Systems and methods for prediction of decrease in sea surface temperature

A system including one or more processors coupled to memory, the memory loaded with computer instructions to predict a decrease in average sea surface temperature, the instructions, when executed on the processors, implement actions comprising:

• setting, by a processor, a value of a first Boolean flag to true when at least one of an eclipse season start date and an eclipse season end date in a particular calendar year occurs in a first predefined number of days preceding a timestamp for a particular partition in a plurality of partitions segmenting the particular calendar year;
• training a machine learning model, wherein the training comprises comparing an output of the machine learning model with ground truth labels;
• predicting, using the trained machine learning model, a probability of decrease in average sea surface temperature relative to a historical average sea surface temperature for a predefined time interval in the particular calendar year wherein the predicting is based at least in part on inputting, to the trained machine learning model, the value of the first Boolean flag;
• and
• invoking an external system to predict occurrence of an extreme climate event in the predefined time interval when the probability of decrease in average sea surface temperature is above a first predefined threshold.

A non-transitory computer readable storage medium impressed with computer program instructions to predict a decrease in average sea surface temperature, the instructions, when executed on a processor, implement a method, comprising:

• setting, by the processor, a value of a first Boolean flag to true when at least one of an eclipse season start date and an eclipse season end date in a particular calendar year occurs in a first predefined number of days preceding a timestamp for a particular partition in a plurality of partitions segmenting the particular calendar year;
• training a machine learning model, wherein the training comprises comparing an output of the machine learning model with ground truth labels;
• predicting, using the trained machine learning model, a probability of decrease in average sea surface temperature relative to a historical average sea surface temperature for a predefined time interval in the particular calendar year, wherein the predicting is based at least in part on inputting, to the trained machine learning model, the value of the first Boolean flag;
• and
• invoking an external system to predict occurrence of an extreme climate event in the predefined time interval when the probability of decrease in average sea surface temperature is above a first predefined threshold.