scholarly journals Technical note: analytical estimation of the optimal parameters for the EOF retrievals of the IASI Level 2 Product Processing Facility and its application using AIRS and ECMWF data

2006 ◽  
Vol 6 (3) ◽  
pp. 831-846 ◽  
Author(s):  
X. Calbet ◽  
P. Schlüssel
Keyword(s):  
Standard Deviation ◽  
Linear Regression ◽  
Mean Difference ◽  
Training Data ◽  
Data Set ◽  
The Real ◽  
Atmospheric Profiles ◽  
The Mean ◽  
Atmospheric State ◽  
The Difference

Abstract. The Empirical Orthogonal Function (EOF) retrieval technique consists of calculating the eigenvectors of the spectra to later perform a linear regression between these and the atmospheric states, this first step is known as training. At a later stage, known as performing the retrievals, atmospheric profiles are derived from measured atmospheric radiances. When EOF retrievals are trained with a statistically different data set than the one used for retrievals two basic problems arise: significant biases appear in the retrievals and differences between the covariances of the training data set and the measured data set degrade them. The retrieved profiles will show a bias with respect to the real profiles which comes from the combined effect of the mean difference between the training and the real spectra projected into the atmospheric state space and the mean difference between the training and the atmospheric profiles. The standard deviations of the difference between the retrieved profiles and the real ones show different behavior depending on whether the covariance of the training spectra is bigger, equal or smaller than the covariance of the measured spectra with which the retrievals are performed. The procedure to correct for these effects is shown both analytically and with a measured example. It consists of first calculating the average and standard deviation of the difference between real observed spectra and the calculated spectra obtained from the real atmospheric state and the radiative transfer model used to create the training spectra. In a later step, measured spectra must be bias corrected with this average before performing the retrievals and the linear regression of the training must be performed adding noise to the spectra corresponding to the aforementioned calculated standard deviation. This procedure is optimal in the sense that to improve the retrievals one must resort to using a different training data set or a different algorithm.

scholarly journals Technical note: analytical estimation of the optimal parameters for the EOF retrievals of the IASI Level 2 Product Processing Facility and its application using AIRS and ECMWF data

2005 ◽  
Vol 5 (5) ◽  
pp. 9691-9730
Author(s):  
X. Calbet ◽  
P. Schlüssel
Keyword(s):  
Standard Deviation ◽  
Linear Regression ◽  
Mean Difference ◽  
Training Data ◽  
Data Set ◽  
The Real ◽  
Atmospheric Profiles ◽  
The Mean ◽  
Atmospheric State ◽  
The Difference

Abstract. The Empirical Orthogonal Function (EOF) retrieval technique consists of calculating the eigenvectors of the spectra to later perform a linear regression between these and the atmospheric states, this first step is known as training. At a later stage, known as performing the retrievals, atmospheric profiles are derived from measured atmospheric radiances. When EOF retrievals are trained with a statistically different data set than the one used for retrievals two basic problems arise: significant biases appear in the retrievals and differences between the covariances of the training data set and the measured data set degrade them. The retrieved profiles will show a bias with respect to the real profiles which comes from the combined effect of the mean difference between the training and the real spectra projected into the atmospheric state space and the mean difference between the training and the atmospheric profiles. The standard deviations of the difference between the retrieved profiles and the real ones show different behavior depending on whether the covariance of the training spectra is bigger, equal or smaller than the covariance of the measured spectra with which the retrievals are performed. The procedure to correct for these effects is shown both analytically and with a measured example. It consists of first calculating the average and standard deviation of the difference between real observed spectra and the calculated spectra obtained from the real atmospheric state and the radiative transfer model used to create the training spectra. In a later step, measured spectra must be bias corrected with this average before performing the retrievals and the linear regression of the training must be performed adding noise to the spectra corresponding to the aforementioned calculated standard deviation. This procedure is optimal in the sense that to improve the retrievals one must resort to using a different training data set or a different algorithm.

scholarly journals The Effectiveness of Mix Impact Aerobic Gymnastics Exercises With SKJ 2000 On Improvement of Physical Freshness

2021 ◽  
Vol 12 (03) ◽  
pp. 39-44
Author(s):  
Anik Maryani ◽  
Fahmy Fachrezzy ◽  
Ramdan Pelana
Keyword(s):  
Standard Deviation ◽  
Physical Fitness ◽  
Aerobic Exercise ◽  
Standard Error ◽  
Exercise Group ◽  
Mean Value ◽  
Mean Difference ◽  
Final Test ◽  
The Mean ◽  
The Difference

This study aims to determine the effectiveness of the effect of aerobic mix impact and SKJ 2000 version (core exercise) to improve physical fitness in female students. The research was conducted at SMEA YASMA Sudirman Cijantung for 8 weeks with 24 meetings. The method used is an experimental method with a pre and post-test design. The sampling technique was random sampling from a total of 40 grade 1 students and 30 samples were taken. The data collection technique used was a physical fitness test using the Indonesian Physical Fitness Test (TKJI). Hypothesis testing uses the t-test at the significant level (α) 0.05. The results showed that the difference between the mean value of the initial test (x) and the final test (y) in the mixed impact aerobic exercise group was obtained = -6.47; the value of the standard deviation of the difference = 1,2; the standard error value of the mean difference = 0.32; and the value becomes = -20,2. The initial test (x) and the final test (y) in the 2000 version of the Physical Fitness exercise obtained the difference in the mean value is = -5; the value of the standard deviation of the difference = 1.1; the standard error value of the mean difference = 0.29; and the value becomes = -17.24. The final test of the mixed impact aerobic exercise group (x) and the final test of the aerobic exercise group (y) version 2000, obtained the mean value of the variable x = 19.33; variable value y = 17; the standard deviation value x = 1.48; standard deviation of the variable y = 2.31; standard error variable x = 0.4; standard error for the variable y = 0.62; standard error for the mean difference between x and variable = 0.74; Hypothesis test results obtained t observation = 3.15, at 28 degrees of freedom and a significant level (α) 0.05, the value of t table = 2.048 is obtained. The conclusion of the study is that the effect of mix impact aerobic exercise is more effective in improving physical fitness compared to those using the 2000 version of the fitness gymnastics version of aerobic exercise.

scholarly journals Improved information about the vertical location and extent of cloud layers from POLDER3 measurements in the oxygen A band

2013 ◽  
Vol 6 (2) ◽  
pp. 2533-2581 ◽  
Author(s):  
M. Desmons ◽  
N. Ferlay ◽  
F. Parol ◽  
L. Mcharek ◽  
C. Vanbauce
Keyword(s):  
Standard Deviation ◽  
Oxygen Pressure ◽  
Optical Thickness ◽  
Mean Difference ◽  
Vertical Extent ◽  
Satellite Platform ◽  
Cloud Optical Thickness ◽  
The Mean ◽  
The Difference ◽  
The One

Abstract. This paper describes new advances in the exploitation of oxygen A band measurements from POLDER3 sensor aboard PARASOL, satellite platform within the A-Train. These developments result from a better account of the dependence of POLDER oxygen parameters to cloud optical thickness τ and to the scene's geometrical conditions, but also and more importantly from the finer understanding of the sensitivity of these parameters to cloud vertical extent. This sensitivity is made possible thanks to the multidirectional character of POLDER measurements. In the case of monolayer clouds that represent most of cloudy conditions, new oxygen parameters are obtained and calibrated from POLDER3 data colocalized with the measurements of the two active sensors of the A-Train, CALIOP/CALIPSO and CPR/CloudSat. From a parameterization that is (μs, τ) dependent, with μs the cosine of the solar zenith angle, a cloud top oxygen pressure (CTOP) and a cloud middle oxygen pressure (CMOP) are obtained which are estimates of actual cloud top and middle pressures. The performance of CTOP and CMOP are presented for the most numerous ISCCP cases in 2008. The coefficient of the correlation between CMOP and the actual cloud middle pressure is 0.81 for cirrostratus, 0.79 for stratocumulus, 0.75 for deep convective clouds. The coefficient of the correlation between CTOP and the actual cloud top pressure is 0.75, 0.73, and 0.79 for the same cloud types respectively. The score obtained by CTOP, defined as the confidence in the retrieval for a particular range of infered value and for a given error, is higher than the one of MODIS CTP. For liquid and ice clouds, the score reaches 50 and 70% respectively for bin value of CTP superior in numbers and accepted errors of 30 and 50 hPa. From the difference between CTOP and CMOP, a first estimate of the cloud vertical extent H is possible. Then, the correlation between the angular standard deviation of POLDER oxygen pressure σPO2 and the cloud vertical extent is described in detail in the case of liquid clouds. The correlation is shown to be spatially and temporally robust, excepted for clouds above land during winter months. The study of the correlation's dependence to cloud optical thickness and to the scene's geometrical conditions leads to parameterizations which provide a second way for retrieving H for this type of clouds. For liquid water clouds above ocean in 2008, the mean difference between the actual cloud vertical extent and the one retrieved from σPO2 (from the pressure difference) is 5 m (−12 m). The standard deviation of the mean difference is close to 1000 m for the two methods. The score of 50% confidence for the retrieval of H corresponds to an error of 20 and 40% for ice and liquid clouds respectively over ocean. These promising results need to be validated outside of the CALIPSO/CloudSat track.

scholarly journals Improved information about the vertical location and extent of monolayer clouds from POLDER3 measurements in the oxygen A-band

2013 ◽  
Vol 6 (8) ◽  
pp. 2221-2238 ◽  
Author(s):  
M. Desmons ◽  
N. Ferlay ◽  
F. Parol ◽  
L. Mcharek ◽  
C. Vanbauce
Keyword(s):  
Standard Deviation ◽  
Oxygen Pressure ◽  
Mean Difference ◽  
Active Sensors ◽  
Convective Clouds ◽  
Vertical Extent ◽  
Satellite Platform ◽  
The Mean ◽  
The Difference ◽  
The One

Abstract. This paper describes new advances in the exploitation of oxygen A-band measurements from POLDER3 sensor onboard PARASOL, satellite platform within the A-Train. These developments result from not only an account of the dependence of POLDER oxygen parameters to cloud optical thickness τ and to the scene's geometrical conditions but also, and more importantly, from the finer understanding of the sensitivity of these parameters to cloud vertical extent. This sensitivity is made possible thanks to the multidirectional character of POLDER measurements. In the case of monolayer clouds that represent most of cloudy conditions, new oxygen parameters are obtained and calibrated from POLDER3 data colocalized with the measurements of the two active sensors of the A-Train: CALIOP/CALIPSO and CPR/CloudSat. From a parameterization that is (μs, τ) dependent, with μs the cosine of the solar zenith angle, a cloud top oxygen pressure (CTOP) and a cloud middle oxygen pressure (CMOP) are obtained, which are estimates of actual cloud top and middle pressures (CTP and CMP). Performances of CTOP and CMOP are presented by class of clouds following the ISCCP classification. In 2008, the coefficient of the correlation between CMOP and CMP is 0.81 for cirrostratus, 0.79 for stratocumulus, 0.75 for deep convective clouds. The coefficient of the correlation between CTOP and CTP is 0.75, 0.73, and 0.79 for the same cloud types. The score obtained by CTOP, defined as the confidence in the retrieval for a particular range of inferred value and for a given error, is higher than the one of MODIS CTP estimate. Scores of CTOP are the highest for bin value of CTP superior in numbers. For liquid (ice) clouds and an error of 30 hPa (50 hPa), the score of CTOP reaches 50% (70%). From the difference between CTOP and CMOP, a first estimate of the cloud vertical extent h is possible. A second estimate of h comes from the correlation between the angular standard deviation of POLDER oxygen pressure σPO2 and the cloud vertical extent. This correlation is studied in detail in the case of liquid clouds. It is shown to be spatially and temporally robust, except for clouds above land during winter months. The analysis of the correlation's dependence on the scene's characteristics leads to a parameterization providing h from σPO2. For liquid water clouds above ocean in 2008, the mean difference between the actual cloud vertical extent and the one retrieved from σPO2 (from the pressure difference) is 5 m (−12 m). The standard deviation of the mean difference is close to 1000 m for the two methods. POLDER estimates of the cloud geometrical thickness obtain a global score of 50% confidence for a relative error of 20% (40%) of the estimate for ice (liquid) clouds over ocean. These results need to be validated outside of the CALIPSO/CloudSat track.

scholarly journals Meta-analysis of the variance ratio

2017 ◽  
Author(s):  
Nicolas Traut
Keyword(s):  
Standard Deviation ◽  
Meta Analysis ◽  
Mean Difference ◽  
Variance Ratio ◽  
Mean Values ◽  
Variance Ratios ◽  
The Mean ◽  
Standard Deviations ◽  
The Difference ◽  
Fisher’S Ratio

IntroductionThe most commonly used effect size when using meta-analysis to compare a measurement of interest in two different populations is the standardised mean difference. This is the mean difference of the measurement divided by the pooled standard deviation in the two groups. The standard deviation is usually supposed to be the same for both groups, although this assumption is often made without any particular evidence. It is possible, however, that the difference of the measurement in the two populations resides precisely in their standard deviations. This could be the case, for example, if a population of patients exhibited more “abnormal” values than a control population – both large and small – even if the mean values were the same. Fisher’s test of equality of variance is designed to compare standard deviations. A variance ratio is a Fisher’s ratio and Fisher distribution can be used to give confidence intervals to the estimate for one study. However, confidence interval for one study can be very wide if the study does not contain enough subjects. Here we present an approach to combine variance ratios of different studies in a meta-analytic way which produces more robust estimates under these circumstances.

A New Equal Area Method to Calculate and Represent Physiologic, Anatomical, and Alveolar Dead Spaces

2006 ◽  
Vol 104 (4) ◽  
pp. 696-700 ◽  
Author(s):  
Yongquan Tang ◽  
Martin J. Turner ◽  
A Barry Baker
Keyword(s):  
Carbon Dioxide ◽  
Dead Space ◽  
Graphical Method ◽  
Mean Difference ◽  
Equal Area ◽  
Area Method ◽  
Physiologic Dead Space ◽  
Anatomical Dead Space ◽  
The Mean ◽  
The Difference

Background Physiologic dead space is usually estimated by the Bohr-Enghoff equation or the Fletcher method. Alveolar dead space is calculated as the difference between anatomical dead space estimated by the Fowler equal area method and physiologic dead space. This study introduces a graphical method that uses similar principles for measuring and displaying anatomical, physiologic, and alveolar dead spaces. Methods A new graphical equal area method for estimating physiologic dead space is derived. Physiologic dead spaces of 1,200 carbon dioxide expirograms obtained from 10 ventilated patients were calculated by the Bohr-Enghoff equation, the Fletcher area method, and the new graphical equal area method and were compared by Bland-Altman analysis. Dead space was varied by varying tidal volume, end-expiratory pressure, inspiratory-to-expiratory ratio, and inspiratory hold in each patient. Results The new graphical equal area method for calculating physiologic dead space is shown analytically to be identical to the Bohr-Enghoff calculation. The mean difference (limits of agreement) between the physiologic dead spaces calculated by the new equal area method and Bohr-Enghoff equation was -0.07 ml (-1.27 to 1.13 ml). The mean difference between new equal area method and the Fletcher area method was -0.09 ml (-1.52 to 1.34 ml). Conclusions The authors' equal area method for calculating, displaying, and visualizing physiologic dead space is easy to understand and yields the same results as the classic Bohr-Enghoff equation and Fletcher area method. All three dead spaces--physiologic, anatomical, and alveolar--together with their relations to expired volume, can be displayed conveniently on the x-axis of a carbon dioxide expirogram.

scholarly journals Comparison of direct and geodetic mass balances on a multi-annual time scale

2010 ◽  
Vol 4 (3) ◽  
pp. 1151-1194
Author(s):  
A. Fischer
Keyword(s):  
Mass Balance ◽  
Direct Method ◽  
Mean Difference ◽  
Published Data ◽  
Glacier Mass Balance ◽  
Mass Balances ◽  
Direct Data ◽  
The Mean ◽  
The Difference ◽  
Geodetic Mass Balance

Abstract. Glacier mass balance is measured with the direct or the geodetic method. In this study, the geodetic mass balances of six Austrian glaciers in 19 periods between 1953 and 2006 are compared to the direct mass balances in the same periods. The mean annual geodetic mass balance for all periods is −0.5 m w.e./year. The mean difference between the geodetic and the direct data is −0.7 m w.e., the minimum −7.3 m w.e. and the maximum 5.6 m w.e. The accuracy of geodetic mass balance resulting from the accuracy of the DEMs ranges from 2 m w.e. for photogrammetric data to 0.002 m w.e. for LIDAR data. Basal melt, seasonal snow cover and density changes of the surface layer contribute up to 0.7 m w.e. for the period of 10 years to the difference to the direct method. The characteristics of published data of Griesgletscher, Gulkana Glacier, Lemon Creek glacier, South Cascade, Storbreen, Storglaciären, and Zongo Glacier is similar to these Austrian glaciers. For 26 analyzed periods with an average length of 18 years the mean difference between the geodetic and the direct data is −0.4 m w.e., the minimum −7.2 m w.e. and the maximum 3.6 m w.e. Longer periods between the acquisition of the DEMs do not necessarily result in a higher accuracy of the geodetic mass balance. Specific glaciers show specific trends of the difference between the direct and the geodetic data according to their type and state. In conclusion, geodetic and direct mass balance data are complementary, but differ systematically.

scholarly journals The influence of kihap on the impact of Dolio-chagui kicks in taekwondo

2014 ◽  
Vol 20 (1) ◽  
pp. 54-57
Author(s):  
Rodrigo Dias Martins ◽  
Debora Cantergi ◽  
Jefferson Fagundes Loss
Keyword(s):  
Martial Arts ◽  
Random Order ◽  
T Test ◽  
Real Effect ◽  
The Real ◽  
The Mean ◽  
The Difference ◽  
The Impact

The kihapis a technique used in several oriental martial arts. It is a yell used by practitioners with the ex pectation of enhancing the force of a hit. However, the real effect of using the kihapis unknown. Therefore, this study aims to compare the peak of acceleration of the Dolio-chaguikick in taekwondo performed with and without the use of kihap. Twenty two experienced taekwondo practitioners performed 30 kicks each against a punching bag, alternating in random order with and without kihap, while the acceleration of the punching bag was measured. A t-test was used to compare the difference between the mean acceleration in both conditions. Higher values were found with the use of kihap(7.8 ± 2.8 g) than without the use of kihap(7.1 ± 2.4 g), p< 0.01, r= 0.57. The results indicate that kihapenhances the impact of the kick.

scholarly journals Study of outcome of the treatment with intravenous iron sucrose in moderately anaemic pregnant women

2019 ◽  
Vol 8 (9) ◽  
pp. 3480
Author(s):  
Kohila Kalimuthu ◽  
Vanusha Avudaithangam
Keyword(s):  
Standard Deviation ◽  
Iron Deficiency ◽  
Pregnant Women ◽  
Iron Deficiency Anaemia ◽  
Intravenous Iron ◽  
Iron Sucrose ◽  
P Value ◽  
Deficiency Anaemia ◽  
The Mean ◽  
The Difference

Background: Moderate anaemia seen in about 15-20% of pregnant women. Iron sucrose complex which is used intravenously for the correction of Iron deficiency anaemia. The drug has been able to raise the haemoglobin to satisfactory level when used in moderately anaemic iron deficient pregnant women. The objective of this study was to study the improvement of Hb% after treatment with intravenous Iron sucrose complex in moderately anaemic pregnant women belonging to 24-32 weeks of gestational age.Methods: 50 antenatal patients between gestational age 24-32 weeks with hemoglobin between 8-9.5g/dl were selected and included in this study. They were subjected to blood hemoglobin estimation, hematocrit and peripheral smear study. In each infusion, the maximum total dose administered was 200 mg iron sucrose in 100 ml of normal saline, slow IV infused over 30 minutes. Monitoring was done throughout the infusion to observe for any side effects.Results: Mean hemoglobin among the 50 patients before starting the therapy was 8.172g/dl and the mean hemoglobin at the end of one month of completing the therapy was 11.066g/dl. The rise in mean hemoglobin i.e. the difference in the mean hemoglobin before and after treatment was 2.894g/dl. The p value is 0.0001 which is statistically significant. The mean hematocrit of the 50 patients studied before starting the treatment was 26.772% with a standard deviation of 1.914. The mean hematocrit after completing the therapy was 33.872% with a standard deviation of 1.321. The difference in the mean hematocrit was 7.100% with a p value of 0.0001 which is statistically significant.Conclusions: Intravenous iron sucrose complex is well tolerated and highly efficacious in improving hemoglobin, hematocrit in the treatment of iron deficiency anaemia in antenatal women.

Data Analysis on the Influencing Factors of the Real Estate Price

2021 ◽  
pp. 52-66
Author(s):  
Huang-Mei He ◽  
Yi Chen ◽  
Jia-Ying Xiao ◽  
Xue-Qing Chen ◽  
Zne-Jung Lee
Keyword(s):  
Data Analysis ◽  
Real Estate ◽  
Influencing Factors ◽  
Real Estate Market ◽  
Training Data ◽  
Market Demand ◽  
Data Set ◽  
The Real ◽  
The Real Estate ◽  
Real Estate Price

China has carried out a large number of real estate market reforms that change the real estate market demand considerably. At the same time, the real estate price has soared in some cities and has surpassed the spending power of many ordinary people. As the real estate price has received widespread attention from society, it is important to understand what factors affect the real estate price. Therefore, we propose a data analysis method for finding out the influencing factors of real estate prices. The method performs data cleaning and conversion on the used data first. To discretize the real estate price, we use the mean ± standard deviation (SD), mean ± 0.5 SD, and mean ± 2 SD of the price and divide it into three categories as the output variable. Then, we establish the decision tree and random forest model for six different situations for comparison. When the data set is divided into training data (70%) and testing data (30%), it has the highest testing accuracy. In addition, by observing the importance of each input variable, it is found that the main influencing factors of real estate price are cost, interior decoration, location, and status. The results suggest that both the real estate industry and buyers should pay attention to these factors to adjust or purchase real estate.

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