Subseasonal to Seasonal (S2S) Experimental Forecasts

A multi-institutional collaboration sponsored by California DWR

Click here to view the latest CW3E S2S Outlook

– Archive of previous S2S Outlooks

Subseasonal Forecasts (Weeks 1-6)

Dynamical Model Atmospheric River Activity Forecasts

A multi-model experimental forecast for AR activity (defined as the # of AR days per week) at week-1, 2, and 3 lead time is shown below for the NCEP dynamical model.

This product was developed in collaboration with NASA’s Jet Propulsion Laboratory.

Weeks 1-2: Shading indicates the odds of AR activity for each day. ARs are defined using the Guan and Waliser (2015) algorithm and probability is calculated by the number of ensemble members predicting an AR at each grid point at 00 UTC on the given forecast day. Click on a panel to open in a new tab, click on title to open seven day panel plot.

Week 3: The top row shows the forecast number of AR days during week-3; the middle row shows the climatological values of AR activity in each model’s hindcast record for the week-3 verification period; the bottom row shows the departure of the AR activity forecast for that same verification period (top panel forecast minus middle panel climatology). For this row, blue values represent higher than average AR activity predicted during week-3; red values represent lower than average AR activity predicted during week-3. Grey rectangles surround grid cells where >75% of forecast ensemble members agree on the sign of the AR activity anomaly with respect to climatology. These regions can be interpreted as having higher confidence in their prediction of week-3 AR activity. The hindcast skill assessment associated with the NCEP, ECMWF, and ECCC hindcast systems is described in DeFlorio et al. 2019b.

Forecast Model:  

Week 1
Week 2
Week 3

Dynamical Model S2S Ridging Forecasts

An atmospheric ridge is defined as elongated area of relatively high atmospheric pressure (American Meteorological Society; 2020). Persistent ridging events play an important role in determining if and where precipitation falls. Ridging forecasts are shown below for lead-times of weeks 1 and 2, weeks 3 and 4 and weeks 5 and 6. These forecasts are for three different ‘ridge types’ that have been shown in recent research [Gibson et al. 2020a; more methodology information here] to strongly influence atmospheric river and precipitation likelihood across the US West Coast. The North-ridge type is typically associated with widespread dry conditions across the entire US West. The South and West-ridge types are typically associated with dry conditions across Southern California and the Colorado River Basin but can also increase the likelihood of wet conditions across the Pacific Northwest. The hindcast skill assessment associated with this outlook is provided in Gibson et al. 2020b.

This product was developed in collaboration with NASA’s Jet Propulsion Laboratory.

Forecast (left): The left panel shows the occurrence frequency of each ridge type (bars) compared to climatology (horizontal line) for each of the model ensemble members. If over 50% of the ensemble members predict more ridging than expected (for this time of year) then the right panel maps are displayed indicating the likelihood of wetter or drier conditions based on how these ridge types typically influence precipitation.

Methodology (right): Atmospheric conditions associated with each ridge type: 500 hPa geopotential height (contours), IVT (shading and vectors; left panel), and precipitation (shading; right panel). For more information on methodology click on the image.

This product was designed and has been tested only during the winter months, this product will be produced and available again starting October 1, 2023.

Sub-seasonal Forecast of North American Weather Regimes

Forecast from IRI.

The graphic shows forecasts of large scale meteorological patterns, issued every day during winter (along x-axis) by the NCEP CFSv2 model. The four large scale “weather regimes” are the West Coast Ridge (reds), Pacific Ridge (blues), Pacific Trough (greens), and Greenland High (yellows), each of which leads to distinct precipitation and temperature conditions over the West. Darker colors indicate more confident forecasts, which run diagonally up the chart from 0 to 45 days ahead. This product was developed with support from NOAA’s Next Generation Global Prediction System (NGGPS) program and the California Department of Water Resources. Please see for the precipitation and temperature signatures of the regimes or Robertson, Vigaud, Yuan & Tippett (2020) for additional details.

This product was designed and has been tested only during the winter months, this product will be produced and available again starting October 1, 2023.

Seasonal Forecasts (Beyond week 6)

During February 2023, negative SST anomalies over central and east-central Pacific Ocean showed weak La Niña conditions; above-normal SST was present in the eastern equatorial and west Pacific. La Niña favors below-normal seasonal precipitation in the far southwestern United States (e.g. over SoCal, Arizona, and New Mexico) and wetter conditions over the Pacific Northwest. The current CCA forecast for Southern California shows dry conditions while for north Sierra Nevada and the Northwest a wet outlook is projected. The current CCA forecast deviates from the canonical pattern since wet conditions extend southward over North and even Central California. The CCA model skill is significant mainly over the far southwestern tier of the desert Southwest where anomalous dryness is predicted. The main canonical patterns contributing to this precipitation forecast is La Niña and the long-term warming trend over the tropical and north-western Pacific Ocean observed over the last several decades. This warming trend accounts for the non-canonical elements of the outlook.

Seasonal Precipitation Forecast based on a Machine Learning Approach

This CW3E forecast product predicts likely seasonal precipitation anomaly patterns for January-February-March over the western U.S. based on machine learning models and dynamical ensemble predictions. Four different machine learning models (Random Forest, XGBoost, LSTM, and neural networks) were pretrained based on learning relationships between remote ocean and atmospheric patterns and characteristic seasonal precipitation anomalies over the Western United States. Instead of training on observations, the machine learning models were here trained on a large climate model ensemble, which enabled more robust relationships to be established due to the very large sample size in the training phase (Gibson et al. 2021). Seasonal forecast skill is also enhanced by focusing on four characteristic large-scale precipitation patterns across the Western United States. Our historical skill assessment found that the probability of a correct forecast improves when the Machine learning models show stronger consensus for a particular pattern. As such, the ensemble agreement (or lack of) provides useful guidance on forecast uncertainty.

This product was developed in collaboration with NASA’s Jet Propulsion Laboratory.

This product was designed and has been tested only during the winter months, this product will be produced and available again in early November 2023.

For additional products and more information visit the Odds of Reaching 100% of Normal Water Year Precipitation webpage.

All products displayed on this page are considered experimental.