EVALUATION OF ICON-LAM FORECASTING OF A STRONG RAIN EVENT IN THE COAST OF SAO PAULO
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EVALUATION OF ICON-LAM FORECASTING OF A STRONG
RAIN EVENT IN THE COAST OF SAO PAULO
Reinaldo Silveira1 (Reinaldo.Silveira@simepar.br)
Gilberto Bonatti2 (Gilberto.Bonatti@inmet.gov.br)
Daniel Rieger3 (Daniel.Rieger@dwd.de)
Juliana M. D. Mol2(Juliana.Mol@inmet.gov.br)
Ricardo R.Santos2 (Ricardo.Raposo@inmet.gov.br)
SIMEPAR-Brazil1
INMET-Brazil2
DWD-Germany3
ICCARUS2021
08th to 12th of March, 2021
Outline
• The Brazilian Met Service (INMET) configuration of ICON model for
South America.
• A case study of a strong rainfall event at city of Iguape in the cost of
Sao Paulo state, at 27th of March, 2019.
• Two-way nested simulation with 2.8 km and 1.4 km grids.
• Examining the total rain rate and accumulation by tuning turbulence
diffusion scaling factors.
• Comparison between model results and observed rainfall.
• Conclusions.
ICCARUS2021
08th to 12th of March, 2021
ICON 7km - South America
Running twice a day for a week forecasting
Supercomputer at INMET:
Aerial information: ICON 2.6.2.2 compiled with Intel Fortran 19 and MPICH-3.2)
Latitude: 60S, 15N
Intel® Xeon® Gold 6130 (22M Cache, 2.10 GHz , 16 GT/s Intel®)
Longitude: 95W, 20W
48 nodes (with 32 cores)
ie: 1200; je: 1200, ke: 65
1536 cores
damp_height = 18000
132Gb RAM per node
top height = 30000
Hard Disk: 31Tb.
dtime = 60
Currently it takes about 2 hours for processing 174 hours forecast
for ICON 7KM, by using 1024 cores
New Supercomputer (April 2021)
HPE Apollo 6000 (Intel Fortran 19.0 and MPT MPI):
Intel® Xeon® Platinum 8270 (35.75M Cache, 2.70 GHz (Turbo 4.00 GHz))
72 nodes (with 52 cores)
3744 cores
192Gb – 384Gb RAM per node
Storage: 1.2Pb
Post Processing Machine (Software Visual Weather)
Dell:
Intel(R) Xeon(R) CPU E5-2620 v4 @ 2.10GHz
32 cores
264Gb RAM
Hard Disk: 18Tb
Motivation • The Summer of 2019 was particularly hotter than climatology, when temperatures greater than 40C were observed at many stations in the South of Brazil, Parana, Santa Catarina, Rio Grande do Sul and in Sao Paulo for many days consecutively. • This anomalous condition lead to strong convection cells in the afternoons and nights, reinforced by the Amazon rainforest humidity transport. • So, we challenge ICON to simulate a key event of river flood due to a localized strong storm strengthened in the morning, when the observed rainfall was greater than 80 mm during few hours.
GOES-16 (BT 10.3u): 27th of March 2019 - 07:45GMT
Courtesy from INPE/CPTEC/DSA
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Iguape-SP
10km Rainfall grid analysis: raingauge + satellite
Source: CPTEC (Rozante et. al., 2010)
Iguape
Iguape
Brazilian Airforce
Weather radar
São Roque, for date:
27.03.2019 – 08:05 UTC
We investigate the convection event by performing a
Two-way nested 36hr simulation and tuning turbulence scaling factors:
Lateral boundaries from ICON (global, 13km) to parent grid
DOMAIN 1 (parent) – 2.8km; DOMAIN 2 (inner) – 1.4km
Land/sea experiments: tuning turbulent diffusion scale factors of
laminar boundary layer for heat
(rat_sea and rlam_heat)
1. REFerence: using default values for ICON
2. REFerence + activation the upper boundary NUDging zone (nudge_type=1)
---------------
(Tuning rat_sea and rlam_heat)
3. LAND: reduce laminar sublayer thickness only over land by 20%;
4. SEA: reduce laminar sublayer thickness by 20% only over sea;
5. COMB: reduce laminar sublayer thickness everywhere by 20%;
Vertical structure of hydrometeor mixing ratio during the time of the maximum rainfall rate (LEFT: REF; RIGHT: NUD for 1.4km grid)
Vertical structure of hydrometeor mixing ratio during the
time of maximum rainfall rate
(LEFT: LAND; CENTER: SEA; RIGHT: COMB for 1.4km grid)36 hours of rainfall simulation of ICON-2.8 and ICON-1.4 for a
point corresponding to A712-INMET observing station
ICON-2.8km ICON-1.4kmSome conclusions
• We examined an isolated storm strengthened during the night, which
caused flooding in part of the city of Iguape.
• We observe that ICON-LAM simulation of the event is timely for both
domains, though it looks like to perform better for the parent grid in this
aspect.
• Also we noticed that the activation of upper boundary nudging zone
improved the vertical structure of hydrometeors (NUD experiment).
• For this particular event, tuning was very sensitive to turbulence diffusion
scaling factors. Nevertheless, COMB experiment improved the
quantification of rainfall in the ICON-1.4 simulation.
ICCARUS2021
08th to 12th of March, 2021Thanks for your attention !
ICCARUS2021
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