Weather Predictions

The SINGV (“Singapore Variable resolution model”) Project

SINGV (“Singapore Variable resolution model”) is a multi-year project in collaboration with the UK Met Office to develop the Numerical Weather Prediction (NWP)/Nowcasting capability for Singapore.

Figure: The simulation domain of SingV.

A Cutting-edge Approach to NWP in the Tropics

SINGV (“Singapore Variable resolution model”) is a multi-year project in collaboration with the UK Met Office to develop the Numerical Weather Prediction (NWP)/Nowcasting capability for Singapore.  It adopts the latest version of the UK Met Office’s Unified Model, aiming to build a tropical convective-scale NWP/Nowcasting system that can provide improved weather forecasts for Singapore and the surrounding region.

Two Significant Future Developments in MSS’ NWP Capability Building

  1. Data Assimilation: Improves Model Initial Conditions and Forecasts

Observations are processed and quality controlled and combined with the forecasts from the NWP model using advanced (variational and ensemble) data assimilation techniques to produce the best estimate of the current state of the atmosphere as initial conditions.  These improved initial conditions will lead to better model forecasts.

  1. Ensemble Forecasting: Provides Forecast Uncertainty Estimates

A group of forecasts (called an ensemble) is run from slightly different initial conditions and/or different model configurations. These forecasts provide a representative sample of possible future weather conditions.  The uncertainty in model forecasts and probabilities of weather events can be estimated quantitatively, which has been established internationally to be the best way to include uncertainty in short term weather forecast. This is particularly the case on the convective-scale and in the tropics.

User Applications

The new forecast system will lead to improvements in severe weather warnings, provide meteorological inputs to dispersion modelling of pollutants and particulate aerosols, improve capability in risk management of weather-related activities such as water resource management and air traffic management.


Diagnostic Studies

One of the primary tools for forecasters are outputs from the global numerical models.  Although for some forecast variables, such as large scale wind patterns, the model fields can be used directly, for rainfall related variables this is generally not the case.  Diagnostic studies can help provide indicators by exploiting the dynamical knowledge of weather systems in the region.

Derived fields, such as isentropic potential vorticity, convective inhibition, depth of the mixed layer, and symmetric and antisymmetric flow components to distinguish equatorial waves, can be used to identify the environments conducive to rainfall over our region.  These studies can be used as forecast guidance as well providing a dynamical interpretation of major events.


Weather Research and Forecasting (WRF) has been serving as the driven NWP model in MSS/CCRS since 2010. The inner-most domain of the current WRF system is in 2 km spatial resolution. The initial and boundary conditions are given by NOAA GFS and ECMWF data. Local observations are ingested into the system through 3DVAR data assimilation scheme. The system provides regional forecasts in the Singapore region up to 72 hours. The current WRF system will be transited into SINGV in the coming year.