Subseasonal Climate Forecasts
Subseasonal Climate Forecasts: Unparalleled Insight
The World Climate Service is a powerful suite of online tools for subseasonal forecasting. The subseasonal forecast range spans the period from 2 weeks to 6 weeks in advance, and entails significant challenges. The WCS provides the tools needed to create, understand, and assess these difficult predictions.Subseasonal Climate Forecasts: Why Use World Climate Service
Commodity trading desk meteorologists using World Climate Service gain multiple benefits. They include: 1) Stay informed with efficient, intuitive access to market-moving multi-model forecasts for weeks 2-6 
World Climate Service customers have access to a powerful and intuitive map interface for viewing subseasonal climate forecast maps from multiple
dynamical models, including zoom and forecast progression functionality.
CFSv2 forecasts to week 6 are updated daily, and a proprietary multi-model blend is available.
2) Understand forecast confidence and risk with calibrated probability forecasts 
World Climate Service probability forecasts are rigorously calibrated using a long history of model reforecasts and observations, and comprehensive information about model skill is provided. Probability and skill information allows users to understand and track model performance, manage risk appropriately, and develop valuable decision systems.
3) Monitor and understand impacts of critical subseasonal climate drivers The World Climate Service provides tools to monitor, understand, and anticipate the key phenomena that affect subseasonal climate fluctuations.
Overview - World Climate Service
The World Climate Service is a web-based information service providing:
- Subseasonal Climate Forecasts - Forecasts of climate and weather conditions from 2 weeks to 6 weeks in advance
- Seasonal Climate Forecasts - Forecasts of climate conditions from 1 month to 6 months in advance

The World Climate Service provides reliable, skillful, hype-free long lead forecasting services. We achieve this by:
- Taking advantage of both dynamical and statistical prediction capabilities
- Emphasizing probabilistic information and quantitative forecast confidence for decision-making
- Creating optimal blends of the information used for prediction
- Providing transparency through on-demand forecast verification and skill statistics
- Predicting and explaining the phenomena and known influences driving the forecast
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