|Title|| APCC: APEC Climate Center held the Final Workshop for the Tonga Climate Prediction Project|
|Date||2017-05-12 PM 1:10:57||Hit||295|
The APEC Climate Center (APCC) held the Tonga Climate Prediction Project Final Workshop to mark the completion of the Tonga Climate Prediction Project. This one-year project, initiated in February 2016, seeks to decrease Tonga’s vulnerability to climate-induced risks. Tonga Meteorological Service (TMS) staff that are heavily involved in the daily seasonal prediction operations travelled to attend the Final Workshop held at the APCC headquarters, located in Busan, South Korea from April 10 to 13, 2017.
Reliable seasonal forecast information regarding temperature, rainfall, and cyclones is critical for the lives and safety of the local Tongan residents. Tonga, like many other countries in the Pacific Islands region, mostly relies on agriculture, fisheries, and tourism for their economy, which are climate-sensitive sectors. TMS delivers this important information through self-produced seasonal information. However, TMS struggles with providing Tongan citizens with reliable seasonal prediction information during Tonga’s dry season.
APCC concluded the Tonga Climate Prediction Project this year by developing a dynamical-statistical downscaling method for generating more reliable seasonal predictions as well as seasonal-to-subseasonal forecast information in Tonga. It is expected that if TMS is able to implement the methods identified through this project, TMS will be able to deliver more reliable seasonal prediction information, allowing the Tongan government and the local residents to effectively prepare for climate change by engaging in more effective resource management of water and agriculture.
During the Final Workshop, the APCC project team shared the outcomes of the project regarding the best methods to improve seasonal forecasts in Tonga. The project team also identified the possible systems that could be developed to help improve the seasonal forecasts that utilize the methods discovered through the implementation of the project. With this information, TMS will be able to decide if the development of this type of system to improve the reliability of seasonal forecasts is a high priority and develop a potential follow up project for the system development.
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