264
H.M. Beggs
15.8.2 TIR Requirements
The World Meteorological Organisation (WMO) goals for spatial resolution and
accuracy of the SST analyses used as a boundary condition for global NWP models are 5 km and 0.3 ◦ C (Eyre et al., 2009). However, accurate ultra-high-resolution
(1 km) SST is becoming more important for regional weather forecasting as regional
NWP models are produced at higher spatial resolutions. Chelton et al. (2007)
showed that using coarse resolution SST analyses as the boundary condition for
regional weather forecast models does not properly portray the fluxes of heat and
moisture from the ocean that drive the formation of low level clouds and precipitation over the ocean. High resolution SST data products (particularly those
combining both IR and MW inputs) preserve SST gradients more effectively compared to analysis systems that rely on cloudy infrared and limited in-situ sources.
A detailed analysis of SST and its diurnal cycle is therefore sometimes needed
locally, in the case of important precipitation events which are very dependent on
evaporation over the ocean. 11
15.9 Seasonal and Interannual Forecasting
Several operational centres routinely issue seasonal forecasts of the Earth’s climate
using coupled ocean-atmosphere models, which require near-real-time knowledge
of the state of the global ocean (Balmaseda et al., 2009). Seasonal forecasting systems are based on coupled ocean-atmosphere general circulation models that predict
SSTs and their impact on atmospheric circulation. The aim of seasonal forecasts is to
predict climate anomalies (e.g. temperature, rainfall, frequency of tropical cyclones)
for the forthcoming seasons (Balmaseda et al., 2009). The strongest relationship
between SST patterns and seasonal weather trends are found in tropical regions.
The horizontal resolution of operational seasonal prediction models are typically of
the order of 1 ◦ due to computational constraints (Balmaseda et al., 2008).
15.9.1 Use of TIR
Global SST analysis products are commonly used to initialize operational seasonal
forecast models. Several operational seasonal prediction models use the global,
weekly, 1 ◦ resolution, Reynolds OI v2 SST analysis of in-situ and GAC AVHRR
SST data (Reynolds and Smith, 1994; Reynolds et al., 2002), either by itself or
combined with the monthly, 1 ◦ resolution, in-situ SST and sea-ice HadISST analysis
(Balmaseda et al., 2008).
11 http://www.wmo.int/pages/prog/sat/documents/SoG-Regional-NWP.doc
H.M. Beggs
15.8.2 TIR Requirements
The World Meteorological Organisation (WMO) goals for spatial resolution and
accuracy of the SST analyses used as a boundary condition for global NWP models are 5 km and 0.3 ◦ C (Eyre et al., 2009). However, accurate ultra-high-resolution
(1 km) SST is becoming more important for regional weather forecasting as regional
NWP models are produced at higher spatial resolutions. Chelton et al. (2007)
showed that using coarse resolution SST analyses as the boundary condition for
regional weather forecast models does not properly portray the fluxes of heat and
moisture from the ocean that drive the formation of low level clouds and precipitation over the ocean. High resolution SST data products (particularly those
combining both IR and MW inputs) preserve SST gradients more effectively compared to analysis systems that rely on cloudy infrared and limited in-situ sources.
A detailed analysis of SST and its diurnal cycle is therefore sometimes needed
locally, in the case of important precipitation events which are very dependent on
evaporation over the ocean. 11
15.9 Seasonal and Interannual Forecasting
Several operational centres routinely issue seasonal forecasts of the Earth’s climate
using coupled ocean-atmosphere models, which require near-real-time knowledge
of the state of the global ocean (Balmaseda et al., 2009). Seasonal forecasting systems are based on coupled ocean-atmosphere general circulation models that predict
SSTs and their impact on atmospheric circulation. The aim of seasonal forecasts is to
predict climate anomalies (e.g. temperature, rainfall, frequency of tropical cyclones)
for the forthcoming seasons (Balmaseda et al., 2009). The strongest relationship
between SST patterns and seasonal weather trends are found in tropical regions.
The horizontal resolution of operational seasonal prediction models are typically of
the order of 1 ◦ due to computational constraints (Balmaseda et al., 2008).
15.9.1 Use of TIR
Global SST analysis products are commonly used to initialize operational seasonal
forecast models. Several operational seasonal prediction models use the global,
weekly, 1 ◦ resolution, Reynolds OI v2 SST analysis of in-situ and GAC AVHRR
SST data (Reynolds and Smith, 1994; Reynolds et al., 2002), either by itself or
combined with the monthly, 1 ◦ resolution, in-situ SST and sea-ice HadISST analysis
(Balmaseda et al., 2008).
11 http://www.wmo.int/pages/prog/sat/documents/SoG-Regional-NWP.doc