SMART WIRELESS TIPPING-BUCKET RAIN GAUGE

Measurement and Automatic Dynamic Calibration

Octavian Postolache, Miguel Dias Pereira

EST Setúbal LabIM/IPS, Rua do Vale de Chaves, Estefanilha, 2914-508, Setúbal, Portugal

Instituto de Telecomunicações, Av. Rovisco Pais, 1049-001, Lisboa, Portugal

Pedro Girão

Instituto de Telecomunicações, Av. Rovisco Pais, 1049-001, Lisboa, Portugal

Keywords: Weather smart sensors, automatic system calibration, wireless communication, neural network.

Abstract: The paper presents the design and implementation of a smart tipping-bucket rain gauge that uses a universal

frequency do digital converter characterised by period and impulse counting measuring capabilities with on-

line accuracy control and a serial interface connected to a transmitter-receiver RF module that provides a

wireless communication between the smart tipping-bucket rain gauge (TBR) and a host unit expressed by a

FieldPoint real-time controller or a laptop PC associated with a weather monitoring network The TBR

sensor tests in dynamic conditions are performed using a FieldPoint based system. The system consist in a

submersible pump that works under the FieldPoint control and assure the accurate control of water flow

rates delivered to the rain gauge funnel. The rain gauge calibration ensures precise conversion of bucket tip

times to actual rainfall rates. The data acquired during the calibration is stored in FieldPoint system memory

and used for an accurate rain fall measurement after an intelligent data processing based on designed and

implemented neural network. Data logging and data communication are parts of the LabVIEW real time

software developed for the present system.

1 INTRODUCTION

The measurement of the rain fall represents an

important task associated with weather stations and

environmental surveying stations. Different types of

sensors are used to measure the quantity of rain

water falling in time (Advanced Measurements,

2003). Tipping-bucket rain gauges (TBRs) have

been used extensively for collecting rainfall intensity

data ever since their inception and subsequent use in

weather station starting form 1970 because they are

simple and durable. Other advantages are that they

can be installed in remote areas, can be connected to

a variety of monitoring or recording devices, and are

relatively inexpensive. Disadvantages are that

measurement errors can be significant during heavy

rainfall or light drizzle, losses from evaporation and

wind effects can occur, and calibration is often

difficult and time consuming (Nemec, 1967).

Referring to the calibration the TBR static and

dynamic calibration methods it can be mentioned.

In the static calibration method, the rain gauge is

levelled, and the stop under a bucket is adjusted until

application of a specified volume of water (usually

added to the bucket drop by drop using a pipette)

causes the bucket to tip. This procedure is repeated

several times for each bucket, and an average

volume for both buckets is calculated. Measured

bucket volumes can vary as much as 5% depending

on factors such as the kind of water used (rainwater

versus tap water), the buckets dry or wet initially

state and the buckets surface quality (Marselek,

1981). An important weakness of the static

calibration method is related to the assumption that

the volume of water needed to cause the bucket to

tip is independent of the rainfall intensity which can

conduct to underestimation of the rainfall intensity

(10 to 40%).

Dynamic calibration methods attempt to account for

undercatchment by calibrating the TBR while the

buckets are in motion and have been proven to be

effective. Different dynamic calibration and system

are reported in the literature (Humphrey, 1997). All

of the preceding methods describing dynamic

calibration of TBRs involve application of many

205

Postolache O., Dias Pereira M. and Girão P. (2006).

SMART WIRELESS TIPPING-BUCKET RAIN GAUGE - Measurement and Automatic Dynamic Calibration.

In Proceedings of the Third International Conference on Informatics in Control, Automation and Robotics, pages 205-209

DOI: 10.5220/0001221002050209

 SciTePress

flow rates and quantifying the response time of the

tipping bucket.

In this conditions a controller water pump and

accurate measurement of time periods associated

with bucket movements during the rainfall

measurement is required.

The article presents a smart wireless tipping-

bucket rain gauge where the TBR is connected to the

counter/period input of a universal frequency digital

converter. An additional frequency measurement

channel is used to acquire the information delivered

by a temperature sensor associated with rainwater

temperature measurement and frost conditions. The

acquired information is wireless transmitted using a

RF receiver-transmitter component to a host real-

time controller that performs tasks such as the TBR

tests and calibration control, data logging, fault

detection and diagnosis, data communication and

data publishing.

2 SMART SENSING SYSTEM

2.1 Rainfall and Temperature

Sensing Unit

The rainfall sensor is represented by a classical

architecture of tipping bucket rain gauge whose

resolution is defined by:

⋅

(1)

where V is the bucket volume, and d is the rain

gauge diameter (of the outer funnel). It extracts the

rainfall information based by the water running

through the collectors funnel into one of the system

twin buckets. When the water flows from one to

other bucket based on the included magnetic reed

switch (low cost proximity sensor) a voltage pulse is

generated. Counting the pulses, N

for 1h time

period the total amount of rainwater can be

calculated using the following relation:

rNhq

RWRW

⋅

)1( (2)

which express the rainfall intensity level expressed

in mm h

-1

The used temperature transducer is based on a LM35

and conditioning circuits expressed by a voltage

amplifier and voltage to frequency conversion stage

(LM331). The nominal dependence of the frequency

output versus temperature is given by:

TAf

⋅⋅

⋅

(3)

where T represents the temperature in ºC,

=0.01V/°C the temperature-to-voltage

conversion coefficient, A=10 the gain of the used

amplifier, and δ=980V

-1

the voltage to frequency

converter internal parameter. Thus for the

temperature included in the 2-20°C range the

frequency signal will varies between 196 Hz and

1960 Hz.

2.2 Muti-channel Data Acquisition

and Wireless Communication

Unit

The signals associated to the rainfall and the

temperature measurement channels expressed by

voltage pulse and frequency variation are acquired

by the multichannel universal frequency to digital

converter (UFDC-01)(Pereira, 2005). It contains two

input channel that are on-line configured to perform

frequency, period or pulse counter measurement

functions. The measurement values are sent through

the wireless connection to the FieldPoint real-time

controller (National Instruments, 2005) or to a host

computer (PC). Referring the UFDC settings the

channel 1 is used for rainfall intensity measurement

based on TBR and set for pulse counter mode (MD)

while the channel 2 is used for temperature

measurement and set for frequency measurement

mode (M0).

Main characteristics of the UFDC include

programmable conversion accuracy than can vary

between 1 % and 0.001 % of FS (full-scale)

amplitude, an auto-calibration capability based on

the 8 MHz quartz crystal oscillator signal and a

RS232 communication port that is connected to the a

wireless interface expressed by a easy Radio

ER400TRS.

The ER400TRS is a complete sub-system that

combines a high performance very low power RF

transceiver, a microcontroller and a voltage regulator

(LPRS,2006). Several characteristics can be

mentioned: RF frequency 434MHz, RF power

output +10dBm, FM deviation 64kHz.

In the present application the RF module Serial Data

Input and Serial Data Output channels operate at the

standard 9600 Baud and the RDY handshake line is

connected to GND. The Easy-Radio transceiver can

accept and transmit up to 180 bytes of data, which it

buffers internally before transmitting in an efficient

over-air code format. Thus the digital values

associated with the number of pulses value (N

)

that corresponds to UFDC channel 1 or the

frequency value (f

)

corresponding to the UFDC

ICINCO 2006 - SIGNAL PROCESSING, SYSTEMS MODELING AND CONTROL

206

channel 2 are transmitted using the TXD line of the

UART port to the serial data input line (SDI) of the

ER1. Based on the low power RF connection the

data is sent to the ER2.(Figure 1).

The ER2 will decode the message and place the

recovered data within a receive buffer that can then

be unloaded to the receiving host (FieldPoint

controller or host PC) for data processing.

Transmission and reception are bi-directional half

duplex i.e. transmit or receive but not

simultaneously.

Fig. 1 represents the main elements of the rainfall

and temperature system with low power wireless

transmission capabilities where ER1 and ER2

represents the RF wireless transmission modules,

FX1 and FX2 the UFDC pulse counter and

frequency acquisition channels, MAX233 interface

chip performs the 0-5V (ER400TRS) to the RS232

voltage levels translation, SDO-serial data output,

SDI serial data input and the E/Wi-Fi b represents

the Ethernet-wireless bridge connected to the

Ethernet port of the FieldPoint FP2000 real time

controller. Based on implemented wireless

communication systems the FP-2000 can work

simultaneously in two wireless networks. The first

one expressed by one or multiple smart rainfall

measurement system or other weather sensors with

RF low cost wireless interfaces and the second one

(Wi-Fi - EE802.11g) that can include different PCs

associated with advanced data processing and

publishing.

2.3 TBR Dynamic Calibrator

In order to increase the accuracy of rainfall

measurements a TBR automated calibrator was

designed and implemented.

The calibration system consists of a FieldPoint

system, voltage controlled submersible pump and a

water reservoir.

The FieldPoint system is represented by NI FP-

2000 controller interface that manages a node

expressed by an analog output module NI FP-AO-

V10. The output voltage of the analog output

module is used to control the pump through a current

buffer (CI). In this way, different values of water

flow are automatically imposed to the measurement

system. The common values obtained in the present

application are between 20 and 1000 mL min

-1

, that

depends on the pump speed (through the imposed

voltage), tubing diameter and tubing composition.

The rain gauge to be calibrated (TBR) is connected

to the UFDC-FX1 input that detects tip occurrence

expressed by pulse signals (Figure 2) and count the

number of pulses.

The “brain” of calibrator (NI FP-2000) sent the start

count (S) and read count (R) commands associated

with Δt

time intervals (Δt

=1min). The read values

are used to calculate the rainfall intensity.

The values of the rain flow intensity are imposed by

the pump of the system that is previously calibrated.

Pump calibration procedure extracts the water

volume versus pump control voltage characteristic.

Thus for different values of the pump control

voltage, V

=[5; 9]V, different pump rates are

obtained and used do deliver several water target

volumes (e.g. 500mL, 1000mL, 2000mL). When the

target volume for a given rate has been delivered to

the collection flask, an optical level sensor

(Honeywell), mounted on the flask, delivers a TTL

signal that is acquired by the digital input module of

the FieldPoint system, and the delivered time Δt

recorded together with the voltage value for the

current pump rate. Using a digital channel of the

Figure 2: The TBR’ magnetic reed switch output PR1(--)

and PR2(-.-) pump rates

TBR

UFDC

RXD

TXD

SDO

SDI

FX2

FX1

ER2

SDO

SDI

FieldPoint

*FP-2000

Wi-Fi

E/Wi-Fi b

ER1

MAX

233

Figure 1: The block diagram rainfall& temperature smart

sensing system

Figure 1: The block diagram rainfall & temperature smart

sensing system.

Figure 2: The TBR’ magnetic reed switch output PR1(--)

and PR2(-.-) pump rates.

SMART WIRELESS TIPPING-BUCKET RAIN GAUGE - Measurement and Automatic Dynamic Calibration

207

FieldPoint digital output module and a conditioning

circuit the collection flask electro valve is opened

for a time interval of Δt

=Δt

+τ

, where the timing

tolerance is expressed by τ defined as 10% of water

delivery time Δt

di.

After the Δt

time interval the next pump rate is

imposed and a new Δt

d(i+1)

is recorded. For the

particular case of 1000mL water target volume the

pump rates (PR) are calculated using the stored t

values and are graphical represented in Figure 3.

Figure 3: Pump calibration results

All the pump calibrations were conducted within a

water temperature range between 15°C and 17°C.

Based on the pump rate characteristics, obtained in

the pump calibration phase, the rain gauge

calibration was perforned together with the TBR

calibration, in the dynamic conditions, for different

values of the pump rate (PR) associated to high level

of rainfall intensity (PR=[446; 963] [mL min

-1

Thus the pump was controlled for voltages in 5 V to

9V interval to inject water on the TBR level for

different time intervals equivalent for 1000mL

rainfall. During the Δt

time intervals the UFDC

channel set as a counter was measured the number of

pulses delivered by TBR for each interval. The

results are presented in Figure 4.

2.4 Neural Network for Field Data

Correction

Considering the experimental values that express the

non-linear dependence of the measured rain-gauge

rate versus an imposed PR an intelligent TBR data

correction algorithm based on single input single

output (SISO) neural network [8] is designed in

ATLAB and implemented for on-line processing at

the FP-2000 level using LabVIEW real-time. The

input of NN is expressed by the normalized values

of measured raingauge rate while the output

represent the underestimating corrected values the

considered true rainfall interval. The raingauge rate

was previously calculated sing the number of pulses

measured by the UFDC and wireless transmitted

data.

3 RESULTS AND DISCUSSIONS

The experimental results obtained from TBR based

measurements in dynamic conditions are presented

in Figure 4.

Figure 4: Departure of measured rain gauge rate

(MRG) from true pump rate along 1:1 line (UL)

MRG

The underestimation maximum level for considered

rainfall intensities was about 205 mL min

-1

which

requires the application of data correction using the

implemented NN.

Using designed MLP neural network characterized

by 2 to 5 hidden neurons with tansignoid activation

function the underestimation rainfall error decrease

according with the designed neural network

architecture (Table 1).

Table 1: Underestimation errors with and without

correction.

Underestimation error with NN based

correction(mL min

-1

)

Number of neurons

U error

without NN

corr.

(mL min

-1

)

2 3 4 5

205.5

14.7 9.1 9.2 12.1

Analyzing the Table 1 can be observed that good

results are obtained for 3 or 4 hidden neurons. For

the particular case of 3 hidden neuron neural

network the TBR rainfall intensity measurement

error, with correction (err

TBR+NN

) and without

correction (err

TBR

) underestimation error, with and

Figure 3: Pump calibration results.

Figure 4: Departure of measured rain gauge rate (MRG)

from true pump rate along 1:1 line (UL).

ICINCO 2006 - SIGNAL PROCESSING, SYSTEMS MODELING AND CONTROL

208

without compensation, associated with tested TBR

in dynamic conditions are presented in Section 3.

400 500 600 700 800 900 1000

-50

100

150

200

250

err

TBR

(mL min

-1

)

err

TBR+NN

(mL min

-1

)

true rainfall(mL min

-1

)

Figure 5: TBR measurement error with and without

correction based on neural network

4 CONCLUSIONS

A smart wireless tipping-bucket rain gauge was

designed and implemented. The proposed solution

combines the flexibility and the measurement

accuracy of the universal frequency to digital

converter with the low cost RF RS232-wireless

bridge in order to perform the weather parameter

monitoring in a wireless network.

Based on the fact that the tipping-bucket rain

gauges suffer from serious non-linear

underestimation errors, especially when rainfall is

characterized by high rates, a real-time system based

on FieldPoint technology for TBR test and

calibration was designed and implemented. Using

the experimental data associated with high rainfall

rates an intelligent algorithm based on Multilayer

Perceptron architecture was designed in order to

decrease the TBR underestimation errors. Several

simulation and experimental results express the

capabilities of the implemented solution.

REFERENCES

Advanced Measurements & Controls, “Weather:

Raingauge”, 2003 on-line at http://www.advmnc.com/

Nemec J., Instruments Methods of observation and

processing of basic data on precipitation and

evaporation, Ed. World Meteorological Organization,

1967

Marselek J. Calibration of the tipping-bucket raingauge,

Journal of Hydrol, Bol. 53, 343-354, 1981

Humphrey M, Istok, J. “A new Method for Automated

Dynamic Calibration of Tipping-Bucket Rain Gauges”

Journal of atmospheric and oceanic technology, VOL

14, pp. 1513-1519. 1997

Dias Pereira, J. M., Postolache O., Girão P. " Using a

Dual-Channel FDC Device and ANN Techniques to

Improve Measurements Accuracy ", Sensors &

Transducers Magazine , Vol. 62 , No. 12 , pp. 462 -

472 , December , 2005

National Instruments, “ FieldPoint and Compact

FieldPoint”, 2004, on-line at www.ni,com

LPRS, “Radio modules with embedded software

interface” on-line at http://www.lprs.co.uk/, 2006

Figure 5: TBR measurement error with and without

correction based on neural network.

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