A Regionalized Stochastic Rainfall Model for the Generation of High Resolution Data in Peninsular Malaysia

2016 ◽  
Vol 10 (5) ◽  
pp. 77 ◽  
Author(s):  
Zalina Mohd Daud ◽  
Siti Musliha Mat Rasid ◽  
Norzaida Abas
Keyword(s):  
High Resolution ◽  
Synthetic Data ◽  
Rectangular Pulse ◽  
Peninsular Malaysia ◽  
Rainfall Series ◽  
Statistical Characteristics ◽  
Model Parameters ◽  
High Resolution Data ◽  
Ungauged Sites ◽  
Hourly Rainfall

<p>High resolution rainfall data is an important input for studies on hydrological systems. Often time synthetic data has to be generated in the absence of historical data.  The stochastic Neyman Scott Rectangular Pulse (NSRP) model has been developed to produce synthetic data of high resolution. To take into account the spatial and temporal nature of rainfall, an extra domain had been added producing the Spatial Temporal NSRP (ST-NSRP) model. However these models require the repeat estimation of all the model parameters for each location. This work develops the Regionalized ST-NSRP model which produces a single model for a region. The regionalization approach is carried out by the station-year method using ten years (2001-2010) of records from sixteen stations. The statistical characteristics used in estimating the model’s parameters are hourly and daily coefficient of variation, hourly and daily lag one auto correlation, hourly cross correlation between sites and hourly skewness of rainfall series. Simulation process is conducted at two independent stations with distinctly different rainfall profile. The Regionalized ST-NSRP model produced statistical characteristics of the ungauged sites which matched those of the observed series fairly well even though there is a tendency to underestimate hourly covariance and lag one autocorrelation. Considering that spatial variability of rainfall is high within the studied region, this model is sufficiently robust in producing reasonable synthetic hourly rainfall series which captured the characteristics and pattern of observed rainfall series. Therefore the model has shown a promising potential in generating high resolution synthetic data for any ungauged site within the region.</p>

scholarly journals Stochastic Modeling of Rainfall in Peninsular Malaysia Using Bartlett Lewis Rectangular Pulses Models

2011 ◽  
Vol 2011 ◽  
pp. 1-6 ◽  
Author(s):  
Ibrahim Suliman Hanaish ◽  
Kamarulzaman Ibrahim ◽  
Abdul Aziz Jemain
Keyword(s):  
Objective Function ◽  
Goodness Of Fit ◽  
Optimization Technique ◽  
Generalized Method Of Moments ◽  
Rain Gauge ◽  
Peninsular Malaysia ◽  
Weight Functions ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Hourly Rainfall

Three versions of Bartlett Lewis rectangular pulse rainfall models, namely, the Original Bartlett Lewis (OBL), Modified Bartlett Lewis (MBL), and 2N-cell-type Bartlett Lewis model (BL2n), are considered. These models are fitted to the hourly rainfall data from 1970 to 2008 obtained from Petaling Jaya rain gauge station, located in Peninsular Malaysia. The generalized method of moments is used to estimate the model parameters. Under this method, minimization of two different objective functions which involve different weight functions, one weight is inversely proportional to the variance and another one is inversely proportional to the mean squared, is carried out using Nelder-Mead optimization technique. For the purpose of comparison of the performance of the three different models, the results found for the months of July and November are used for illustration. This performance is assessed based on the goodness of fit of the models. In addition, the sensitivity of the parameter estimates to the choice of the objective function is also investigated. It is found thatBL2nslightly outperformsOBL. However, the best model is the Modified Bartlett LewisMBL, particularly when the objective function considered involves weight which is inversely proportional to the variance.

scholarly journals A High-Resolution Dataset of Water Fluxes and States for Germany Accounting for Parametric Uncertainty

2016 ◽  
Author(s):  
Matthias Zink ◽  
Rohini Kumar ◽  
Matthias Cuntz ◽  
Luis Samaniego
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Groundwater Recharge ◽  
Land Surface ◽  
Large Scale ◽  
Parametric Uncertainty ◽  
Model Parameters ◽  
High Resolution Data ◽  
Daily Discharge ◽  
Resolution Dataset

Abstract. Long term, high-resolution data about hydrologic fluxes and states are needed for many hydrological applications. Because continuous large-scale observations of such variables are not feasible, hydrologic or land surface models are applied to derive them. This study aims to analyze and provide a consistent high-resolution dataset of land surface variables over Germany, accounting for uncertainties caused by equifinal model parameters. The mesoscale Hydrological Model (mHM) is employed to derive an ensemble (100 members) of evapotranspiration, groundwater recharge, soil moisture and generated runoff at high spatial and temporal resolutions (4 km and daily, respectively) for the period 1951–2010. The model is cross-evaluated against the observed runoff in 222 catchments, which are not used for model calibration. The mean (standard deviation) of the ensemble median NSE estimated for these catchments is 0.68 (0.09) for daily discharge simulations. The modeled evapotranspiration and soil moisture reasonably represent the observations from eddy covariance stations. Our analysis indicates the lowest parametric uncertainty for evapotranspiration, and the largest is observed for groundwater recharge. The uncertainty of the hydrologic variables varies over the course of a year, with the exception of evapotranspiration, which remains almost constant. This study emphasizes the role of accounting for the parametric uncertainty in model-derived hydrological datasets.

scholarly journals Circulation pattern based parameterization of a multiplicative random cascade for disaggregation of observed and projected daily rainfall time series

2013 ◽  
Vol 17 (7) ◽  
pp. 2487-2500 ◽  
Author(s):  
D. Lisniak ◽  
J. Franke ◽  
C. Bernhofer
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Global Climate ◽  
Statistical Properties ◽  
Rainfall Data ◽  
Time Slice ◽  
Model Parameters ◽  
Rainfall Time Series ◽  
Rainfall Characteristics ◽  
Hourly Rainfall

Abstract. The use of multiplicative random cascades (MRCs) for temporal rainfall disaggregation has been extensively studied in the past. MRCs are appealing for rainfall disaggregation due to their formal simplicity and the possibility to extract the model parameters directly from observed high resolution rainfall data. These parameters, however, represent the rainfall characteristics of the observation period. Since rainfall characteristics of different time slices are changing due to climate variability, we propose a parameterization approach for MRCs to adjust the parameters according to past (observed) or future (projected) time series. This is done on the basis of circulation patterns (CPs) by extracting a distinct MRC parameterization from high resolution rainfall data, as observed on days governed by each individual CP. The parameterization approach is tested by comparing the statistical properties of disaggregated rainfall time series of two time slices, 1969–1979 and 1989–1999, to the results obtained by two other disaggregation methods (a conceptually similar MRC without CP-based parameterization and a recombination approach) and to the statistical properties of observed hourly rainfall data. In this context, all three approaches use rainfall data of the time slice 1989–1999 for parameterization. We found that the inclusion of CPs into the parameterization of a MRC yields hourly time series that better reproduce the properties of observed rainfall in time slice 1989–1999, as compared to the simple MRC. Despite similar results of both MRCs in the validation period of 1969–1979, we can conclude that the CP-based parameterization approach is applicable for temporal rainfall disaggregation in time slices distinct from the parameterization period. This approach accounts for changes in rainfall characteristics due to changes in the frequency of occurrence of the CPs and allows generating hourly rainfall from daily data, as often provided by a statistical downscaling of global climate change.

scholarly journals Circulation pattern based parameterization of a multiplicative random cascade for disaggregation of daily rainfall under nonstationary climatic conditions

2012 ◽  
Vol 9 (9) ◽  
pp. 10115-10149 ◽  
Author(s):  
D. Lisniak ◽  
J. Franke ◽  
C. Bernhofer
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Climatic Conditions ◽  
Statistical Properties ◽  
Rainfall Data ◽  
Time Slice ◽  
Model Parameters ◽  
Rainfall Time Series ◽  
Rainfall Characteristics ◽  
Hourly Rainfall

Abstract. The use of multiplicative random cascades (MRCs) for temporal rainfall disaggregation has been extensively studied in the past. MRCs are appealing for rainfall disaggregation due to their formal simplicity and the possibility to extract the model parameters directly from observed high resolution rainfall data. These parameters, however, represent the rainfall characteristics of the observation period. Since rainfall characteristics of different time slices are changing due to climate variability, we propose a parameterization approach for MRCs to adjust the parameters according to past (observed) or future (projected) time series. This is done on the basis of circulation patterns (CPs) by extracting a distinct MRC parameterization from high resolution rainfall data, as observed on days governed by each individual CP. The parameterization approach is tested by comparing the statistical properties of disaggregated rainfall time series of two time slices, 1969–1979 and 1989–1999, to the results obtained by two other disaggregation methods (a conceptually similar MRC without CP-based parameterization and a recombination approach) and to the statistical properties of observed hourly rainfall data. In this context, all three approaches use rainfall data of the time slice 1989–1999 for parameterization. We found that the inclusion of CPs into the parameterization of a MRC yields hourly time series that better reproduce the properties of observed rainfall in time slice 1989–1999, as compared to the simple MRC. Despite similar properties of both MRCs for the time slice 1969–1979, we can conclude that the CP-based parameterization approach is applicable for temporal rainfall disaggregation in time slices distinct from the parameterization period. This approach accounts for changes in rainfall characteristics due to changes in the frequency of occurrence of the CPs and allows generating hourly rainfall from daily data, as often provided by a statistical downscaling of global climate change.

scholarly journals A high-resolution dataset of water fluxes and states for Germany accounting for parametric uncertainty

2017 ◽  
Vol 21 (3) ◽  
pp. 1769-1790 ◽  
Author(s):  
Matthias Zink ◽  
Rohini Kumar ◽  
Matthias Cuntz ◽  
Luis Samaniego
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Groundwater Recharge ◽  
Land Surface ◽  
Large Scale ◽  
Parametric Uncertainty ◽  
Model Parameters ◽  
High Resolution Data ◽  
Daily Streamflow ◽  
Resolution Dataset

Abstract. Long-term, high-resolution data about hydrologic fluxes and states are needed for many hydrological applications. Because continuous large-scale observations of such variables are not feasible, hydrologic or land surface models are applied to derive them. This study aims to analyze and provide a consistent high-resolution dataset of land surface variables over Germany, accounting for uncertainties caused by equifinal model parameters. The mesoscale Hydrological Model (mHM) is employed to derive an ensemble (100 members) of evapotranspiration, groundwater recharge, soil moisture, and runoff generated at high spatial and temporal resolutions (4 km and daily, respectively) for the period 1951–2010. The model is cross-evaluated against the observed daily streamflow in 222 basins, which are not used for model calibration. The mean (standard deviation) of the ensemble median Nash–Sutcliffe efficiency estimated for these basins is 0.68 (0.09) for daily streamflow simulations. The modeled evapotranspiration and soil moisture reasonably represent the observations from eddy covariance stations. Our analysis indicates the lowest parametric uncertainty for evapotranspiration, and the largest is observed for groundwater recharge. The uncertainty of the hydrologic variables varies over the course of a year, with the exception of evapotranspiration, which remains almost constant. This study emphasizes the role of accounting for the parametric uncertainty in model-derived hydrological datasets.

scholarly journals The Effects of Chemical Bonding at Subatomic Resolution: A Case Study on α-Boron

Molecules ◽  
2021 ◽  
Vol 26 (14) ◽  
pp. 4270
Author(s):  
Andreas Fischer ◽  
Georg Eickerling ◽  
Wolfgang Scherer
Keyword(s):  
High Resolution ◽  
Core Region ◽  
Model Parameters ◽  
High Quality ◽  
Single Crystal Diffraction ◽  
High Resolution Data ◽  
Theoretical Structure ◽  
Structure Factors ◽  
Resolution Data

Similar to classical asphericity shifts, aspherical deformations of the electron density in the atomic core region can result in core asphericity shifts in refinements using a Hansen-Coppens multipolar model (HCM), especially when highly precise experimental datasets with resolutions far beyond sin(θ)/λ ≤ 1.0 Å−1 are employed. These shifts are about two orders of magnitude smaller than their counterparts caused by valence shell deformations, and their underlying deformations are mainly of dipolar character for 1st row atoms. Here, we analyze the resolution dependence of core asphericity shifts in α-boron. Based on theoretical structure factors, an appropriate Extended HCM (EHCM) is developed, which is tested against experimental high-resolution (sin(θ)/λ ≤ 1.6 Å−1) single-crystal diffraction data. Bond length deviations due to core asphericity shifts of α-boron in the order of 4–6·10−4 Å are small but significant at this resolution and can be effectively compensated by an EHCM, although the correlation of the additional model parameters with positional parameters prevented a free refinement of all core model parameters. For high quality, high resolution data, a proper treatment with an EHCM or other equivalent methods is therefore highly recommended.

scholarly journals High-Resolution Traffic Sensing with Probe Autonomous Vehicles: A Data-Driven Approach

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 464
Author(s):  
Wei Ma ◽  
Sean Qian
Keyword(s):  
High Resolution ◽  
Autonomous Vehicles ◽  
Penetration Rate ◽  
Data Driven ◽  
Traffic Information ◽  
Flow Density ◽  
Massive Data ◽  
Market Penetration ◽  
High Resolution Data ◽  
Data Driven Approach

Recent decades have witnessed the breakthrough of autonomous vehicles (AVs), and the sensing capabilities of AVs have been dramatically improved. Various sensors installed on AVs will be collecting massive data and perceiving the surrounding traffic continuously. In fact, a fleet of AVs can serve as floating (or probe) sensors, which can be utilized to infer traffic information while cruising around the roadway networks. Unlike conventional traffic sensing methods relying on fixed location sensors or moving sensors that acquire only the information of their carrying vehicle, this paper leverages data from AVs carrying sensors for not only the information of the AVs, but also the characteristics of the surrounding traffic. A high-resolution data-driven traffic sensing framework is proposed, which estimates the fundamental traffic state characteristics, namely, flow, density and speed in high spatio-temporal resolutions and of each lane on a general road, and it is developed under different levels of AV perception capabilities and for any AV market penetration rate. Experimental results show that the proposed method achieves high accuracy even with a low AV market penetration rate. This study would help policymakers and private sectors (e.g., Waymo) to understand the values of massive data collected by AVs in traffic operation and management.

scholarly journals A flexible framework for sequential estimation of model parameters in computational hemodynamics

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Christopher J. Arthurs ◽  
Nan Xiao ◽  
Philippe Moireau ◽  
Tobias Schaeffter ◽  
C. Alberto Figueroa
Keyword(s):  
Unscented Kalman Filter ◽  
Three Dimensional ◽  
Synthetic Data ◽  
Sequential Estimation ◽  
Wall Material ◽  
Patient Specific ◽  
Model Parameters ◽  
Computational Framework ◽  
Lumped Parameter ◽  
Estimation Of Model Parameters

AbstractA major challenge in constructing three dimensional patient specific hemodynamic models is the calibration of model parameters to match patient data on flow, pressure, wall motion, etc. acquired in the clinic. Current workflows are manual and time-consuming. This work presents a flexible computational framework for model parameter estimation in cardiovascular flows that relies on the following fundamental contributions. (i) A Reduced-Order Unscented Kalman Filter (ROUKF) model for data assimilation for wall material and simple lumped parameter network (LPN) boundary condition model parameters. (ii) A constrained least squares augmentation (ROUKF-CLS) for more complex LPNs. (iii) A “Netlist” implementation, supporting easy filtering of parameters in such complex LPNs. The ROUKF algorithm is demonstrated using non-invasive patient-specific data on anatomy, flow and pressure from a healthy volunteer. The ROUKF-CLS algorithm is demonstrated using synthetic data on a coronary LPN. The methods described in this paper have been implemented as part of the CRIMSON hemodynamics software package.

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