Fazekas, Ádám

Lehotai, Balázs

Nagy, Gábor

Tóth, László

Vas, Ádám

University of Debrecen Electronic Archive

                 A. Vas et al. / Carpathian Journal of Electronic and Computer Engineering 5 (2012) 139-142                                   139 
________________________________________________________________________________________________ 
ISSN 1844 – 9689 
 
Microcontroller-based network for 
meteorological sensing and weather forecast 
calculations 
 
Á. Vas*, Á. Fazekas*, B. Lehotai**, G. Nagy** and L. Tóth* 
* University of Debrecen, Faculty of Informatics, Department of Informatics Systems and Networks, H-4032, Debrecen, 
Egyetem tér 1, Hungary, e-mail: vas.adam@inbox.com, adam.j.fazekas@gmail.com, toth.laszlo@inf.unideb.hu 
** National Instruments Hungary Kft, H-4031, Debrecen, Határ út 1/A, Hungary, e-mail: balazs.lehotai@gmail.com, 
gabor3.nagy@ni.com 
 
 
 
Abstract— Weather forecasting needs a lot of computing 
power. It is generally accomplished by using 
supercomputers which are expensive to rent and to 
maintain. In addition, weather services also have to 
maintain radars, balloons and pay for worldwide weather 
data measured by stations and satellites. 
Weather forecasting computations usually consist of 
solving differential equations based on the measured 
parameters. To do that, the computer uses the data of close 
and distant neighbor points. Accordingly, if small-sized 
weather stations, which are capable of making 
measurements, calculations and communication, are 
connected through the Internet, then they can be used to 
run weather forecasting calculations like a supercomputer 
does. It doesn’t need any central server to achieve this, 
because this network operates as a distributed system. 
We chose Microchip’s PIC18 microcontroller (µC) 
platform in the implementation of the hardware, and the 
embedded software uses the TCP/IP Stack v5.41 provided 
by Microchip. 
I. INTRODUCTION 
The improvement of meteorology is strongly correlated 
to the development of science and technology like 
electronics, where only few better examples exist than that 
of numerical weather forecasting. 
The development of thermodynamics in the 19th 
century resulted in a completion of the set of fundamental 
physical principles. It was recognized that meteorology is 
essentially the application of hydro- and thermodynamics 
to the atmosphere that requires the measurement of seven 
basic variables (pressure, temperature, density, humidity 
and the vector of velocity) and the solution of seven 
independent equations (three dimensional hydrodynamic 
equations of motion, the continuity equation, the equation 
of state and the equations of laws of thermodynamics). 
Therefore, the weather forecasts are made by collecting 
data about the current state of the atmosphere and using 
models to predict how the atmosphere will evolve [1,2]. 
The effective data collection was not possible until the 
invention of electric telegraph in 1835 but by the next 
decade, the telegraph allowed reports of weather 
conditions collecting from a wide area [1]. 
That time the way of making a forecast was based only 
on visual pattern recognition. This method required 
remembering a previous similar weather event which was 
expected to be similar to an upcoming event. However, 
this technique was difficult to use since there is rarely a 
perfect analog for an event in the future [1]. 
A great progress was made then in the science of 
meteorology during the middle of 20th century. The 
complexity of numerical weather prediction models have 
increased dramatically when the earliest modeling work 
was done by John von Neumann and Jule Charney 
likewise when the first electronic computer appeared. As 
the computing power grew, so did the speed capacity and 
complexity of the models. And as new observations 
became available from weather balloons, radar systems 
and satellites the forecast methods received more 
sophisticated initial data [1]. 
As mentioned above, weather forecasting methods are 
based on complex physical and mathematical models, 
which are very compute-intensive to calculate and consist 
of solving differential equations numerically based on the 
measured parameters. This task is typically done by 
supercomputers, but to maintain such systems is quite 
costly (considering not only the price but energy 
consumption, cooling etc.), and this is true for buying 
computing time, too. 
Our idea is to construct a network, in which every node 
communicates with each other and does calculations based 
on its near and far neighbors’ data. The nodes are 
connected to each other through the Internet. This network 
can be used to approximate solutions of differential 
equations, without using a central computer. If one uses 
more nodes, the calculations become more accurate as the 
field becomes better covered. However, the computing 
power required by one node does not change, because 
they use their neighbors’ data only. 
Our work for developing an autonomous 
meteorological sensor network that can also function as a 
forecasting supercomputer has started three years ago 
[3,4] and here we present our first results. 
II. SYSTEM DESCRIPTION 
Our present system consists of three main parts. The µC 
based hardware with temperature, humidity and pressure 
sensors, as well as Ethernet connector and power supply 
circuits. The software on the microcontroller is 
                 A. Vas et al. / Carpathian Journal of Electronic and Computer Engineering 5 (2012) 139-142                                   140 
________________________________________________________________________________________________ 
ISSN 1844 – 9689 
 
responsible for the system and measurement control along 
with making the necessary calculations and controlling the 
network connections. Finally, since at the present stage we 
are working with only three test panels a physical and 
software simulated test environment was necessary to be 
built. 
A. The Hardware of the Sensor and Computation 
 Network 
Regarding the available development experiences and 
tools we have within doors and also considering the goal 
of the project the hardware (Fig. 1) has been designed on 
the basis of the following features: 
 
• Low power Ethernet interface for networking 
• Integrated sensors providing easy access to 
measurement results 
• Minimize power supply and digital noise to get 
accurate results 
• Sufficient computation power to solve planned 
differential equations 
• Low voltage (and current) system to provide safe 
laboratory usage 
• Low layer count of PCB to ease debugging and any 
necessary modification 
• Preferably an 8-bit µC based 
• Possible migration with SW reusability 
• Low system and development cost 
 
The Ethernet based network capability is an essential 
part of the system as it is the base of the geographical 
distribution. As the Ethernet communication is widely 
used in commercial and industrial applications, several 
Integrated Circuit (IC) manufacturers provide a proven 
physical layer device (PHY), media access control 
protocol (MAC) or PHY + MAC in one package. These 
solutions are targeting the best performance that can be 
achieved with the chosen network media and for that 
usually providing a (8/)16/32Bit parallel interface with a 
maximum flexibility to interface with industry standard 
µCs/µProcessors. The recently available µCs developed 
for network enabled embedded systems often include 
MAC and provide standard MII/RMII/GRMII... interfaces 
to the PHY. With this integration the system can use a low 
pin count package for µC and PHY as well which results a 
lower system cost. This integration still provides a 
maximum flexibility on MAC to µC (direct internal bus 
connection) and MAC to PHY for using any network 
media with choosing the sufficient PHY only. Several 
semiconductor companies stepped further and integrated 
the whole Ethernet chain into a chip, providing a 
maximum integration (single chip solution) for the most 
widely used media (10Base-T, 100Base-TX). Microchip is 
providing 10Base-T and 100Base-TX solutions integrating 
the MAC+PHY and µC+MAC+PHY in small footprint 
circuits for targeting low-cost Ethernet enabled embedded 
systems. As the team already had some experience with 
Microchip's µCs we chose the µC+MAC+PHY part for 
the central of our experiment. 
B. The Software of the Microcontroller and the 
 Simulated Test Environment 
Microchip’s MPLAB X is a single integrated 
environment to develop code for embedded 
microcontrollers. It also includes an ANSI compliant C 
compiler called MPLAB C18, which comes with many 
essential libraries containing functions for many 
applications like I2
Until now two calculation methods have been 
implemented: temperature gradients and isotherms. The 
temperature gradient as a vector shows the direction 
where the temperature rises most quickly and its length 
shows how fast the temperature rises in that direction. In a 
two-dimensional Euclidean space it can be calculated by 
C, Timers and Analog-to-Digital 
Converters (ADC). The software that we have been 
developing uses the TCP/IP Stack library by Microchip. 
This library includes the implementation of many network 
protocols, e.g. UDP, TCP, IP or DHCP. Two important 
parts of our software are the TCP server and TCP client 
running on each board. These are responsible for the 
exchange of weather data (temperature, air pressure, 
humidity) with the neighbors. This exchange process is 
done once in every second. When the neighbors’ data 
arrive, the station does calculations based on the measured 
parameters. 
 





∂
∂
∂
∂
=
y
yxT
x
yxTyxTgrad ),(,),(),( 000000 , (1) 
where T(x 0 ,y 0 )  is the measured temperature at point 
(x 0 ,y 0
x
yxxTyxxT
x
yxT
∆
∆−−∆+
≈
∂
∂
2
),(),(),( 000000
) . The partial derivatives are approximated in our 
case with the first-order central difference method (2,3). 
 (2) 
y
yyxTyyxT
y
yxT
∆
∆−−∆+
≈
∂
∂
2
),(),(),( 000000  (3) 
Now we use the temperature but later also the pressure 
and wind velocity data from the neighbors of the station to 
approximate the solution of the differential equation. 
Isotherms are curves connecting points that have the 
same temperature. In our case each node calculates 
 
Figure 1. The block diagram of the hardware of our first 
experimental weather station. 
                 A. Vas et al. / Carpathian Journal of Electronic and Computer Engineering 5 (2012) 139-142                                   141 
________________________________________________________________________________________________ 
ISSN 1844 – 9689 
 
isotherms with 5°C differences taking into account its 
eastern and southern neighbors. If there is a point between 
them whose temperature is a multiple of 5 (or one of them 
has that temperature), the node calculates its position with 
linear interpolation and reports it as a point on an isotherm 
curve. 
To verify the operation of the microcontroller grid we 
have made a software environment that can monitor and 
visualize the measurements, calculations and 
communication between the nodes. At the present state the 
test system with the simulated test environment, written in 
Java language, gathers the temperature, pressure and 
humidity measurement data from the microcontrollers and 
displays a thermal map with isotherms and gradients. It 
also logs the measured and calculated data transmitted by 
the nodes to verify their calculations. 
The software accepts incoming TCP connections from 
the nodes and renders coordinates to them in a predefined 
way. Each node placed in the thermal map according to 
the given coordinates. It was necessary to simulate nodes 
because of the small number of hardware stations. For that 
reason we made a TCP application on the PC. Multiple 
instances of that program are running, and they work the 
same way as the boards except that they cannot measure 
their own temperature, obviously. 
The values in the thermal map could be assigned in two 
ways: a point in the thermal map is either a 
microcontroller unit with a temperature sensor or a virtual 
point with a calculated temperature. To calculate the 
temperature in the virtual points the first-order central 
difference approximation of the Heat Equation was used: 
 
4
),(
),(
),(
),(
),( 00
00
00
00
00 1














∆−+
∆++
∆−+
∆+
≈
+
i
i
i
i
i
t
t
t
t
t
yyxT
yyxT
yxxT
yxxT
yxT , (4) 
where T(x 0 ,y 0 ) t i + 1  is the measured temperature at point 
(x 0 ,y 0 )  a t  t ime t i + 1
The temperature and gradient values in the real nodes 
are assigned by the measured and computed data, which 
are transmitted over the network. The communication 
process between a node and the test environment is 
divided into two parts. In the first part the nodes send the 
measured data to the test software, and after that they send 
the results of the gradient and isotherm calculations. Using 
these data the Java application draws the vectors and 
isotherms (fitting a curve on the isotherm points received 
from the stations) on the thermal map. As we mentioned 
before, the nodes transmit data once in every second thus 
the thermal map always represents the momentary 
conditions in the grid. 
. 
III. TEST ENVIRONMENT AND RESULTS 
At the moment we’re working with 3 stations connected 
to each other and to a PC through an Ethernet switch (Fig. 
2). Each board is placed on a plastic can filled with water. 
The temperature of the water is different at each position. 
One is filled with hot (~60°C, red), the second is with 
warm water (~35°C, orange) while the third one with 
mixture of ice and water (~0°C, blue). This way heat 
propagation on the test field can be simulated efficiently. 
Also, due to the large temperature differences between the 
stations, the temperature gradient vectors become clearly 
visible. The stations are placed on a grid paper to make it 
easier to show their positions in the Java application 
(Figure 2), and they display the measured temperature in 
Celsius degrees on their 7-segment displays for debugging 
purposes. 
Based on the measured and calculated data by the real 
and virtual stations the Java application can display the 
thermal map of the test field (Fig. 3), the gradient vectors 
of the real nodes (Fig. 4) and isotherms (Fig. 5). 
According to the pictures, the calculation of the 
gradient vectors and the isotherms seems to be accurate: 
the gradients at nodes 2 and 3 point at the direction of 
node 1, the hottest point of the field; and there are many 
isotherms drawn around node 1, where the temperature 
changes most quickly. 
 
Figure 3. The thermal map of the test field. 
 
Figure 2. The test environment with the 3 weather stations and the 
Java application running on the PC. 
                 A. Vas et al. / Carpathian Journal of Electronic and Computer Engineering 5 (2012) 139-142                                   142 
________________________________________________________________________________________________ 
ISSN 1844 – 9689 
 
 
IV. SUMMARY 
We succeeded to build a simple network which is 
capable of calculating isotherms and temperature 
gradients on a test field by distributed calculations. 
Although it consists of 3 stations now, we could 
understand the basic physical processes, test several 
different experimental configurations, the necessary basic 
instrumentation, algorithms and software environments as 
well as get sufficient experiences for further work towards 
using more stations and implementing further 
meteorological models on the boards. 
ACKNOWLEDGEMENT 
The authors wish to thank National Instruments 
Hungary Kft. for providing the possibility to use their 
facilities and SciTech Műszer Kft. for financially and 
technically supporting our work. This work is supported 
by the TÁMOP-4.2.2.C-11/1/KONV-2012-0001 project. 
The project has been supported by the European Union, 
co-financed by the European Social Fund. 
REFERENCES 
[1] P. Lynch, “The origins of computer weather prediction and 
climate modeling,” J. Computational Physics, vol. 227, pp. 3431–
3444, 2008. 
[2] F. G. Shuman, “Weather prediction,” U.S. Department of 
Commerce National Oceanic and Atmospheric Administration 
National Weather Service, Office Note 198, April 1979. 
[3] B. Simon, G. Pásztor, “Mikrokontrolleren alapuló időjárás 
előrejelző hálózat,” University of Debrecen, Faculty of 
Informatics, BSc thesis, supervisor: L. Tóth and G. Nagy, 2010. 
[4] B. T. Kulcsár, D. Virág, “Mikrokontrolleren alapuló időjárás 
előrejelző hálózat II.,” University of Debrecen, Faculty of 
Informatics, BSc thesis, supervisor: L. Tóth and G. Nagy, 2011. 
 
 
Figure 4. The temperature gradients displayed on the thermal map. 
 
Figure 5. Isotherms displayed on the thermal map. 


Microcontroller - based network for meteorological sensing and weather forecast calculations

https://dea.lib.unideb.hu/dea/bitstream/2437/194786/1/file_up_28_Vas.pdf

Microcontroller - based network for meteorological sensing and weather forecast calculations

Abstract

Similar works

Full text

Available Versions

University of Debrecen Electronic Archive