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Development of a Cheap Deployable Pyranometer: Interim Report

Date: 31/10/2012

Student Name: Tom Cartlidge

Matriculation: s0786957

School of Engineering and Electronics

University of Edinburgh

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Abstract

This interim report documents the research associated with the design of a cheap deployable pyranometer, many of which will eventually form a dense wireless network in Edinburgh to assist studies in cloud cover effects on photovoltaic generation. The background motivations of a solar power framework set up by a research partnership (UKSIS) between the University of Edinburgh and University of Reading are presented, and a comprehensive literature review of the main design features and considerations is performed. Recommendations and industry standards in sensor type, data acquisition, calibration and quality control are analysed as options for this new design.

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Table of Contents 1. Introduction .............................................................................................................................. 4

1.1 Background ............................................................................................................................ 4

1.2 UK Solar Integration Study (UKSIS) Initiative ........................................................................... 4

1.3 Objectives & Planning ............................................................................................................ 4

2. Principles of Solar Radiation....................................................................................................... 7

3. Pyranometer Types .................................................................................................................... 8

4. Data Acquisition ........................................................................................................................ 9

5. Calibration and Quality Control ................................................................................................ 10

6. Pyranometer Classification ...................................................................................................... 12

7. Conclusions ............................................................................................................................. 14

8. References ............................................................................................................................... 15

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1. Introduction

1.1 Background

As future global energy supply tends towards the reliance on a cocktail of renewable technologies, solar photovoltaic (PV) power is constantly developing and becoming more commercially available and reliable. International Energy Agency (IEA) forecasts estimate that by 2050, PV cells will provide approximately 11% of global electricity production, offsetting 2.3 billion tonnes of CO2 emissions annually (International Energy Agency, 2010). Optimal technological advances and cost reduction in the field are essential to achieving this vision. A pyranometer is a device used for measuring global solar irradiance; the total solar energy available at the sensors location at any given time. This is the sum of both direct (direct line from sun) and diffuse (scattered by atmospheric particles) solar radiation, measured in W/m2 (British Standards Institution, 2000). Pyranometers have multiple applications including climatology, meteorology, agriculture (Trnka, et al., 2007), solar energy studies (Ertekin, Evrendilek, & Kulcu, 2008) and building physics. This interim report will focus on the design of a cheap deployable pyranometer in the context of photovoltaic (PV) generation and provide a review of some of the key literature surrounding the topic.

1.2 UK Solar Integration Study (UKSIS) Initiative

UKSIS, a partnership between the University of Edinburgh and the University of Reading, have compiled a research framework in view of optimising solar PV’s contribution capacity and stability into the UK’s national electricity grid. This comprises a number of separate work packages which will combine to create a state of the art solar energy model. Work package 3 aims to deliver a ‘sub-grid cloud model’ which will stochastically approximate spatial variation solar data at a very local level, downscaled from models approximating data to 3km2 resolution. The importance of this is in understanding the effect of cloud cover on global solar irradiance, the dominating yet presently sparsely understood factor in PV generation. Previous attempts have been carried out at a 400m resolution under stratocumulus cloud conditions (Venema, Garc, & Simmer, 2010) with promising results – here analysis of other cloud types will be possible.

Around 75 bespoke pyranometer designs with wireless data logging will be implemented across Edinburgh in order to record accurate data at a high spatial density, for comparison and validation of the mathematical models. This paper is the beginning of the design of such a sensor network, aiming to deliver each individual unit for around £300; 25% the cost of existing options.

1.3 Objectives & Planning

Project objectives are defined as follows:

1. Research and present the principles behind current pyranometer technologies and solar radiation databases.

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2. Select/refine most appropriate sensor design. 3. Select/refine optimal data storage design. 4. Employ data transmission method. 5. Merge components and develop cheap deployable pyranometer product. 6. Build & test at least one prototype – document in final report.

From these objectives a more detailed work breakdown structure was written, and by consideration of critical paths, a project plan Gantt chart was drafted. This can be seen in Figure 1:

ID Task Name Duration Start Finish

1 Pyranometer Design Project 147 days Thu 20/09/12 Mon 15/04/132 Project Kick Off 0 days Thu 20/09/12 Thu 20/09/123 Project Proposal submission 0 days Thu 27/09/12 Thu 27/09/124 Interim Report & Literature Review 0 days Thu 01/11/12 Thu 01/11/125 Interview with Project Examiner (TBC) 0 days Mon 14/01/13 Mon 14/01/136 Web Page submission (TBC) 0 days Mon 11/02/13 Mon 11/02/137 Final Report submission 0 days Thu 04/04/13 Thu 04/04/138 Oral Exam (TBC) 0 days Mon 15/04/13 Mon 15/04/139 Research 31 days Thu 20/09/12 Thu 01/11/12

10 General topic reading 31 days Thu 20/09/12 Thu 01/11/1211 Existing technologies 31 days Thu 20/09/12 Thu 01/11/1212 Industry Standards 31 days Thu 20/09/12 Thu 01/11/1213 Industry Practises 31 days Thu 20/09/12 Thu 01/11/1214 Identify potential advancements 31 days Thu 20/09/12 Thu 01/11/1215 Sensor Design 7.5 days Fri 02/11/12 Tue 13/11/1216 Technology comparison 0.5 wks Fri 02/11/12 Tue 06/11/1217 Select/design optimum 1 wk Tue 06/11/12 Tue 13/11/1218 Data Logging 15.5 days Tue 13/11/12 Tue 04/12/1219 Investigate commerically available solutions 1 wk Tue 13/11/12 Tue 20/11/1220 Comparison to custom design 0.5 days Tue 20/11/12 Tue 20/11/1221 Custom build circuitry 2 wks Wed 21/11/12 Tue 04/12/1222 Processing (Raspberry Pi?) & Operating System 2 wks Wed 21/11/12 Tue 04/12/1223 Communication 33 days Wed 05/12/12Fri 18/01/1324 Data Transmission 15.5 days Wed 05/12/12Wed 26/12/1225 Evaluate necessity 0.5 days Wed 05/12/12 Wed 05/12/1226 Research and decide on best method 1 wk Wed 05/12/12 Wed 12/12/1227 Design transmission circuitry 2 wks Wed 12/12/12 Wed 26/12/1228 Data Receiver 17.5 days Wed 26/12/12Fri 18/01/1329 Receiver design 1.5 wks Wed 26/12/12 Fri 04/01/1330 Data handling software 2 wks Mon 07/01/13 Fri 18/01/1331 Design integration 25 days Mon 21/01/13Fri 22/02/1332 Integrate sensor, logger & transmitter 1 wk Mon 21/01/13 Fri 25/01/1333 Mechanical packaging design 2 wks Mon 28/01/13 Fri 08/02/1334 Finalise drawings & schematics for manufacturing 1 wk Mon 11/02/13 Fri 15/02/1335 Create Bill of Materials (BOM) 1 wk Mon 18/02/13 Fri 22/02/1336 Prototyping & Testing 22.5 days Mon 25/02/13Wed 27/03/1337 Source & procure full BOM 1 wk Mon 25/02/13 Fri 01/03/1338 Build Prototype 1 wk Mon 04/03/13 Fri 08/03/1339 Test Prototype 2 wks Mon 11/03/13 Fri 22/03/1340 Design improvements study 0.5 wks Mon 25/03/13 Wed 27/03/1341 Final Project Report 6 days Wed 27/03/13Thu 04/04/1342 Write report 5 days Wed 27/03/13 Wed 03/04/1343 Check report 1 day Wed 03/04/13 Thu 04/04/13

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Project: 003-Project PlanDate: Fri 12/10/12

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2. Principles of Solar Radiation

The sun is by far our closest star and therefore responsible for the significant part of solar radiation reaching the earth’s atmosphere. The power produced by the sun is around 3.9x1026W (Brooks, Monitoring Solar Radiation and Its Transmission Through the Atmosphere, 2006) and its transmitted intensity follows the inverse square law. The solar constant is defined as the average power density of solar radiation falling on a surface of a sphere at distance r from the sun, described by Equation (1):

푆퐸

4휋푟 (1)

where: S0= solar constant (Wm-2) E= total power of sun (W) r = distance from sun (m) (=150,000,000,000m to edge of earth’s atmosphere) At the edge of earth’s atmosphere the solar constant is taken as 1370 Wm-2. More accurately, it is in the range 1324<S0<1417 when the variation of ‘r’ is accounted for due to eccentricity of the earth’s orbit. It has been calculated that around 70% reaches the surface of the earth, giving a global irradiance of around 1000Wm-2 on a clear-sky day (Brooks, Measuring Sunlight at Earth's Surface: Build Your Own Pyranometer, 2007). The breakdown of irradiance sources are shown in Figure 2:

Figure 2 Direct, diffuse, and total insolation for a standard atmosphere, with relative air mass of 1.5 (Brooks, Monitoring

Solar Radiation and Its Transmission Through the Atmosphere, 2006)

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It is seen that the energy density varies across the spectrum – 99% of global solar radiation incident at earth’s surface is in the range 300<λ<3000nm (ISO, 1990). The light spectrum is divided as in Table 1:

Table 1 Light categories by wavelength (Brooks, Monitoring Solar Radiation and Its Transmission Through the Atmosphere, 2006)

Category Approximate wavelength (nm) UV-C 100-280 UV-B 280-315 UV-A 315-400

Visible 400-750 Infrared (IR) 750+

Because we know the solar constant value at the top and have knowledge of the properties of various atmospheric constituents (e.g. aerosols, ozone, CO2, H20), we can indirectly deduce what is happening in the sky by placing a pyranometer at the bottom of the atmospheric column.

3. Pyranometer Types

Different sensor types are capable of measuring solar irradiance. Ideally, the spectral response should be flat so every wavelength is accounted for equally. A bandwidth of 300nm – 3000nm is common in thermal pyranometers (World Meteorological Organisation , 2008), the most popular of which uses a thermopile (multiple thermocouples connected in series) to produce a voltage output proportional to the heat and hence incident solar radiation (Kipp & Zonen, 2012).

Black and white type pyranometers, which use thermopile sensors, are extremely tilt sensitive (McArthur, et al., 1995). Additional to direct and diffuse radiation, tilted collectors receive radiation reflected from surrounding ground objects which can account for up to 25% of total energy incident on the cell. This highlights the important of sensor orientation in a near perfect horizontal place.

Many commercial pyranometers rely on thermopile sensors due to their increased accuracy. However, they can thousands of pounds and therefore it is not financially viable to deploy them in dense networks. A subset of solar radiation sensors known as ‘surrogate’ pyranometers (Brooks, Monitoring Solar Radiation and Its Transmission Through the Atmosphere, 2006) exist at a fraction of the price and rely on silicon based photodetectors (King & Myers, 1997). Such detectors include reference solar cells, photodiodes and light emitting diodes (LEDs) in reverse biased mode. These optical components produce tiny currents proportional to the intensity of light they absorb, which are often passed through transimpedence amplifiers to produce voltages large enough to be read by data loggers (Forrest, 2003). Si-photodetectors are sensitive to spectral effects (McArthur, et al., 1995), as illustrated in Figure 1. This also explains the biggest inefficiency in PV cells – the movement of electrons in the p-n doped semiconductor is only excited by a small portion of the spectrum; the remainder is wasted as heat energy.

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Figure 3 Typical Spectral Response of Silicon Photodetector (Brooks, Measuring Sunlight at Earth's Surface: Build Your

Own Pyranometer, 2007)

Despite this drawback, surrogate pyranometers are so commercially viable for denser networks that much research has been put in to understanding and improving them (King & Myers, 1997). It has already been shown possible to classify cloud amount and types with these devices, even when data is not highly accurate (Duchon & O'Malley, 1998). Device internal temperature sensors can be used for calculating temperature sensitivity correction coefficients, and make it possible to specify accuracy values over various operating temperature ranges (McArthur, et al., 1995).

4. Data Acquisition

Data acquisition (DAQ) is the process of reading the analogue input signal from the system sensors and converting them into digital signals which computers can then process. This section presents literature recommendations on the topic.

The input voltage range from the sensors must be established (amplified or not). Processing accuracy is governed by bit resolution, as expressed in Equation (2):

푉 =푉 .

(2 − 1) (2)

For instance, a logger with a 0 to 2.5V input with 12-bit resolution will achieve accuracy to 0.6mV per individual reading. Required accuracy is relative to the magnitude of input signals (Brooks, Measuring Sunlight at Earth's Surface: Build Your Own Pyranometer, 2007). It should typically be 10 times better than the accuracy required for the signal.

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Multiplexing is required where there is more than one input channel into the DAQ. For example, inputs from radiation and temperature sensors would be switched sequentially into a single voltage measuring unit. This is achieved by either magnetic relay contacts or semiconductor switches (McArthur, et al., 1995). It is recommended that relay multiplexing be used for radiation measurements due to their small contribution to noise (1 – 2µV). Semiconductor multiplexing is suitable where number of input channels is much greater and so higher switching rate is required, but at the cost of additional noise.

Overall system uncertainty must be kept as low as possible; a DAQ uncertainty of <5µV over a 10 minute mean voltage input is ideal (McArthur, et al., 1995). Below this, the DAQ’s contribution to overall system is insignificant. Anything up to <15µV (0.3% of signal) is acceptable, equivalent to around 2Wm-2.

Measurement frequency of the data logger is described by two time parameters. The ‘recording interval’ is the length of time that the recorded average value is taken over. Instantaneous readings are prone to much greater inaccuracies and so time averages are taken to smooth them out. One recommendation is to set the recording interval at 1 minute; longer time intervals are then computable with higher quality control ability (McArthur, et al., 1995). Storage media size and transmission/processing rates will place constraints on this. The second time parameter is the ‘sampling interval’, defined as the period between successive measurements on any single input. This is ideally less than 0.5 of the pyranometer response time but because radiation changes are not particularly quick, 2s is appropriate. If connected to an alternating current (AC) power supply, sampling duration is an important factor. If sampling is set at the same frequency e.g. 50Hz then the device will be insensitive to power line frequency noise. This is not a factor when using a direct current (DC) source.

An ideal system is inevitably compromised by logistics, accuracy, convenience and cost. With this in mind it will be worth considering a separate central server for data logging, with the field device merely transmitting raw calibrated data. Full feasibility studies will be carried out at this later stage in the project.

5. Calibration and Quality Control

Sophisticated calibration techniques and device maintenance is crucial to the success of pyranometry; professional laboratory calibration is advisable, certainly for any credible commercial product (Brooks, Measuring Sunlight at Earth's Surface: Build Your Own Pyranometer, 2007). ISO 9847 is the standard for the calibration of field pyranometers against a reference pyranometer (British Standards Institute, 1993). It provides guidance and instructions to instrument calibration either indoor (by artificial light) or outdoor in natural light under a particular condition (advises to pick a condition that is typical of the field the instrument will be measuring). The procedure involves recording output from the two devices in the same orientation at the same time and then calculating calibration factors based on mathematical treatment of the error ratios. It has guidance where more advanced treatment is desired, for example temperature variation compensation and factors for special values of solar angles. The reference pyranometer should always be of higher quality/class

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than the instrument being calibrated. All procedures are traceable to the world radiometric reference (WRR), which has been the basis for meteorological radiation measurements for the last 25 years (Rüedi & Finsterle, 2012). Additional correction factors (King & Myers, 1997) and more exhaustive implementations of the standards (Martinez, Andújar, & Enrique, 2009) are available, tailored specifically towards silicon-photodiode type detectors. One idea for a more rigorous calibration is to have conditional correction factors, relative to what noise is being detected in the readings for at that time. This would be in attempt to remove the error caused by the calibration being performed under just one specific condition. It is known that clear skies are less ‘noisy’ than cloudy skies (Brooks, Monitoring Solar Radiation and Its Transmission Through the Atmosphere, 2006). A final error to explore is directional response - deviation away from the ideal cosine response to incidence angle of the sun, described by Equation (3):

퐼 = 퐼 cos(푧) (3)

where: Iz = irradiance at any zenith angle ‘z’ I0 = irradiance at zero zenith angle z = zenith angle The orientation of solar zenith angle is measure as shown in Figure 4:

Figure 4 Measuring solar zenith angle (Science Glossary, 2011)

No real device has a perfect cosine response due to build imperfections, spectral response and light reflection rather than absorption into the photodetector at larger solar zenith angles. This can be alleviated physically by applying a layer of Teflon over the sensor window/dome, improving the spectral transmission properties (Brooks, Measuring Sunlight at Earth's Surface: Build Your Own Pyranometer, 2007). Global solar irradiance now is computed according to Equation (4):

퐸 =푉 − 푉푅

(4)

where: E = mean irradiance (Wm-2) Vs= mean signal voltage (µV)

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V0= mean night voltage (µV) – for long term testing this can be based on two 1hr means, one 1hr after sunset and the other 1hr before sunrise (saves recording unnecessary dark values) R = overall corrected calibrated response (µVW-1m2) Researchers (McArthur, et al., 1995) claim that by following their guidelines with a good quality pyranometer and DAQ system holding 10x greater resolution than the accuracy required in the measured signal, overall system uncertainty in the region of 15 – 25 Wm-2 should be achieved(over a 10 minute averaging period). There is no quote for 1min time averages however. Guidelines advise quality control measures such as taking device resistance, electrical zero’s, lead resistance and outside inductance effects to including further correction factors to reduce undesirable effects. Other good practises in quality control include alerts on data out with the range -6<E<1050 W/m2. It is not uncommon for night time signals to give small negative outputs(McArthur, et al., 1995). Furthermore, producing a daily graphical record at 1 minute resolution can be used to screen for any data spikes in data, which may have been caused by measurement dropouts, animal shadows etc. The aim here is to build a trustworthy sensor network and by building in such intelligence will assist data reliability.

6. Pyranometer Classification

There are three classification categories of pyranometer, shown in order from best to worst: Secondary standard First Class Second Class

Criteria for each are defined by ISO 9060 (ISO, 1990) and presented in Table 2:

Table 2 Pyranometer specification list (ISO, 1990)

Reference No.

Specification Pyranometer category

Secondary standard

First class Second class

1 Response time: time for 95% response <15s <30s <60s

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Zero offset: a) Response to 200wm-2 net

thermal radiation (ventilated)

b) response to 5Kh-1 change in ambient temperature

+7Wm-2

±2Wm-2

+15Wm-2

±4Wm-2

+30Wm-2

±8Wm-2

3a Non-stability Percentage change in responsivity per year

±0.8% ±1.5% ±3%

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3b

Non-linearity: Percentage deviation from the responsivity at 500Wm-2due to change in irradiance within 100Wm-2 to 1000Wm-2

±0.5% ±1% ±3%

3c

Directional response (for beam radiation): The range of errors caused by assuming that the normal incidence responsivity is valid for all directions when measuring from any direction a beam radiation whose normal incidence irradiance is 1000Wm-2

±10Wm-2 ±20Wm-2 ±30Wm-2

3d

Spectral selectivity: Percentage deviation of the product of spectral absorbtance and spectral transmittance from the corresponding mean within 0.35µm and 1.5µm

±3% ±5% ±10%

3e

Temperature response: Percentage deviation due to change in ambient temperature within an interval of 50K

±2% ±4% ±8%

3f

Tilt response: Percentage deviation from the responsivity at 0deg tilt (horizontal) due to change in tilt from 0deg to 90deg at 1000Wm-2 irradiance

±0.5% ±2% ±5%

The criteria for the uniform spectral responsivity can only be realised with thermal sensor types equipped with absorbing surfaces, and so strictly speaking a surrogate pyranometer does not meet classification requirements. However, the standard (ISO, 1990) does also state that a pyranometer may be categorised as first class for solar energy test applications only if criteria for azimuth and cosine response are satisfied.

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7. Conclusions

The design of a bespoke cheap deployable pyranometer with wireless transmission is being developed to make it financially viable to host dense irradiance sensor networks

The data will validate mathematically models and provide insight to effects of cloud cover on PV potential

Si-photodetector (surrogate) type designs are likely to be used for cost reduction and it is understood that the success of these device relies on good design practices and advanced calibration techniques based on ISO 9847

It is important to use DAQ systems with a high enough bit-resolution to minimise accumulating system errors

Calibration aims to smooth out deviations caused by poor spectral responsivity, temperature effects and imperfect cosine responses

Although surrogate pyranometers do not strictly meet the full criteria set to be classified by IS 9060, they can be termed ‘first class’ if they are for solar energy tests and satisfy both azimuth and cosine response requirements

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8. References

British Standards Institute. (1993). BS 7621:1993 ISO 9847:1992Calibrating field pyranometers by reference to a reference pyranometer. BSi.

British Standards Institution. (2000). Solar energy - vocabulary. BS EN ISO 9488 , 10.

Brooks, D. (2007, February). Measuring Sunlight at Earth's Surface: Build Your Own Pyranometer. Retrieved October 27, 2012, from Institute for Earth Science Research and Education: http://www.instesre.org/construction/pyranometer/pyranometer.htm

Brooks, D. (2006, August). Monitoring Solar Radiation and Its Transmission Through the Atmosphere. Retrieved 10 28, 2012, from Drexel University: http://www.pages.drexel.edu/~brooksdr/DRB_web_page/papers/UsingTheSun/using.htm#where

Duchon, C. E., & O'Malley, M. S. (1998). Estimating Cloud Type From Pyranometer Observationa. Journal of Applied Meterology , 132-141.

Ertekin, C., Evrendilek, F., & Kulcu, R. (2008). Modeling spatio-temporal dynamics of optimum tilt angles for solar collectors in Turkey. Sensors , 2913-2931.

Forrest, M. M. (2003). Gettin Started in Electronics. Lincolnwood: Master Publishing.

International Energy Agency. (2010). Solar photovoltaic energy. Technology Roadmap , 5.

ISO. (1990). Solar energy - Specification and classification of Instruments for measuring hemispherical solar and direct solar radiation. ISO 9060 .

King, D. L., & Myers, D. R. (1997). Silicon Photodiode Pyranometers: Operational Characteristics, Historical Experiences, and New Calibration Procedures. Albuquerque: Sandia National Laboratories.

Kipp & Zonen. (2012). Passion for Precision. Retrieved from Kipp & Zonen: http://www.kippzonen.com/?product/20151/SMP3.aspx

Martinez, M. A., Andújar, J. M., & Enrique, J. M. (2009). A New and Inexpensive Pyranometer for the Visible Spectral Range. sensors , 4615-4634.

McArthur, L., Dahlgren, L., Dehne , K., Hamalainen, M., Leidquist, L., Maxwell, G., et al. (1995). Using Pyranometers in tests of solar energy converters. International Energy Agency Solar Heating and Cooling Program .

Rüedi, I., & Finsterle, W. (2012). The World Radiometric Reference and its Quality System. World Radiation Centre.

Science Glossary. (2011). Retrieved October 30, 2012, from My NASA Data: http://mynasadata.larc.nasa.gov/glossaryapp/

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Trnka, M., Eitzinger, J., Kapler, P., Dubrovský, M., Semerádová, D., Žalud, Z., et al. (2007). Effect of stimated daily global solar radiation data on the result of crop growth models. Sensors , 2330-2362.

Venema, V., Garc, S., & Simmer, C. (2010). A new algorithm for the downscaling of cloud fields. Quarterly Journal of the Royal Meteorological Society , 91-106.

World Meteorological Organisation . (2008). WMO-No.8. Guide to Meteorological Instruments and Methods of Observation , I7-1 to I.7-40.