scholarly journals Tropical Cyclone Center Positioning Using Single Channel Microwave Satellite Observations of Brightness Temperature

2021 ◽  
Vol 13 (13) ◽  
pp. 2466
Author(s):  
Yanyang Hu ◽  
Xiaolei Zou
Keyword(s):  
Brightness Temperature ◽  
Root Mean Square ◽  
Single Channel ◽  
Horizontal Resolution ◽  
Satellite Observations ◽  
Advanced Technology ◽  
Tropical Storms ◽  
Mean Square ◽  
The Difference ◽  
Atlantic Hurricanes

Satellite observations of brightness temperature from the Advanced Technology Microwave Sounder (ATMS) and Microwave Humidity Sounder (MHS) humidity sounding channels can provide relatively high horizontal resolution information about cloud and atmospheric moisture in the troposphere, thus revealing the structures of tropical cyclones (TCs). There is usually a high brightness temperature in a TC eye region and low brightness temperature reflecting spiral rain bands. An azimuthal spectral analysis method is used as a center-fixing algorithm to determine the TC center objectively using the brightness temperature observations of the ATMS humidity-sounding channel 18 (183.31 ± 7.0 GHz) and MHS humidity-sounding channel 5 (190.31 GHz). The position in the brightness temperature field encompassing a TC that achieves the largest symmetric component is regarded as the TC center. Two Atlantic hurricanes in 2012, Hurricanes Sandy and Isaac, are first used to analyze the performance of the TC center-fixing technique. Compared with the National Hurricane Center best track, the root-mean-square differences of the center fixing results for Hurricanes Sandy and Isaac are less than 47.3 and 34.3 km, respectively. It is found that the uncertainty of the TC center-fixing algorithm and thus the difference from the best track increases when the brightness temperature distribution within a TC is significantly asymmetric. Then, the TC center-fixing technique is validated for all tropical storms and hurricanes over Northern Atlantic and Western Pacific in 2019. Compared with the best track data, the root-mean-square differences for tropical storms and hurricanes are 33.81 and 26.20 km, respectively. The demonstrated successful performance of the proposed TC center-fixing algorithm to use the single channel of microwave humidity sounders for TC positioning is important for vortex initialization in operational hurricane forecasts.

Detecting pneumatic actuator leakage using acoustic emission monitoring

2020 ◽  
Vol 62 (1) ◽  
pp. 22-26
Author(s):  
H Mahmoud ◽  
P Mazal ◽  
F Vlašic
Keyword(s):  
Acoustic Emission ◽  
Root Mean Square ◽  
Diagnostic Procedure ◽  
Pneumatic Cylinder ◽  
Mean Square ◽  
Emission Testing ◽  
Acoustic Emission Testing ◽  
Maximum Root ◽  
The Difference ◽  
Pneumatic Cylinders

This paper focuses on the development of an efficient new diagnostic procedure for checking the function of pneumatic cylinders using acoustic emission. This diagnostic procedure is able to detect distinctive differences that determine whether the cylinder is damaged or undamaged. Moreover, the paper aims to find the diagnostic criteria that can be used to evaluate the pneumatic cylinder and detect defects. In this study, acoustic emission testing of several undamaged cylinders is carried out before artificial defects are created in each one. The signals from the progress and retreat strokes are recorded and analysed according to many parameters. The root mean square is normalised and the different responses of damaged and undamaged pneumatic cylinders are recognised by the time delay of the strokes. The differences are identified by comparing the maximum root mean square from sensor A and the maximum root mean square from sensor B for one cycle in the retreat stroke. The damaged and undamaged cylinders are distinguished using the difference in energy values present in the signals of the two sensors in the retreat stroke. The final evaluation of the cylinder is determined by calculating the total value of the root mean square. This paper is a continuation of a prior article and the extension of that work.

scholarly journals Snow depth mapping with unpiloted aerial system lidar observations: a case study in Durham, New Hampshire, United States

2021 ◽  
Vol 15 (3) ◽  
pp. 1485-1500
Author(s):  
Jennifer M. Jacobs ◽  
Adam G. Hunsaker ◽  
Franklin B. Sullivan ◽  
Michael Palace ◽  
Elizabeth A. Burakowski ◽  
...  
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Confidence Intervals ◽  
Root Mean Square ◽  
Snow Depth ◽  
Coniferous Forest ◽  
Horizontal Resolution ◽  
Absolute Difference ◽  
Mean Square ◽  
Mean Absolute Difference

Abstract. Terrestrial and airborne laser scanning and structure from motion techniques have emerged as viable methods to map snow depths. While these systems have advanced snow hydrology, these techniques have noted limitations in either horizontal or vertical resolution. Lidar on an unpiloted aerial vehicle (UAV) is another potential method to observe field- and slope-scale variations at the vertical resolutions needed to resolve local variations in snowpack depth and to quantify snow depth when snowpacks are shallow. This paper provides some of the earliest snow depth mapping results on the landscape scale that were measured using lidar on a UAV. The system, which uses modest-cost, commercially available components, was assessed in a mixed deciduous and coniferous forest and open field for a thin snowpack (< 20 cm). The lidar-classified point clouds had an average of 90 and 364 points/m2 ground returns in the forest and field, respectively. In the field, in situ and lidar mean snow depths, at 0.4 m horizontal resolution, had a mean absolute difference of 0.96 cm and a root mean square error of 1.22 cm. At 1 m horizontal resolution, the field snow depth confidence intervals were consistently less than 1 cm. The forest areas had reduced performance with a mean absolute difference of 9.6 cm, a root mean square error of 10.5 cm, and an average one-sided confidence interval of 3.5 cm. Although the mean lidar snow depths were only 10.3 cm in the field and 6.0 cm in the forest, a pairwise Steel–Dwass test showed that snow depths were significantly different between the coniferous forest, the deciduous forest, and the field land covers (p < 0.0001). Snow depths were shallower, and snow depth confidence intervals were higher in areas with steep slopes. Results of this study suggest that performance depends on both the point cloud density, which can be increased or decreased by modifying the flight plan over different vegetation types, and the grid cell variability that depends on site surface conditions.

scholarly journals Inverse modeling of fire emissions constrained by smoke plume transport using HYSPLIT dispersion model and geostationary satellite observations

2020 ◽  
Vol 20 (17) ◽  
pp. 10259-10277
Author(s):  
Hyun Cheol Kim ◽  
Tianfeng Chai ◽  
Ariel Stein ◽  
Shobha Kondragunta
Keyword(s):  
Root Mean Square Error ◽  
Cost Function ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Inverse Modeling ◽  
Dispersion Model ◽  
Satellite Observations ◽  
Mean Square ◽  
Fire Emissions ◽  
Wildfire Smoke

Abstract. Smoke forecasts have been challenged by high uncertainty in fire emission estimates. We develop an inverse modeling system, the HYSPLIT-based Emissions Inverse Modeling System for wildfires (or HEIMS-fire), that estimates wildfire emissions from the transport and dispersion of smoke plumes as measured by satellite observations. A cost function quantifies the differences between model predictions and satellite measurements, weighted by their uncertainties. The system then minimizes this cost function by adjusting smoke sources until wildfire smoke emission estimates agree well with satellite observations. Based on HYSPLIT and Geostationary Operational Environmental Satellite (GOES) Aerosol/Smoke Product (GASP), the system resolves smoke source strength as a function of time and vertical level. Using a wildfire event that took place in the southeastern United States during November 2016, we tested the system's performance and its sensitivity to varying configurations of modeling options, including vertical allocation of emissions and spatial and temporal coverage of constraining satellite observations. Compared with currently operational BlueSky emission predictions, emission estimates from this inverse modeling system outperform in both reanalysis (21 out of 21 d; −27 % average root-mean-square-error change) and hindcast modes (29 out of 38 d; −6 % average root-mean-square-error change) compared with satellite observed smoke mass loadings.

The Loss of Dexterity in the Bilateral Lower Extremities in Patients With Stroke

2011 ◽  
Vol 27 (2) ◽  
pp. 122-129 ◽  
Author(s):  
Ryoji Kiyama ◽  
Kiyohiro Fukudome ◽  
Toshiki Hiyoshi ◽  
Akihide Umemoto ◽  
Yoichi Yoshimoto ◽  
...  
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Knee Extensor ◽  
Lower Extremities ◽  
Response Force ◽  
Mean Square ◽  
Control Subjects ◽  
The Difference ◽  
Mean Square Errors

The aim of this study was to examine the dexterity of both lower extremities in patients with stroke. Twenty patients with stroke and 20 age-matched control subjects participated in this study. To determine the dexterity of the lower extremities, we examined the ability to control muscle force during submaximal contractions in the knee extensor muscles using a force tracking task. The root mean square errors were calculated from the difference between the target and response force. The root mean square error was significantly greater in the affected limb of patients with stroke compared with those of the unaffected limb and the control subjects, and in the unaffected limb compared with that of the control subjects. Furthermore, the root mean square error of the affected limb was related significantly to motor function as determined by Fugl-Myer assessment. These results demonstrate impairment of the dexterity of both the affected and the unaffected lower extremities in patients with stroke.

A Revised Speech Intelligibility Rating (RSIR) Test: Listeners with Normal Hearing

1994 ◽  
Vol 110 (1) ◽  
pp. 75-83 ◽  
Author(s):  
Charles Speaks ◽  
Timothy D. Trine ◽  
Thomas R. Crain ◽  
Nancy Niccum
Keyword(s):  
Root Mean Square ◽  
Speech Intelligibility ◽  
Normal Hearing ◽  
Average Level ◽  
Mean Square ◽  
Critical Difference ◽  
Connected Discourse ◽  
The Difference ◽  
Method Of Adjustment

Two experiments were conducted to examine the intelligibility of 72 passages of connected discourse prepared by Cox and McDaniel1,2 in their development of the Speech Intelligibility Rating (SIR) test. Intelligibility was assessed with a method-of-adjustment (MOA) procedure in which listeners adjusted the level of a multi-talker babble until they could just understand 50% of a passage; the measure of intelligibility was the signal-to-babble ratio, dB S/B. The objective was to develop a Revised Speech Intelligibility Rating (RSIR) test that would comprise a large number of equivalent passages that produce reliable intelligibility measures. In experiment 1, the S/B ratio was based on the overall root-mean-square (rms) levels of speech and babble, as represented by the average level of frequent peaks observed on a VU meter. Across all 72 passages, mean intelligibility was −1.43 dB S/B, and the measure of intelligibility for 42 passages was within ±0.5 dB of the overall mean for all 72 passages. In experiment 2, the S/B ratio was based on long-term rms levels of speech and babble measured in 16 one-third-octave bands, with center frequencies from 160 to 5000 Hz. In an effort to achieve greater equivalence in intelligibility among passages, the overall rms level of each passage was attenuated by the difference between SB16-band for an individual passage and S/B16-band for a reference passage. Mean intelligibility across all 72 passages was — 8.06 dB, and the measure of intelligibility was within ±0.5 dB of the overall mean for 64 of the 72 passages. For those 64 passages, the 95% critical difference for five MOAs was 0.72 dB, which corresponds to an estimated percentage critical difference Of 10.8%.

scholarly journals Which method is more accurate? or errors have error bars

2017 ◽  
Author(s):  
Jan H Jensen
Keyword(s):  
Root Mean Square ◽  
Mean Square ◽  
Data Set ◽  
Error Bars ◽  
The Difference ◽  
Mean Square Errors

This document is my attempt at distilling some of the information in two papers published by Anthony Nicholls (J. Comput. Aided Mol. Des. 2014, 28, 887; ibid 2016, 30, 103). Anthony also very kindly provided some new equations, not found in the papers, in response to my questions. The paper describes how one determines whether the difference in accuracy of two methods in predicting some properties for the same data set is statistically significant using root-mean-square errors, mean absolute errors, mean errors, and Pearsons r values.

scholarly journals PERABIAKAN ALGORITMA SUHU PERMUKAAN LAUT LANDSAT 8 UNTUK PERAIRAN PONELO

2018 ◽  
Vol 5 (2) ◽  
pp. 84
Author(s):  
Romansah Wumu ◽  
Iqrimha Staddal ◽  
Evi Sunarti Antu ◽  
Burhan Liputo ◽  
Farid Darise
Keyword(s):  
Root Mean Square Error ◽  
Brightness Temperature ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Landsat 8 ◽  
Mean Square

Suhu permukaan laut (SPL) merupakan salah satu parameter yang mempengaruhi kehidupan biota laut. Landsat menyediakan data suhu permukaan bumi sejak tahun 1978. Penelitian ini memfokuskan pada pebraikan algoritma Brightness Temperature (BT) pada band 10 dan 11 data Landsat 8 (L8) menjadi Suhu Permukaan Laut (SPL) untuk perairan Ponelo dengan metode regresi. Regresi yang diperoleh diuji menggunakan metode R-squared (R 2 ) dan Root Mean Square Error (RMSE). Berdasarkan hasil pengambilan data dan analisis data L8 pada tanggal 21 Mei 2017 diperoleh algoritma polinomial 2 merupakan algoritma terbaik dengan dengan koefisien a 0 , a 1 , dan a 2 secara berturut turut 3568.14, 329.64, dan -7.55 (R 2 = 0.86 dan RMSE = 0.73).

scholarly journals Experimental Investigation of Ground Radiation on Dielectric and Brightness Temperature of Soil Moisture and Soil Salinity

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2806
Author(s):  
Weizhen Wang ◽  
Leilei Dong ◽  
Chunfeng Ma ◽  
Long Wei ◽  
Feinan Xu ◽  
...  
Keyword(s):  
Dielectric Constant ◽  
Soil Moisture ◽  
Brightness Temperature ◽  
Root Mean Square ◽  
Soil Salinity ◽  
Saline Soil ◽  
Mean Square ◽  
Horizontal Polarization ◽  
Vertical Polarization ◽  
Mean Square Errors

Soil moisture and salinity are crucial parameters of the Earth’s ecosystem; how to understand the radiation properties of them is of great significance for remote sensing monitoring. In this study, the application of mixed soil dielectric models (Dobson and generalized refractive mixing dielectric model (GRMDM)) and saline soil dielectric models (Dobson-S, HQR (Qingrong Hu), and WYR (Yueru Wu)) were analyzed to select the optimal models to simulate brightness temperature based on observational data. The brightness temperature of the soil moisture and multilevel salinity was simulated by using the Q-H (parameter of polarization mixing and parameter of characterizing height) model and Holmes parameterization scheme of soil effective temperature. The results show that both the Dobson model and the GRMDM model can well reproduce the real part and imaginary part of the dielectric constant of non-saline soil, and the GRMDM model was better. With the increase of the frequency, the simulation error of the dielectric constant of the saline soil by using the Dobson-S model, HQR model, and WYR model also increased, and the simulation result of the WYR model was better in the L band. The simulated result of the brightness temperature of soil moisture between the observation value and simulation value presented a high correlation both in the horizontal polarization and vertical polarization, with R greater than 0.967 and 0.948, and the root mean square error smaller than 3.998 K and 2.766 K, respectively. Meanwhile, the correlation coefficients of the brightness temperature of the saline soil in the horizontal polarization and vertical polarization were 0.935 and 0.971, and the root mean square errors were 5.808 K and 4.65 K, respectively. The brightness temperature decreased as the soil salinity increased, and the higher the salinity content was, the quicker the brightness temperature decreased. We expect that the experimental results can be used as a reference for algorithm developers to further enhance the accuracy of soil moisture and soil salinity retrievals.

scholarly journals Forecast and analysis assessment through skill scores

2007 ◽  
Vol 4 (1) ◽  
pp. 189-212 ◽  
Author(s):  
M. Tonani ◽  
N. Pinardi ◽  
C. Fratianni ◽  
S. Dobricic
Keyword(s):  
Root Mean Square ◽  
Comprehensive Evaluation ◽  
Skill Score ◽  
Data Availability ◽  
Forecast Errors ◽  
Mean Square ◽  
Atmospheric Forcing ◽  
Skill Scores ◽  
The Mediterranean ◽  
The Difference

Abstract. This paper describes a first comprehensive evaluation of the quality of the ten days ocean forecasts produced by the Mediterranean ocean Forecasting System (MFS). Once a week ten days forecasts are produced. The forecast starts on Tuesday at noon and the prediction is released on Wednesday morning with less then 24 hr delay. In this work we have considered 22 ten days forecasts produced from the 16 August 2005 to the 10 January 2006. All the statistical scores have been done for the Mediterranean basin and for 13 regions in which the Mediterranean sea has been subdivided. The forecast evaluation is given here in terms of root mean square (rms) values. The main skill score is computed as the root mean square of the difference between forecast and analysis (FA) and forecast and persistence (FP), where the persistence is defined as the average of the day of the analysis corresponding to the first day of the forecast. A second skill score (SSP) is defined as the ratio between rms of FA and FP, giving the percentage of accuracy of the forecast with respect to the persistence (Murphy 1993). The rms of FA is always better than FP and the FP rms error is double than the rms of FA. It is found that in the surface layers the error growth is controlled mainly by the atmospheric forcing inaccuracies while at depth the forecast errors could be due to adjustments of the data assimilation scheme to the data insertion procedure. The predictability limit for our ocean forecast seems to be 5–6 days connected to atmospheric forcing inaccuracies and to the data availability for assimilation.

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