scholarly journals Planning of Optimized Irrigation Decision in Weather to Extended Range using Weather Forecast with a Coupled Framework of Optimization and Ecohydrological Model

2021 ◽  
Author(s):  
Adrija Roy
2016 ◽  
Vol 31 (3) ◽  
pp. 697-711 ◽  
Author(s):  
D. Hudson ◽  
A. G. Marshall ◽  
O. Alves ◽  
G. Young ◽  
D. Jones ◽  
...  

Abstract There has been increasing demand in Australia for extended-range forecasts of extreme heat events. An assessment is made of the subseasonal experimental guidance provided by the Bureau of Meteorology’s seasonal prediction system, Predictive Ocean Atmosphere Model for Australia (POAMA, version 2), for the three most extreme heat events over Australia in 2013, which occurred in January, March, and September. The impacts of these events included devastating bushfires and damage to crops. The outlooks performed well for January and September, with forecasts indicating increased odds of top-decile maximum temperature over most affected areas at least one week in advance for the fortnightly averaged periods at the start of the heat waves and for forecasts of the months of January and September. The March event was more localized, affecting southern Australia. Although the anomalously high sea surface temperature around southern Australia in March (a potential source of predictability) was correctly forecast, the forecast of high temperatures over the mainland was restricted to the coastline. September was associated with strong forcing from some large-scale atmospheric climate drivers known to increase the chance of having more extreme temperatures over parts of Australia. POAMA-2 was able to forecast the sense of these drivers at least one week in advance, but their magnitude was weaker than observed. The reasonably good temperature forecasts for September are likely due to the model being able to forecast the important climate drivers and their teleconnection to Australian climate. This study adds to the growing evidence that there is significant potential to extend and augment traditional weather forecast guidance for extreme events to include longer-lead probabilistic information.


MAUSAM ◽  
2021 ◽  
Vol 69 (1) ◽  
pp. 29-44
Author(s):  
N. CHATTOPADHYAY ◽  
K. V. RAO ◽  
A. K. SAHAI ◽  
R. BALASUBRAMANIAN ◽  
D. S. PAI ◽  
...  

2016 ◽  
Vol E99.C (1) ◽  
pp. 143-146
Author(s):  
Roger Yubtzuan CHEN ◽  
Zong-Yi YANG ◽  
Hongchin LIN

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