Modernisation of Modernisation of Surface Observations Surface Observations

Network in KENYA Network in KENYA

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PresenterPresenterKennedy Omondi Obong’o. y gGrad. Eng. ERB/IEK Kenya Meteorological Department ICT Divisiony g pP.O. Box 30259-00100 G.P.ONAIROBI, KENYATel No: 254-20-3867880-5 Ext 2071-2Cell Phone No: 254-722857807E-mail: kobongo@meteo.go.ke

kobongo33@yahoo.com

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ABSTRACTABSTRACT

In order to modernise surface observation technologies and systems, the Kenya Government has

d t k il t f t id i th i t ll ti fundertaken a milestone of stride in the installations of Meteorological, hydro-meteorological and oceanic observation technologies & systems. This paper f d i b i lfocuses on modern automatic observational systems that are state of the art technology that provide real-time data. The systems have improved the temporal resolution of observed weather data but, hitherto, provide spatial resolution meteorological data. A cluster of 45 automatic surface observing systems g yhave been installed but, the target is to install over 200 stations country wide by 2015.

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ABSTRACT ContinuesABSTRACT Continues

The triumphant entry of these systems has notably resulted in improved data accuracy, quality, availability, and efficiency in transmission to the collecting centre/Nairobi RTH. From statistical comparisons we deduced that more than 98% of thecomparisons, we deduced that more than 98%, of the observations are consistent and do correlate well with human manned observation stations. Embracing these technologies convincingly demonstrates that staffs IT skill levels have improved considerably by more than 20% However some teething challenges are also20%. However, some teething challenges are also implicitly appreciated and will come out clearly in the paper.

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KeywordsKeywordsKeywordsKeywords

SustainabilitySustainabilitySustainability,Sustainability,GSM/GPRS,GSM/GPRS,A i Ob i l h l i (A SAutomatic Observational technologies (AWS, AWOS, Hydro-met, Tidal gauges, RG ), T h l fTechnology transfer,ICT , Special and Temporal Resolutions

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BACKGROUNGBACKGROUNG

HistoricallyHistorically;;--FirstFirst KMDKMD AutomaticAutomatic SurfaceSurface ObservationalObservational SystemsSystemsFirstFirst KMDKMD AutomaticAutomatic SurfaceSurface ObservationalObservational SystemsSystemsinstalledinstalled midmid 19901990ss..--BiggestBiggest challenge,challenge, TelemetryTelemetry--SystemSystem timetime couldcould notnotgggg g ,g , yy yysynchronisesynchronise soso hashas toto locklock toto thethe satellitesatellite allocatedallocated slotslot forfortransmissiontransmission andand thusthus uploadupload datadata toto necessitatenecessitate furtherfurthercommunicationcommunication andand distributionsdistributions..

--Otherwise,Otherwise, monthlymonthly datadata wouldwould bebe retrievedretrieved byby directlydirectlyh kih ki ll hh dd dd l dl dhookinghooking aa laptoplaptop computercomputer toto thethe systemsystem andand downdown loadloaddatadata manuallymanually.. DataData then,then, hadhad toto undergoundergo variousvariouscumbersomecumbersome processesprocesses beforebefore storedstored

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cumbersomecumbersome processesprocesses beforebefore storedstored..66

BACKGROUNG BACKGROUNG -- contcont

The introduction of GSM/GPRS communications technology in Kenya at the beginning of this millennium was an eye opener for the implementations of several technologies including Automatic Observational Systems.

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IntroductionIntroductionThe Automatic observational System -highlymodular in design and architecture

What are the advantages?Troubleshooting, Repair and Maintenance easier.S t d tiSaves on system down time.Minimises/reduces on data lost during maintenance/repair. e ce/ ep .Scalability another characteristic of this system. Sensors can be added to the system without

i i li f h d i i icompromising on quality of the data acquisition. Self calibrating and operate in automatic mode.

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AWS BLOCK DIAGRAMAWS BLOCK DIAGRAMTELEMETRY

DATA LOGGERFunctions

1 Acquire Process POWER UNIT1. Acquire, Process,2. Archival

POWER UNIT

SENSORS Figure 1

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SENSORS g

REMOTE AUTOMATIC SYSTEM

AWS CENTRAL BASE SYSTEM

GSM/GPRS NETWORK

SOLAR PANEL

APPLICATION SOFTWARE COMMUNICATES TO UPDATE THE SYNOPTIC

S C

E E W K

PANEL USER INTERFACE WITH NEWEST DATA

DATAFig 13: Observer Window

GSM Network (900/1800MHz)GSM Modem GSM Modem

LOGGER

PYROWS/WD

RG/TBAT/RH

BARO

BASE STATION COMPUTERFigure 2

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BASE STATION COMPUTER

1010

SOME AWS SENSORSSO S S SO SAT/RH BAROMETRIC PRESSURERGAUGE EVAPORATION PAN

WIND ANEMOMETERS SOLAR RADIATION NET RADIATION SUNSHINE HOURS

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Data logger

The data acquisition system heart of theThe data acquisition system , heart of the remote monitoring and control system.S l d l thSamples, processes, measures and logs the sensors as per the Setup configuration. Based on a microprocessor controlled system environment.

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SensorsCollecting raw data. In engineering terms coverts the processIn engineering terms, coverts the process variables in different energy forms to electrical energy then signal conditionedenergy then signal conditioned Measured elements -Air temperature, Relative h idit B R i Wi d dhumidity, Baro pressure, Rain, Wind speed and direction, Solar radiation, Soil moisture, E ti tEvaporation etc.

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TelemetryyCommunication ability of the system to relay data in real time through available communication media.Supported; satellite, radio, fixed line telephony, wireless through GSM/GPRS p y, g

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PowerThe life line of almost all blocks. AC power is used were available, but since most of the stations are installed in remote

l l d h bareas solar panels are used to charge battery and thus supply the systems. Areas with AC power still have solar panel connected as a redundant alternative.

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AWS distribution in KenyaAWS distribution in Kenya

Moyale

Lodwar

Ki l

WajirMarsabit

Moyale

Kitale

Kisumu EmbuGarissa

EldoretKakamega

Nyanyuki

NyahururuMeru

Kericho

ThikaNairobi

Lamu

NarokKisii

NyanyukiNakuru Nyeri

Makindu

Voi Msabaha

Lamu

Figure 3

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Figure 3

Where KMD Automatic System?Where KMD Automatic System?MeteorologicalMeteorological NetworkNetwork distributeddistributed

nationallynationallynationallynationally37 Synoptic stations ( 24 AWS 37 Synoptic stations ( 24 AWS --Automated)Automated)Automated)Automated)17 Automatic Hydro17 Automatic Hydro--met Systems met Systems

G G A d d A d d 3 River Gauging3 River Gauging--Automated Radar Automated Radar based Water Level based Water Level

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ContsConts

10 Airports ( 3 AWOS + 2 up c10 Airports ( 3 AWOS + 2 up c--10 Airports ( 3 AWOS + 2 up c10 Airports ( 3 AWOS + 2 up c--Automated )Automated )4 Automatic Tidal Gauges in India 4 Automatic Tidal Gauges in India 4 Automatic Tidal Gauges in India 4 Automatic Tidal Gauges in India oceanocean

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From figure 3, the systems have improved the From figure 3, the systems have improved the temporal and spatial resolution of observed temporal and spatial resolution of observed weather data in particularly in the western part ofweather data in particularly in the western part ofweather data in particularly in the western part of weather data in particularly in the western part of the county along the Nzoia basin that is currently the county along the Nzoia basin that is currently well cover and therefore predictions have been 90 well cover and therefore predictions have been 90 we cove d e e o e p ed c o s ve bee 90we cove d e e o e p ed c o s ve bee 90percent accurate. In fact from these systems’ data, percent accurate. In fact from these systems’ data, the disastrous floods that have caused numerous the disastrous floods that have caused numerous calamities over the years were averted thanks to calamities over the years were averted thanks to prompt real time information for flood forecast, prompt real time information for flood forecast,

li i d iti ti l t ili i d iti ti l t iearlier warning and mitigations alert issuance.earlier warning and mitigations alert issuance.

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The country spatial resolution of observedThe country spatial resolution of observed weather data still remains a challenge thus calling for more efforts and resources to becalling for more efforts and resources to be mobile to alleviate this trend.From statistical comparisons we deduced thatFrom statistical comparisons, we deduced that more than 98%, of the observations are consistent and do correlate well with humanconsistent and do correlate well with human manned observation stations.

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The system comes with WMO message formats of SYNOP METER SPECI BUFR CREXSYNOP, METER, SPECI, BUFR, CREX, reports, graphs, exporting, importing, printing, troubleshooting tools and many other capabilitiestroubleshooting tools and many other capabilities.Thus Manual entries made easy.The system is also observer interactive it allow anThe system is also observer interactive, it allow an observer to enter weather phenomenon and information.information.This has made work easier at station levels and thus work that was previously being done by three people p y g y p pis now comfortably being done by one person and more interesting just by a lick of a mouse.

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BOTTLENECKSBOTTLENECKS

Maintenance SustainabilityMaintenance SustainabilityTechnology transfer and capacity buildingGS /G S i idGSM/GPRS service providersLarge data VolumesVandalisms

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Maintenance SustainabilityMaintenance Sustainability

The recommended routine and preventiveThe recommended routine and preventive maintenance/repair should be performed quarterly per year but being a developingquarterly per year, but being a developing country with many financial constraints, sometimes a year passes withoutsometimes, a year passes without maintenance/repair being carried out. However should it be done it is a curativeHowever, should it be done, it is a curative maintenance or when the system has completely failed

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completely failed.

Maintenance Sustainabilityy

Thus sustainability is one of the greatestThus, sustainability is one of the greatest challenges. S ki f d d i fStocking of recommended spare pairs for maintenance/repair and computer virus are l bl h llalso notable challenges.

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Technology transfer and capacity building

Technology transfer and capacity building task was first gy p y gdifficult and was meet with resistance. However, as days went by the staff started appreciating the technology suffice to say that some of them even took a bold step of registering p g gthemselves to IT teaching institutions. To day, considerable numbers of staff in the out stations are computer literate Embracing these technologies convincinglycomputer literate. Embracing these technologies convincingly demonstrates that staffs IT skill levels have improved considerably by more than 20%. In all out stations officers were trained on the away to interact with the systems and morewere trained on the away to interact with the systems and more so the computer receiving stations.

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GSM/GPRS service providersGSM/GPRS service providersGSM/GPRS service providers have also contributed pnegatively to data exchange. Circuit switched data (CSD) used

i l h iPoor signal strength or no coverage in some parts of the country. Another difficulty is in remote login via GSMAnother difficulty is in remote login via GSM network. The communications often hangs while interrogating the remote station. Lastly, the cost of GSM/GPRS service is still exorbitant.

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Large data VolumesLarge data Volumes

The Automatic Observational Systems provideThe Automatic Observational Systems provide real-time data at frequency that is determined by the user of the system For Meteorologicalby the user of the system. For Meteorological purposes, we have configured our system to log data every 10 minutes This is definitelylog data every 10 minutes. This is definitely large volumes of Data. Currently we are using access database withCurrently, we are using access database with storage capacity of 2GB.

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VandalismsVandalisms

Last but not least 10 percent of all theLast but not least, 10 percent of all the installed Automatic Observational Systems have had solar panel vandalized so farhave had solar panel vandalized so far.

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NZOIA RIVER HydroNZOIA RIVER Hydro--met network met network --figure 4figure 4NZOIA RIVER HydroNZOIA RIVER Hydro met network met network figure 4figure 4

MT. ELGON CHERANGANY HILLS

MAU WATER MAU WATER TOWER

Figure 4

FLOOD PLAIN LAKE

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PLAIN LAKE VICTORIA

2929

Figure 5

WEBUYE

Automatic Telemetric Hydromet Stations

(Automatic Telemetric)

SEMOGERE Kaimosi FTC

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3030

AWS INSTALLATION AT LODWAR Fig re 6

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AWS INSTALLATION AT LODWAR- Figure 6

7 00 0 0

PRODUCT - Forecast chart - Figure 7

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20.0

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2 2 2 2 3 3 01

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Rainfall Forecast Level Water Level Flood Alert Flood Warning

3232

Observed and Predicted River levels at Rwambwa RGS

6Observed Predicted

4

5

m)

2

3

River level (m

1

0

1‐Sep‐08

6‐Sep‐08

11‐Sep

‐08

16‐Sep

‐08

21‐Sep

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26‐Sep

‐08

1‐Oct‐08

6‐Oct‐08

11‐Oct‐08

16‐Oct‐08

21‐Oct‐08

26‐Oct‐08

31‐Oct‐08

5‐Nov

‐08

10‐Nov

‐08

15‐Nov

‐08

20‐Nov

‐08

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CONCLUSIONSCONCLUSIONSIn a nutshell, Automatic Observational system is the , ytechnology that envisages assuring availability of meteorological data for posterity development.

h i h h h blThe triumphant entry of these systems has notably resulted in improved data accuracy, quality, availability and efficiency in transmission to theavailability, and efficiency in transmission to the collecting centre/Nairobi RTH. Installation of these systems in remote area will definitely improve on spatial data resolution and reduce manual observational constraints that are experienced in remote areas

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experienced in remote areas.

G d bl d i iGod bless and inspire thi t h l ithis technology in developing countries anddeveloping countries and world overworld over.

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THANK YOU FOR YOUR THANK YOU FOR YOUR TIMETIME

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