This interactive climate model is based on seasonal forecast data sets from Copernicus. In the climate forecasting space, anomalies are often the most frequently examined data. It helps us to understand how much warmer, cooler, drier, or wetter than normal an area may be. Therefore, this climate model looks at forecast anomalies through the Copernicus sea surface temperature anomaly(SSTA) data. I used the python CDSAPI to access the climate data store on Copernicus website.
Anomalies
This model looks at the forecast air temperature anomaly, in Celsius, for the month of March 2019. Red colors show warmer-than-average temperatures while blue indicates below-average temperatures.
The model also examines precipitation rate anomaly in mm/second for the month of March 2019. Negative values (blues) indicate below-normal monthly precipitation while positive values (yellows, oranges) indicate above-normal monthly precipitation.
The interactive map can be found here https://olanrewajumuili.github.io/MyClimMod.Website/