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Group Title: AREC-H research report - Agricultural Research and Education Center-Homestead ; SB-85-2
Title: Establishing a modern weather station for use by agriculture
CITATION PAGE IMAGE ZOOMABLE
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Permanent Link: http://ufdc.ufl.edu/UF00067846/00001
 Material Information
Title: Establishing a modern weather station for use by agriculture
Series Title: Homestead TREC research report
Physical Description: 37 leaves : ; 28 cm.
Language: English
Creator: Orth, Paul G
TREC (Agency)
Publisher: University of Florida, Agricultural Research and Education Center
Place of Publication: Homestead Fla
Publication Date: 1985
 Subjects
Subject: Crops and climate -- Florida   ( lcsh )
Automatic meteorological stations -- Florida   ( lcsh )
Genre: government publication (state, provincial, terriorial, dependent)   ( marcgt )
bibliography   ( marcgt )
non-fiction   ( marcgt )
 Notes
Bibliography: Includes bibliographical references (leaf 37).
Statement of Responsibility: Paul G. Orth.
General Note: "December 23, 1985."
 Record Information
Bibliographic ID: UF00067846
Volume ID: VID00001
Source Institution: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
Resource Identifier: oclc - 72810470

Table of Contents
    Historic note
        Historic note
    Main
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        Page 2
        Page 3
        Page 4
        Page 5
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        Page 21
        Page 22
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        Page 35
        Page 36
        Page 37
Full Text





HISTORIC NOTE


The publications in this collection do
not reflect current scientific knowledge
or recommendations. These texts
represent the historic publishing
record of the Institute for Food and
Agricultural Sciences and should be
used only to trace the historic work of
the Institute and its staff. Current IFAS
research may be found on the
Electronic Data Information Source
(EDIS)

site maintained by the Florida
Cooperative Extension Service.






Copyright 2005, Board of Trustees, University
of Florida






Homestead TREC Research Report SB85-2 December 23, 1985



Establishing a Modern Weather Station for Use by Agriculture

Paul G. Orth
University of Florida, IFAS
Mzitica Research and Education Center
Homestead, FL 33031

S Abstract

e prog Imlng of ~{i ut matic data logger for meteorological data
collect on at TREC --96 estead is documented. Processing the data using
available softwareNY scribed and examples of the final product are shown.
Factors r lat the routine collection of weather data are discussed.


Introduction

Weather data are so important to agriculture that the Weather Service was
originally a part of the U. S. Department of Agriculture. Weather forecasts are
useful, and at times essential, for the planning of agricultural operations.
Records of recent weather, climatological records, can be applied to activities
such as scheduling irrigation, predicting crop pest problems, and estimating crop
maturity. Long term climatological records are important for general planning of
crop planting and harvesting. Frost probability and average daily heat unit
accumulation are the applicable statistics. Climatological records also help
toward an understanding of year to year yield variability caused by weather.
Published climatological data for the United States are based primarily on
data collected at hundreds of cooperative weather stations plus a few observation
stations operated by the National Weather Service under N.O.A.A., a part of the
U.S. Department of Commerce. Class A climatological stations are operated by
individual volunteers or sponsored by commercial enterprises and educational
institutions. Often the data collected are limited to daily maximum and minimum
temperature and precipitation, rain or snow. Of the 105 active stations listed
for Florida in April 1985, 12 were operated by IFAS, 8 by the National Weather
service and 3 by the Federal Aviation Authority.(3)
The application of electronics and computers to the collection and
processing of weather data enable the measurement of more climatologically
significant parameters in whatever detail is necessary. Automated weather
stations can be connected by phone lines or other communication means to a
central computer for data processing and dissemation in any time frame necessary.
This report documents the establishment of one automated climatological
station. It discusses the parameters monitored and how research impacts the
writing of the microprocessor and computer programs. It presents the programs
that were developed, demonstrates their use, and suggests some of the
applications of the resulting data to agricultural research problems.




-'I -_


-2-



Materials and Methods

The heart of the system described here is the CR 21 micrologger manufactured
by Campbell Scientific, Inc.* This unit was chosen based on previous experiences
at Homestead and the selection of the unit for use at other IFAS installations.
The latter allows compatible data processing for all units placed in a network of
stations. The parameters chosen to be collected were air temperature, rainfall,
relative humidity, solar radiation, wind movement, soil temperature and leaf
wetness. This used 7 of the 9 available input channels.
These parameters were chosen for the following reasons. (1) Air temperature
and precipitation are the standard parameters for a climatological station and
are important weather factors in crop production. Climatological data becomes
more useful as the length of continuous record increases. (2) Solar radiation
change is responsible for change in plant dry matter production and is one of the
parameters used in estimating evapotranspiration. (3) Relative humidity combined
with temperature characterizes air moisture (dew point, vapor pressure deficit,
etc.) which is a factor in evapotranspiration, of comfort/stress for animals and
plants, and survival of some insects and microorganisms. (4) Wind movement is a
factor in gas exchange in crop systems, has some effect on evapotranspiration and
animal comfort, and at higher velocities can damage crops. (5) Leaf wetness
intensity and duration can be a factor in spread of plant disease, and length of
dew period is a factor in planning crop management activities. (6) Soil
temperature is a factor in biological activities in the soil such as pest
activity and nutrient uptake by crop roots, and it is a consideration in
scheduling planting in the Spring. The above list is not exhaustive but
indicates some potential applications for the data collected.
The following sensors were used for data input: (1) #201 thermistor and
relative humidity probe (2) Met-One* wind speed sensor, (3) Li Cor* Pyranometer,
(4) Sierra* tipping bucket rain gage, (5) wetness sensing grid coated with latex
paint, and (6) #102 thermistor probe.
Two methods were used to store the raw data. (1) A VAX computer operated by
the Institute of Food and Agricultural Sciences (IFAS) in Gainesville, Fla. was
programmed to daily query the CR 21 through the phone line and modem connected to
the data unit. On command from the computer all data in the internal memory of
the CR 21 was output through the phone line to the computer. (2) A tape recorder
permanently connected to the data logger received periodic updates automatically
from the CR 21. Tapes are read into a microcomputer through a special interface
unit that is compatible with the microcomputer and tape recorder. The data are
checked for completeness and for errors before the file is transferred to the
mainframe computer.
Two important capabilities of the CR 21 are its sensor scan every minute and
preprocessing of raw data before storage in its internal memory. This allows
data collection in great detail plus summarization to limit the quantity of
numbers which actually is stored. Thus wind movement (wind run) is measured
every minute and the data logger can be programmed to measure the strongest wind
for a one minute period each day as well as the total movement for a day, or for
shorter periods of time if required. Thus the data logger makes 1440
observations which are reduced to three numbers, a total, an extreme, and the
time the extreme occurred. One important fact to consider when using the data is


* Specific products and manufacturers are identified solely for information
purposes, and such identification does not constitute an endorsement or
recommendation.










that each observation takes a fraction of a second every minute and thus is a
sample. Thus a parameter such as solar radiation, which can vary greatly from
second to second in response to clouds is only measured correctly if the
"samples" collected give the same total as that from an integrated continuous
measurement. Because of the large number of samples taken in a day, this
assumption is valid. However, if the researcher wants data for a period of time
shorter than an hour, the probability of getting a biased estimate increases as
the number of samples decreases. Note that "sampling" does not apply to wind or
rain measurement since those data are based on a counter system which records
each rotation of the anemometer cups and each tip of the tipping bucket in the
rain gauge.
Frequency of sampling and preprocessing were aspects of the micrologger
utilized by the author in programming the micrologger. In addition, the
Agricultural Engineering Department (AED) at the University of Florida developed
data processing programs for use with a statewide network of weather data
loggers. An effort was made to accommodate network needs without compromising the
collection of weather data for research at TREC. Two important limitations in
developing compatibility are the availability of only 27 output program entry
lines and an internal memory of 640 data point storage locations. The 27 output
entry lines are divided equally between three output tables and each table can be
set for a different frequency of output. The AED program requires output at
hourly intervals and thus one table was set for hourly output. Other tables were
programmed so as not to exceed the 640 data point storage locations in 24 hours
and so the output intervals were logical for the planned data applications.
Output from the CR 21 was to be processed at monthly intervals using
Minitab* software. This imposed one more restriction on programming the CR 21.
All output lines were made 16 numbers long which made it easier to use the
Minitab software. Minitab is a statistical package designed for interactive use.
It was selected for this research because: (1) the author was familiar with the
software, (2) it was available on two conveniently available mainframe computers
capable of accommodating the large workspace needed to process the data, (3) it
was capable of handling output from the CR 21 without additional processing, and
(4) the software was easy to use, and accomplished all current processing needs
with room for expansion.

Results and Discussion

The micrologger was programmed in accordance with instructions supplied by
the manufacturer.(1) The input table (Fig. 1) activates the sensor input section
of the micrologger. The solar sensor was attached to the first input channel.
The multiplier was determined by the manufacturer to give an output in units
needed for the VAX program. The soil temperature and air temperature sensors
were connected to channels 3 and 4 respectively. Programs 10 and 7 are for the
specific sensors used. These programs give an output in degrees Celsius and thus
a multiplier of 1 was used. If the sensor used is out of calibration, a
corrective linear regression equation can be programmed to increase data
accuracy. The relative humidity sensor, input 5, required the air temperature
sensor for its operation. It reads directly in percent relative humidity. The
leaf wetness sensor was connected to a resistance network to minimize current


* Specific products and manufacturers are identified solely for information
purposes, and such identification does not constitute an endorsement or
recommendation.




*I I Jr


CR21 Input Table Coding Form

CR21 ID ______ Start Date ____ ..__ Slart Time

NOTE: Select Input program number from appendix A of the CR21 Operator's Manual. Speify a multplier (a) and offset (b) for each sensor using the equaton
EU aX + b to convert the sensor ouput (X) to engineering units EU Final Output Engmeenng Units U -Input Uruts e volts (VL.mnullvolts (MV).
and count.


Senso Sensor Description and Calibration
Nuber(EU) InuPr
Range (EU) Input Program


Final Output (EU)
Multiplier
Program No. (EU/IU) Offset (EU)I


(V. MV. DC Restane)

Solar Radiation
1
11: 2 12: 0.144 13: 0
(V. MV, DC Resistance)



21: 22: 23:
(V. MV, DC Resistance)

SSoil Temperature (Bare 2 inches 5 cm.)

31: 10 32: 1 33: 0
(V, MV. DC Resistance)

Air Temperature

41: 7 42: 1 43: 0
(V, MV. DC & AC Resistnce)

Relative Humidity

151: 8 52: 1 53: 0
(V. MV. DC & AC Res)stance)

Leaf Wetness

61: 5 62: 4 163+
(V, MV. DC & AC Resince)



_71: 72: 73:
Pulse counter (4095 counts per scan maximum)

Wind MovementI

181: 6 1 82: .0621 83: 0
Pulse counter (15 counts per scan maximum)


Rain
_ ________ 91:


6 92: .025 93: 0


Figure 1. Datalogger input program documentation for TREC weather


station.




'I -I


-5-



consumption and to achieve a suitable output voltage range. In addition, output
programs were of the histogram type. Thus a multiplier of 4 and an offset of
+0.7 was used to give required sensitivity and readability. The wind speed
sensor was programmed with the multiplier 0.0621 to give an output in km./hr.
This sensor has a circuit closure device and the number of closures are counted
by the CR 21. The tipping bucket rain gauge also makes circuit closures, and a
multiplier of .025 produces an output in cm.; each tip of the bucket is .025 cm
of rain.
The three output tables programmed for 1, 12, and 24 hours are reproduced in
figures 2-4. Three outputs every 60 minutes give air temperature data requested
by AED, Fig. 2. Relative humidity, solar radiation, precipitation and wind run
are other parameters they want monitored, but the programming is not identical to
that which they suggest since all one-hour output had to be in one table at this
TREC site. Leaf wetness is monitored for research at TREC. The histogram output
characterizes the situation the previous hour. In conjunction with readings the
previous and following hours it tells whether the sensor was wet or dry and if
wet, whether the intensity was changing. From such data the continuous periods
of wetness can be determined. The output occupies 2 lines and equals 16 output
number pairs. (Each output pair consists of an item identifier and a data value
as discussed below.) Flexibility to achieve 2 full lines was accomplished by
including a time output with entries 2 and 3 and selecting 4 bins for the
histogram.
To some extent the output assigned to the other two output tables was
arbitrary. An effort was made to fill Output Table 2, Fig. 3, and to leave
Output Table 3, Fig. 4 with room for expansion. The leaf wetness histogram must
be every 12 hours not 24 in order for a combined histogram, noon to noon, to be
produced. The histogram gives the length of time for wetness of various
intensities. Since most wetness occurs at night, determining length of
continuous wetness requires the centerpoint of such data be midnight not, noon.
The hourly leaf wetness histogram, discussed above, can be used to detect breaks
in leaf wetness.
Maximum and minimum temperature output on a 12 hour basis is useful for
detecting day-to-day changes in weather. A daily minimum temperature in the PM
or a maximum in the AM often indicates a notable weather change or variation.
Maximum solar radiation intensity may help identify a sensor out of calibration.
It also may serve to indicate the clarity of the sky, and one could calculate
what the total radiation would have been with a cloudless sky all day. Two
readings a day allows two estimates of sky clarity. There is no overriding
reason to have total radiation or soil temperature summaries more than once a
day. The main reason they appear in Output Table 2 is to fill the table and to
achieve 3 full lines, 24 number pairs. The primary flexibility in accomplishing
this comes from the leaf wetness histogram which could have had less than 11
bins.
Output Table 3, Fig. 4, gives two full lines, with flexibility achieved by
the 10 bins in the temperature histogram, the three vacant table entry lines and
the greater flexibility in Output Table 2. Thus, any future modification in
Table 3 would consider possible modification in Table 2 also. The parameters
shown in Fig. 4 complete the data output objectives planned at the start of
program preparation. The daily reading of internal battery voltage allows the
batteries to be changed on a timely schedule. The ID number identifies the
station used to collect the data. It, 691, comes from a rearrangement of the
last three digits of the U.S. Weather Service identification, 08-4091-6, for the
climatic station located here. The back-up unit uses ID 692.





-U-


CR21 Output Table Coding Form

CR21 ID ____Start Date ____ Start Time .... -----
NOTE: Select output program number and parameters from appendix B of the CR21 Operator'l Manual. Outpul ID numbers 1. 2 and 3 identify table
number, day and time. ID numbers 4 and greater idently data generated by oupu programs Only positive integers are used to program the output table
Output Table Number (1. 2 or 3) 1 Output Time Interval (minutes) 03: 60

Table Output Program and Data Description Output ID No.
Entry
Number Param 1 descrip. Param 2 descrip. Program No. Parameter 1 Parameter 2


Average Air Temperature
1111: 51 1
11: 31 12: 4 13: 0


Maximum Air Temperature, Time

21:


S5,6

53 22: 4 23: 1


Minimum Air Temperature Time 7.8
31: 54 32: 4 33: 1


Average Relative Humidity 9
4-
41: 51 42: 5 43: 0


Total Radiation 10
51: 52 52: 1 53: 0


Total Rain 11
6
61: 52 62: 9 6: 0


Wind Movement 12
7
71: 51 72: 8 73: 0


Leaf Wetness Histogram
81: 55 82: 6 83: 4


S13-16

91: 0 92: 1 93: 8


Figure 2. Datalogger documentation for hourly output of processed


weather data.


9


I i ,




I ( -' I


CR21 Output Table Coding Form

CR21 ID ________ Start Date___ Start Time _----
NOTE: Select output program number and parameters from appendix B ol the CR21 Operator's Manual. Output ID numbers 1. 2 and 3 identify table
number, day and time. [D numbers 4 and greater identify data generated by ouput programs. Only positive integers are used to program the output table
Output Table Number (1, 2 or 3) 2 Output Time Interval (minutes) 03: 7 20

Table Output Program and Data Description Output ID No.
Entry
Number Param 1 descrip. Param 2 descrip. Program No. Parameter 1 Parameter 2


Maximum Temperature, Time 14 2,3
11: 53 12: 4 13: 1

Minimum Temperature, Time 4,5
2
21: 54 22: 4 23: 1


E Maximum Radiation Time 6 6,7
31: 53 32:1 33:1


Total Radiation 8
41: 527 42: 43:


S Leaf Wetness Histogram
51: 52: 53: 1

Continued 9-19
61: 0 62:0 6: 11

Average Soil Temperature 20
71: 51 72: 3 73: 0

Maximum Soil Temperature Time 21,22
81: 53 82: 3 83: 1

Minimum Soil Temperature 23,24
:91: 54 92: 3 93: 1


Figure 3. Datalogger documentation for 12-hr output of processed


weather data.




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CR21 Output Table Coding Form

CR21 ID Start Date ____ Start Time ------- ..
NOTE: Select output program number and puarmeter from appendix B of the CR21 Operator's Manual. Output ID number 1. 2 and 3 identify table
number, day and time ID numbers 4 and greater identify data generated by ouput program. Only positive integes are used to program the output table
Output Table Number (1, 2 or 3) 3 Output Time Interval (minutes) 03: 1440


Tab Output Program and Data Description Output ID No.
EntyI
Number Param 1 desaip. Param 2 dip. Param 2 de p. Program No. Parameter 1 Parameter 2


I


Rain, Total 4 2
11: 52 12: 9 13: 0


2 Maximum Rain Time 3,4
21: 53 22: 9 23: 1


3 Temperature Histogram
31: 55 32: 4 33: 10


4 Continued 5-14
41: 0 42: 3 43: 30


5 Battery Supply (To CR21) 15
51: 50 52: 0 53: 0

SID No. 16
61: 62 62: 691 d: 0


7
71: 72: 73:


8
81: 82: 83:
[ "


9


Figure 4. Datalogger documentation


for 24-hr output of processed


weather data.


SI 1


I




, I J I


-9-



Figures 5 and 6 show typical output from the CR 21 programmed as described
above. Fig. 5 identifies each output item and gives an example. Fig. 6 is 14
hours of output, showing 14 hourly sets of data, two 12-hr sets and one 24-hr.
set. The first number of each number pair identifies each reading and refers
back to the appropriate output table. Thus, to decode the data the Output Table
Number must be identified first. It is indicated by the identifier 01. The
output tables are labeled 0001, 0002, or 0003 respectively. In Fig. 5 the output
is divided into three groupings according to output table, and the identity of
each reading is shown. Since Output Tables 2 and 3 follow an output table 1
occurring at the same time, day number and time are not repeated. Each day has
56 lines of output, 48 for Table 1, 6 lines for Table 2, and 2 lines for Table 3.
Output is in chronological order and thus requires further processing to compile
the output in more easily read tables for each parameter.
Minitab was the statistical software chosen to sort the data and to do the
statistical analyses. Commands in Minitab can be entered one line at a time from
a computer terminal. This is a common practice for extracting statistical
information and doing simple plotting of a data file. Minitab commands also
allow sorting of a composite of data such as generated by the CR 21 data logger.
These commands, and others, can be put together in a subroutine and called from
an interactive terminal at the appropriate time. An outline of the steps in
processing CR 21 data is given in Fig. 7. First the Minitab software is
activated and then the file of data readied for processing is transferred into
the Minitab workspace. Since there are 56 lines of data for each day and these
lines must be sorted into specific locations, the SET command is used to put
repetitive sequences of 1 through 56 in column 17, C17.
The number of sequences, N, must be the same as the number of data days read
into the workspace. Then the data can be sorted by the subroutine SORTP.EXE.
This subroutine is given in figures 8 and 9. Note it is repetitive since 34
lines are sorted into selected locations. This sorting does not save the hourly
temperature data since it was not needed at this research center. However the
program was written so it could be expanded easily at a future date to sort out
hourly temperature data. Thus groups of columns not now used e.g. C36-C44, were
reserved for that use. The two PRINT statements are included so the processing
can be monitored and any serious problems be detected. (See additional
discussion below with examples of output.) Some errors in the data file will
cause problems that prevent the file from being read into Minitab, and an error
statement will be generated. This is another way to detect errors. The ADD
command generates the total 24-hr solar radiation from the two 12-hr totals. The
LET command converts the two 12-hr soil temperature averages into a 24-hr
average. This subprogram ends with the naming of some important columns for
easier reading of the output, Fig. 9.
A storage file is opened with the OUTFILE command (Fig. 7). All output from
the next two subprograms will go to a computer file, and thus the computer
terminal screen does not require constant monitoring. When OUTFILE is not used
and a program is executed, all commands and output appear at the terminal.
Minitab periodically prompts with "Continue?" and the operator must reply for
data processing to continue or for the program to jump to the next command. This
is avoided by using the OUTFILE command. The PROCP.EXE subroutine is given in
figures 10 and 11, along with a description of the output generated. The PRINT
command brings out the contents of the columns selected and the DESCRIBE command
gives basic statistics on the contents of the columns. The values generated are:
N (the number of items in the column), mean, median, tmean (a 5% trimmed mean),
standard deviation, standard error of the mean, maximum value, minimum value,
first quartile, and third quartile.




i 1 1


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Figure 5. Key to data output from automated weather station, TREC Homestead


Output Table 1


CODE/CONTENTS:


01 table 1 02 day 03
05 maximum air temperature
07 minimum air temperature


time
06
08


04 average air
time of maximum
time of minimum


temperature


EXAMPLE:
01 0001. 02 0261. 03 2400. 04 23.86 05 24.10 06 2332. 07 23.61

CODE/CONTENTS:


09 average relative humidity 10 total solar radiation
12 wind movement 13-16 leaf wetness histogram


11 rain


EXAMPLE:
09 23.46 10 -.411 11 0.000 12 0.000 13 0.000 14 0.000 15 1.000


Output Table 2

CODE/CONTENTS:


01 table 2
04 minimum
06 maximum


02 maximum air
air temperature
solar radiation


temperature 03 time of maximum
05 time of minimum
07 time of maximum 08 total solar radiation


EXAMPLE:
01 0002. 02 27.56 03 1555.


04 22.56 05 2021. 06 4.559


07 1441. 08 467.2


CODE/CONTENTS:


09-19 leaf wetness histogram
21 maximum soil temperature
23 minimum soil temperature


EXAMPLE:
09 0.824
17 0.000


20 average soil temperature
22 time of maximum
24 time of minimum


10 0.017 11 0.005 12 0.099 13 0.050 14 0.003
18 0.000 19 0.000 20 30.40 21 32.04 22 1426.


15 0.000 16 0.000
23 28.18 24 2400.


Output Table 3

CODE/CONTENTS:

01 table 3 02 total rainfall 03 maximum 1 minute rain 04 time of maximum
05-14 temperature histogram 15 CR21 battery my 16 ID (691)

EXAMPLE:
01 0003. 02 0.400 03 0.075 04 329.0 05 0.000 06 0.000 07 0.000 08 0.000
09 0.000 10 0.104 11 0.577 12 0.236 13 0.080 14 0.000 15 1039. 16 0691.


08 2356.


16 0.000




1 I


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Figure 6. Typical data stream from automated weather station, TREC Homestead


01 0001. 02 0262. 03 1100. 04 23.44 05 23.80 06 1100. 07 23.06 08 1008.
09 22.61 10 12.63 11 0.150 12 0.000 13 0.000 14 1.000 15 0.000 16 0.000
01 0001. 02 0262. 03 1200. 04 23.78 05 24.04 06 1114. 07 23.68 08 1200.
09 23.23 10 12.60 11 0.225 12 0.000 13 0.000 14 1.000 15 0.000 16 0.000
01 0002. 02 24.76 03 102.0 04 22.06 05 0907. 06 0.372 07 1104. 08 33.31
09 0.058 10 0.056 11 0.309 12 0.462 13 0.112 14 0.000 15 0.000 16 0.000
17 0.000 18 0.000 19 0.000 20 -47.8 21 11.22 22 1059. 23 -82.4 24 337.0
01 0001. 02 0262. 03 1300. 04 23.57 05 23.74 06 1220. 07 23.31 08 1300.
09 22.98 10 11.14 11 0.250 12 0.000 13 0.000 14 1.000 15 0.000 16 0.000
01 0001. 02 0262. 03 1400. 04 24.10 05 25.18 06 1358. 07 23.31 08 1304.
09 23.76 10 111.3 11 0.025 12 0.000 13 0.433 14 0.566 15 0.000 16 0.000
01 0001. 02 0262. 03 1500. 04 26.25 05 27.27 06 1442. 07 25.00 08 1401.
09 26.74 10 276.6 11 0.000 12 0.000 13 0.216 14 0.000 15 0.000 16 0.000
01 0001. 02 0262. 03 1600. 04 26.86 05 27.16 06 1552. 07 26.35 08 1559.
09 27.29 10 176.7 11 0.000 12 0.000 13 0.000 14 0.000 15 0.000 16 0.000
01 0001. 02 0262. 03 1700. 04 26.45 05 26.93 06 1616. 07 26.01 08 1656.
09 26.65 10 108.5 11 0.000 12 0.000 13 0.000 14 0.000 15 0.000 16 0.000
01 0001. 02 0262. 03 1800. 04 26.04 05 26.30 06 1727. 07 25.60 08 1757.
09 26.24 10 48.87 11 0.000 12 0.000 13 0.000 14 0.000 15 0.000 16 0.000
01 0001. 02 0262. 03 1900. 04 24.99 05 25.60 06 1802. 07 24.34 08 1900.
09 24.89 10 3.237 11 0.000 12 0.000 13 0.350 14 0.000 15 0.000 16 0.000
01 0001. 02 0262. 03 2000. 04 22.99 05 24.28 06 1901. 07 22.37 08 2000.
09 22.94 10 -.902 11 0.175 12 0.000 13 0.083 14 0.533 15 0.116 16 0.000
01 0001. 02 0262. 03 2100. 04 22.66 05 22.87 06 2058. 07 22.31 08 2002.
09 22.48 10 -.965 11 0.100 12 0.000 13 0.000 14 1.000 15 0.000 16 0.000
01 0001. 02 0262. 03 2200. 04 22.84 05 22.94 06 2139. 07 22.75 08 2121.
09 22.49 10 -1.02 11 0.000 12 0.000 13 0.000 14 1.000 15 0.000 16 0.000
01 0001. 02 0262. 03 2300. 04 22.74 05 23.06 06 2300. 07 22.62 08 2248.
09 22.30 10 -1.22 11 0.000 12 0.000 13 0.000 14 1.000 15 0.000 16 0.000
01 0001. 02 0262. 03 2400. 04 22.69 05 23.06 06 2303. 07 22.44 08 2354.
09 22.18 10 -1.17 11 0.050 12 0.000 13 0.000 14 1.000 15 0.000 16 0.000
01 0002. 02 27.27 03 1442. 04 22.31 05 2002. 06 5.517 07 1415. 08 0731.
09 0.391 10 0.065 11 0.025 12 0.306 13 0.201 14 0.009 15 0.000 16 0.000
17 0.000 18 0.000 19 0.000 20 -27.5 21 22.37 22 1559. 23 -82.4 24 2014.
01 0003. 02 1.425 03 0.050 04 1957. 05 0.000 06 0.000 07 0.000 08 0.000
09 0.000 10 0.000 11 0.672 12 0.314 13 0.012 14 0.000 15 1200. 16 0692.





-12-



Fig. 7. Flow sheet of commands used in processing weather data from the
automated station.

MINITAB
READ 'filename' C1-C16
SET C17
N(1:56)
END
EXECUTE 'sortp.exe'
OUTFILE 'filename'
EXECUTE 'procp.exe'
EXECUTE 'prunl.exe'
EXECUTE 'ckrun.exe'
SAVE 'filename'
STOP
(N = number of days in data file)



Subroutine PRUN1.EXE, Fig. 12, continues the data summarizing and closes the
outfile. It was separated from the previous subroutine because when sensors
were not working or not used and most or all data in a column consisted of zeros,
Minitab flagged that condition with a message to indicate a possible error.
Sometimes this flagging aborted execution of subsequent commands. Such "error"
messages appear on the screen even if other data are going to an outfile. When
such messages appear the operator should check for completeness of output. These
messages also appear in the outfile. The author uses other subroutines if
Minitab aborts.
Subroutine CKRUN.EXE, Fig. 13, erases columns no longer needed and checks
for errors in histogram columns. Some computers using Minitab have less
workspace than others. With limited workspace unneeded columns must be erased to
make room for new columns. Whether one needs to keep close tally on workspace
available because of limited computer memory or has ample workspace, it is good
housekeeping to erase columns no longer needed. A check for histogram
completeness is based on the fact that a group of histogram columns should add up
to 0.99-1.00. The describe command is used to efficiently survey 24 hourly, two
12-hr, and one 24-hr histograms.
The last step before terminating the Minitab run is to SAVE the workfile for
future processing needs. Thus another reason for erasing columns is so that no
more is saved with the SAVE command than is actually needed in the future.
Refinement, and to some extent development of the programs discussed above
was partially through a process of trial and error. The program should be
efficient in form, and the output should be easily read. As with most computer
programs there is no best way to achieve a goal. The author is confident better
programs could be written, and others using this information should feel free to
make modifications.
One aim of this report is to explain the programs used to generate the
following tables of data. The second aim is to show a sample of the output data
and indicate some of the applications of such data.


I I II






-13-



Figure 8. First portion of Minitab program SORTP used to process
weather data from micrologger.


CHOOSE
CHOOSE
CHOOSE
CHOOSE
CHOOSE


CHOOSE 10
CHOOSE 12
CHOOSE 14
CHOOSE 16
CHOOSE 18
CHOOSE 20
CHOOSE 22
CHOOSE 24
CHOOSE 28
CHOOSE 29
CHOOSE 31
CHOOSE 33
CHOOSE 35
CHOOSE 37
CHOOSE 39
CHOOSE 41
CHOOSE 43
CHOOSE 45
CHOOSE 47
CHOOSE 49
CHOOSE 51
CHOOSE 25
CHOOSE 26
CHOOSE 27
CHOOSE 52
CHOOSE 53
CHOOSE 54
CHOOSE 55
CHOOSE 56
ADD C458 C
LET C523 =
PRINT C19
PRINT C20


I


(


C17,
C17,
C17,
C17,
C17,
C17,
C17,
C17,
C17,
C17,
C17,
C17,
C17,
C17,
C17,
C17,
C17,
C17,
C17,
C17,
C17,
C17,
C17,
C17,
C17,
C17,
C17,
C17,
C17,
C17,
C17,
C17,
C17,
C17,
;485,


C6, INTO C18-C21
C6 C8 C10 C12 C14
C6 C8 C10 C12 C14
C6 C8 C10 C12 C14
C6 C8 C10 C12 C14


C
C
C
C


C2 (
C2 (
C2 (
C2 (
C2 (
C2
C2
C2
C2
C2
C2
C2
C2
C2
C2
C2
C2
C2
C2
C2
C2
C2
C2
C2
C2
C2
C2
C2
C2
C2
C2
C2
C2
C2


16,
16,
16,
16,
C16,
C16,
C16,
C16,
C16,
C16,
C16,
C16,

C16,
C16,
C16,
C16,
C16,
C16,
C16,
C16,
C16,
C16,
C16,
C16,
:16,
C16,
C16,
C16,
C16,
,16,
C16,
C16,


INTO
INTO
INTO
INTO
INTO
INTO
INTO
INTO
INTO
INTO
INTO
INTO

INTO
INTO
INTO
INTO
INTO
INTO
INTO
INTO
INTO
INTO
INTO
INTO
INTO
INTO
INTO
INTO
INTO
INTO
INTO
INTO


C27-C35
C45-C53
C63-C71
C81-C89
) C99-C107
) C117-C125
SC135-C143
I C153-C161
SC171-C179
SC189-C197
I C207-C215
) C225-C233

I C243-C251
SC261-C269
C279-C287
C297-C305
C315-C323
C333-C341
C351-C359
C369-C377
C387-C395
C405-C413
C423-C431
C441-C449
C450-C458
C459-C467
C468-C476
C477-C485
C486-C494
C495-C503
C504-C512
C513-C521


INTO C522


(C472 + C499) / 2
C235 C451 C478 C505
C21 C236 C237 C520 C521


I I I I


C8 C10 C12 C14
C8 C10 C12 C14
C8 C10 C12 C14 I
C8 C10 C12 C14
C8 C10 C12 C14
C8 C10 C12 C14 i
C8 C10 C12 C14
C8 C10 C12 C14
,INTO C234-C237
C8 C10 C12 C14 l
C8 C10 C12 C14 l
C8 C10 C12 C14
C8 C10 C12 C14
C8 C10 C12 C14
C8 C10 C12 C14
C8 C10 C12 C14
C8 C10 C12 C14
C8 C10 C12 C14
C8 C10 C12 C14
C8 C10 C12 C14
C8 C10 C12 C14
C8 C10 C12 C14
C8 C10 C12 C14
C8 C10 C12 C14
C8 C10 C12 C14 (
C8 C10 C12 C14
C8 C10 C12 C14 (
C8 C10 C12 C14 (
C8 C10 C12 C14 (




SI


-14-



Figure 9. Last portion of Minitab program SORTP used to process
weather data from micrologger.

NAME C452 'MAXTEMP', C453 'TIMEMAX', C454 'MINTEMP', C522 'TDRAD' &
C455 'TIMEMIN,' C456 'MAXRAD', C457 'RADTIME', C458 'TOTALRAD' &
C523 'SOILAVG', C473 'SOILMAX', C474 'SMAXTIME', C475 'SOILMIN', &
C476 'SMINTIME', C460 'LE', C461 'AF', C462 'WE', C463 'TN', C464 'ES', &
C465 'S', C466 'HI', C467 'ST', C469 'OG', C470 'RA', C471 'M' &
C479 'MAXTEMP2',C480 'TIMEMAX2', C481 'MINTEMP2', C482 'TIMEMIN2', &
C483 'MAXRAD2', C484 'RADTIME2', C485 'TOTLRAD2', C 523 'STAV' &
C500 'SOILMAX2', C501 'SMAXTIM2', C502 'SOILMIN2', C503 'SMINTIM2', &
C487 'L', C488 'EA', C489 'FW', C490 'ET', C491 'NE', C492 'SS', &
C493 'H', C494 'IS', C496 'TO', C497 'GR', C498 'AM.', C506 'RAIN', &
C507 'MAXRATE', C508 'TIME', C509 'TE', C510 'MP', C511 'ER.', &
C512 'A', C514 'TU', C515 'RE.', C516 'HIS', C517 'T', C518 'OGR', &
C519 'AM', C520 'BATTMV' C521 'ID', C19 'TBLNO', C20 'DAYNO', &
C21 'ENDTIME' C451 'NOTBL.', C236 'NODAY', C237 'TIMEND', C505 'TABLENO.' &
C 522 'DAYTOT'
END


Figure 10. First portion of Minitab program PROCP used to process
weather data from micrologger.


C19 C235 C451 C478 C505
C20 C21 C236 C237 C520 C521
C28 C46 C64 C82 C100 C118 C136 C154
C172 C190 C208 C226 C244 C262 C280 C298
C316 C334 C352 C370 C388 C406 C424 C442
C29 C47 C65 C83 C101 C119 C137 C155
C173 C191 C209 C227 C245 C263 C281 C299
C317 C335 C353 C371 C389 C407 C425 C443
C30 C48 C66 C84 C102 C120 C138 C156
C174 C192 C210 C228 C246 C264 C282 C300
C318 C336 C354 C372 C390 C408 C426 C444
C31 C49 C67 C85 C103 C121 C139 C157
C175 C193 C211 C229 C247 C265 C283 C301
C319 C337 C355 C373 C391 C409 C427 C445
C32-C35 C50-C53
C68-C71 C86-C89
C104-C107 C122-C125
C140-C143 C158-C161
C176-C179 C194-C197
C212-C215 C230-C233
C248-C251 C266-C269
C284-C287 C302-C305
C320-C323 C338-C341
C356-C359 C374-C377
C392-C395 C410-C413
C428-C431 C446-C449


diagnostic and data log

hourly relative humidity


hourly radiation


hourly rain


hourly wind


hourly
leaf wetness
histogram


PRINT
PRINT
PRINT
PRINT
PRINT
PRINT
PRINT
PRINT
PRINT
PRINT
PRINT
PRINT
PRINT
PRINT
PRINT
PRINT
PRINT
PRINT
PRINT
PRINT
PRINT
PRINT
PRINT
PRINT
PRINT
PRINT






-15-



Figure 11. Last portion of Minitab program PROCP used to process
weather data from micrologger.


PRINT C452 C453 C479 C480 12
PRINT C454 C455 C481 C482 12
PRINT C456-C458 C483-C485 C522 12
PRINT C473 C474 C500 C501 12
PRINT C475 C476 C502 C503 C523 12
PRINT C460-C467 12
PRINT C469-C471
PRINT C487-C494
PRINT C496-C498
PRINT C506-C508 24
PRINT C509-C512 24
PRINT C514-C519
DESCRIBE C28 C46 C64 C82 C100 C118 C136 C154
DESCRIBE C172 C190 C208 C226 C244 C262 C280 C298
DESCRIBE C316 C334 C352 C370 C388 C406 C424 C442
DESCRIBE C29 C47 C65 C83 C101 C119 C137 C155
DESCRIBE C173 C191 C209 C227 C245 C263 C281 C299
DESCRIBE C317 C335 C353 C371 C389 C407 C425 C443
DESCRIBE C31 C49 C67 C85 C103 C121 C139 C157
END







Figure 12. Minitab program PRUN1 used to conclu


hr. maximum temperature
hr. minimum temperature
hr. radiation data summary
hr. soil max. temperature
hr. soil min. temperature
hr. leaf wetness histogram



hr. rain summary
hr. temperature histogram

hourly relative humidity


hourly solar radiation


hourly wind


de output from initial


processing of weather data from micrologger.


C229 C247 C265 C283 C301
C373 C391 C409 C427 C445
C481
C484
C475 C502


hourly wind continued

air temperature
ii If
soil temperature
air temp. histogram
11 11 it
total daily solar rad.


C211
C355
C454
C483
C500


DESCRIBE
DESCRIBE
DESCRIBE
DESCRIBE
DESCRIBE
DESCRIBE
DESCRIBE
DESCRIBE
OUTFILE
END


C175 C193
C319 C337
C452 C479
C456 C457
C523 C473
C509-C512
C514-C519
C522


>I I t




% I r 1


-16-


Figure 13.


Minitab program CKRUN which checks histogram data for completeness.


C1-C21 C27-C31 C45-C49 C63-C67 C81-C85 C99-C103
C117-C121 C135-C139 C153-C157 C171-C175 C189-C193 C207-C211
C225-C229 C234-C237 C243-C247 C261-C265 C279-C283
C297-C301 C315-C319 C333-C337 C351-C355 C369-C373
C387-C391 C405-C409 C423-C427 C441-C445 C450-C459 C468
C472-C486 C495 C499-C508 C513 C520-C522


C33 +
C51 +
C69 +
C87 +
- C105
- C123
- C141
- C159
- C177
- C195
- C213
- C231
- C249
- C267
- C285
- C303
- C321
- C339
- C357
- C375
- C393
- C411
- C429
- C447
- C510
- C461


+ C469 + C470 + C471
LET C557 = C487 + C488
+ C496 + C497 + C498
DESCRIBE C531-C554
DESCRIBE C555-C557
END


C34 + C35
C52 + C53
C70 + C71
C88 + C89
+ C106 + C107
+ C124 + C125
+ C142 + C143
+ C160 + C161
+ C178 + C179
+ C196 + C197
+ C214 + C215
+ C232 + C233
+ C250 + C251
+ C268 + C269
+ C286 + C287
+ C304 + C305
+ C322 + C323
+ C340 + C341
+ C358 + C359
+ C376 + C377
+ C394 + C395
+ C412 + C413
+ C430 + C431
+ C448 + C449
+ C511 + C512
+ C462 + C463


LET
LET
LET
LET
LET
LET
LET
LET
LET
LET
LET
LET
LET
LET
LET
LET
LET
LET
LET
LET
LET
LET
LET
LET
LET
LET


+ C489 + C490 + C491 + C492 + C493 + C494&


ERASE
ERASE
ERASE
ERASE
ERASE
ERASE


C531
C532
C533
C534
C535
C536
C537
C538
C539
C540
C541
C542
C543
C544
C545
C546
C547
C548
C549
C550
C551
C552
C553
C554
C555
C556


C32
C50
C68
C86
C104
C122
C140
C158
C176
C194
C212
C230
C248
C266
C284
C302
C320
C338
C356
C374
C392
C410
C428
C446
C509
C460


+ C514 + C515 + C516 + C517 + C518 + C519
+ C464 + C465 + C466 + C467&




I I tI


-17-



The output shown in Tables 1 and 2 first appear as display on the computer
terminal, program SORTP. When errors appear in either table the person running
the program usually must exit Minitab and re-edit the data file. Table 1 shows
the number of days of data entered, column 1, the first output table number each
day, column 2, the 14th table number each day, column 3, the 13th and 26th table
numbers each day, columns 4 and 5, and the 27th or last table number of the day,
column 6. All the values in table 1 are correct.
Table 2 again starts with the number of days of data entered. Columns 3 and
5 are the time of the first AM output and the time of the first PM output
respectively. Columns 2 and 4 are the year day numbers found in the output data
tables associated with the times in columns 3 and 5. These identify the date of
rows of data, and the relationship between Cl and C2 is the key to interpreting
the data in the following tables where column 1, row number, is always repeated.
These numbers correspond to the dates January 31 through March 1. Column 6 is the
potential, in millivolts, of the power source operating the micrologger. The
reading of 1401 indicates a rechargeable battery source was in use and was being
recharged until day number 39 (February 8). The station or data identification
number is the last value output each day, column 7.
Table 3 contains an example of data collected on an hourly basis. In this
case it is average relative humidity for an hour, and 8 hours of data are given in
the table. There are 24 values each day. These are sensor readings without
corrections for calibration as are all data given in this report. This sensor is
of limited use for night readings in the Homestead area because it is not accurate
at high relative humidity, and most nights have relative humidity between 90 and
100%.
Examples of daily values from the hourly leaf wetness histogram are given in
table 4. The datum is the decimal fraction of an hour the intensity reading was
in one of the bins indicated by the column heading. If intensity was out of
range, the total of all four bins was less than 1.0. There are 4 times 24 data
values for each day. Table 4 gives 2 hours of data for each day. Other data
collected on an hourly basis, but not demonstrated here are: average, maximum,
and minimum air temperature, total solar radiation, total rainfall, and wind run.
(See Output Table 1, Fig. 5.) Thus the amount of data each month is large
demonstrating two features of this type of data collection. First, data can be
collected economically in much more detail than by any manual method. Second, a
computer and appropriate software is required for processing the data.
Table 5, maximum air temperature and time of maximum was put into output
memory every twelve hours. This is done, as previously stated, to highlight
weather changes. Three days had AM maximum temperatures higher than the PM
maximum. However, only one was significant. Day 39, row 9, had a temperature
just after midnight which was 1C higher than the afternoon maximum. This
indicates the entire day was cooler than the early AM. Days 38 and 54, rows 8 and
24, reached the daily maximum a little before noon; the afternoon was slightly
cooler. Thus one can conclude there was cloudiness, rain or some other factor
preventing additional afternoon heat buildup. Corresponding minimum air
temperatures are seen in table 6. Seven PM minima were lower than the AM value.
Four of these, days 39, 43, 44, and 57, rows 9, 13, 14, and 27, were associated
with significant weather changes; it was significantly cooler the following
morning.
Similarly, soil temperature data are given in tables 7 and 8. These tables
includes AM and PM averages.




WI I '


-18-



Table 1. Output used to check original data formatting and the performance
of the data processing programs.


ROW TBLNO C235 NOTBL. C478 TABLENO.


1 1 1 2 2 3
2 1 1 2 2 3
3 1 1 2 2 3
4 1 1 2 2 3
5 1 1 2 2 3
6 1 1 2 2 3
7 1 1 2 2 3
8 1 1 2 2 3
9 1 1 2 2 3
10 1 1 2 2 3
11 1 1 2 2 3
12 1 1 2 2 3
13 1 1 2 2 3
14 1 1 2 2 3
15 1 1 2 2 3
16 1 1 2 2 3
17 1 1 2 2 3
18 1 1 2 2 3
19 1 1 2 2 3
20 1 1 2 2 3
21 1 1 2 2 3
22 1 1 2 2 3
23 1 1 2 2 3
24 1 1 2 2 3
25 1 1 2 2 3
26 1 1 2 2 3
27 1 1 2 2 3
28 1 1 2 2 3
29 1 1 2 2 3
30 1 1 2 2 3




V a I


-19-



Table 2. Output used to verify days processed and monitor daily changes in
power supply to micrologger.


DAY
ROW NO.


DAY
TIME NO.


100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100


TIME POWER STA.


1300
1300
1300
1300
1300
1300
1300
1300
1300
1300
1300
1300
1300
1300
1300
1300
1300
1300
1300
1300
1300
1300
1300
1300
1300
1300
1300
1300
1300
1300


1276
1275
1271
1269
1266
1401
1401
1401
1318
1315
1313
1317
1307
1303
1301
1300
1297
1296
1297
1296
1294
1291
1288
1286
1283
1278
1274
1270
1268
1266


691
691
691
691
691
691
691
691
691
691
691
691
691
691
691
691
691
691
691
691
691
691
691
691
691
691
691
691
691
691





-20-



Table 3. Hourly averages of relative humidity for days 31-60 in 1985

AVERAGE RELATIVE HUMIDITY ENDING AT:

0100 0200 0300 0400 0500 0600 0700 0800

ROW* C28 C46 C64 C82 C100 C118 C136 C154

1 86.0 86.1 86.1 86.1 86.0 86.0 86.0 85.7
2 81.9 82.4 83.3 84.3 84.6 84.5 84.8 85.0
3 83.9 83.9 84.2 84.5 84.7 84.6 84.4 84.3
4 85.0 84.8 84.4 84.1 84.0 84.0 84.0 84.0
5 84.4 84.4 84.4 84.4 84.4 84.2 83.9 83.9
6 84.7 84.9 85.0 85.0 84.7 84.2 84.3 84.3
7 84.3 84.2 84.3 84.1 83.9 83.9 83.6 83.1
8 84.7 84.7 84.3 83.9 83.8 83.9 84.0 84.1
9 83.3 82.3 80.2 79.3 79.0 79.1 79.6 81.8
10 82.7 83.4 84.4 85.5 86.1 86.3 86.3 86.0
11 85.2 86.5 86.1 85.3 84.6 85.4 86.3 86.7
12 86.3 86.4 86.0 83.2 81.1 83.0 85.5 86.4
13 84.7 84.7 84.8 85.1 85.0 83.7 82.6 81.1
14 78.1 78.3 76.5 73.6 72.4 72.3 73.3 75.4
15 85.8 87.6 87.9 87.4 86.6 87.0 85.5 86.0
16 81.7 81.7 82.7 84.3 85.8 87.5 87.5 87.9
17 76.2 83.1 86.2 87.5 87.9 88.1 88.1 87.4
18 85.5 86.0 86.6 86.9 87.0 87.1 87.1 87.0
19 86.2 86.5 86.8 86.9 86.9 86.4 85.9 85.0
20 85.5 85.7 85.9 85.8 85.4 85.0 84.7 84.7
21 84.7 84.4 84.2 84.1 84.1 84.0 83.9 83.8
22 75.4 76.2 76.9 77.3 77.6 78.1 77.9 77.9
23 72.8 73.8 76.6 77.1 78.8 77.1 76.6 75.2
24 80.1 79.8 79.5 79.4 79.5 78.9 79.7 79.9
25 79.7 79.8 80.2 80.1 80.6 81.6 82.5 82.0
26 76.9 77.2 77.2 77.8 79.1 79.9 81.7 81.5
27 84.2 84.2 84.3 83.9 83.5 83.4 83.3 83.2
28 84.5 84.5 84.4 84.5 84.6 84.6 84.7 84.5
29 84.7 84.8 84.9 84.8 84.8 84.7 84.5 84.0
30 84.5 84.4 84.4 84.5 84.5 84.5 84.4 84.1

* See table 2 for conversion from ROW number to day number


T I 9 )






-21-



Table 4. Data produced for hourly leaf wetness histogram, days 31-60, 1985.

ENDING AT TIME LISTED

0100 0200

INTENSITY


ROW 0-2


4-6 6-8 0-2


2-4

0.000
0.000
0.000
1.000
1.000
1.000
1.000
1.000
0.000
0.000
0.216
1.000
0.000
0.000
1.000
0.000
0.033
1.000
0.000
1.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.450
1.000
1.000


2-4

0.000
0.000
0.000
1.000
1.000
1.000
1.000
1.000
0.000
0.000
0.583
1.000
0.000
0.000
1.000
0.400
0.000
1.000
0.000
1.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
1.000
1.000
1.000


4-6 6-8


1.000
1.000
1.000
0.000
0.000
0.000
0.000
0.000
1.000
1.000
0.416
0.000
0.000
1.000
0.000
0.600
1.000
0.000
1.000
0.000
0.000
1.000
1.000
1.000
1.000
1.000
0.000
0.000
0.000
0.000


1.000
1.000
1.000
0.000
0.000
0.000
0.000
0.000
1.000
1.000
0.783
0.000
0.000
1.000
0.000
1.000
0.966
0.000
1.000
0.000
0.000
1.000
1.000
1.000
1.000
1.000
0.000
0.550
0.000
0.000


I CI




16 I >


-22-



Table 5. Twelve-hour maximum air temperature and time of occurence,
days 31-60, 1985

ROW PM MAX TIME AM MAX TIME

1 26.54 1400 25.46 1152
2 28.09 1448 27.47 1156
3 29.43 1331 28.38 1200
4 29.73 1223 29.37 1146
5 28.21 1411 27.47 1159
6 28.27 1255 27.19 1200
7 30.33 1436 29.67 1154
8 28.09 1510 28.79 1153
9 15.90 1842 16.85 2
10 21.51 1350 21.18 1200
11 22.92 1338 22.18 1200
12 24.53 1400 23.91 1126
13 19.10 1233 19.01 1142
14 18.56 1551 14.88 1143
15 19.97 1458 16.07 1155
16 18.97 1300 18.03 1159
17 22.18 1427 20.25 1156
18 22.48 1405 21.99 1158
19 24.84 1430 24.07 1152
20 26.86 1227 26.21 1134
21 24.53 1321 22.87 1017
22 24.53 1404 24.37 1150
23 25.68 1231 25.52 1156
24 24.78 1230 24.99 1145
25 26.48 1348 26.16 1132
26 26.54 1426 26.05 1009
27 27.59 1219 27.42 1150
28 27.47 1318 26.97 1200
29 29.08 1312 28.27 1200
30 28.96 1329 27.81 1200





-23-



Table 6. Twelve-hour minimum air temperature and time of occurence,
days 31-60, 1985

ROW AM MIN TIME PM MIN TIME

1 11.620 558 19.060 2024
2 18.340 703 21.560 2253
3 18.880 730 17.990 2336
4 17.330 725 18.970 2358
5 16.240 412 17.990 2400
6 15.640 346 18.650 2257
7 17.680 538 17.370 2400
8 16.890 106 16.890 2400
9 12.400 850 11.020 2400
10 8.700 512 10.900 2400
11 9.460 652 11.380 2400
12 11.020 152 18.340 2400
13 15.310 637 9.690 2141
14 7.680 719 6.075 2400
15 2.323 535 6.036 2356
16 2.400 704 8.940 2400
17 1.939 608 8.420 2400
18 5.958 645 10.210 2400
19 7.830 349 14.050 2400
20 12.030 310 17.680 2358
21 17.240 353 20.660 2400
22 19.740 114 20.290 2400
23 18.830 132 19.880 2249
24 19.190 653 21.460 2400
25 18.250 715 21.700 1949
26 20.060 643 19.600 2332
27 18.120 242 17.500 2400
28 13.840 712 15.900 2400
29 13.590 423 17.060 2400
30 14.670 556 17.150 2400


, I I I





-24-


Table 7. Twelve-hour maximum and
time of occurence, days


average soil temperature and
31-60, 1985


ROW PM MAX


27.16
28.12
29.84
29.51
30.33
28.85
31.30
29.29
21.23
23.49
26.18
25.24
24.58
26.01
26.06
24.10
26.12
25.95
26.47
27.33
24.82
25.36
27.84
25.77
28.90
29.51
30.17
31.03
30.71
31.14


TIME AM MAX


1627
1631
1636
1636
1638
1636
1627
1623
1652
1634
1630
1611
1528
1613
1612
1559
1603
1613
1612
1702
1654
1646
1633
1734
1652
1625
1700
1613
1516
1703


22.31
23.98
25.06
25.83
25.42
25.36
26.24
27.22
23.86
19.92
20.64
21.35
22.18
19.92
18.84
19.31
19.24
20.58
21.87
23.18
23.37
22.87
24.52
23.86
25.12
26.64
26.53
26.35
26.87
26.82


TIME AVERAGE


1200
1200
1200
1200
1200
1200
1200
1200
4
1200
1200
1200
2
1200
1200
1200
1200
1200
1200
1200
1200
1200
1200
1200
1200
1200
1200
1200
1200
1200


19.27
21.43
22.98
22.91
23.12
22.83
23.09
24.13
21.47
17.35
17.12
18.13
20.63
17.42
15.64
16.09
15.80
16.86
17.93
20.10
21.99
21.55
21.98
22.67
22.80
24.39
23.68
23.12
23.20
23.57


I




TI I


-25-


Table 8. Twelve-hour minimum and
time of occurence, days


average soil
31-60, 1985


temperature and


ROW AM MIN


18.01
20.58
21.80
22.12
21.93
21.68
22.25
23.31
20.11
16.31
15.94
16.95
19.65
16.16
13.92
14.30
14.07
15.35
16.52
19.18
21.42
21.16
21.16
21.93
21.87
23.25
22.75
21.61
21.68
22.31


TIME PM MIN


853
849
844
851
835
852
828
906
1040
847
839
819
853
910
908
915
848
826
719
602
659
830
814
838
829
902
815
832
835
841


22.18
23.92
23.98
24.76
24.46
24.04
25.12
23.86
18.56
18.01
19.51
21.42
18.43
18.29
18.56
18.29
18.63
19.45
21.29
22.87
22.18
22.87
23.55
23.43
24.88
24.82
24.88
25.06
25.00
25.60


TIME AVERAGE


2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
1202
2400
2400
2400
2400
2400
2400
2400
2400
2400
1202
2400
2400
2400
2400
2400
2400
2400
2400


24.84
26.18
27.37
27.62
27.82
27.00
28.67
27.21
20.27
21.25
23.39
23.67
22.10
22.76
22.67
21.52
22.80
23.22
24.28
25.40
23.88
24.27
26.11
24.89
26.99
27.59
28.28
28.56
28.44
28.96




I I


-26-



Table 9 gives AM histogram data data from a leaf wetness sensor. PM data are
compiled in the same way. Each number represents the decimal fraction of time
wetness was in the range indicated by the column heading. The total of all times
in a twelve hour period should be 1.0 unless intensity went out of range.
Differences between days are shown in the data. For example, day 43 (row 13) was
wet in the AM with over 6 hours, 53.7% times 12, of intensities 7-8. Day 53, row
23, on the other hand, was almost all at maximum dryness. The intensity scale is
arbitrary. The goal of the electronic circuit design and micrologger programming
was to obtain a range of values between maximum dryness and maximum wetness.
Application of these data to field biological models may lead to refinements in
the intensity scale.
Table 10 gives the total daily rainfall and the maximum 1 minute intensity.
This period was relatively dry with 0.150 cm or 0.06 in. on day 51.
Table 11 is the last daily data table and contains temperature histogram
data. Similar to the leaf wetness histogram each number represents the decimal
fraction of time air temperature was in the range indicated by the column heading.
The total for a 24-hr period should be 1.0 unless the temperature went below 30C
or above 33C. One application of such data is in models requiring heat units.
Typical output statistics begin with table 12. Average hourly relative
humidity is characterized by the mean, extremes, and varability. These data are
from an uncalibrated sensor, and therefore the values may be inaccurate. However,
the lower day time humidities with greater variability from day to day are
apparent.
Further statistics are given in table 13, air temperature extremes, table 14,
soil temperature extremes, and table 15, temperature histogram. These can be
useful when summarizing and interpreting data and comparing one period of time
with another. In addition to the statistics shown here, similar statistics can be
calculated for the other sets of data collected.
Finally, Tables 16 and 17 show typical output from checks of histogram data
for completeness.




, I I I


-27-




Table 9. Leaf wetness histogram data for AM on days 31-60, 1985

INTENSITY


ROW


0-1 1-2


0.255
0.265
0.261
0.254
0.281
0.268
0.309
0.258
0.969
1.000
0.262
0.290
0.430
1.000
0.300
0.291
0.323
0.283
0.302
0.338
0.231
1.000
0.995
0.991
0.779
0.902
0.301
0.337
0.308
0.302


2-3


0.338
0.337
0.291
0.018
0.004
0.023
0.000
0.044
0.000
0.000
0.459
0.022
0.002
0.000
0.018
0.152
0.269
0.162
0.501
0.002
0.004
0.000
0.004
0.008
0.027
0.027
0.006
0.000
0.000
0.004


3-4


0.020
0.000
0.000
0.005
0.225
0.630
0.484
0.116
0.001
0.000
0.272
0.662
0.002
0.000
0.290
0.172
0.384
0.536
0.093
0.475
0.002
0.000
0.000
0.000
0.073
0.033
0.000
0.190
0.640
0.491


4-5


0.256
0.000
0.000
0.366
0.100
0.077
0.001
0.086
0.000
0.000
0.005
0.025
0.011
0.000
0.386
0.377
0.022
0.018
0.102
0.091
0.011
0.000
0.000
0.000
0.119
0.036
0.000
0.368
0.051
0.201


5-6


0.127
0.000
0.000
0.093
0.388
0.000
0.040
0.494
0.026
0.000
0.000
0.000
0.000
0.000
0.005
0.005
0.000
0.000
0.000
0.005
0.058
0.000
0.000
0.000
0.000
0.000
0.001
0.104
0.000
0.000


6-7


0.000
0.375
0.344
0.262
0.000
0.000
0.163
0.000
0.002
0.000
0.000
0.000
0.015
0.000
0.000
0.000
0.000
0.000
0.000
0.086
0.529
0.000
0.000
0.000
0.000
0.000
0.690
0.000
0.000
0.000


7-8


0.000
0.022
0.102
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.537
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.161
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000





-28-


Table 9 concluded.

INTENSITY

8-9 9-10 10-11
ROW

1 0.000 0 0
2 0.000 0 0
3 0.000 0 0
4 0.000 0 0
5 0.000 0 0
6 0.000 0 0
7 0.000 0 0
8 0.000 0 0
9 0.000 0 0
10 0.000 0 0
11 0.000 0 0
12 0.000 0 0
13 0.000 0 0
14 0.000 0 0
15 0.000 0 0
16 0.000 0 0
17 0.000 0 0
18 0.000 0 0
19 0.000 0 0
20 0.000 0 0
21 0.001 0 0
22 0.000 0 0
23 0.000 0 0
24 0.000 0 0
25 0.000 0 0
26 0.000 0 0
27 0.000 0 0
28 0.000 0 0
29 0.000 0 0
30 0.000 0 0





-29-



Table 10. Total daily rainfall, cm, and most intense minute for days 31-60, 1985

ROW RAIN INTENSITY TIME

1 0.000 0.000 2400
2 0.000 0.000 2400
3 0.050 0.025 945
4 0.025 0.025 913
5 0.000 0.000 2400
6 0.000 0.000 2400
7 0.000 0.000 2400
8 0.000 0.000 2400
9 0.075 0.025 1252
10 0.000 0.000 2400
11 0.000 0.000 2400
12 0.125 0.025 2206
13 0.025 0.025 54
14 0.000 0.000 2400
15 0.000 0.000 2400
16 0.000 0.000 2400
17 0.000 0.000 2400
18 0.000 0.000 2400
19 0.000 0.000 2400
20 0.000 0.000 2400
21 0.150 0.025 1123
22 0.000 0.000 2400
23 0.000 0.000 2400
24 0.000 0.000 2400
25 0.000 0.000 2400
26 0.000 0.000 2400
27 0.000 0.000 2400
28 0.000 0.000 2400
29 0.000 0.000 2400
30 0.000 0.000 2400


, I I I




, I I


-30-



Table 11. Twenty-four hour temperature histogram data for days 31-60, 1985
fraction of time temperature within range

TEMPERATURE RANGE C

3-6 6-9 9-12 12-15
ROW

1 0.000 0.000 0.028 0.299
2 0.000 0.000 0.000 0.000
3 0.000 0.000 0.000 0.000
4 0.000 0.000 0.000 0.000
5 0.000 0.000 0.000 0.000
6 0.000 0.000 0.000 0.000
7 0.000 0.000 0.000 0.000
8 0.000 0.000 0.000 0.000
9 0.000 0.000 0.049 0.597
10 0.000 0.102 0.292 0.193
11 0.000 0.000 0.390 0.137
12 0.000 0.000 0.161 0.174
13 0.000 0.000 0.153 0.103
14 0.000 0.195 0.390 0.154
15 0.320 0.097 0.145 0.115
16 0.231 0.092 0.293 0.093
17 0.188 0.090 0.181 0.073
18 0.006 0.325 0.112 0.081
19 0.000 0.104 0.195 0.084
20 0.000 0.000 0.000 0.219
21 0.000 0.000 0.000 0.000
22 0.000 0.000 0.000 0.000
23 0.000 0.000 0.000 0.000
24 0.000 0.000 0.000 0.000
25 0.000 0.000 0.000 0.000
26 0.000 0.000 0.000 0.000
27 0.000 0.000 0.000 0.000
28 0.000 0.000 0.000 0.141
29 0.000 0.000 0.000 0.238
30 0.000 0.000 0.000 0.090




, k


-31-



Table 11 concluded.

TEMPERATURE RANGE C

15-18 18-21 21-24 24-27 27-30 30-33
ROW

1 0.021 0.245 0.135 0.269 0.000 0.000
2 0.000 0.345 0.299 0.161 0.193 0.000
3 0.002 0.379 0.221 0.119 0.277 0.000
4 0.106 0.384 0.120 0.093 0.294 0.000
5 0.218 0.359 0.090 0.138 0.193 0.000
6 0.265 0.250 0.109 0.287 0.086 0.000
7 0.069 0.392 0.141 0.090 0.246 0.059
8 0.223 0.305 0.180 0.190 0.100 0.000
9 0.352 0.000 0.000 0.000 0.000 0.000
10 0.092 0.237 0.081 0.000 0.000 0.000
11 0.068 0.116 0.286 0.000 0.000 0.000
12 0.013 0.238 0.386 0.026 0.000 0.000
13 0.565 0.177 0.000 0.000 0.000 0.000
14 0.177 0.081 0.000 0.000 0.000 0.000
15 0.112 0.188 0.000 0.000 0.000 0.000
16 0.104 0.145 0.000 0.000 0.000 0.000
17 0.072 0.163 0.134 0.000 0.000 0.000
18 0.090 0.107 0.276 0.000 0.000 0.000
19 0.095 0.144 0.291 0.084 0.000 0.000
20 0.150 0.149 0.161 0.318 0.000 0.000
21 0.256 0.154 0.468 0.120 0.000 0.000
22 0.000 0.401 0.569 0.029 0.000 0.000
23 0.000 0.449 0.283 0.267 0.000 0.000
24 0.000 0.295 0.487 0.216 0.000 0.000
25 0.000 0.238 0.425 0.336 0.000 0.000
26 0.000 0.261 0.370 0.367 0.000 0.000
27 0.038 0.452 0.129 0.337 0.042 0.000
28 0.300 0.119 0.092 0.242 0.103 0.000
29 0.168 0.150 0.087 0.206 0.148 0.000
30 0.304 0.118 0.104 0.140 0.241 0.000




, IIt 1


-32-



Table 12. Basic statistics on February 1985 hourly relative humidity data

ENDING TIME

0100 0200 0300 0400 0500 0600 0700 0800

STATISTIC
N 30 30 30 30 30 30 30 30
MEAN 82.79 83.22 83.42 83.36 83.37 83.43 83.55 83.53
MEDIAN 84.45 84.40 84.40 84.35 84.55 84.20 84.35 84.10
TMEAN 83.19 83.56 83.65 83.66 83.70 83.77 83.89 83.85
STDEV 3.59 3.32 3.24 3.43 3.41 3.48 3.28 3.14
SEMEAN 0.66 0.61 0.59 0.63 0.62 0.64 0.60 0.57
MAX 86.30 87.60 87.90 87.50 87.90 88.10 88.10 87.90
MIN 72.80 73.80 76.50 73.60 72.40 72.30 73.30 75.20
Q3 85.05 85.10 85.93 85.35 85.50 85.55 85.60 85.78
Q1 81.30 82.15 82.07 82.42 80.97 82.65 82.57 81.95


ENDING TIME

0900 1000 1100 1200 1300 1400 1500 1600

STATISTIC
N 30 30 30 30 30 30 30 30
MEAN 80.64 72.65 66.21 62.1 60.1 59.4 58.7 59.3
MEDIAN 82.45 74.80 68.36 62.9 61.0 58.6 59.7 61.5
TMEAN 81.36 73.27 66.70 62.4 60.5 60.0 59.5 60.0
STDEV 4.48 7.87 9.63 10.7 12.4 13.4 13.5 12.8
SEMEAN 0.82 1.44 1.76 2.0 2.3 2.4 2.5 2.3
MAX 85.00 82.10 81.00 82.0 85.5 86.3 85.3 84.2
MIN 66.44 54.82 43.89 37.5 32.2 28.4 23.2 24.0
Q3 83.33 79.43 72.58 67.9 67.6 68.6 69.0 68.9
Q1 79.30 67.54 59.55 58.3 56.2 53.1 52.3 52.2



ENDING TIME

1700 1800 1900 2000 2100 2200 2300 2400

STATISTIC
N 30 30 30 30 30 30 30 30
MEAN 62.0 64.9 70.82 75.51 78.24 79.83 80.66 81.70
MEDIAN 64.7 68.4 73.80 76.85 80.10 82.40 83.75 84.05
TMEAN 62.8 65.9 71.73 76.25 78.99 80.61 81.43 82.28
STDEV 12.1 11.8 9.18 6.67 5.81 5.66 5.53 4.55
SEMEAN 2.2 2.2 1.68 1.22 1.06 1.03 1.01 0.83
MAX 82.3 82.8 84.10 84.00 84.10 84.90 85.10 85.70
MIN 25.6 28.2 40.18 54.22 60.22 60.28 64.85 68.06
Q3 70.1 72.1 76.30 80.62 82.68 83.80 84.30 84.53
Q1 56.2 60.1 64.62 71.88 76.00 77.18 78.35 79.93




,i a t 1


-33-



Table 13. Basic statistics on 12-hr air temperature readings, OC
days 31-60, 1985.


N
MEAN
MEDIAN
TMEAN
STDEV
SEMEAN
MAX
MIN
Q3
QI


PM MAX
30
25.07
26.08
25.29
3.80
0.69
30.33
15.90
28.12
22.41


AM MIN
30
13.44
14.99
13.81
5.48
1.00
20.06
1.94
18.15
9.27


AM MAX
30
24.30
25.49
24.57
4.13
0.75
29.67
14.88
27.47
21.79


PM MIN
30
15.75
17.44
16.04
4.78
0.87
21.70
6.04
19.19
10.99


Table 14. Basic statistics on 12-hr soil temperature
days 31-60, 1985.


N
MEAN
MEDIAN
TMEAN
STDEV
SEMEAN
MAX
MIN
Q3
Q1


PM MAX
30
27.41
27.25
27.51
2.58
0.47
31.30
21.23
29.59
25.67


AM MIN
30
19.64
21.16
19.80
3.02
0.55
23.31
13.92
21.93
16.47


AM AVG
30
20.77
21.76
20.90
2.82
0.52
24.39
15.64
23.10
17.80


PM AVG
30
25.27
25.15
25.34
2.54
0.46
28.96
20.27
27.60
23.11


AM MAX
30
23.51
23.86
23.58
2.63
0.48
27.22
18.84
25.93
21.17


readings, OC


PM MIN
30
22.26
23.15
22.34
2.65
0.48
25.60
18.01
24.77
19.25




, k ,1 a


-34-


Table 15.


Basic statistics on temperature histogram data,
days 31-60, 1985

RANGE (oC)


9-12 12-15


STATISTIC


30
0.0248
0.0000
0.0075
0.0772
0.0141
0.3200
0.0000
0.0000
0.0000


30
0.0335
0.0000
0.0187
0.0736
0.0134
0.3250
0.0000
0.0225
0.0000


30
0.080
0.000
0.062
0.123
0.023
0.390
0.000
0.155
0.000


30
0.093
0.077
0.073
0.129
0.023
0.597
0.000
0.144
0.000


RANGE (oC)


15-18 18-21 21-24


30
0.129
0.094
0.113
0.134
0.024
0.565
0.000
0.219
0.010


30
0.235
0.238
0.233
0.119
0.022
0.452
0.000
0.349
0.145


30
0.197
0.138
0.187
0.159
0.029
0.569
0.000
0.293
0.089


24-27 27-30 30-33


30
0.134
0.120
0.128
0.124
0.023
0.367
0.000
0.248
0.000


30
0.0641
0.0000
0.0520
0.0991
0.0181
0.2940
0.0000
0.1143
0.0000


30
0.0020
0.0000
0.0000
0.0108
0.0020
0.0590
0.0000
0.0000
0.0000


N
MEAN
MEDIAN
TMEAN
STDEV
SEMEAN
MAX
MIN
Q3
Q1


STATISTIC

N
MEAN
MEDIAN
TMEAN
STDEV
SEMEAN
MAX
MIN
Q3
Q1




-35-


Table 16. Check on completeness of hourly leaf wetness histogram bins.


ROW C531 C532 C533 C534 C535 C536

1 1.000 1.000 1.000 0.999 1.000 1.000
2 1.000 1.000 1.000 0.999 1.000 1.000
3 1.000 1.000 1.000 1.000 1.000 0.999
4 1.000 1.000 1.000 1.000 0.999 1.000
5 1.000 1.000 1.000 1.000 1.000 1.000
6 1.000 1.000 1.000 1.000 1.000 1.000
7 1.000 1.000 1.000 1.000 1.000 0.999
8 1.000 1.000 0.999 1.000 1.000 1.000
9 1.000 1.000 1.000 1.000 1.000 1.000
10 1.000 1.000 1.000 1.000 1.000 1.000
11 0.999 0.999 1.000 1.000 1.000 0.999
12 1.000 1.000 1.000 1.000 1.000 1.000
13 1.000 1.000 1.000 1.000 1.000 1.000
14 1.000 1.000 1.000 1.000 1.000 1.000
15 1.000 1.000 1.000 1.000 1.000 1.000
16 1.000 1.000 1.000 1.000 1.000 1.000
17 0.999 1.000 1.000 1.000 0.999 0.999
18 1.000 1.000 0.999 1.000 0.999 1.000
19 1.000 1.000 1.000 1.000 1.000 0.999
20 1.000 1.000 1.000 1.000 1.000 1.000
21 1.000 1.000 1.000 1.000 1.000 0.999
22 1.000 1.000 1.000 1.000 1.000 1.000
23 1.000 1.000 1.000 1.000 1.000 1.000
24 1.000 1.000 1.000 1.000 1.000 1.000
25 1.000 1.000 1.000 1.000 1.000 1.000
26 1.000 1.000 1.000 1.000 1.000 1.000
27 1.000 1.000 1.000 1.000 1.000 1.000
28 1.000 1.000 1.000 1.000 1.000 1.000
29 1.000 1.000 1.000 1.000 1.000 1.000
30 1.000 1.000 1.000 1.000 1.000 1.000


C537 C538

0.999 1.000
1.000 1.000
1.000 1.000
1.000 1.000
1.000 1.000
1.000 1.000
1.000 1.000
1.000 1.000
1.000 1.000
1.000 1.000
1.000 1.000
1.000 1.000
0.999 1.000
1.000 1.000
1.000 1.000
1.000 1.000
0.999 1.000
0.999 1.000
1.000 1.000
1.000 1.000
0.999 1.000
1.000 1.000
1.000 1.000
1.000 1.000
1.000 0.999
1.000 0.999
1.000 1.000
1.000 0.999
1.000 1.000
1.000 1.000








Table 17. Check for completeness of 12-hr leaf wetness and 24-hr
temperature histogram bins.

ROW C555 C556 C557

1 0.997 0.996 0.998
2 0.998 0.999 0.998
3 0.998 0.998 0.998
4 0.997 0.998 0.999
5 0.998 0.998 0.999
6 0.997 0.998 0.999
7 0.997 0.997 0.999
8 0.998 0.998 0.996
9 0.998 0.998 0.997
10 0.997 1.000 0.999
11 0.997 0.998 0.999
12 0.998 0.999 0.997
13 0.998 0.997 1.000
14 0.997 1.000 0.999
15 0.977 0.999 0.999
16 0.958 0.997 0.999
17 0.901 0.998 0.998
18 0.997 0.999 0.998
19 0.997 0.998 0.998
20 0.997 0.997 0.997
21 0.998 0.997 0.999
22 0.999 1.000 1.000
23 0.999 0.999 0.999
24 0.998 0.999 0.998
25 0.999 0.998 1.000
26 0.998 0.998 0.998
27 0.998 0.998 0.999
28 0.997 0.999 0.999
29 0.997 0.999 0.998
30 0.997 0.998 0.999




-37-


SUMMARY AND CONCLUSIONS

Weather data collection can be automated as well as the routine manipulation
of the data and preparation of useful tables of data. Such automation makes it
possible:to collect data on a greater number of parameters and in greater detail
than possible with manual readings. It is forseen that such detailed data will be
necessary in future crop modeling. Automation does not eliminate the need for
human involvement in data collection. For the foreseeable future someone will have
to be responsible for checking reliability and accuracy of the instrumentation,
correcting problems, and deciding what applications are appropriate for the data.
The procedures described and currently utilized are not automated to the fullest
extent possible. However no additional automation is planned at the present time.
There have been a number of problems with the equipment, and if automation is
relied on too heavily there is a tendency to ignore problems with resulting large
gaps in data.

References

1. Campbell Scientific Inc., CR21 Micrologger Operator's Manual, Revision
IM CR21-9.1
2. Stringer, E.T., Techniques of Climatology, W.H. Freeman & Co. San Francisco,
Calif. 1972
3. U.S. Dept. of Commerce, NOAA Climatological Data, Florida 89(4) pp 19-21
4. Wiesner, C.J., Climate, irrigation & Agriculture, Angus & Robertson, London,
1970, 246 pp.




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