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TITLE: Improving Efficiency and Quality in Weather Observation and Climate Monitoring by

Using Artificial Intelligence and Information Communication Technology (ICT) Infrastructure

By

Kikwasi, W. K.*1,2

*1. Wilberforce.kikwasi@meteo.go.tz, wkikwasi@yahoo.com, wkikwasi@gmail.com

*2. Tanzania Meteorological Agency (2016)

ABSTRACT

Weather forecast and climate monitoring are complex processes, requiring high-level skills in

gathering initial conditions to minimize error, which can mislead the entire process. Use of

traditional observation process which depends on human ability in extraction of the weather

parameters is time consuming, error generic and labor demanding. These challenges include

handling decimal places during parameter manipulation where truncations, neglecting extraction

of complex parameters, communicating data through different communicators at different levels

until it reaches final destination. Archiving process require time, accuracy, papers and space. In

order to reduce the challenges, data should be collected with high level of accuracy, timely and

controlled environment to increase accuracy in forecast and monitoring of weather and climate.

Application of Artificial Intelligence (AI) and Information Communication Technology (ICT) is

the solution to weather and climate monitoring. Software with web-based browser interface has

been developed for weather observers, forecasters, analysts and climatologists to improve their

efficiency. The software interface has an architecture that encapsulate visualization functionality

supporting observed data entry, automatic (generation of derived parameters, climate data

archiving, weather report, compilation, generating BUFR and sending report to GTS integrated

systems), online parameter trend and severe weather analysis and visualization. The tool has

capability of data error minimazation, cost reduction and climate change mitigation.

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1. PROBLEM STATEMENT

The Tanzania Meteorological Agency like many other 3rd world NMHs its

professionalism, quality service policy has been facing challenges including some of the

parameters being missing in synoptic report for global exchange, data error generation due to

application of tradition methods during parameter generation example are slide rules, constant

correction tables, decimal place truncation at different levels of observation data coding,

communication and archiving contributing to data error generation, delay in data communication

and data loss.

The most significant outcome from the investigation was the recommendation of looking

for the most reliable method that will remove or reduce all of these challenges.

2. INTRODUCTION

Tanzania being member of WMO, its contribution to world weather forecast is vital.

However, while trying to make its effort to give accurate data and information, there are

some challenges need to get solution. Among those challenges, are the missing geopotential

information in the synoptic report from most observation stations which area crucial to weather

forecast and climate monitoring. The use of traditional methods is not only making the data

collection process been very complicated and sometime impossible but also are error generating

due to measuring instrument, observation (observer), manipulation tools (Slide rule, table, etc.),

communication and data archiving from papers (cards).

In view of this we have taken next level of assignment of looking for and develop a

mechanism which will help to remove the complication and impossibilities (extract geopotential

height), hence remove the Triple oblique (///) in synoptic reports, improve accuracy, timelines in

communication and archiving of data.

Furthermore the mechanism should be capable to generate more parameters, archive the

extracted data or parameter to database in an easy readable format ready for further data analysis

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wherever and whenever needed. The expected result will reduce some of the challenges that

TMA and the world weather forecast centres are facing on accuracy, timeliness and data bank

and early detection of developing micro scale feature at local levels.

The solution to the mentioned challenges is by application of AI and ICT by automation

of the process. The machines (computer) as hardware and the Software programs and AI (high

level programming languages) should be applied to reach the goal. The computer machine(s) are

needed to store the AI programs used to execute the equations using the observed data

(parameters), store the products or results of the manipulation ready for further use, security and

control to data at station or remote.

In order to act in solving the problem, the very basic thing is to have the meteorological

observation and parameter generation theory the AI and ICT knowledge.

3. METHODOLOGY

3.1 Understanding the problem and collection of fundamental model for data generation

Understanding of the theoretical and practical of observation principals, physical

atmosphere and the physical meaning of the governing meteorological equations, associated with

solution leading to collection of the correct meteorological parameters while maintaining WMO

weather parameter observation and collection standards. These equations also involve extraction

of other derived parameter e.g. geopotential height, etc. [1][2][4][5][6][7][8]

3.1.1 Determination of station pressure by interpolation using constant correction table. [27]

3.1.1.1 Determination of station pressure by interpolation. [27]

( )rrclpclp TPPP ,= (1)

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Fig 1 Interpolation scheme

3.2 Determination of station pressure using physical equations

3.2.1 If Digital barometer used as pressure measuring equipment the station pressure is

read directly (Pstn)

3.2.2 Non-digital barometer (Q-Pattern and Fortin) used as pressure measuring equipment

(Pstn);

Pstn = Pstn gs, z,Pread,Tattached,g45,ϕ( ) (1)

3.2.2.1 Reducing measured station pressure to mean sea level pressure (mslp) for coastal

station

Pmsl = Pmsl Pstn,γ, z( ) (2)

3.3 Determination of saturated vapor pressure at wet bulb temperature [5]

( )wbswbswb TEE = (3)

3.4 Determination of actual mixing ratio of dry air [1]

( )wbTTww ,= (4)

3.5 Determination of saturation vapor pressure [6]

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( )TEE ss = (5)

3.6 Determination of saturation mixing ratio of the air [1]

( )sss Eww = (6)

3.7 Determination of relative humidity [1]

( )swwRHRH ,= (7)

3.8 Determination actual vapor pressure [6]

( )sERHEE ,= (8)

3.9 Determination of geopotential height of a station from mean sea level (H_stn1) [7]

( )mslclpstnstnstn PPTvHH ,,11 = (9)

3.10 Determination of height of 850 hPa level from station level (thickness) [7]

( )clpPTHH ,850850 = (10)

3.11 Determination of saturation Vapor at 850 hPa level [6]

( )850850850 TEsEs = (11)

3.12 Determination of virtual temperature at 850 hPa level [7]

( )850850850850 ,ETTvTv = (12)

3.13 Determination of geopotential height of a station [7]

( )PclpvTHH stnstn ,= (13)

3.14 Determination of Dewpoint temperature [7]

( )TRHDPDP ,= (14)

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3.15 Determination of Dewpoint temperature depression [7]

( )DPTDD ,= (15)

3.16 Determination of Station tropopause pressure [7]

( )clpTrTr PPP = (16)

3.17 Determination of station tropopause elevation [7]

( )mslTrTrTr PPHH ,= (17)

3.18 Design of the Artificial Intelligence manipulation algorithm

Organization of an order on how the selected equations from the collection are going to

be read and executed and setting boundary conditions the input data.

Fig 2. Ideally extraction steps of the weather parameters from observed data

3.19 Setting or design of observers operation interface using ICT

Design a user-friendly interface, which will help a user to filling the electronic

observation form and internally access and execute the AI programs modules to generate derived

parameters and weather report (SYNOP, METAR and BUFR). [12][15]

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This interface should have other sub-interfaces, such as observation input (SYNOP and

METAR), Observation monitoring, severe weather monitoring and trend analysis, and climate

data accessibility. [11]

3.20 Data archive architecture using AI and ICT

Data archiving is a fundamental investment and concern. Mechanism of serving data to

database is studied [17][19][21][23][25] and a database designed to which all products or results

of the execution are stored in different format to meet the user’s requirements. Data are

automatically archived to permanent files after every 24 hours of observation in a CSV file

system.

3.21 Data visualization and observation process monitoring

Headquarters (DG), Central forecast office, Forecast and Briefing bench at airport,

Climate centre, marine, agro met offices, etc. need to have connectivity to real time weather

parameter trend data. An interface to connect to remote (HQ) server is designed with difference

level of priority to access different types of data [25].

3.22 Selection of ICT programming tools

PHP programming language has been implemented since they are scripting language used

for different applications on the web [13][14][15][16][18]. PHP stands for Hypertext pre-

processor. PHP runs on the remote server and processes the web pages before they are sent to

browser. Thus it can be used as CGI (Client Gateway Interface). Besides being a web-sided pre-

processor, it also has mathematical libraries to perform mathematical expressions. PHP has

libraries powerful in creating images, graphs and display the on the web using built-in functions

that handle images generation using GD-graphics library.

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3.23 Server connectivity

The free source database server Mysql is used to accept command from different desks to

run execution programs stored in it. The server should send results of execution to the requested

desk and store the copy to special folder in a server.

Mysql server is a reliable open source database and very friendly to PHP command

[17][19][21][23] [25][28].

4. RESULTS.

4.1 Server link and data input interface.

A page / window through which the user can enter observed data used to generate other

parameters and weather report

Fig 3 Data input window an interface of observer and server execution tools

4.2 Database and output interface.

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Located in a server, used to store the results of execution, through data base the process

of switching the date of data is done the data in database are swapped to yesterday living today

room ready for new entry.

Fig 4 Database where results of execution are store after every observation

4.3 Archiving and data analysis files creation

The files with different readable format were created for further uses

Fig 5 Interface used to view or monitor observations at remote stations

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Fig 6 Trend curves plotted automatically by ICT tools

4.4 Comparing results of some data of observation made by human and those generated by a

tool.

Fig 7 Comparison between CLP data extracted by system to those extracted by an observer

(human)

Fig 8 Comparison between DPT data extracted by system to those extracted by an observer

(human)

VERIFICATION OF WPFT (DMO) Vs PHYSICAL OBSERVER 26/09/2008 (Station Level Pressure generation): DIA

10031004100510061007100810091010101110121013

1 3 5 7 9 11 13 15 17 19 21 23

Time of Observation (GMT)

Stat

ion

Leve

l Pre

ssur

e (C

LP)

(hPa

)

Station Level Pressure(CLP) by PhysicalobserverStation Level Pressure(CLP) by WPFT (DMO)

VERIFICATION OF WPFT (DMO) Vs PHYSICAL OBSERVER (Dew Point Temperature generation)

0

5

10

15

20

25

30

1 3 5 7 9 11 13 15 17 19 21 23

Time of Observation (GMT)

Dew

Poi

nt T

empe

ratu

re

(Deg

.C) Dew Point Temperature by

Physical ObserverDew Point Temperature byWPFT (DMO)

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Fig 9 Comparison between RH data extracted by system to those extracted by an observer

(human)

4.4 The system was done successfully as all manned meteorological stations are capable to use

the tool at one time without conflict

Fig 10 Accessing Server, Database and other gaph modules Via Internet by different users

VERIFICATION OF WPFT(DMO) Vs PHYSICAL OBSERVER (Relative humidity generation)

0

20

40

60

80

100

120

1 3 5 7 9 11 13 15 17 19 21 23

Time of Obsevation (GMT)

Rel

ativ

e hu

mid

ity (%

)

RH by Physical ObserverRH by WPFT(DMO)

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5. DISCUSSION

5.1 Geopotential results

The geopotential were generated to all stations supposed to report the parameter. The

results indicated a very good relationship for stations using geopotential tables.

5.2 Other parameters generated.

Other generated parameters were compared with results produced by human and those

generated by system digitally the relationship very good results with very few departure between

the two but with same trend (Fig 7, 8 and 9). The departure might have been caused by

difference in execution skills including truncation of decimal places in several execution

processes, tools used to extract other parameter consists of instrumental error associated with

change in weather at different time of the day (Slide rule), but the system does not assume

decimal places at it works under controlled environment until the final result of execution is

obtained.

5.3 Interface results

Interface has indicated a very significant improvement in minimization of time used by

an observer when preparing weather report for transmission (coding), reducing communication

error multiplication, timely trend analysis, timely severe weather analysis and detection.

5.4 Data archive

The data archive saves data to special file in a database ready for use by the scientist

without a need of retyping either doesn’t need any paper to keep the records. Problem of data

loss and typing error is solved.

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6. Conclusion

The system has been fully developed and tested over all manned meteorological observatories in

Tanzania. The produced results were of very high quality. We recommend the system to be fully

operational and take-over the traditional data collection method. Some advantage we expect by

use of the system is operational cost reduction and climate change mitigation.

8. REFERENCES

[1] Pemmaraju S. Pant (1968) “Problem workbook for the Training of class II Meteorological

Personnel (WMO-N0. 223. TP. 118)”

[2] Paul Van Delst, CIMSS/SSEC (1999) “Geopotential height calculation”

[3] “Clearing confusion over sea level pressure analysis” [Online] [Cited: 2008]

http://www.crh.noaa/unr/?n=mslp

[4] “…conversion factors” [Online] [Cited: 2006]

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[5] “The surface Automated surface observing System (ASOS) Algorithm Tutorial” [Online]

[Cited: 2008]

http://www.nwstc.noaa.gov/DATAACQ/d.ALGOR/d.PRES/PRESalgoProcessW8.htm

[6] “NASA Vapor Pressure” [Online] [Cited: 2008]

http://atmos.nmsu.edu/education_and_outreach/encyclopedia/sat_vapor_pressure.htm

[7] M. J. Mahoney. (2005) “A discussion of various measures of Altitude”

[8] O. Owen Parish and Terrill W. Putnam, 1977. “NASA Technical note: Equations for the

determination of humidity from dewpoint and Psychrometric data.”

[9] N. N. Kussul and A. U. Shyelyestov (2005) “PHP5 applications - Self training (1-257) ”

[10] M. Zandstra (2004) “Train yourself PHP4 for 24 hours”

[11] Manirupa Das and M. Pauline Baker, 2008. “NOAA coastal sites: An N-tiered service-

oriented approach towards visualization of Meteorology and water level data”.

[12] Web user Interface Design: Forgotten Lesson. Nerurkav, Uttara. IEEE,

November/December 2001, IEEE software.

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[13] Bogdan Ksiezoposki, Pawel Luka (2003). Annale UMCS Informatica AI1(2003) 325-

2330 “Using PHP and HTML languages to create graphical interfaces and remote control of

programs”

[14] Lamar, L. Introduction to a user interface Design/ information Architecture processes

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[15] Hypertext Transfer Protocol—HTTP/1.1 World wide web consortium W3C

[Online][cited: February 23,2008.]

http://www.w3.org/protocals/rfc2616/rfc2616,html

[16] Alan Levine, “Writing HTML” Maricopa center for learning and Instruction (MCLI).

http://www.mcli.dist.maricopa.edu/tut (June, 2000).

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http://httpdapache.org.

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S., 4, IEEE Transactions on software Engineering, 1989, Vol.14.

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APPENDEX I

DEFINITION OF SYMBOLS USED

Symbol Unit Definition

clpP (hPa) Actual Station Pressure

Pr (hPa) Pressure as read from a barometer at station

Tr (°C) Temperature as read from attached thermometer on a barometer

Cr (hPa) Interpolated correction factor

P1,P2, …, Pn (hPa) Pressure bounding the correction factor in correction table

T1,T2, …, Tm (°C) Temperature bounding the correction factor in correction table

C1,C2, …,

C(2m+i)

(hPa) Fixed correction factor at the bounding Pressure and Temperature

Tpred (°C) Predicted interpolation ration

T (°C) Temperature as read from dry bulb (air temperature)

Twb (°C) Temperature as read from wet bulb temperature

Eswb (hPa) Saturation vapor pressure at wet bulb temperature

w (g/kg) Actual mixing ratio of dry air

Es (hPa) Saturation vapor pressure

ws (g/kg) Saturation mixing ratio

RH (%) Relative humidity

E (hPa) Actual vapor pressure

Tvstn (°C) Station virtual Temperature

Pmsl (hPa) Station pressure reduced to mean sea level

ΦH (m) Geopotential height

Hs (m) Station elevation

DP (°C) Dewpoint temperature

D (°C) Dewpoint temperature depression

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PTr (hPa) Station Tropopause pressure

HTr (m) Station Tropopause elevation

ϕ Latitude

APPENDEX II

INTERNATIONAL STANDARD CONSTANTS USED

Symbol Value Definition

T_msl 288.16 K Standard temperature at sea level

P_msl 1013.25 hPa Standard atmospheric pressure at sea level

Rd 287.053 J/(kg*degK) Gas constant for dry air

Rv 461.495 J/(kg*degK) Gas constant for water vapor

g45 9.80665 m/s2 Acceleration due to gravity (g) at latitude 45 WMO standards

Cp 1.005 J/gm Specific heat of dry air at constant pressure

Cpv 4.186 J/gm Specific heat of water vapor at constant pressure

Lv 2500 J/gm Latent heat of vaporization

ε 0.622 Ratio of molecular weights of wet and dry air

Md 28.9644 gm/mol Molecular weight of dry air

Mv 18.016 g/mol Molecular weight of water

Req ~ 637890 m Earth's radius at the Equator

L or γ 0.0065 degK/m Temperature lapse rate