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Convective-scale NWP for Singapore
ECMWF Symposium on Dynamical Meteorology and NWP 8 June 2017
Hans Huang and the weather modelling and prediction section
MSS, Singapore
Dale Barker and the SINGV team
Met Office, Exeter, UK
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Tropical weather systems are complex
• Convective rain clouds develop rapidly and have short-life
span
• Largely driven by winds, which tend to be weaker and
more variable in direction in the tropics
• Difficult to give precise forecasts of onset, location and
intensity
• “Off-the-shelf” Numerical Weather Prediction (NWP)
models - Low skill in predicting convective-scale systems
(rain showers, localised thunderstorms)
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Anurag Dipankar
SINGV, 500m
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Jeff Lo
WRF, 500m
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Singapore Weather:
Surface Temperature
• Little variability through year….
Raizan, MSS
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Monsoon Seasons in Singapore
NE Monsoon
SW Monsoon
Inter-Monsoon
Inter-Monsoon
NE Monsoon
Cheong Wee Kiong, MSS
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0
5
10
15
20
25
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Da
ys
Average Number of Thunderstorm Days
0
1
2
3
4
5
6
7
8
9
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Mo
nth
ly F
req
ue
ncy
Month
Average Number of Sumatra squalls making landfall in Singapore
Main weather systems which bring heavy rainfall:
Localized convective thunderstorms
“Sumatra” squalls
Monsoon surges
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SINGV
– the convective-scale NWP system for
Meteorological Service Singapore
Collaboration: Met Office and Meteorological Service Singapore.
~4FTE/yr from each partner.
Tropical, km-scale NWP R&D plus operational
implementation target.
Core Model R&D -> Evaluation -> DA -> Ensemble
+ Tech Infrastructure
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SINGVDownScaler (DS)
Data Assimilation (DA) Ensemble System (ENS)
• Downscaler uses a 1.5 km mesh, runs 2
times per day and produces 36h
forecasts.
• Data assimilation system uses a 4.5
mesh, runs in full cycling with 3h
assimilation windows and produces
12h forecasts from each analysis.
• Ensemble system uses 12 4.5km DS
members, runs 4 times a day and
produces 36h forecasts.
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SINGV (DownScaler) started in
real time at MSS in Feb 2015!Version 2.0:
• Based on PS35 UKV ENDGame • No MURK aerosol
• But L80 rather than L70
• Changes from Version 1
1. P2A blended BL scheme
2. Single 1.5 km domain• As opposed to version 1 = double nest
• 1092 x 1026 x L80, dt=50s
• Fixed not variable resolution
• ~5 times cost of Version 1 configuration
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Model (vn 2.0)
Radar
Estimated Rainfall
and
Precipitation
Forecasts from
SINGV (UM),
WRF-GFS, and
WRF-ECM
SINGV – clumpiness
(blobbiness)
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Known Model Issues• Cloud-scale biases
– Too much heavy rain and too high peak rainfall rates.
– Too strong and deep updrafts.
– Not enough light rain.
– Too many small cells, too few large if convection is well resolved.
– Too few cells if under-resolved
• Organisation biases
– Cells too circular if under-resolved, too elongated if well resolved and orientation tends to be too much along wind.
– Lack of propagation of squall lines (noted particularly in Singapore).
• Biases in response to large-scale / boundary layer / diurnal forcing
– Timing of initiation of convection.
– Other timing issues.
– Land-sea contrast issues - in particular excessive convective rainfall over land and light rain over the ocean.
• Biases in response to driving model
– Spin up effects at edge of domain
– Errors passed from larger scale driving models.
(from convection WG)
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SINGV configuration vn2.0 → vn2.1• Fixed the reconfiguration coding error
• Treatment of the convective boundary layers1. Applied the blended boundary layer approach
2. Introduced stochastic temperature perturbations
• Increase of the mixing length in 3D Smagorinsky scheme
• Switched off the excessive graupel production
Improvements:
- Excessive rainfall reduced;- Less intensive storm cells;- Spin-up time reduced.
Counts of Storm Cells(Storm cells are defined as spanning at least 12 model grid-boxes, with hourly rainfall in excess of 20 mm/hr in every grid-box)
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• The moisture conservation scheme
• Temporally correlated stochastic θ perturbations
• Stochastic moisture perturbations
SINGV configuration vn2.1 → vn3.0
Improvements:
- Rainfall patterns are more realistic compared with vn2.1;
- More rainfall is produced over the sea than vn2.1.
• Areal coverage of rainfall
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vn2.0
vn2.1
vn2.1
vn3.0
The clumpiness was still an issue
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● Use of prognostic cloud cover scheme (PC2) instead of
Smith’s scheme.
● Revised stochastic boundary layer perturbations
● Revised moisture conservation
Remarks
● Cloud cover is too variable over tropics.
● Switch to the prognostic cloud cover scheme (PC2) from
Smith’s scheme made significant impact. Reason not fully
understood yet!
SINGV configuration vn3.1 → vn4.0
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Reduction in intense convection
August November
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Reduced clumpiness
Increase in light rainfall and reduction in high rainfall intensity gives the impression of reduced clumpiness
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Improved squall lines
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FSS score August, full SINGV domain
Light Rain Heavy Rain
vn4.0 is more skilful than vn3.1 vn4.0 is of comparable skill
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FSS score for November, full SINGV domain
Light Rain Heavy Rain
vn4.0 is more skilful than vn3.1 vn4.0 is more skilful than v3.1
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SINGV Data Assimilation
SINGV DA Version 1: conventional observations
+ amsub + iasi + satwind + mtsatclear (with global bias
configuration)
SINGV DA Version 2: conventional observations
+ amsub + iasi + satwind + scatwind + airs + saphir (with
SINGV specific configuration) + Singapore radar
• Initial technique: 3D-Var cycling every 3hr
• Real time implementation: April 2016
• Consider 4D-Var: 2016
• Adding ensemble to DA:2017
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Observation for SINGV
3DVAR
LHN
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SINGV Data Assimilation System
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Analysis
Forecasts at the same valid time (left->right: T+3h, T+6h, T+9h and T+12h)
Cycling DA produces more forecasts at one valid time, with reasonable consistency
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“Dryness” issue in the DA cycling system
DA is too dry.
Relative humidity at surface for (left) DS (T+3h)(right) DA (T+3h) 2016-05-24 03:00:00
DA produces much less rain.
Hourly precipitation for (left) DS (T+9h)(right) DA (T+6h)2016-10-25 09:00:00
DS (downscaler) DA (data assimilation)
Remarks: not a DA problem ?! May be due to model and lateral boundaries.
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Experiments with
different DA
configurations
Precipitation scores for
different forecasting schemes
over a week (01-08 Feb 2017)
LHN helps at T+3h
Restart helps at T+12h
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SINGV Ensemble Configuration• Model settings
– Model: UM 10.6
– Resolution: 4.5 km
– Grid: 364x342 grid points
– Vertical levels: 80 levels (model top 38.5km)
– Forecast range: T+36
– Time step: 100 seconds
• Operational details
– Real time (planned): by 31st March 2017
– Run 4 cycles per day (3, 9, 15, 21 UTC), the four cycles are driven from the T+3 dumpsof 00/06/12/18z Global ensemble MOGREPS-G cycles
– Forecast length: T+36
– 12 ensemble members - 1 control run + 11 perturbed members
• Initial conditions
– Initial conditions from global ensemble: MOGREPS-G
– Perturbations generated by Ensemble Transform Kalman Filter (ETKF).
– The 03 and 15 UTC cycles receive perturbations from the global ensemble members 1 to11, whereas the 09 and 21 UTC cycles use perturbed members 12 to 22
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Postage stamp map
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Hourly Rainfall Probability
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ppn>10mm/hr ppn>20mm/hr ppn>60mm/hr
Temp<20°C Temp>30°C Temp>35°C
Wind>5kt Wind>10kt Wind>20kt
Summary Plot
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Forecaster’s Evaluation
Skills in rainfall prediction over Singapore and the surrounding region Spatial coverage of the rainfall;
Structure and orientation of the rain cluster;
Rainfall intensity
1. Does SINGV or WRF-EC provide value-added guidance in comparison with
EC global model?
2. Does SINGV offer better guidance than WRF-EC?
(MSS-UKMO joint effort)
• Focused on deterministic assessment of the forecasts
• Of course, with only a downscaler, it may not be expected that the model
will perform well. We may need to wait for the inclusion of ensembles
before the potential can be fully assessed
Assessment Criteria
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Forecaster Evaluation
• SINGV had captured this event relatively well
• EC Global had captured this event relatively well but had under-forecast the rainfall intensity
• WRF-EC missed this event.
Radar SINGV
WRF-EC EC Global
SINGV > EC Global > WRF-EC
5am local time
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Forecaster evaluation
• WRF-EC had captured this event relatively well
• SINGV missed this event totally
• EC Global missed the rainfall over Singapore and Peninsular Malaysia
Radar SINGV
WRF-EC EC Global
WRF-EC > EC Global > SINGV
5am local time
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Remarks and Ongoing Work• Convective-scale NWP in tropics, especially for Singapore, is a
difficult problem
• SINGV has definitely helped put forward NWP challenges in the deep tropics
• Updates have been made in model physics to tackle some of the known problems and collaborative effort is needed to understand the issues
• Improements are only incremental due to limited resources
• The current focus in model development is to test the grey-zone convection scheme
• SINGV DA will be focussing on LHN and tackling some of the issues
• SINVG EPS will be focussing on putting it to real-time
• In objective verification, we are working on issues on using GPM and Radar as references
• We are also working with forecasters on subjective evaluation, which will assess the usefulness of SINGV in operational applications