The Seasonal Forecaster’s Holy Grail
When launched back in 2004, the Climate Forecast System or CFS, drew excitement from the climate science community. Dr. Suranjana Saha, of the Environmental Modeling Center at NOAA writes, “The CFS provides important advances in operational seasonal prediction on a number of fronts. For the first time in the history of U.S. operational seasonal prediction, a dynamical modeling system has demonstrated a level of skill in forecasting U.S. surface temperature and precipitation that is comparable to the skill of the statistical methods used by the NCEP Climate Prediction Center (CPC).” The significance here, she goes on to write, is a “overall improvement in the operation of seasonal forecasts.”
An Amalgam of Insight:
Dr. Saha does right to point out CFS’s unique character. It is a merger of atmospheric modeling from Global Forecast System (GFS) and oceanic modeling from the GFDL Modular Ocean Model version 3 (MOM3). The two components, together, encompass observations from variety of different vantage points including surface observations, upper air balloon observations, aircraft observations, and satellite observations.
While weather forecasts beyond two weeks are generally considered unreliable, the comprehensive nature of CFS makes it more an exception to the rule. This is especially true for anyone working in domains subject to high uncertainty, like insurance and agriculture. CFS is a particularly good resource for those intent on ensemble forecasting.
Ensemble forecasting is a traditional technique in medium range (up to 10 days) weather forecasts, seasonal forecasts and climate modelling. By changing initial conditions or model parameters, a range of forecasts is created that differ from each other slightly. This is due to the chaotic nature of fluid dynamics (which weather modelling is a subset of). For weather forecasting, the ensemble is usually created by small changes in initial conditions, but for seasonal forecasts, it is much easier to just take real initial conditions every 6-hours.
This week, the one and only Andres Luhamaa provides us with an example of how one can use the Planet OS Datahub API to merge 9-month forecasts started at different initial times, into a single ensemble forecast. Check out Andres’s example of how to use CFS in our Github notebook.
As a quick and final note, Andres’s example only concerns precipitation, a number of other variables like pressure, wind and temperature can all be investigated. If you are interested in variables that we have not already provided, please do drop us a line, we love hearing how our users plan to use the Datahub!