Temperature, Pressure, and Humidity - Howard University Physics ...
Temperature, Pressure, and Humidity (Tempredity) Quantifications From the Howard University Beltsville Campus Atmospheric Research Center in Beltsville, Maryland REU SCHOLAR: OLUWASEUN OJIMI 1 MENTORS: DR. VENABLE, D.2, DR. SAKAI, R. 3 REU Research Experience for Undergraduate Program Engineering, Baltimore City Community College, BCCC 2 Physics and Astronomy Department, Howard University, Washington, DC 3 NOAA Center for Atmospheric Sciences, Howard University, Washington, DC 1
ABSTRACT
Quantifications of atmospheric parameters have played a vital role in weather and climate research which has contributed to an extensive data network used for modeling. For data model input on climate and weather research, a significant number of findings from the atmosphere are needed. The purpose of this study is to find cheap but efficient alternative sensors to the standard temperature, pressure and humidity sensors. The preliminary results showed that the inexpensive sensors can assemble more precise data when reading atmospheric parameters.
INTRODUCTION
This research focused on the Intercomparison of the standard Temperature, pressure and humidity sensors with inexpensive sensors. The two inexpensive sensors were tested for accuracy in comparison with the standard sensors which were the National Weather Service (NWS) sondes. The goal of the study was to find and compare affordable atmospheric parameters sensors substitute for the standard sensors that are also validated for accuracy. The hypothesis was that DHT22 temperaturehumidity and BMP180 Barometric Pressure/Temperature/Altitude Sensor will emulate close accurate data as the Model 278
Barometric Pressure Transducer and Model 41382VC Relative humidity / Temperature probe sensor in reading atmospheric parameters.
INSTRUMENTS CHARACTERISTICS
It is obvious and important to consider the accuracy and precision simultaneously for the quantification of TEMPREDITY measurements. Tempredity is an abbreviation formed from the initial letters of the words temperature, pressure and humidity. Based on given research, it would be a waste of time and energy to determine result with high precision if we know that the result would be highly inaccurate. Equally, a result cannot be
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considered to be extremely accurate if the precision is low.
meteorological and atmospheric science observation.
It is very crucial to differentiate between the terms accuracy and precision in any measuring experiment. The accuracy of an experiment shows how close the experiment is to the true value. Therefore, it is the measure of correctness to the accepted true value. The precision of an experiment is a measure of how well the result has been determined, without reference to its agreement to the true value. Precise result can also be reproduce to give a well and accurate data result. Two forms of precision data are absolute and relative precision. Absolute precision indicates the magnitudes of the uncertainty in the result. While relative precision indicates the uncertainty in terms of a fraction of the value of the result.
Atmospheric pressure: the surface of the earth is at the bottom of the atmospheric sea. The standard atmospheric pressure is measured in various units. For weather applications, the standard atmospheric pressure is frequently called 1 bar or 1000 milibars or hectopascals. This has been found to be easy for recording the relatively small deviations from standard atmospheric pressure with normal weather patterns.
UNDERSTANDING OF THERMODYNAMIC PROPERTIES
Temperature measurement: The thermodynamic temperature shows the degree of warmth or coldness of an object or substance and one of the seven quantities in the international system unit (SI). The kelvin, unit of thermodynamic temperature, is the fraction 1/273.16 of the thermodynamic temperature of the triple point of water. Degree Celsius is commonly used in
Humidity: this describes the fact that the atmosphere contain water vapor. The most commonly used term to quantify humidity is called relative humidity. Relative humidity is defined as the amount of water in the air relative to the saturation amount the air can hold at a given temperature multiplied by 100 when pressure is considered to be constant. INSTRUMENT PROTOTYPE
Sonde is a National Weather Service (NWS) instrument design with different sensors that are used for calibration of atmospheric parameters. The schematic below shows the schematic design of a sonde prototype.
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Schematic 1. Sensor Function PAR (Photo synthetically Active Radiation) Sensor: sensor reports the photosynthetic photon flux density (PPFD). This is the power of electromagnetic radiation in the spectral range that is used by plants for photosynthesis (400‐ 800nm). This sensor is ideal for experiments investigating photosynthesis. T, RH, Air Pressure sensor: the sensor is used for the quantification of thermodynamic parameters GPS: gives the location of every measurements A/D board converter: this converts ultra‐violet radiation voltage to numbers.
TIME LAG
The lagged-correlation is the correlation of two time series shifted in time. According to Meko [2011]: “Lagged correlation is important in studying the relationship between time series for two reasons. First, one series may have a delayed response to the other series, or perhaps a delayed response to a common stimulus that affects both series. Second, the response of one series to the other series or an outside stimulus may be “smeared” in time, such that a stimulus restricted to one observation elicits a response at multiple observations.”
The majority of atmospheric data are recorded as a time series. These time series are documented and plotted to later be manipulated in order to process some form of
statistical analysis. Each time series represents a collection of data, each one being recorded at a specific time. Time series provide compact descriptions of data, and can be used to predict future values of the time series [Davis, 2012]. The location of the Model 278 Barometric Pressure/Model 41382vc relative humidity & temperature sensor and DHT 22/BMP180 probes are similar. The plotted time series are from June 10, 2015 to June 11, 2015. To observe the time series on a smaller scale, the time series were plotted based on days of the year: Figure 1 displays the time series plot of the three temperature data of sonde. Figure 2 displays the time series of relative humidity sensors. Figure 3 displays the time series of pressure sensors.
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Figure 1
Figure 2
Figure 3
LINEAR REGRESSION
In measurement, linear regression is a method used for modeling the relationship
between a scalar dependent variable y and independent variable x. Mathematical representation of linear fit implies
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The model function created for modeling linear fits for the experiment was: function[a0 a1]=Linear_Regression(P1,P2t) n=length(P1); a1=(n*sum(P1.*P2t)sum(P1)*sum(P2t))/(n*sum(P1.^2) (sum(P1))^2); a0=mean(P2t)- a1*mean(P1); end.
The plots below indicate the linear regression or fit to the preliminary results of the experiment. Hysteresis occurs from this
Figure 14 Linear Fit of True T. vs T1
Figure 26 TT v T2
result due to differences in time series (i.e., Time lag).
Figure 5 RHT v RH1
Figure 7 RHT v RH2 Linear Fit
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Figure 8 PT v P Linear Fit
CORRELATION COEFFICIENT
How well does linear regression equation truly represent the set of data? The only way to determine the answer to this question is to examine the correlation coefficient (r) and the coefficient of determination (r2). Lin. Reg equation: Y=a x + b Where a=slope b= y intercept 2 r =coefficient of determination r=correlation coefficient
The quantity r, called the linear correlation coefficient, measures the strength and the direction of a linear relationship between the two variables. The importance of r is such that -1 < r < +1. The + and – signs are used for positive linear correlations and negative linear correlations, respectively. The mathematical formula for computing (r) correlation coefficient implies –
where n = the number of pairs of the data. The Matlab function created for finding r is– function [r]=correlation_coefficient n=length(x) r=(n*sum(x.*y)sum(x)*sum(y)/sqrt(n*sum(x.^2)(sum(x))^2)*sqrt(n*sum(y.^2)(sum(y))^2); end
Positive correlations: If x and y have a strong positive correlation, r value is close to +1. An r value of exactly +1 shows a perfect positive fit. Positive values indicate a relationship between x and y variables such that as values for x increase, values for y also increase. Negative correlation: If x and y have a strong negative linear correlation, r is close to -1. An 6
r value of exactly -1 indicates a perfect negative fit. Negative values indicate a relationship between x and y such that as values for x increase, values for y decrease. A perfect correlation of ± 1 occurs only when the data points all lie exactly on a straight line. If r = +1, the slope of this line is positive. If r = -1, the slope of this line is negative. A correlation greater than 0.8 is generally described as strong, whereas a correlation less than 0.5 is generally described as weak. COEFFICIENT OF DETERMINATION, r2
of the change (fluctuation) of one variable that is predictable from the other variable and indicates how well the regression line represents the data. If the regression line passes exactly through every point on the scatter plot, it would be able to explain all of the variation. The further the line is away from the points, the less it is able to explain. This provides more information on how well the inexpensive pressure sensor can be compare to the standard one. The table below displays the correlation between each sensors and describe the final statistical data result of the project.
The coefficient of determination, r2, is very important because it provides the amount
TABLE 1. Correlation and Statistical Data Sensor comparison T1 &TT T2&TT T1&T2 P1&TP TRH&RH1 TRH&RH2 RH1&RH2
Correlation Coefficient of coefficient (r) determination (r^2) 0.9935 0.9870 0.9948 0.9896 0.9998 0.9995 0.9985 0.9995 0.9848 0.9699 0.9863 0.9729 0.9982 0.9965
The coefficient of determination is a measure that cultivated me to determine how certain one can be in making predictions from a certain plot/model. r2 is the ratio of the explained variation to the total fluctuation or change. Is such that 0 < r 2 < 1, and denotes the strength of the linear association between x and y. For example, if r = 0.922, then r 2 = 0.850, which means that 85% of the total variation in TT can be explained by the linear
Root Mean Square (RMS) 0.2877 0.2574 0.0518 0.0172 1.4392 1.3656 0.3350
Y = ax + b Slope (a) 1.0912 1.0828 1.0088 1.0522 1.2146 1.4449 1.0088
Y‐Intercept (b) ‐4.0436 ‐3.5361 ‐0.4972 ‐51.4888 ‐0.1775 8.9307 ‐0.4972
relationship between T1 and TT (as described by the regression equation). The other 15% of the total variation in TT remains unexplained. SENSORS SPECIFICATION DISCOVERY
It was found that Barometric pressure transducer Setra’s Model 278 is the ultimate solution for quantifying barometric pressure 7
for remote environmental applications. The 278 is designed using the SETRACERAM™ ceramic sensor, allowing it to meet tough accuracy desires over wide operating temperatures in remote applications. Model 278 is as well ideal for solar powered applications because of its low power consumption and sleep mode feature. Under normal operation, this feature minimizes current draw when readings are not being taken. Setra’s Model 278 is considered to be the standard sensor for pressure quantification and it is costly compare to other barometric pressure sensor. The Model 41382VC Relative Humidity/Temperature Probe combines high accuracy humidity and temperature sensors in a single probe. The output signal is 0-1 V (standard) or 0-5 V (user selected option) for
both relative humidity and temperature. RH range is 0-100%. Temperature range is -50 to +50°C. Model 41382VC also considered to be the expensive standard Relative humidity temperature sensor. The DHT22 is an elementary, low-cost digital temperature and humidity sensor. It uses a capacitive humidity sensor and a thermistor to measure the surrounding air, and spits out a digital signal on the data pin (no analog input pins needed). It is fairly simple to operate, but requires careful timing to grab data. The only real downside of this sensor is you can only get new data from it once every 2 seconds, so when using a library, sensor readings can be up to 2 seconds old.
Table 2
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DISCUSSION AND CONCLUSIONS
The collection of data enabled the direct analysis and comparison of Tempredity quantification from both DHT22/BMP180 (inexpensive sensor) and Model 278/Model 41382 VC (standard sensor). Based on the final results, I am competent to back up my educated guess (hypothesis) that the two inexpensive sensors are well-suited for collecting more precise and accurate data alternatives to the standard ones. I detected that entrancing measurements with
Figure 9. Comparison of TT & T1
inexpensive sensors require strong sampling of data. The plots below give an overview of strong correlation coefficient r /coefficient of determination r2 between different sensor data collected at the Howard University Beltsville atmospheric research site and a weaker correlation coefficient for the inexpensive relative humidity sensor data with the expensive relative humidity sensor data from Beltsville.
Figure 10. Comparison of TT & T2
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Figure 11. Comparison of T1 & T2
Figure 12 Comparison of P1 & PT
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Figure 13 Comparison of RH1 & RHT
Figure 14 Comparison of RH2 & RHT
Figure 15 Comparison of RH1 & RH2
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ACKNOWLEDGEMENTS
This study was supported by the National Science Foundation Research Experiences for Undergraduates. I would like to acknowledge the National Science Foundation for financial support of the Research Experience for Undergraduates (REU) summer program and the Howard University REU in Physics Site (NSF Grant PHY-1358727).
http://www.ltrr.arizona.edu/~dmeko/note s_10.pdf Wallace, J.M & Hobbs, V.P. (2006). Atmospheric Science an Introductory Survey. Burlington, MA: Elsevier Inc.
I would also like to thank Dr. Demetrius Venable, Dr. Ricardo Sakai, and Dr. Misra Prabhakar, for the opportunity given to me to be able to perform this research. I would also like to thank Dr. Siwei Li and my REU partner Francios Junior, for all of their help in conducting this research. REFERENCES
Bevington, P.R. & Keith Robinson, D (1992). Data reduction and error analysis for the physical sciences. United States of America: McGraw-Hill, Inc. Davis, R. A. [July 25, 2012]. Introduction to Statistical Analysis of Time Series [PDF document]. Retrieved from http://www.stat.columbia.edu/~rdavis/lect ures/Session6.pdf Mat bit, Correlation Coefficient. (n.d) retrieved July 24 2015, from finding your way around Web Site: http://mathbits.com/MathBits/TISection/ Statistics2/correlation.htm Meko, D. M. [2011]. Course outline (lessons): 10. Lagged correlation. [PDF document]. Retrieved from Lecture Notes Online Website: 12
