The Numerical Simulations of the June 16, 2010, Heavy Rainfall Event over Singapore are highlighted by an unprecedented precipitation which produced widespread, massive flooding in and around Singapore. The objective of this study is to check the ability of Weather Research Forecasting version 3 (WRFV3) model to predict the heavy rain event over Singapore. Results suggest that simulated precipitation amounts are sensitive to the choice of cumulus parameterization. Various model configurations with initial and boundary conditions from the NCEP Final Global Analysis (FNL), convective and microphysical process parameterizations, and nested-grid interactions have been tested with 48-hour (June 15–17, 2010) integrations of the WRFV3. The spatial distributions of large-scale circulation and dynamical and thermodynamical fields have been simulated reasonably well in the model. The model produced maximum precipitation of ~5 cm over Changi airport which is very near to observation (6.4 cm recorded at Changi airport). The model simulated dynamic and thermodynamic features at 00UTC of June 16, 2010, lead to understand the structure of the mesoscale convective system (MCS) that caused the extreme precipitation over Singapore. It is observed that Singapore heavy rain was the result of an interaction of synoptic-scale weather systems with the mesoscale features.
On 16th June 2010, a heavy rainfall event occurred in Singapore producing devastating flash flood and tremendous amount of property damage (Singapore’s national water agency (PUB) report, Annual Weather Review, 2010, NEA, Singapore). Heavy rainfall is usually resulted from individual mesoscale storms or mesoscale convective systems (MCSs) embedded in synoptic-scale disturbances [
In the present study,
Many attempts have been made to assess and evaluate the performance of numerical weather predictions from model configurations for the region, because on the basis of flood forecasting and even rainfall forecasting as well as safety evaluation model for Mumbai, the early warning could be avoiding disaster. During the last two decades, weather forecasting all over the world has greatly benefitted from the guidance provided by the Numerical Weather Prediction (NWP). Significant improvement in accuracy and reliability of NWP products has been driven by advances in numerical techniques, explosive growth in computer power, and the phenomenal increase in satellite-based soundings. The prediction of these systems is subject to the limitations of synoptic forecasting methods, which only indicate probable occurrence of heavy precipitation but not the quantity. Although numerical models provide the quantitative prediction of precipitation, they are subject to the limitations of initial data, model dynamics, and physics which can lead to uncertainties model output. Uncertainties are “data uncertainties,” “modeling uncertainties,” “completeness uncertainties.” Data uncertainties arise from the quality or appropriateness of the data used as inputs to models. Modeling uncertainties arise from an incomplete understanding of the modeled phenomena, or from approximations that are used in the formal representation of the processes. Completeness uncertainties refer to all omissions due to the lack of knowledge. They are, in principle, nonquantifiable and irreducible. The prediction of the mesoscale systems requires the use of high-resolution atmospheric mesoscale models and observations with a mesoscale network. Some studies of the numerical prediction of heavy rainfall event over India using high resolution mesoscale models show the predictability of events with precipitation less than 20 cm/day [
The model used in this study is the Advanced Research Weather Research and Forecast model (WRF) version 3.3. The WRF is the next generation forecast model and data assimilation system that has advanced both the understanding and prediction of weather. It has been designed to support operational forecasting and atmospheric research needs. The WRF model is a fully compressible, nonhydrostatic model [
The model supports both idealized and real-data applications with various lateral boundary condition options. Due to meteorological complexities involved in replicating the rainfall occurrences over Singapore, the WRFV3 modeling system is tested for different physics schemes. In the present study, we tested out the possible combination of Microphysics, Cu-physics, and PBL schemes provided in the Table
Microphysics, Cu-physics, and PBL schemes.
Microphysics options | Cumulus parameterization | Boundary-layer option |
---|---|---|
WSM 6-class graupel |
Kain-Fritsch (new Eta) scheme |
YSU scheme |
It is to be noted that MYJ PBL scheme can only be used with the MYJ SFC layer scheme. Other physics options include longwave radiation from RRTM Scheme, shortwave radiation from Dudhia scheme, surface physics from unified Noah land-surface model, and Surface Clay Physics from Surface scheme Monin-Obukhov similarity theory. As grid spacings decrease, convective parameterizations become more inappropriate (and scientifically questionable given the underlying assumptions), whereas the explicit representation of microphysical processes can be computed for increasingly small clouds, cloud particles, water droplets, and so forth. So therefore in the present case, we conducted multinested experiments of 3 model domains of 27, 9, and 3 km horizontal resolution (Figure
Domain used (three nested domains having resolution 27 km : 9 km : 3 km).
In the present study, the model is forced with initial and boundary condition form NCEP Final global analysis (FNL) and model integrated 48 hr with initial and boundary condition which started from June 15, 2010. Forcing variables are air temperature, cloud, amount/frequency, cloud liquid, water/ice, convection, geopotential height, humidity, hydrostatic pressure, ice extent, land cover, maximum/minimum temperature, planetary, boundary layer height, potential temperature, precipitable water, sea surface temperature, skin temperature, snow water, equivalent soil, moisture/water content, surface air temperature, surface pressure surface winds, tropospheric ozone, upper level winds, and wind shear.
Rainfall is an important parameter in many operational and research activities, ranging from weather forecasting to climate research. The WRFV3 model with the WSM6 microphysics is observed to provide useful information on high-resolution weather phenomena over Singapore. The study demonstrated that the WSM6 schemes are competitive options in WRF by reproducing precipitating convection and associated meteorological phenomena over Singapore. Apart from this, it has been observed that performance with GD scheme is found to be the best in both spatial and temporal bases. The spatial distributions of large-scale circulation and thermodynamics features have been simulated reasonably well in this model. The model produced maximum precipitation of ~5 cm at Changi station (the observed rainfall over Changi is 6.4 cm during the day). Figure
(a) TRMM rainfall (mm) and (b) WRFV3 total accumulated precipitation (mm) for 16th June 2010. Resolution for the model precipitation shown is 3 km.
An attempt has been carried out with available sources of data to analyse mesoscale characteristics at Singapore favorable for the formation of the event during 16th June 2010. Here, we used Finite Global Analysis (FNL) available data for the analysis. Figure
Mean sea level pressure for 00UTC of 16 June 2010 (left panel), 06UTC of 16 June 2010 (centre panel), and 12UTC of 16 June 2010 (right panel).
Surface convective inhibition (CIN) for 00UTC of 16 June 2010 (left panel), 06UTC of 16 June 2010 (centre panel), and 12UTC of 16 June 2010 (right panel).
Wind magnitude (shaded) and wind vector (arrow) at 850 mb level on 16th June 2010 (a) GFS, (b) FNL, and (c) model.
Wind magnitude (shaded) and wind vector (arrow) at 500 mb level on 16th June 2010 (a) GFS, (b) FNL, and (c) model.
The model derived dynamical and thermodynamic fields were analyzed to understand the characteristics of the convective system which was responsible for the 16th of June, 2010 heavy rainfall over Singapore. Figure
Model derived relative humidity (RH) at 500 mb level over Singapore (a) and on 00UTC of 16th June 2010 over Singapore with respect to vertical level (b).
Wind direction (WD) in degrees with respect to vertical levels at Singapore on 00UTC of 16 June of 2010.
Differential model fields which can be represented by Z-component of wind at 250 mb Minus Z-component of wind at 850 mb over Singapore latitude on 00UTC of the 16th of June, 2010 have also been analyzed (Figure
Z-component of wind at 250 mb minus Z-component of wind at 850 mb at Singapore.
Model derived high cloud fraction in percentage over Singapore.
Figure
Potential temperature (in degrees) over Singapore on 00UTC of 16 June 2010 at 400 mb (a) and at 700 mb (b).
Wind vector on 00UTC of the 16th of June, 2010 with respect to vertical levels.
The WRFV3 was observed to make good estimate for the MCS and its timing which leads to heavy rainfall event over Singapore. The model produced maximum precipitation of ~5 cm over Changi airport which is very near to observation (6.4 cm recorded at Changi airport). The model simulated circulation features shows the mesoscale characteristics of the convective system. The model simulated dynamic and thermodynamic features at 00UTC of 16th June 2010 which lead to understand the structure of the MCS that caused the heavy precipitation over Singapore. In meteorology, the vertical wind shear perspective is superior to other meteorological parameters, because it establishes the kind of physical cause and effect link between storm structure and the prestorm environment that forecasters can readily apply when attempting to assess storm potential on any given day. To the degree that the prestorm environment is known and that convection is going to occur, a forecaster can simply look at the hodograph and determine to a good approximation whether there is sufficient shear over a sufficient depth to promote supercell development (e.g., 20 ms−1 of wind variation over the lowest 4–6 km above ground level). Significant vertical shear presented during the time, and it is speculated that the strong vertical Z component of wind causes the cloud burst during the event which contributed to heavy rainfall. It is to be noted that model simulations are always sensitive to the initial conditions, domain size and location, model dynamics, and physics, and hence research is still continuing to improve the prediction. From model derived results, we can conclude that the highly localized, heavy rain was the result of an interaction of synoptic-scale weather systems with the mesoscale features.
Dr. B. H. Vaid would like to express his gratitude to the WRF group, GrADs, and Ferret for online support and help. Dr. S. Y. Liong is acknowledged for the kind help and support. The author very much thankful to the reviewers and editor for valuable suggestions and comments, which really helped the author to improve the paper in the present stage.