scholarly journals Evaluation of the Impacts of Assimilating the TAMDAR Data on 12/4 km Grid WRF-Based RTFDDA Simulations over the CONUS

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Yongxin Zhang ◽  
Yubao Liu ◽  
Thomas Nipen
Keyword(s):  
Wind Speed ◽  
Data Assimilation ◽  
Real Time ◽  
Mean Squared Error ◽  
Meteorological Data ◽  
Horizontal Resolution ◽  
Weather Research And Forecasting ◽  
Squared Error ◽  
Forecasting System ◽  
Four Dimensional Data Assimilation

An analysis of the impacts of assimilating the Tropospheric Airborne Meteorological Data Report (TAMDAR) data with the Weather Research and Forecasting- (WRF-) real-time four-dimensional data assimilation (RTFDDA) and forecasting system over the Contiguous US (CONUS) is presented. The impacts of the horizontal resolution increase from 12 km to 4 km on the WRF-RTFDDA simulations are also examined in conjunction with the TAMDAR data impacts. The assimilation of the TAMDAR data reduces the root mean squared error of the moisture field predictions and increases the correlation between the predictions and the observations for both domains with 12 km and 4 km grid spacings. The TAMDAR data reduce the model dry biases in the middle and lower levels by adding moisture at those levels. Assimilating the TAMDAR data improves temperature predictions at middle to high levels and wind speed predictions at all levels especially for the 12 km domain. Increasing the horizontal resolution from 12 km to 4 km results in significantly larger impacts on surface variables than assimilating the TAMDAR data.

Slip-Clutch Torque Estimation via Real-Time Adaptive Lookup Table

2021 ◽  
Vol 2 (2) ◽  
Author(s):  
Wenpeng Wei ◽  
Hussein Dourra ◽  
Guoming Zhu
Keyword(s):  
Friction Coefficient ◽  
Real Time ◽  
Mean Squared Error ◽  
Surface Friction ◽  
Lookup Table ◽  
Recursive Least Squares ◽  
Time Varying ◽  
Squared Error ◽  
Output Torque ◽  
Clutch Torque

Abstract Transfer case clutch is crucial in determining traction torque distribution between front and rear tires for four-wheel-drive (4WD) vehicles. Estimating time-varying clutch surface friction coefficient is critical for traction torque control since it is proportional to the clutch output torque. As a result, this paper proposes a real-time adaptive lookup table strategy to provide the time-varying clutch surface friction coefficient. Specifically, the clutch-parameter-dependent (such as clutch output torque and clutch touchpoint distance) friction coefficient is first estimated with available low-cost vehicle sensors (such as wheel speed and vehicle acceleration); and then a clutch-parameter-independent approach is developed for clutch friction coefficient through a one-dimensional lookup table. The table nodes are adaptively updated based on a fast recursive least-squares (RLS) algorithm. Furthermore, the effectiveness of adaptive lookup table is demonstrated by comparing the estimated clutch torque from adaptive lookup table with that estimated from vehicle dynamics, which achieves 14.8 Nm absolute mean squared error (AMSE) and 2.66% relative mean squared error (RMSE).

scholarly journals A GSI-Based Coupled EnSRF–En3DVar Hybrid Data Assimilation System for the Operational Rapid Refresh Model: Tests at a Reduced Resolution

2014 ◽  
Vol 142 (10) ◽  
pp. 3756-3780 ◽  
Author(s):  
Yujie Pan ◽  
Kefeng Zhu ◽  
Ming Xue ◽  
Xuguang Wang ◽  
Ming Hu ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Hybrid System ◽  
Control Variable ◽  
Three Dimensional ◽  
Horizontal Resolution ◽  
Precipitation Forecast ◽  
Background Error ◽  
Hybrid Data Assimilation ◽  
Noticeable Improvement ◽  
Forecasting System

Abstract A coupled ensemble square root filter–three-dimensional ensemble-variational hybrid (EnSRF–En3DVar) data assimilation (DA) system is developed for the operational Rapid Refresh (RAP) forecasting system. The En3DVar hybrid system employs the extended control variable method, and is built on the NCEP operational gridpoint statistical interpolation (GSI) three-dimensional variational data assimilation (3DVar) framework. It is coupled with an EnSRF system for RAP, which provides ensemble perturbations. Recursive filters (RF) are used to localize ensemble covariance in both horizontal and vertical within the En3DVar. The coupled En3DVar hybrid system is evaluated with 3-h cycles over a 9-day period with active convection. All conventional observations used by operational RAP are included. The En3DVar hybrid system is run at ⅓ of the operational RAP horizontal resolution or about 40-km grid spacing, and its performance is compared to parallel GSI 3DVar and EnSRF runs using the same datasets and resolution. Short-term forecasts initialized from the 3-hourly analyses are verified against sounding and surface observations. When using equally weighted static and ensemble background error covariances and 40 ensemble members, the En3DVar hybrid system outperforms the corresponding GSI 3DVar and EnSRF. When the recursive filter coefficients are tuned to achieve a similar height-dependent localization as in the EnSRF, the En3DVar results using pure ensemble covariance are close to EnSRF. Two-way coupling between EnSRF and En3DVar did not produce noticeable improvement over one-way coupling. Downscaled precipitation forecast skill on the 13-km RAP grid from the En3DVar hybrid is better than those from GSI 3DVar analyses.

Artificial Neural Networks Estimation for Thicknesses of Multilayer Nano-Scale Films

2019 ◽  
Vol 962 ◽  
pp. 41-48
Author(s):  
Tzong Daw Wu ◽  
Jiun Shen Chen ◽  
Ching Pei Tseng ◽  
Cheng Chang Hsieh
Keyword(s):  
Thin Films ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Real Time ◽  
Film Growth ◽  
Mean Squared Error ◽  
Squared Error ◽  
Thin Film Optics ◽  
The Mean ◽  
Artificial Neural

This study presents a real-time method for determining the thickness of each layer in multilayer thin films. Artificial neural networks (ANNs) were introduced to estimate thicknesses from a transmittance spectrum. After training via theoretical spectra which were generated by thin-film optics and modified by noise, ANNs were applied to estimate the thicknesses of four-layer nanoscale films which were TiO2, Ag, Ti, and TiO2 thin films assembled sequentially on polyethylene terephthalate (PET) substrates. The results reveal that the mean squared error of the estimation is 2.6 nm2, and is accurate enough to monitor film growth in real time.

scholarly journals A study on WRF radar data assimilation for hydrological rainfall prediction

2013 ◽  
Vol 17 (8) ◽  
pp. 3095-3110 ◽  
Author(s):  
J. Liu ◽  
M. Bray ◽  
D. Han
Keyword(s):  
Data Assimilation ◽  
Real Time ◽  
Meteorological Data ◽  
Weather Radar ◽  
Radar Data ◽  
Rain Gauge ◽  
Radar Reflectivity ◽  
Storm Event ◽  
Hydrological Applications ◽  
Radar Data Assimilation

Abstract. Mesoscale numerical weather prediction (NWP) models are gaining more attention in providing high-resolution rainfall forecasts at the catchment scale for real-time flood forecasting. The model accuracy is however negatively affected by the "spin-up" effect and errors in the initial and lateral boundary conditions. Synoptic studies in the meteorological area have shown that the assimilation of operational observations, especially the weather radar data, can improve the reliability of the rainfall forecasts from the NWP models. This study aims at investigating the potential of radar data assimilation in improving the NWP rainfall forecasts that have direct benefits for hydrological applications. The Weather Research and Forecasting (WRF) model is adopted to generate 10 km rainfall forecasts for a 24 h storm event in the Brue catchment (135.2 km2) located in southwest England. Radar reflectivity from the lowest scan elevation of a C-band weather radar is assimilated by using the three-dimensional variational (3D-Var) data-assimilation technique. Considering the unsatisfactory quality of radar data compared to the rain gauge observations, the radar data are assimilated in both the original form and an improved form based on a real-time correction ratio developed according to the rain gauge observations. Traditional meteorological observations including the surface and upper-air measurements of pressure, temperature, humidity and wind speed are also assimilated as a bench mark to better evaluate and test the potential of radar data assimilation. Four modes of data assimilation are thus carried out on different types/combinations of observations: (1) traditional meteorological data; (2) radar reflectivity; (3) corrected radar reflectivity; (4) a combination of the original reflectivity and meteorological data; and (5) a combination of the corrected reflectivity and meteorological data. The WRF rainfall forecasts before and after different modes of data assimilation are evaluated by examining the rainfall temporal variations and total amounts which have direct impacts on rainfall–runoff transformation in hydrological applications. It is found that by solely assimilating radar data, the improvement of rainfall forecasts are not as obvious as assimilating meteorological data; whereas the positive effect of radar data can be seen when combined with the traditional meteorological data, which leads to the best rainfall forecasts among the five modes. To further improve the effect of radar data assimilation, limitations of the radar correction ratio developed in this study are discussed and suggestions are made on more efficient utilisation of radar data in NWP data assimilation.

scholarly journals THE EFFECT OF OPENING TYPE ON THERMAL CONVENIENCE OF THE OLD AND NEW BUILDING CLASSROOM

2019 ◽  
Vol 24 (2) ◽  
pp. 75-87
Author(s):  
Ali Anton Senoaji ◽  
Arif Kusumawanto ◽  
Sentagi Sesotya Utami
Keyword(s):  
Wind Speed ◽  
Air Temperature ◽  
Mean Squared Error ◽  
Comparative Method ◽  
Field Measurements ◽  
Root Mean Squared Error ◽  
Wind Speeds ◽  
Squared Error ◽  
Simulation Results ◽  
New Buildings

This study was aimed at analyzing the effect of opening type on the thermal convenience of classrooms in old and new buildings at SMK Negeri 3 Yogyakarta. This study used a qualitative comparative method and the simulation of IES VE 2018. The field air measurement is carried out at 10 measurement points and 5 measurement points in each class, with a height of 1.5 m. Field measurements were carried out in March 2019, at 06.30-16.30 WIB. The parameters used in the study were air temperature, humidity and wind speed. Air temperature and humidity were measured using a Thermo hygrometer. Wind speed was measured using an anemometer. The data collection method is carried out by observation and measurement. Root Mean Squared Error (RMSE) was used to validate the data. The results show the best thermal convenience of the classroom was obtained during the simulation using the type of Windows Awning, with a full aperture area. Simulation results show a comfortable distribution of airflow in the classroom at wind speeds above 0.15-0.28 m/sec, Temperature 25.07-27.10oC.PENGARUH TIPE BUKAAN TERHADAP KENYAMANAN TERMAL RUANG KELAS BANGUNAN LAMA DAN BARU Tujuan dari penelitian yaitu menganalisis pengaruh bukaan terhadap kenyamanan termal ruang kelas pada bangunan lama dan baru, di SMK Negeri 3 Yogyakarta. Penelitian ini menggunakan metode komparatif kualitatif yaitu dan hasil simulasi IES VE 2018. Pengukuran udara luar dilakukan pada 10 titik pengukuran dan sebanyak 5 titik pengukuran disetiap kelasnya, dengan ketinggian 1,5 m. Pengukuran lapangan dilakukan pada bulan Maret tahun 2019, waktu 06.30-16.30 WIB. Parameter yang digunakan dalam penelitian yaitu temperatur udara, kelembaban dan kecepatan angin. Temperatur udara dan kelembaban diukur dengan menggunakan alat thermo hygrometer. Kecepatan angin diukur dengan menggunakan alat anemometer. Metode pengumpulan data dilakukan dengan metode pengamatan dan pengukuran. Validasi data menggunakan Root Mean Squared Error (RMSE). Hasil penelitian menunjukkan kenyamanan termal ruang kelas terbaik diperoleh pada saat simulasi menggunakan tipe bukaan ke atas atau Awning Windows, dengan area bukaan penuh. Hasil simulasi menunjukkan distribusi aliran udara yang nyaman di dalam ruang kelas pada kecepatan angin di atas 0,15-0,28 m/det, Temperatur 25,07 -27,10o C. 

scholarly journals The Impact of Assimilating Atmospheric Infrared Sounder Observation on the Forecast of Typhoon Tracks

2011 ◽  
Vol 2011 ◽  
pp. 1-10 ◽  
Author(s):  
Chien-Ben Chou ◽  
Huei-Ping Huang
Keyword(s):  
Data Assimilation ◽  
Three Dimensional ◽  
Wrf Model ◽  
Weather Research And Forecasting ◽  
Atmospheric Infrared Sounder ◽  
Background Error ◽  
Forecasting System ◽  
Typhoon Tracks ◽  
The Impact ◽  
Decorrelation Scale

This work assesses the effects of assimilating atmospheric infrared sounder (AIRS) observations on typhoon prediction using the three-dimensional variational data assimilation (3DVAR) and forecasting system of the weather research and forecasting (WRF) model. Two major parameters in the data assimilation scheme, the spatial decorrelation scale and the magnitude of the covariance matrix of the background error, are varied in forecast experiments for the track of typhoon Sinlaku over the Western Pacific. The results show that within a wide parameter range, the inclusion of the AIRS observation improves the prediction. Outside this range, notably when the decorrelation scale of the background error is set to a large value, forcing the assimilation of AIRS data leads to degradation of the forecast. This illustrates how the impact of satellite data on the forecast depends on the adjustable parameters for data assimilation. The parameter-sweeping framework is potentially useful for improving operational typhoon prediction.

scholarly journals Implementation of a Demand-Side Management Solution for South Korea’s Demand Response Program

2020 ◽  
Vol 10 (5) ◽  
pp. 1751 ◽  
Author(s):  
Wonsuk Ko ◽  
Hamsakutty Vettikalladi ◽  
Seung-Ho Song ◽  
Hyeong-Jin Choi
Keyword(s):  
South Korea ◽  
Real Time ◽  
Demand Response ◽  
Mean Squared Error ◽  
Demand Side Management ◽  
Demand Side ◽  
Root Mean Squared Error ◽  
Squared Error ◽  
Demand Response Program ◽  
Relative Root

In this paper, we show the development of a demand-side management solution (DSMS) for demand response (DR) aggregator and actual demand response operation cases in South Korea. To show an experience, Korea’s demand response market outline, functions of DSMS, real contracted capacity, and payment between consumer and load aggregator and DR operation cases are revealed. The DSMS computes the customer baseline load (CBL), relative root mean squared error (RRMSE), and payments of the customers in real time. The case of 10 MW contracted customers shows 108.03% delivery rate and a benefit of 854,900,394 KRW for two years. The results illustrate that an integrated demand-side management solution contributes by participating in a DR market and gives a benefit and satisfaction to the consumer.

Kalman-filter-based sensor fusion applied to road-objects detection and tracking for autonomous vehicles

2020 ◽  
pp. 095965182097552
Author(s):  
Wael Farag
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Extended Kalman Filter ◽  
Mean Squared Error ◽  
Unscented Kalman Filter ◽  
Measurement Data ◽  
Root Mean Squared Error ◽  
Time Performance ◽  
Detection And Tracking ◽  
Squared Error

In this article, a real-time road-Object Detection and Tracking (LR_ODT) method for autonomous driving is proposed. This method is based on the fusion of lidar and radar measurement data, where they are installed on the ego car, and a customized Unscented Kalman Filter is employed for their data fusion. The merits of both devices are combined using the proposed fusion approach to precisely provide both pose and velocity information for objects moving in roads around the ego car. Unlike other detection and tracking approaches, the balanced treatment of both pose estimation accuracy and its real-time performance is the main contribution in this work. The proposed technique is implemented using the high-performance language C++ and utilizes highly optimized math and optimization libraries for best real-time performance. Simulation studies have been carried out to evaluate the performance of the LR_ODT for tracking bicycles, cars, and pedestrians. Moreover, the performance of the Unscented Kalman Filter fusion is compared to that of the Extended Kalman Filter fusion showing its superiority. The Unscented Kalman Filter has outperformed the Extended Kalman Filter on all test cases and all the state variable levels (−24% average Root Mean Squared Error). The employed fusion technique shows how outstanding is the improvement in tracking performance compared to the use of a single device (−29% Root Mean Squared Error with lidar and −38% Root Mean Squared Error with radar).

scholarly journals Improving soil moisture prediction of a high-resolution land surface model by parameterising pedotransfer functions through assimilation of SMAP satellite data

2021 ◽  
Vol 25 (3) ◽  
pp. 1617-1641
Author(s):  
Ewan Pinnington ◽  
Javier Amezcua ◽  
Elizabeth Cooper ◽  
Simon Dadson ◽  
Rich Ellis ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Land Surface ◽  
Mean Squared Error ◽  
Land Surface Model ◽  
Cosmic Ray ◽  
Pedotransfer Functions ◽  
Surface Model ◽  
Squared Error ◽  
Surface Models

Abstract. Pedotransfer functions are used to relate gridded databases of soil texture information to the soil hydraulic and thermal parameters of land surface models. The parameters within these pedotransfer functions are uncertain and calibrated through analyses of point soil samples. How these calibrations relate to the soil parameters at the spatial scale of modern land surface models is unclear because gridded databases of soil texture represent an area average. We present a novel approach for calibrating such pedotransfer functions to improve land surface model soil moisture prediction by using observations from the Soil Moisture Active Passive (SMAP) satellite mission within a data assimilation framework. Unlike traditional calibration procedures, data assimilation always takes into account the relative uncertainties given to both model and observed estimates to find a maximum likelihood estimate. After performing the calibration procedure, we find improved estimates of soil moisture and heat flux for the Joint UK Land Environment Simulator (JULES) land surface model (run at a 1 km resolution) when compared to estimates from a cosmic-ray soil moisture monitoring network (COSMOS-UK) and three flux tower sites. The spatial resolution of the COSMOS probes is much more representative of the 1 km model grid than traditional point-based soil moisture sensors. For 11 cosmic-ray neutron soil moisture probes located across the modelled domain, we find an average 22 % reduction in root mean squared error, a 16 % reduction in unbiased root mean squared error and a 16 % increase in correlation after using data assimilation techniques to retrieve new pedotransfer function parameters.

Sign in / Sign up

Export Citation Format

Share Document