numerical weather and climate forecasting in indonesia: a
TRANSCRIPT
Numerical Weather and Climate Forecasting in Indonesia: A Capacity Building Experience
Presented by:
Tri Wahyu Hadi
Weather and Climate Predic0on Laboratory Bandung Ins0tute of Technology
The Beginning of Modern Meteorology (The “Mind “ Era) Vilhelm Bjerkness : “the central problem of Meteorology is the prediction of future weather” 1904: Weather forecasting
as a problem in physics
Exact solution to the system of equations is impossible
Lewis Fry Richardson: 1922: Weather prediction
by numerical process
Two weeks calculation by hand produced erroneous forecast
Early Numerical Weather Prediction (NWP): 1950: John von Neuman’s first
successful run of “simple” barotropic model for retrospective 24-forecast using ENIAC
1954: C.G. Rossby team’s first
numerical weather prediction in real-time in Sweden
Imaginary “human” computer
Electronic Numerical Integrator and Computer (ENIAC) à speed: 5000 opera0ons per second It works!
Global Numerical Weather Prediction Model: Basic Idea of The Spectral Transform Method Signal on sphere at par0cular 0me t
The simple Barotropic Vor0city Equa0on (BVE)
Predict the future value of each spectral coefficient
Weather Forecasting Today (Mind and More Machines ) • Clusters installed in Maryland and West Virginia • Each have 156 Power 575 nodes linked by double data rate (DDR) InfiniBand networks.
• (IBM) Power6 processors run at 4.7GHz and deliver a total of 4,992 cores, 18.7TB of main memory, 170TB of disk capacity, and 13PM of tape archiving capacity.
(h^p://www.theregister.co.uk/2012/03/08/ibm_noaa_ncep_weather_super/
European Center for Middle Range Weather Forecasts
NOAA -‐ USA
JMA -‐ JAPAN
ECMWF – European ConsorGum
The Ultimate Goal: Seamless Suite of Weather and Climate Prediction
Tran
spor
tatio
n
Fore
cast
Lea
d Ti
me
Warnings & Alert Coordination
Watches
Forecasts
Threats Assessments
Guidance
Outlook P
rote
ctio
n of
Li
fe &
Pro
perty
Spa
ce
Ope
ratio
n
Rec
reat
ion
Eco
syst
em
Sta
te/L
ocal
P
lann
ing
Env
ironm
ent
Floo
d M
itiga
tion
& N
avig
atio
n
Agr
icul
ture
Res
ervo
ir C
ontro
l
Ene
rgy
Com
mer
ce
Societal Benefits
Hyd
ropo
wer
Fire
Wea
ther
Hea
lth
Forecast Uncertainty
Minutes
Hours
Days
1 Week
2 Week
Months
Seasons Years
Current range of skillful forecasts
Range of forecasts with developing skill
Adapted from Lord et al. (Symposium on 50th Anniversary of OperaGonal Numerical Weather PredicGon, University of Maryland College Park, July 15, 2004)
What we have been doing… Near real –time downscaling experiments
Mesoscale Weather Model
(MM5/WRF)
Output of global model
1° ≈ 111 km
Output
27 km
9 km
Data freely available on the internet, NCEP GFS global model output : • Horizontal resoluGon : 0.5° & 1° available • VerGcal resoluGon : 24 sigma levels • Time resoluGon : 3 hr • PredicGon range : up to 384 hour (var. res.) • Number of output parameters : 128 • GRIB ver. 2 data format
Targeeed regional model characterisGcs • Coarse grid res. : 27 km x 27km • Finer grid res. : 9 km x 9 km • VerGcal resoluGon : 32 sigma levels • Time resoluGon : 3 hr • Two-‐way nesGng between coarse and finer domain
No local data assimilaGon
Mainly developed to support educaGon and research in Meteorology at ITB
IC & BC
Number of node (total cores) 2 (1) 8 (8) 24 (6) 48 (2) + GPU
Widyatmoko (2006); Junnaedhi (2006);
2011 3rd Gen.
2006 1st Gen.
2004 Gen. 0
2009 2nd Gen.
Trilaksono (2004); Wahyudi (2004)
Near real-‐Gme predicGon
In-house Development of Computational Resources
• NWP was first introduced into curriculum of undergraduate program in Meteorology in 2003 • Minimum sustainable facility • Thanks to open source codes of NWP models (MM5, WRF, etc.)
Basic System Design
NOAA
Dept. Inf. Science Kochi University
Space Science and Engineering Center (SSEC) University of Wisconsin
Block A : Internet Resources
Dept. Atm. Sci. University of Wyoming
GFS Data
Sonde Data
MTSAT IR mages
MTSAT/GOES latest images
Block B : DataServer
Regional model Preprocessing (if data is adequate)
Daily rainfall esGmaGon
Model run up to 48-‐hour lead Gme predicGon
Topgraphy and land-‐use data (fixed)
Post processing of regional model output
-Monitoring -‐PredicGon -‐NowcasGng -‐Forcast VerificaGon, etc
Block D : Web Server
Postprocess
Block C : PC Cluster
? ?
Automated Run of the Forecast Cycle
NCEP-GFS Forecast Run at 1200 UTC
Download Time
2100 UTC
1200 UTC
Meso Forcast Run
1200 UTC
0000 UTC
Meso Regional Prediction effective forecast lead time
Data downloaded at 3 hr fcst interval
Cluster assembled inhouse (typhoon & tornado): • 2 nodes, each with 2 processor AMD 12 core • 48 GB total memory • 5 TB total data storage
Mesoscale models are : MM5, and WRF-‐ARW
1700 UTC
Note: improved network infrastructure of ITB has made it possible to reduce latency with beeer spaGal and temporal resoluGon of global output data , and overall process to finish earlier; Since 2008 more collaboraGons have been made with more individuals.
Example of logged data transfer Downloading : f.0000_tl.press_gr.1p0deg at Wed Oct 13 01:15:01 WIT 2010 -‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐ -‐-‐01:15:01-‐-‐ ip://tgip.nws.noaa.gov/SL.us008001/ST.opnl/MT.gfs_CY.12/RD.20101012/PT.grid_DF.gr2/f.0000_tl.press_gr.1p0deg => `/ops/noaa/avn-‐tgip/20101012/f.0000_tl.press_gr.1p0deg' Resolving cache.itb.ac.id... 167.205.22.103 Connec0ng to cache.itb.ac.id|167.205.22.103|:8080... connected. Proxy request sent, awai0ng response... 200 OK Length: 15,707,776 (15M) [text/plain] 0K ........ ........ ........ ........ ........ ........ 20% 114.64 KB/s 3072K ........ ........ ........ ........ ........ ........ 40% 133.81 KB/s 6144K ........ ........ ........ ........ ........ ........ 60% 133.64 KB/s 9216K ........ ........ ........ ........ ........ ........ 80% 133.61 KB/s 12288K ........ ........ ........ ........ ........ ....... 100% 133.54 KB/s 01:17:14 (129.35 KB/s) -‐ `/ops/noaa/avn-‐tgip/20101012/f.0000_tl.press_gr.1p0deg' saved [15707776/15707776]
• Download of NOAA -‐ GFS model output through ITB Proxy • In 2010 we used minimum data for ini0al and boundary condi0on : :
Latency à Six-‐hourly data à 9 files x (~2.5 min) ~ 25 min Size à 9 x 15 MB = 135 MB / day
Main Outputs Disseminated Online
Available variables: q Integrated cloud q Rainfall with 10m wind
q Temperature q Equivalent poten0al temperature
2 days forecast 3 hours interval
• Updated daily at hep://weather.meteo.itb.ac.id/ • More features from student research including SMS and mobile applicaGon • No English pages yet (sorry)
Examples of Core Research: Improving Short Range Prediction with Radar Data Assimilation
Luthfi Imanal Satrya, Undergraduate final project 2012 Indra Gustari (on leave from BMKG), Doctoral Research 2011-‐2014
Experiments using WRF Model for 24-‐hour lead 0me predic0on of rainfall over Jakarta Area
Model Domain
Several weather radars have been installed and operated by BMKG, LAPAN, and BPPT but data have not been used in NWP à long way for opera0onal implementa0on
Introducing End-to-end Modeling Concept: Research on impact modeling of weather events APPLICATION IMPACT MODELING
Early Warning System:
-‐ Flood Mesoscale weather forecast – Flood modeling à e.g., WRF-‐ANUGA
-‐ Transporta0on safety – Volcanic ash dispersion
Volcanic ash dispersion modeling à e.g., GFS-‐PUFF, WRF-‐PUFF
-‐ Transporta0on safety – opera0on of long-‐span bridge
Coupled mesoscale weather forecast – Computa0onal Fluid Dynamics àe.g., WRF-‐Open FOAM
• AND…OTHER APPLICATIONS MAY REQUIRE OTHER IMPACT MODELING APPROACH • MANY IMPACT MODELS ARE COMMERCIAL SOFTWARES BUT WE PREFER OPEN SOURCE
Research on Weather-Impact Modeling: Coupled WRF-AnuGA Flood Model
Manggarai Area of experiment
Katulampa
Summary SpecificaGon
Nodes 4 nodes
Cores 16 cores (4 cores/node)
Processors (per nodes)
AMD FX (tm) – 8350, 4 Ghz
Memory (per nodes)
8 GB DDR 3, 800 Mhz
Dedicated Server
ANUGA is Free and Open Source (FOSS) soiware developed by Australian Na0onal University and Geoscience Australian ( h^p://anuga.anu.edu.au/) à Collabora0ve research involving MAIPARK, IRISIKO, ANU, and (joining soon) LIPI
Research on Impact Modeling: Example of Riverine Flood Simulation
Domain
Triangular mesh More than 330 thousands of total element Fine element (area < 10m2) near the Ciliwung River
Simulated case of January 2013 Finite vloume formula0on
Research on Impact Modeling: Volcanic Ash Dispersion
*) Muhammad Rais Abdillah, Undergraduate final project 2012
Simpe trajectory model (iniGal Gaussian size distribuGon) with PUFF model:
Effect of parGcle size à parGcle “life Gme” in the atmosphere
CASE I: 5 November 2010
CASE II: 10 November 2010
Research on Impact Modeling: Volcanic Ash Dispersion
α1 α2
α1 = 25.30° α2 = 36.26° AR1 = 2.46 AR2 = 1.33
α1 α2
α1 = 22.07° α2 = 41.84° AR1 = 1.19 AR2 = 0.76
PREDICTED HORIZONTAL DISTRIBUTION OF PARTICLES
CASE I CASE II
Black arrows indicate direc0on of horizontal ash dispersion es0mated from MTSAT imageries (Asri Susilawa0, 2012) à discrepancies between model and satellite products are large for CASE I à similar results using GFS and WRF wind forecasts
AR = 5.43 α = 1.01°
AR = 3.05 α = 0.09°
α
α
Research on Impact Modeling: Prediction of Wind Gust over Long Span Bridge (Coupled WRF – CFD )
*) Bimo Adi Kusumo, Undergraduate final project 2012
Suramadu Bridge
• Bridge is some0mes closed when cross-‐wind speed exceeds certain threshold (40-‐60 km/hour) • How to predict wind gust over the deck of the bridge? • Must combine weather forecast with CFD modeling
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Varia
nsi
Waktu
a.)
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1.5 2
2.5 3
3.5 4
4.5
18 19 20 21 22 23 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
Varia
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Time (hours; UTC)
10-‐minute moving variance
Case I
Case II
A B C D
Timur Barat
Timur
Barat
CASE I : 22 September 2010 (bridge close around 0500 UTC) CASE II : 2 October 2010 (bridge closed around 0900-‐1100 UTC)
Calculated increase of maximum wind speed ra0o from 2-‐hour mean values are around 60 % (Case I) and 43% (Case II) à may be used for early warning if local wind observa0ons are available
Research on Impact Modeling: Wind Gust
Climate Prediction : Statistical Downscaling
For predic0on of longer 0me range, and climate projec0on we currently use sta0s0cal downscaling of ensemble model output
ensemble members
ensemble mean
There are six million ways !
Climate Prediction : Analogue Method
database target
1982
1982
2000
2000 2005
2005
predictor
predictand
best pa^ern similarity S(u) F(t) F(u)
CH(u) CH(t) analogue
-‐ reduce dimensionality : EOF
-‐ pa^ern similarity: cosine similarity
-‐ CH(t) ≈ CH(u) where S(u) =
-‐ Constructed analog (untuk mul0 window):
Predictor window
Preliminary results of hindcast experiments for predicGng 10-‐day accumulated staGon rainfall in Indramayu, West Java
Ongoing doctoral research by Elza Surmaini (2013)-‐-‐> Applica0ons for early warning of paddy drought
Climate Prediction : Seasonal Rainfall Prediction
What we lack of: Science Policy
Workshop at NCAR in November 2013 sponsored by :
Giant enterprises with ten’s of billions of dollars of annual budget!
What we lack of: Incomplete pillars of S & T
Weak Meteorological
Society
Research Ac0vi0es
Users Community
Educa0on
Very limited number of higher educaGon with
program in Meteorology
24
Plenty Rooms for Collaborations & Sharing
Weather Portals
ObservaGonal data!
AWS
SODAR
Thank You