CURRENT STATUS OF BMKG’s WEATHER RADAR DATA QUALITY CONTROL

and IN-HOUSE PRODUCTS DEVELOPMENT

Riris Adriyanto1), Donaldi S. Permana2), Iddam H. Umam1), Wahyu Argo1)

1) Division for Remote Sensing Data Management, Center for Public Weather Services2)Division for Meteorology R&D, Center for Research and Development

BMKG Indonesia

BMKG

Existing & Planned Weather Radar NetworkBMKG

36 C-Band Radars(Single Polarization)4 X-Band Radars(1 Single Polarization and 3 Dual Polarization)27 Radar Site are currently connected to BMKG’s HQ (HDSS Radar Integration)

Existing:

Existing Radars (40)

Remark : Radar’s Coverage 150 km (C-band) ; 400 km (S-band) ; 75 km (X-band)

Planned in 2017 (1 Radar)Planned 19 new D-pol radars (2018 ~ …)

Existing & Planned:

Weather Radar Sites (as of March 2017)

No Radar Name Manufacturer Band1 Ambon Vaisala C-Band2 Balikpapan EEC C-Band3 Banda Aceh Gematronik C-Band4 Bandar Lampung EEC C-Band5 Banjarmasin EEC C-Band6 Bengkulu Gematronik C-Band7 Biak EEC C-Band8 Bima Gematronik C-Band9 Cilacap EEC X-Band

10 Denpasar EEC C-Band11 Gorontalo EEC C-Band12 Jambi Gematronik C-Band13 Jayapura EEC C-Band14 Kendari EEC C-Band15 Kupang Baron C-Band16 Majene EEC X-Band17 Makassar Gematronik C-Band18 Manado Gematronik C-Band19 Masamba EEC X-Band20 Mataram Gematronik C-Band21 Maumere Gematronik C-Band

No Radar Name Manufacturer Band22 Medan EEC C-Band

23 Merauke EEC C-Band24 Nias Baron C-Band25 Padang Gematronik C-Band26 Palangkaraya Gematronik C-Band27 Palembang Gematronik C-Band

28 Palu EEC X-Band

29 Pangkal Pinang Baron C-Band

30 Pangkalanbun Gematronik C-Band

31 Pekanbaru EEC C-Band

32 Pontianak EEC C-Band

33 Semarang Baron C-Band

34 Sorong EEC C-Band

35 Surabaya Gematronik C-Band

36 Tangerang EEC C-Band

37 Tarakan EEC C-Band

38 Ternate Gematronik C-Band

39 Timika EEC C-Band

40 Yogyakarta Baron C-Band

BMKG

Black = Integrated to HQ System Red = Dual-PolarizationBlue = Not yet integrated to HQ System

BMKG RDQC INTERNAL1. Quality of Calibration2. Optimizing Scanning Strategy3. Post Processing have been implemented from 2015

EXTERNAL1. Dual / Multi Radar Doppler (Composite) have been

implemented from 20092. Other Surface Observation System (AWS/ARG & LD)

next plan

INTERNAL RDQC1. Quality of Calibration

Calibration Technique:Hardware and Software CalibrationSupporting System Calibration

BMKG’s schedule for Weather Radar Maintenance and Calibration with its contractors (local companies and engineer from radar manufacturer): 2 x/year

This Quality Control include :Optimizing Parameter of Scanning Strategy (Clutter Filtering and Parameter Thresholding)

INTERNAL RDQC2. Optimizing Scanning Strategy

Standard Parameter of Filtering and Thresholding

Optimized Parameter of Filtering and Thresholding

Implementing Effective Maximum Range, Maximum Velocity (Nyquist) for Operational UseImplementing Best Scanning Strategy that consider Climate and Topography Condition Implementing Conditional Scheduler to cope with Severe Weather

INTERNAL RDQC2. Optimizing Scanning Strategy part 2

3. Post Processing1.Clutter Filtering2.Attenuation Correction3.Bright Band Echo Correction4.Masking / Occultation5.Vertical Profile Correction

INTERNAL RDQCPost Processing Implementation

Post Processing Implementation

Post Processing Implementation

This data QC has been implemented for Weather Radars those are connected to HQ Integration System

The Composite RDQC algorithm was developed by the National Severe Storms Laboratory (NSSL)/NWS/NOAA & has been implemented into the preprocessing of radar integration system (HDSS) installed at BMKG-HQ.

This algorithm ingests high resolution raw radar data and performs quality control on the data from each individual radar

very important in order to produce clean radar mosaics and excellent estimates of precipitation.

The Composite RDQC uses several steps to perform quality control on the individual radars including the use of a neural network.

Steps contained in the RDQC are :

Terrain blockages are removed - only beam clearance >50 m and beam blockage <50% allowed to contain radar reflectivity echoes.

Speckle filter - range gates are pre-classified so that echo sizes less than a threshold value will be removed.

Noise removal is performed - reflectivity below noise thresholds are removed

Sun strobes and test patterns are eliminated - each radial is checked for reflectivity fill >90% and correlation coefficient >0.80.

Non-precipitation echoes are removed - echo top thresholds are applied to consider only radar echo where the height of echo top is greater than a threshold height as precipitation.

EXTERNAL RDQC1. Dual/Multi Doppler Radar (Composite)

EXTERNAL RDQCImplementation

Figure below shows examples of the RDQCA processing applied to a radar (EEC C-band radar in Jakarta), before and after images (data after application of the RDQCA).

This QC include :1. Performs an objective analysis on the available rain gauge data.2. Compares the radar derived estimates to the rain gauge

analysis to determine biases in the radar data.3. Corrects the radar derived rainfall estimates based on the

gauge measurements.

Challenges that we experience in this QC method :1. Distribution of AWS/ARG 2. Data Quality and Continuity

EXTERNAL RDQC2. Other Surface Observation System (not yet implemented)

Processing

NetcdfLat-lon

Clutter removalAttenuation correction

FLOWCHART

Permana et al (2016) – Weather radar data processing and recovery using Python-based Wradlib , J. Met. Geo.

Clutter removal

clutter removalGabella et al (2002)

Data radar Palangkaraya 6 Mei 2016 pukul 15:36 UTC

Permana et al (2016) – Weather radar data processing and recovery using Python-based Wradlib , J. Met. Geo.

Attenuation correction

Data radar Palangkaraya 6 Mei 2016 pukul 15:36 UTC

Kraemer et al (2008)

Permana et al (2016) – Weather radar data processing and recovery using Python-based Wradlib , J. Met. Geo.

WRADLIB VS. RAINBOW® (GEMATRONIK)

Permana et al (2016) – Weather radar data processing and recovery using Python-based Wradlib , J. Met. Geo.

WRADLIB VS. FROG-MURAN® (BARON)

Permana et al (2016) – Weather radar data processing and recovery using Python-based Wradlib , J. Met. Geo.

OUTPUT (1)

TANGERANG

LAMPUNG

OUTPUT (2)SURABAYA BENGKULU

REGIONAL KOMPOSITE-2 (SUMATERA)

REGIONAL KOMPOSITE-1 (JAWA-BALI)

Summary As of 2017, 40 Doppler weather radars (single / dual polarized C-band / X-

band) have been deployed by BMKG; 20 additional new generation weather radars are needed to obtain minimum nation-wide radar coverage.

Current radar data QC implemented in BMKG : Internal radar data QC : built-in radar data processing software (on site) External radar data QC : using RDQCA developed by NSSL-NWS/NOAA (at HQ

radar integration system) Future plan for External radar data QC : open for external

collaborations/technical assistance Using rain gauges data – network density issues Spsce-borne/satellite precipitation radar products - GSMaP, etc (?)

On-going development by BMKG: In-house Radar Integration System. Expected outcome :

Processing raw data to produce radar QPE and QPF – open for external collaborations

Challenges : Non-homogeneity in surface precipitation measurements Data continuity due to radar hardware problems Radar data for NWP assimilations – external collaborations needed

THANK YOU …..