CFAN, Climate Forecast Applications Network, LLC.

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 Telephone:  USA  +1.404.803.2012

 Email:  info@cfanclimate.com 

NOAA SBIR Project

CFAN has been awarded a Phase II grant from the NOAA SBIR program, entitled Probabilistic subseasonal weather forecasts for the energy and agriculture sector.
 

Under this project, CFAN is developing advanced solutions that address the challenges of providing probabilistic predictions on subseasonal (weeks 3 to 4) timescales.  The expected outcomes are innovations in ensemble interpretation and calibration suitable for subseasonal time scales that provide the basis for web-based decision support tools for the energy and agricultural sectors.

 

Technical abstract.  This proposal addresses the challenge of providing business-relevant subseasonal forecasts for the energy and agricultural sectors, including applications to renewable energy. An innovative multi-model prediction system using the CFSv2 and ECMWF forecasts will be developed to exploit the advantages of each model. An interactive web-based dashboard system is designed to display and deliver the forecast information in a flexible and dynamic manner to aid decision support integration.  A comprehensive assessment of predictability of business-relevant variables by region, initial and target month, and atmospheric flow regimes provides the basis for assessing the confidence of individual forecasts and for identifying forecast ‘windows of opportunity’. An ensemble calibration scheme uses predictability assessment, reforecasts and recent forecast errors to correct for model bias error and to improve the shape of the ensemble distribution. Advanced ensemble interpretation techniques support scenario predictions of extreme events. A strategy for assessing confidence of each forecast is based on a comprehensive forecast evaluation, predictability assessment, and ensemble characteristics

 

Further information (link to pdf)

 

We are seeking collaborators, partners and clients to help develop these products in a way that optimizes their utility for the energy and agricultural sectors.