B M K G

Darman Mardanis, SEStasiun Klimatologi Pondok Betung BMKG

1.How to make climate data qualified; to make better climate prediction

2.So many UPT BMKG have a lot of climate data but fortunetly without validation

3.Climatological station main job : Observation, Collection,Analyse data and finally come up with information.

Doing validation for climate data qualification

To provide climate data with good qualification (especially for Banten dan DKI Jakarta Province)

Create a simple database system for Banten and Jakarta Province

To support climate prediction

Entry Data

Data Processing

Output

Validationyes

no

Checking

Create Valid Data File

As general,Data validation is the means by which data are checked to ensure that the final data stored in database system is the best possible representation of the true value of the variable at the measurement site at a given time or in a given interval of time or a certain time period.The important thing is,Validation process is just to find the correct data but not to find the mistakes.

Validation data process is same as quality data control

Primary Validation Method:Validation data process is on the place that doing the observation.

- For single data series; comparing observation data value with pre-set physical limits. ex. Range for temperature data are 20oC-37oC

- Doing the sequence test with graph, to detect the trend or deviation from normal value

- Compare value from two unit obeservation instrument.ex. Dry termometer and wet termometer

Time (GMT) Value from Dry Termometer

00.00 25.0oC

01.00 28.0oC

02.00 40.0oCIs it true?

Expected:1.Human error2.Instrument error, etc

Secondary Validation Method:Validation data process > comparison the climate data from two place.Note. Searching nearby place

- For multiple station:1. Balance series 2. Analysis Regressi3. Double Mass Curve, etc

- For single station:1. Trend Analysis2. Residual Mass Curve3. Run Test, etc

PRIMARY VALIDATION FOR CLIMATE DATAAT BANTEN AND DKI JAKARTA PROVINCE

Rainfall measurement:1.Obs Type (manually)2.Hillman Type (automatically)3.Tipping Bucket/ARG/ AWS (digitally)

Comparison value of daily data rainfall from Obs, Hellman and Tipping Bucket type:1.Rainfall data from 3 measurements must be different2.Obs type is still needed although Automatically and digitally type are available3.Value from Obs type assesed correctly. Assumption, error value from two other devices is so high 4.If there’s an error from Hillman and digital, it’ll be came from instrumentation error or wrong observation

Primary validation temperature principles:1.Maximum temperature must be higher than minimum temperature2.Maximum temperature must be higher than temperature 13.00 local time3.Minimum temperature must be lower than temperature 07.00 local time

No. Field Type Lebar Miss Value Deskripsi Satuan Keterangan1 TH Num 4 Tahun2 BL Num 2 Bulan 1 123 TG Num 2 Tanggal 1 314 t07 Num 4.1 99.9 Suhu jam 07 oC 19.0 29.65 t13 Num 4.1 99.9 Suhu jam 13 oC 22.5 37.06 t18 Num 4.1 99.9 Suhu jam 18 oC 22.8 33.87 tavg Num 4.1 99.9 Suhu rata2 oC 22.9 31.08 tx Num 4.1 99.9 Suhu maksimum oC 25.0 38.09 tm Num 4.1 99.9 Suhu minimum oC 15.0 26.0

10 rh07 Num 3.0 999 Kelembapan jam 07 % 50 10011 rh13 Num 3.0 999 Kelembapan jam 13 % 15 10012 rh18 Num 3.0 999 Kelembapan jam 18 % 25 10013 rhavg Num 3.0 999 Kelembapan rata2 % 45 10014 QFF Num 6.1 9999.9 Tekanan Stasiun mb 980 103015 QFE Num 6.1 9999.9 Tekanan Perm Laut mb 980 102016 ffavg Num 3.0 999 Kecepatan rata2 knot 0 1517 dd Num 3.0 999 Arah angin derajat 0 36018 ffmax Num 3.0 999 Kecepatan maksimum knot 0 4019 ddmax Num 3.0 999 Arah angin rata2 derajat 0 36020 rainfall Num 5.1 999.9 Curah Hujan mm 0 500 8888 : TTU21 lpm Num 3.0 999 Penyinaran matahari % 0 100

Nilai

Step-step:1.Open Microsoft Office Excel2.Open compilation climate data file3.Click menu data-filter-auto filter4.Flag data suspect (based on range data table)

Grafik Perbandingan Suhu Udara Maksimum dengan Suhu Jam 13.00 WIB di Pondok Betung

22.0

24.0

26.0

28.0

30.0

32.0

34.0

36.0

38.0

40.0

Jan Peb Mar Apr Mei Jun Jul Ags Sep Okt Nop Des

Waktu (Bulan)

Su

hu

Ud

ara

(oC

)

Tm ax T13.00

From graph, we can compare maximum temperature value (blue line) with temperature 13.00 LT value (red line).

Base on our principle before that Max temperature must be higher than temperature 13.00 LT.

On the graph, max temp on Feb 2007 lowest than temperature 13.00 LT (suspect value)

Cross check with hardcopy

Runs Test is a nonparametric test because no assumption is made about population distribution parameters. This test can be determine if the order of responses above or below a specified value is random. A run is a set of consecutive observations that are all either less than or greater than a specified value.

Grafik Curah Hujan Bulan Januari Periode 2000-2009 Pondok Betung

0

100

200

300

400

500

600

700

800

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Waktu (Tahun)

Cu

rah

Hu

jan

(m

m)

Rainfall on 2000-2009 at Pondok Betung

Doing secondary validation with Run Test-Minitab

Runs Test: Rainfall

Runs test for RainfallRuns above and below K = 332The observed number of runs = 8The expected number of runs = 5.84 observations above K, 6 below* N is small, so the following approximation may be invalid.P-value = 0.1223

(P-value)< 0.05 tidak homogen

(p-value)> 0.05 homogen

HOMOGEN

The right climate information support by good quality of data.

We must have a simple validation method

STASIUN KLIMATOLOGI PONDOK BETUNG

JL. RAYA KODAM BINTARO NO.82 JAKARTA SELATAN – INDONESIA

TEL/ FAX. +62217353018 / 7355262 http://www.staklimpondokbetung.net

staklim.pondok.betung@gmail.com