scholarly journals A Dynamical Z-R Relationship for Precipitation Estimation Based on Radar Echo-Top Height Classification

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Wenxin Wu ◽  
Haibo Zou ◽  
Jiusheng Shan ◽  
Shanshan Wu
Keyword(s):  
Mean Squared Error ◽  
The Other ◽  
Intense Rainfall ◽  
Rainfall Events ◽  
Quantitative Precipitation Estimation ◽  
Squared Error ◽  
Hourly Rainfall ◽  
Precipitation Estimation ◽  
Radar Echo ◽  
Short Time

Using echo-top height and hourly rainfall datasets, a new reflectivity-rainfall (Z-R) relationship is established in the present study for the radar-based quantitative precipitation estimation (RQPE), taking into account both the temporal evolution (dynamical) and the types of echoes (i.e., based on echo-top height classification). The new Z-R relationship is then applied to derive the RQPE over the middle and lower reaches of Yangtze River for two short-time intense rainfall cases in summer (2200 UTC 1 June 2016 and 2200 UTC 18 June 2016) and one stratiform rainfall case in winter (0000 UTC 15 December 2017), and then the comparative analyses between the RQPE and the RQPEs derived by the other two methods (the fixed Z-R relationship and the dynamical Z-R relationship based on radar reflectivity classification) are accomplished. The results show that the RQPE from the new Z-R relationship is much closer to the observation than those from the other two methods because the new method simultaneously considers the echo intensity (reflecting the size and concentration of hydrometers to a certain extent) and the echo-top height (reflecting the updraft to a certain extent). Two statistics of 720 rainfall events in summer (April to June 2017) and 50 rainfall events in winter (December 2017) over the same region show that the correlation coefficient (root-mean-squared error and relative error) between RQPE derived by the new Z-R relationship and observation is significantly increased (decreased) compared to the other two Z-R relationships. Besides, the new Z-R relationship is also good at estimating rainfall with different intensities as compared to the other two methods, especially for the intense rainfall.

scholarly journals Thematic Maps for the Variation of Bearing Capacity of Soil Using SPTs and MATLAB

Geosciences ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 329
Author(s):  
Mahdi O. Karkush ◽  
Mahmood D. Ahmed ◽  
Ammar Abdul-Hassan Sheikha ◽  
Ayad Al-Rumaithi
Keyword(s):  
Bearing Capacity ◽  
Mean Squared Error ◽  
Ground Level ◽  
The Other ◽  
Thematic Maps ◽  
First Order ◽  
Squared Error ◽  
Order Polynomial ◽  
Interpolation Polynomials ◽  
Penetration Tests

The current study involves placing 135 boreholes drilled to a depth of 10 m below the existing ground level. Three standard penetration tests (SPT) are performed at depths of 1.5, 6, and 9.5 m for each borehole. To produce thematic maps with coordinates and depths for the bearing capacity variation of the soil, a numerical analysis was conducted using MATLAB software. Despite several-order interpolation polynomials being used to estimate the bearing capacity of soil, the first-order polynomial was the best among the other trials due to its simplicity and fast calculations. Additionally, the root mean squared error (RMSE) was almost the same for the all of the tried models. The results of the study can be summarized by the production of thematic maps showing the variation of the bearing capacity of the soil over the whole area of Al-Basrah city correlated with several depths. The bearing capacity of soil obtained from the suggested first-order polynomial matches well with those calculated from the results of SPTs with a deviation of ±30% at a 95% confidence interval.

scholarly journals Improved Attenuation-Based Radar Precipitation Estimation Considering the Azimuthal Variabilities of Microphysical Properties

2020 ◽  
Vol 21 (7) ◽  
pp. 1605-1620
Author(s):  
Hao Huang ◽  
Kun Zhao ◽  
Haonan Chen ◽  
Dongming Hu ◽  
Peiling Fu ◽  
...  
Keyword(s):  
Variational Approach ◽  
Optimization Approach ◽  
Radar Rainfall ◽  
Microphysical Properties ◽  
Quantitative Precipitation Estimation ◽  
Hourly Rainfall ◽  
Precipitation Estimation ◽  
Raindrop Size ◽  
Differential Reflectivity ◽  
The Impact

AbstractThe attenuation-based rainfall estimator is less sensitive to the variability of raindrop size distributions (DSDs) than conventional radar rainfall estimators. For the attenuation-based quantitative precipitation estimation (QPE), the key is to accurately estimate the horizontal specific attenuation AH, which requires a good estimate of the ray-averaged ratio between AH and specific differential phase KDP, also known as the coefficient α. In this study, a variational approach is proposed to optimize the coefficient α for better estimates of AH and rainfall. The performance of the variational approach is illustrated using observations from an S-band operational weather radar with rigorous quality control in south China, by comparing against the α optimization approach using a slope of differential reflectivity ZDR dependence on horizontal reflectivity factor ZH. Similar to the ZDR-slope approach, the variational approach can obtain the optimized α consistent with the DSD properties of precipitation on a sweep-to-sweep basis. The attenuation-based hourly rainfall estimates using the sweep-averaged α values from these two approaches show comparable accuracy when verified against the gauge measurements. One advantage of the variational approach is its feasibility to optimize α for each radar ray, which mitigates the impact of the azimuthal DSD variabilities on rainfall estimation. It is found that, based on the optimized α for radar rays, the hourly rainfall amounts derived from the variational approach are consistent with gauge measurements, showing lower bias (1.0%), higher correlation coefficient (0.92), and lower root-mean-square error (2.35 mm) than the results based on the sweep-averaged α.

scholarly journals An evaluation framework for identifying the optimal raingauge network based on spatiotemporal variation in quantitative precipitation estimation

2016 ◽  
Vol 48 (1) ◽  
pp. 77-98 ◽  
Author(s):  
Che-Hao Chang ◽  
Shiang-Jen Wu ◽  
Chih-Tsung Hsu ◽  
Jhih-Cyuan Shen ◽  
Ho-Cheng Lien
Keyword(s):  
Temporal Variation ◽  
Evaluation Framework ◽  
Spatial And Temporal Variation ◽  
Cluster Number ◽  
Data Set ◽  
Rainfall Characteristics ◽  
Quantitative Precipitation Estimation ◽  
Hourly Rainfall ◽  
Precipitation Estimation ◽  
Grid Based

This study proposes an evaluation framework to identify the optimal raingauge network in a watershed using grid-based quantitative precipitation estimation (QPE) with high spatial and temporal resolution. The proposed evaluation framework is based on comparison of the spatial and temporal variation in rainfall characteristics (i.e. rainfall depth and storm pattern) from the gauged data compared with those from QPE. The proposed framework first utilizes cluster analysis to separate raingauges into various clusters based on the locations and rainfall characteristics. Then, a cross-validation algorithm is used to identify the influential raingauge in each cluster based on evaluating performance of fitting weighted spatiotemporal semivariograms of rainfall characteristics from the gauged rainfall to the QPE data. Thus, the influential raingauges for a specific cluster number form the representative network. The optimal raingauge network is the one corresponding to the best fitness performance among the representative networks considered. The study area and data set are the hourly rainfall from 26 raingauges and 1,336 QPE grids for 10 typhoons in the Wu River watershed located in central Taiwan. The proposed evaluation framework suggests that a 10-gauge network is the optimal and can describe a good spatial and temporal variation in the rain field similar to the grid-based QPE from two additional typhoon events.

scholarly journals RainFARM: Rainfall Downscaling by a Filtered Autoregressive Model

2006 ◽  
Vol 7 (4) ◽  
pp. 724-738 ◽  
Author(s):  
Nicola Rebora ◽  
Luca Ferraris ◽  
Jost von Hardenberg ◽  
Antonello Provenzale
Keyword(s):  
Random Field ◽  
Autoregressive Model ◽  
Gaussian Random Field ◽  
Statistical Properties ◽  
The Other ◽  
Small Scale ◽  
Intense Rainfall ◽  
Rainfall Events ◽  
The Tropics ◽  
Meteorological Models

Abstract A method is introduced for stochastic rainfall downscaling that can be easily applied to the precipitation forecasts provided by meteorological models. Our approach, called the Rainfall Filtered Autoregressive Model (RainFARM), is based on the nonlinear transformation of a Gaussian random field, and it conserves the information present in the rainfall fields at larger scales. The procedure is tested on two radar-measured intense rainfall events, one at midlatitude and the other in the Tropics, and it is shown that the synthetic fields generated by RainFARM have small-scale statistical properties that are consistent with those of the measured precipitation fields. The application of the disaggregation procedure to an example meteorological forecast illustrates how the method can be implemented in operational practice.

scholarly journals Deep Learning for Precipitation Estimation from Satellite and Rain Gauges Measurements

2019 ◽  
Vol 11 (21) ◽  
pp. 2463
Author(s):  
Arthur Moraux ◽  
Steven Dewitte ◽  
Bruno Cornelis ◽  
Adrian Munteanu
Keyword(s):  
Deep Learning ◽  
Multiscale Analysis ◽  
Mean Squared Error ◽  
Rain Gauge ◽  
Probability Of Detection ◽  
Precipitation Rate ◽  
Rain Gauges ◽  
Squared Error ◽  
Precipitation Estimation ◽  
Deep Learning Model

This paper proposes a multimodal and multi-task deep-learning model for instantaneous precipitation rate estimation. Using both thermal infrared satellite radiometer and automatic rain gauge measurements as input, our encoder–decoder convolutional neural network performs a multiscale analysis of these two modalities to estimate simultaneously the rainfall probability and the precipitation rate value. Precipitating pixels are detected with a Probability Of Detection (POD) of 0.75 and a False Alarm Ratio (FAR) of 0.3. Instantaneous precipitation rate is estimated with a Root Mean Squared Error (RMSE) of 1.6 mm/h.

Finite Sample Properties of Several Predictors From an Autoregressive Model

1987 ◽  
Vol 3 (3) ◽  
pp. 359-370 ◽  
Author(s):  
Koichi Maekawa
Keyword(s):  
Maximum Likelihood ◽  
Sample Size ◽  
Asymptotic Expansions ◽  
Autoregressive Model ◽  
Mean Squared Error ◽  
The Other ◽  
Finite Sample ◽  
Squared Error ◽  
Distributional Properties ◽  
Finite Sample Properties

We compare the distributional properties of the four predictors commonly used in practice. They are based on the maximum likelihood, two types of the least squared, and the Yule-Walker estimators. The asymptotic expansions of the distribution, bias, and mean-squared error for the four predictors are derived up to O(T−1), where T is the sample size. Examining the formulas of the asymptotic expansions, we find that except for the Yule-Walker type predictor, the other three predictors have the same distributional properties up to O(T−1).

scholarly journals Radar Quantitative Precipitation Estimation improvement in Champagne vineyard.

2018 ◽  
Vol 50 ◽  
pp. 02013
Author(s):  
Basile PAUTHIER ◽  
Sébastien DEBUISSON ◽  
Arnaud DESCOTES ◽  
Julien PERGAUD ◽  
Sylvain MAILLARD
Keyword(s):  
Soil Erosion ◽  
Nitrogen Mineralization ◽  
Radar Data ◽  
Crucial Importance ◽  
Rainfall Events ◽  
Quantitative Precipitation Estimation ◽  
Spatial Coverage ◽  
Precipitation Estimation ◽  
Weather Stations ◽  
External Drift

Rainfall has a crucial importance in viticulture, especially in Champagne vineyards, where irrigation is prohibited. Rainfall directly influences the phytosanitary pressure, nitrogen mineralization, flowering conditions, parcel practicability, soil erosion etc… In these conditions, implementing a weather stations network is the solution that the Comité Champagne chose to monitor rainfall all over the Champagne appellation since the 1990's. This networks is actually composed of 42 weather stations implemented in order to have the best spatial coverage as possible. The Comité Champagne also obtain some weather stations data from Météo France, the French national weather service. Even with that network, capturing all rainfall events accurately is difficult, especially in convective cases. Therefore, the interest in radar data has increased, to capture rainfall everywhere. Some tests have been previously made with PANTHERE radar data from Météo France with a resolution of 1 km2, results were promising, but presented inaccuracies particularly in convective events. In this article, we use a radar merging technique similar to the ANTILOPE method from Météo France, with a higher resolution network. The tool employed is the Estimages toolbox merger, based on krigine with external drift (KED) which has been demonstrated to give good results in quantitative precipitation estimation (QPE) improvement.

scholarly journals Deep Learning Based Retrieval of Forest Aboveground Biomass from Combined LiDAR and Landsat 8 Data

2019 ◽  
Vol 11 (12) ◽  
pp. 1459 ◽  
Author(s):  
Linjing Zhang ◽  
Zhenfeng Shao ◽  
Jianchen Liu ◽  
Qimin Cheng
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Mean Squared Error ◽  
Forest Biomass ◽  
The Other ◽  
Biomass Estimation ◽  
Lidar Data ◽  
Landsat 8 ◽  
Squared Error ◽  
Forest Aboveground Biomass

Estimation of forest aboveground biomass (AGB) is crucial for various technical and scientific applications, ranging from regional carbon and bioenergy policies to sustainable forest management. However, passive optical remote sensing, which is the most widely used remote sensing data for retrieving vegetation parameters, is constrained by spectral saturation problems and cloud cover. On the other hand, LiDAR data, which have been extensively used to estimate forest structure attributes, cannot provide sufficient spectral information of vegetation canopies. Thus, this study aimed to develop a novel synergistic approach to estimating biomass by integrating LiDAR data with Landsat 8 imagery through a deep learning-based workflow. First the relationships between biomass and spectral vegetation indices (SVIs) and LiDAR metrics were separately investigated. Next, two groups of combined optical and LiDAR indices (i.e., COLI1 and COLI2) were designed and explored to identify their performances in biomass estimation. Finally, five prediction models, including K-nearest Neighbor, Random Forest, Support Vector Regression, the deep learning model, i.e., Stacked Sparse Autoencoder network (SSAE), and multiple stepwise linear regressions, were individually used to estimate biomass with input variables of different scenarios, i.e., (i) all the COLI1 (ACOLI1), (ii) all the COLI2 (ACOLI2), (iii) ACOLI1 and all the optical (AO) and LiDAR variables (AL), and (iv) ACOLI2, AO and AL. Results showed that univariate models with the combined optical and LiDAR indices as explanatory variables presented better modeling performance than those with either optical or LiDAR data alone, regardless of the combination mode. The SSAE model obtained the best performance compared to the other tested prediction algorithms for the forest biomass estimation. The best predictive accuracy was achieved by the SSAE model with inputs of combined optical and LiDAR variables (i.e., ACOLI1, AO and AL) that yielded an R2 of 0.935, root mean squared error (RMSE) of 15.67 Mg/ha, and relative root mean squared error (RMSEr) of 11.407%. It was concluded that the presented combined indices were simple and effective by integrating LiDAR-derived structure information with Landsat 8 spectral data for estimating forest biomass. Overall, the SSAE model with inputs of Landsat 8 and LiDAR integrated information resulted in accurate estimation of forest biomass. The presented modeling workflow will greatly facilitate future forest biomass estimation and carbon stock assessments.

Rain Distribution in a Mountainous Watershed

1993 ◽  
Vol 24 (4) ◽  
pp. 225-242 ◽  
Author(s):  
A. Loukas ◽  
M.C. Quick
Keyword(s):  
The Other ◽  
Regression Equations ◽  
Watershed Model ◽  
Rainfall Events ◽  
Creek Watershed ◽  
Lower Elevation ◽  
Hourly Rainfall ◽  
Dependent Variables ◽  
Mountainous Watershed ◽  
Rainfall Depth

The orographic and temporal gradients of rainfall in a mountainous watershed in southwestern British Columbia have been analyzed and streamflow has been estimated using a watershed model. The study watershed is the Jamieson Creek watershed located approximately 30 km north of Vancouver in the Coastal Mountains. The purpose of the study was to determine whether rainfall follows a definable pattern in this mountainous watershed. Regression analysis has been performed for the total rainfall depth per event and hourly intensity for the period 1972-1975. Data is taken from the rainfall season of June to mid-November in order to avoid complications of combined rain and snow events. In this analysis, the rainfall data from a gauge at the lower elevation was used as the set of independent variables and the data from the other four gauges in the watershed as dependent variables. The results showed that the rainfall depth per event increased up to the mid-elevation of the watershed, and then decreased at the upper elevations. On the other hand, the hourly rainfall intensity was found to decrease with increase of elevation in the watershed, so that longer duration of rainfall events occurs at the middle and upper watershed. The regression equations, developed from the analysis of the distribution of the hourly intensity, were used for the prediction of rainfall events of the years 1976-1977. The agreement between the predicted and the observed rain was statistically good. Also, the simulation of the watershed streamflow using the predicted rainfall gave good results. Consequently, because the rainfall follows a definable distribution as a function of elevation, it is possible to use data from one station located at the lower elevation in combination with the developed predictor equations to accurately describe the rainfall over the watershed.

Estimation of genetic parameters for litter size in Danish Landrace and Yorkshire pigs

1995 ◽  
Vol 60 (2) ◽  
pp. 315-324 ◽  
Author(s):  
J. Estany ◽  
D. Sorensen
Keyword(s):  
Litter Size ◽  
Mean Squared Error ◽  
Predictive Ability ◽  
The Other ◽  
Genetic Trend ◽  
Genetic Groups ◽  
Squared Error ◽  
Random Models ◽  
Yorkshire Pigs ◽  
Predicted Values

AbstractVariance components for litter size (total number of piglets born) were estimated from Danish purebred Landrace and Yorkshire litters by restricting maximum likelihood. The data were collected from the national Danish breeding programme and consisted of 19 666 litters in Danish Landrace and 29 336 litters in Danish Yorkshire. Four different analyses for litter size were conducted within breed. In the first two, genetic groups were included in the model in order to account for the importation of animals from other countries; in the other two, genetic groups were removed from the model. Within each case, herd-year-type of insemination effects were fitted as fixed (H-fixed models), or herd-year-season-type of insemination effects were fitted as random (H-random models). Estimates of heritability ranged from about 0·11 to 0·14 in Landrace and from 0·10 to 0·11 in Yorkshire. Variance due to herd-year-season-type of insemination ranged from 0·029 to 0·041 of total variance, values somewhat lower than those obtained for non-genetic permanent effects. In order to compare the four models, data were divided into different subsets, and records from one subset were predicted using parameters estimated from the other subset. Both the correlation between observed and predicted values, and the mean squared error of prediction indicated that predictive ability was higher in the case of H-random models. There was no evidence that genetic groups improved the predictive ability for litter size. However, group effects affected inferences about genetic trend, particularly in Landrace, where genetic group composition changed consistently over the years.

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