Ping Zhu, AHC5 234, Office Hours: M/W/F 10AM - 12 PM, or by appointment M/W/F, 9:00 -9:50 AM, AHC5 357 MET 4400 Meteorological Instrumentation and Observations

For climatologically purposes, and to measure climate variability Why do we make atmospheric observations? For current weather observation, now-casting, and forecasting Vital for atmospheric research, and process studies The Basic parameters include: pressure, temperature, humidity, winds, clouds, precipitation, etc Two types of observations In situ measurement: refers to measurements obtained through direct contact with the respective object. Remote sensing measurement: acquisition of information of an object or phenomenon, by the use of either recording or real-time sensing devices that are wireless, not in physical or intimate contact with the object. Active remote sensingPassive remote sensing Chapter 1: Introduction

Active remote Sensing: Makes use of sensors that detect reflected responses from objects that are irradiated from artificially-generated energy sources, such as radar. Passive Remote Sensing: Makes use of sensors that detect the reflected or emitted electro-magnetic radiation from natural sources.

Steps needed to make measurements for a specific application: 1. Define and research the problem. What parameters are required and what must be measured. What is the frequency of the observations that will be required? How long will the observations be made? What level of error is acceptable? 2. Know and understand the instruments that will be used (consider cost, durability, and availability). 3. Apply instruments and data processing (consider deployment, and data collection). 4. Analyze the data (apply computational tools, statistics, ect.).

What are covered in this class? 1. Data Processing 2. Temperature measurement Basic principles Sensor types Response time 3. Pressure measurement Basic principles Sensors 4. Moisture measurement Moisture Variables Basic Principles Sensors 6. Wind measurement Mechanical method Electrical method 7. Radiation Basic principles Sensors 5. Precipitation measurement Rain gauges Radars for precipitation

10. Weather radar 8. Clouds measurement 9. Upper atmosphere measurement 11. Satellite observations

General Concepts Accuracy is the difference between what we measured and the true (yet unknown) value. Precision (also called reproducibility or repeatability) describes the degree to which measurements show the same or similar results. Probability density Reference value Average Measured value Accuracy Precision Quantifying accuracy and precision

Measurement errors can be divided into: random error and systematic error Random error is the variation between measurements, also known as noise. UnpredictableZero arithmetic mean Random error is caused by (a)unpredictable fluctuations of a measurement apparatus, (b)the experimenter's interpretation of the instrumental reading; Random error can be reduced by taking many measurements Systematic errors are biases in measurement which lead to the situation where the mean of many separate measurements differs from the actual value of the measured attribute.

Systematic errors: (a) constant, or (b) varying depending on the actual value of the measured quantity, or even to the value of a different quantity. e.g.the systematic error is 2% of the actual value actual value: 100°, 0°, or −100° +2°0°−2° A common method to remove systematic error is through calibration of the measurement instrument. When they are constant, they are simply due to incorrect zeroing of the instrument. When they are not constant, they can change sign. Systematic versus random error predictable imperfect calibration of measurement imperfect methods of observation interference of the environment with the measurement process unpredictable inherent fluctuations imperfect reading

Drift Measurements show trends with time rather than varying randomly about a mean. A drift may be determined by comparing the zero reading during the experiment with that at the start of the experiment However, if no pattern of repeated measurements is evident, drifts (or systematic error) can only be found either by measuring a known quantity or by comparing with readings made using a different apparatus, known to be more accurate. How to express errors Expression of Measures: e (unit) ± Δe, e.g., Unit Error: Percent Error: Absolute error: ± Δe, e.g., Relative error

Fundamentals Data processing concepts Averaging

Two variables

Mean and perturbation quantities

Introducing Variance Standard deviation

What does standard deviation mean? In probability theory, standard deviation is a measure of the variability of a data set. A low standard deviation indicates that the data points tend to be very close to the mean, while high standard deviation indicates that the data are spread out over a large range of values. Example: observations 2, 4, 4, 4, 5, 5, 7, 9 Mean: 5Standard deviation: 2

Confidence intervalRange Rules for normally distributed data

Two variables covariance Correlation coefficient

Example 1

Example-2 Example-3

Example-4 Scientific meaning of covariance

Sensible heat flux Specific heat at constant pressure Kinematic sensible heat flux, sh Sensible heat flux, SH z T z T daytimenighttime

Significant figures The rules for identifying significant digits when writing or interpreting measurements: 1. All non-zero digits are significant. 2. In a number without a decimal point, only zeros between non-zero digits are significant : 5 significant figures 20, 300? ; 10001; 3. Leading zeros are not significant ; 0.12;

(b) Using scientific notation The significance of trailing zeros in a number not containing a decimal point can be ambiguous. (a) A decimal point may be placed after the number; for example "100." indicates specifically that three significant figures are meant 4. In a number with a decimal point, all zeros to the right of the first non-zero digit are significant ; ; ; 120.

For multiplication and division, the result should have as many significant figures as the measured number with the smallest number of significant figures. For addition and subtraction, the result should have as many decimal places as the measured number with the smallest number of decimal places. Rule of arithmetic computation Example: A sprinter is measured to have completed a m race in seconds, what is the sprinter's average speed? A calculator gives: m/s. Superfluous precision! Applying significant-figures rules, expressing the result would be m/s Example: =53.8

Example: Let's calculate the cost of the copper in an old penny that is pure copper. Assuming that the penny has grams of copper, and copper cost 67.0 dollar per pound. How much it costs to make the penny? 1lb=453.6 gram

= = = = x 2.5 = / = x 273 = 8. (5.5) 3 = x (4x ) = x 3.00 = x x 10 2 = 12. What is the average of , , , , and ?

= = = = x 2.5 = / = x 273 = (5.5) 3 = 1.7 x x (4.x ) = x 3.00 = 1.4 x x x 10 2 = 3.0 x What is the average of , , , , and ? Answer =