that can act as substitutes for ground precipitation observations have important significance for research development in the meteorology and hydrology of river basins. In this paper, statistical analysis methods were employed to quantitatively assess the usage accuracy of three precipitation products, China Meteorological Assimilation Driving Datasets for the Soil and Water Assessment Tool (SWAT) model (CMADS), next-generation Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) and Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA), for the Jinsha River Basin, a region characterized by a large spatial scale and complex terrain. The results of statistical analysis show that the three kinds of data have relatively high accuracy on the average grid scale and the correlation coefficients are all greater than 0.8 (CMADS:0.86, IMERG:0.88 and TMPA:0.81). The performance in the average grid scale is superior than that in grid scale. (CMADS: 0.86(basin), 0.6 (grid); IMERG:0.88 (basin),0.71(grid); TMPA:0.81(basin),0.42(grid)). According to the results of hydrological applicability analysis based on SWAT model, the three kinds of data fail to obtain higher accuracy on hydrological simulation. CMADS performs best (NSE:0.55), followed by TMPA (NSE:0.50) and IMERG (NSE:0.45) in the last. On the whole, the three types of satellite precipitation data have high accuracy on statistical analysis and average accuracy on hydrological simulation in the Jinsha River Basin, which have certain hydrological application potential.

scholarly journals REAL-TIME BIOFEEDBACK INTEGRATED INTO NEUROMUSCULAR TRAINING INCREASES BRAIN FUNCTIONAL CONNECTIVITY AND REDUCES HIGH-RISK KNEE BIOMECHANICS

2019 ◽  
Vol 7 (3_suppl) ◽  
pp. 2325967119S0002 ◽  
Author(s):  
Jed A. Diekfuss ◽  
Dustin R. Grooms ◽  
Kim Barber Foss ◽  
Scott Bonnette ◽  
Chris Dicesare ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Real Time ◽  
Injury Risk ◽  
Pearson Correlation ◽  
Correlation Coefficients ◽  
Acl Injury ◽  
Neuromuscular Training ◽  
Acl Injury Risk ◽  
Left Thalamus ◽  
The Right

Background: Anterior cruciate ligament (ACL) injury is associated with alterations in the central nervous system and resultant sensorimotor control (Courtney et al., 2005; Grooms et al., 2017). Our prospective data indicates that altered knee-motor functional brain connectivity is associated with increased risk for ACL injury (Diekfuss et al., revisions invited), revealing novel neural targets for neuromuscular training interventions. Specifically, interventions that integrate concomitant sensorimotor feedback with injury prevention techniques have the potential to enhance brain functional connectivity to optimize ACL injury risk reduction strategies. To deliver concomitant sensorimotor feedback, we have developed an augmented neuromuscular training (aNMT) system that utilizes interactive, real-time biofeedback to simultaneously target multiple biomechanical variables associated with ACL injury risk (Bonnette et al., in press; Figure 1A). aNMT calculates and maps key biomechanical parameters to an interactive graphical shape that responds in real time as a function of participants’ movements. Participants are instructed to perform exercises to achieve a goal shape, which equates to producing biomechanical parameters associated with ACL injury risk reduction, while deviations toward injury risk factors result in specific shape distortions. We hypothesized that aNMT would significantly improve biomechanics associated with ACL injury risk and also increase knee-motor functional connectivity. We further predicted that the identified connectivity changes would be associated with the hypothesized changes in biomechanics. Methods: Over six weeks of training, participants (n = 25) performed a series of aNMT-based progressive exercises (e.g., squat, overhead squat, squat jump, tuck jump, single-leg Romanian dead lift, pistol squat) and completed a drop vertical jump (DVJ) task while fully instrumented for 3D motion analysis pre- and post-training. Peak knee abduction moment (pKAM; bilateral average) from the DVJ was used as the biomechanical outcome variable. Resting-state functional magnetic resonance imaging (fMRI) scans were also collected pre- and post-training on a subset of the cohort (n = 17). Thirteen additional participants were recruited to serve as untrained controls and completed the DVJ and resting-state fMRI on two testing sessions separated by approximately 6 weeks. Twenty-five knee-motor regions of interest (ROIs) were created based on the areas of brain activation derived from previously published data (Grooms et al., 2015; Kapreli et al., 2007). Paired-samples t tests with a false discovery rate correction for multiple comparisons determined differences in functional connectivity among these 25 ROIs (post > pre). Fisher-transformed Pearson correlation coefficients between the average residual blood oxygen level dependent (BOLD) signal time series extracted from ROIs that demonstrated significant group level changes were associated with pKAM in DVJ task at pre- and post-training. The pre- and post-training Pearson correlation coefficients were subsequently compared using the cocor package (Diedenhofen & Musch, 2015) to determine if the two relationships were significantly different. Results: Results showed that pKAM in the aNMT group was significantly lower following aNMT (p < .05), while no significant changes were found between the two time points for controls (p > .05). Results also revealed significantly greater functional connectivity between the right supplementary motor area (SMA) and the left thalamus at post-training relative to pre-training for the aNMT group, t(16) = 3.37, p = .04 (Figure 1B). No significant differences between the two time points were observed for the controls (all p > .05). The association between pKAM and the right SMA and left thalamus at pre-training (r = -.22; Figure 1C) was significantly different compared to that at post-training (r = .26; Figure 1D), p < .05, with a positive relationship between pKAM and SMA and thalamus activation following aNMT biofeedback. No similar changes in pKAM and right SMA and left thalamus connectivity were observed for the untrained controls, p > .05. Conclusions/Significance: The right SMA is involved in the planning and coordination of movement, and the left thalamus is associated with neuromotor control. The increased functional connectivity between these regions, combined with the reduction in pKAM, which is associated with reduced risk of ACL injury, indicate a possible neural mechanism for improved motor adaption associated with aNMT biofeedback. These findings have distinct implications for ACL injury prevention strategies. Biofeedback tools such as aNMT can be designed to target specifically the neural drivers of aberrant movement biomechanics underlying increased ACL injury risk. [Figure: see text]

scholarly journals Development of a Spatial Decision Support System for Real-Time Flood Early Warning in the Vu Gia-Thu Bon River Basin, Quang Nam Province, Vietnam

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1667 ◽  
Author(s):  
Hong T. Nguyen ◽  
Trung Q. Duong ◽  
Liem D. Nguyen ◽  
Tram Q.N. Vo ◽  
Nhat T. Tran ◽  
...  
Keyword(s):  
Real Time ◽  
River Basin ◽  
Local Community ◽  
Warning System ◽  
Spatial Decision Support System ◽  
Flood Events ◽  
Spatial Decision Support ◽  
Flood Warning ◽  
Socio Economic Development ◽  
Flood Warning System

Vu Gia-Thu Bon (VGTB) river basin is an area where flash flood and heavy flood events occur frequently, negatively impacting the local community and socio-economic development of Quang Nam Province. In recent years, structural and non–structural solutions have been implemented to mitigate damages due to floods. However, under the impact of climate change, natural disasters continue to happen unpredictably day by day. It is, therefore, necessary to develop a spatial decision support system for real-time flood warnings in the VGTB river basin, which will support in ensuring the area’s socio-economic development. The main purpose of this study is to develop an online flood warning system in real-time based on Internet-of-Things (IoT) technologies, GIS, telecommunications, and modeling (Soil and Water Assessment Tool (SWAT) and Hydrologic Engineering Center’s River Analysis System (HEC–RAS)) in order to support the local community in the vulnerable downstream areas in the event of heavy rainfall upstream. The structure of the designed system consists of these following components: (1) real-time hydro-meteorological monitoring network, (2) IoT communication infrastructure (Global System for Mobile Communications (GSM), General Packet Radio Service (GPRS), wireless networks), (3) database management system (bio-physical, socio-economic, hydro-meteorological, and inundation), (4) simulating and predicting model (SWAT, HEC–RAS), (5) automated simulating and predicting module, (6) flood warning module via short message service (SMS), (7) WebGIS, application for providing and managing hydro-meteorological and inundation data, and (8) users (citizens and government officers). The entire operating processes of the flood warning system (i.e., hydro-meteorological data collecting, transferring, updating, processing, running SWAT and HEC–RAS, visualizing) are automated. A complete flood warning system for the VGTB river basin has been developed as an outcome of this study, which enables the prediction of flood events 5 h in advance and with high accuracy of 80%.

scholarly journals Comparison of the GPCP 1DD Precipitation Product and NEXRAD Q2 Precipitation Estimates over the Continental United States

2016 ◽  
Vol 17 (6) ◽  
pp. 1837-1853 ◽  
Author(s):  
Wenjun Cui ◽  
Xiquan Dong ◽  
Baike Xi ◽  
Ronald Stenz
Keyword(s):  
United States ◽  
Warm Season ◽  
Correlation Coefficients ◽  
Heavy Rain ◽  
Global Precipitation Climatology Project ◽  
Cold Season ◽  
Precipitation Estimation ◽  
Precipitation Estimates ◽  
Annual Means ◽  
Mean Differences

Abstract This study compares the Global Precipitation Climatology Project (GPCP) 1 Degree Daily (1DD) precipitation estimates over the continental United States (CONUS) with National Mosaic and Multi-Sensor Quantitative Precipitation Estimation (NMQ) Next Generation (Q2) estimates. Spatial averages of monthly and yearly accumulated precipitation were computed based on daily estimates from six selected regions during the period 2010–12. Both Q2 and GPCP daily precipitation estimates show that precipitation amounts over southern regions (&lt;40°N) are generally larger than northern regions (≥40°N). Correlation coefficients for daily estimates over selected regions range from 0.355 to 0.516 with mean differences (GPCP − Q2) varying from −0.86 to 0.99 mm. Better agreements are found in monthly estimates with the correlations varying from 0.635 to 0.787. For spatially averaged precipitation values averaged from grid boxes within selected regions, GPCP and Q2 estimates are well correlated, especially for monthly accumulated precipitation, with strong correlations ranging from 0.903 to 0.954. The comparisons between two datasets are also conducted for warm (April–September) and cold (October–March) seasons. During the warm season, GPCP estimates are 9.7% less than Q2 estimates, while during the cold season GPCP estimates exceed Q2 estimates by 6.9%. For precipitation over the CONUS, although annual means are close (978.54 for Q2 vs 941.79 mm for GPCP), Q2 estimates are much larger than GPCP over the central and southern United States and less than GPCP estimates in the northeastern United States. These results suggest that Q2 may have difficulties accurately estimating heavy rain and snow events, while GPCP may have an inability to capture some intense precipitation events, which warrants further investigation.

scholarly journals The Evaluation of the Tropospheric Ozone Variability in the Municipality of Suceava from the Meteorological Perspective

2018 ◽  
Vol 12 (2) ◽  
pp. 195-213 ◽  
Author(s):  
Dumitru Mihăilă ◽  
Andrei-Emil Briciu ◽  
Gina Ursul
Keyword(s):  
Wind Speed ◽  
Tropospheric Ozone ◽  
Meteorological Parameters ◽  
Road Traffic ◽  
Sunshine Duration ◽  
Pearson Correlation ◽  
Warm Season ◽  
Correlation Coefficients ◽  
Weekend Effect ◽  
Air Humidity

Abstract The daily, monthly and annual variations of the tropospheric ozone in the area of Suceava municipality are described and explained by using the correlations between the ozone concentration and the local meteorological parameters. The meteorological parameters are as follows: the air temperature, the air humidity, the sunshine duration, the wind speed and direction. All parameters represent hourly datasets recorded in the interval 2004-2007. The Pearson correlation coefficients and the linear regressions were obtained for daily, monthly and yearly scales. Significant positive correlations between the O3 and the temperature and strong negative correlations between the O3 and the air humidity were found, especially between 11 a.m. and 8 p.m., during the warm season. The sunshine duration and the wind speed and direction were found to have weaker, but good positive correlation with the O3 during the same hourly interval. The weekend effect of the ozone exists in the City of Suceava too, as the reduced road traffic during the weekend causes higher concentrations of ozone. The wavelet analyses were conducted in order to further explain the variability of the ozone.

scholarly journals Adequacy of Near Real-Time Satellite Precipitation Products in Driving Flood Discharge Simulation in the Fuji River Basin, Japan

2021 ◽  
Vol 11 (3) ◽  
pp. 1087
Author(s):  
Li Zhou ◽  
Mohamed Rasmy ◽  
Kuniyoshi Takeuchi ◽  
Toshio Koike ◽  
Hemakanth Selvarajah ◽  
...  
Keyword(s):  
Real Time ◽  
River Basin ◽  
Bias Correction ◽  
Large Scale ◽  
River Flow ◽  
Hydrologic Model ◽  
Satellite Precipitation ◽  
Flood Discharge ◽  
Discharge Simulation ◽  
The Impact

Flood management is an important topic worldwide. Precipitation is the most crucial factor in reducing flood-related risks and damages. However, its adequate quality and sufficient quantity are not met in many parts of the world. Currently, near real-time satellite precipitation products (NRT SPPs) have great potential to supplement the gauge rainfall. However, NRT SPPs have several biases that require corrections before application. As a result, this study investigated two statistical bias correction methods with different parameters for the NRT SPPs and evaluated the adequacy of its application in the Fuji River basin. We employed Global Satellite Mapping of Precipitation (GSMaP)-NRT and Integrated Multi-satellitE Retrievals for GPM (IMERG)-Early for NRT SPPs as well as BTOP model (Block-wise use of the TOPMODEL (Topographic-based hydrologic model)) for flood runoff simulation. The results showed that the corrected SPPs by the 10-day ratio based bias correction method are consistent with the gauge data at the watershed scale. Compared with the original SPPs, the corrected SPPs improved the flood discharge simulation considerably. GSMaP-NRT and IMERG-Early have the potential for hourly river-flow simulation on a basin or large scale after bias correction. These findings can provide references for the applications of NRT SPPs in other basins for flood monitoring and early warning applications. It is necessary to investigate the impact of number of ground observation and their distribution patterns on bias correction and hydrological simulation efficiency, which is the future direction of this study.

Post and near real-time satellite precipitation products skill over Karkheh River Basin in Iran

2020 ◽  
Vol 41 (17) ◽  
pp. 6484-6502
Author(s):  
Hamidreza Mosaffa ◽  
Amin Shirvani ◽  
Davar Khalili ◽  
Phu Nguyen ◽  
Soroosh Sorooshian
Keyword(s):  
Real Time ◽  
River Basin ◽  
Satellite Precipitation ◽  
Karkheh River Basin

Building a Real Time Plantar Force Measurement Sensor With LC Resonator

2017 ◽  
Author(s):  
Xinyu Chen ◽  
Yanrui Guo ◽  
Jiaoyun Yang ◽  
Hongtu Chen ◽  
Ning An
Keyword(s):  
Real Time ◽  
Force Measurement ◽  
Pearson Correlation ◽  
Correlation Coefficients ◽  
Heel Strike ◽  
Resonant Circuit ◽  
Sensing System ◽  
Coil Inductance ◽  
Lc Resonator ◽  
Plantar Force

The development of wearable equipment is posing new challenges to traditional sensors. Currently, it is important to reduce sensor size and improve sensor sensitivity so that data could be collected without interfering users’ common behavior. This article illustrates a new method for real time plantar force measurement. By paralleling planar inductive sensing coils with capacitors to form a LC resonant circuit and monitoring the change of resonant frequency or inductance, the occurrence of heel-strike and toe-off could be detected, because the change of the clearances between foot and insole triggers the simultaneous alternation of coil inductance. We conducted experiments on two different types of coils and compared them with force sensitive resistor (FSR). It is found that the Pearson correlation coefficients of these two coils’ inductance with the output voltage of FSR conversion circuit are −0.9780 and −0.7788, respectively. With smaller size and less expensive than traditional resistive sensors, this new sensing system could precisely reflect different gaits of walkers when tested in realistic situations.

Sign in / Sign up

Export Citation Format

Share Document