The Geostationary Operational Environmental Satellite (GOES)-R convective initiation (CI) algorithm predicts CI in real time over the next 0-60 min. While GOES-R CI has been very successful in tracking nascent clouds and obtaining cloud-top growth and height characteristics relevant to CI in an object-tracking framework, its performance has been hindered by elevated false-alarm rates, and it has not optimally combined satellite observations with other valuable data sources. Presented here are two statistical learning approaches that incorporate numerical weather prediction (NWP) input within the established GOES-R CI framework to produce probabilistic forecasts: logistic regression (LR) and an artificial-intelligence approach known as random forest (RF). Both of these techniques are used to build models that are based on an extensive database of CI events and nonevents and are evaluated via cross validation and on independent case studies. With the proper choice of probability thresholds, both the LR and RF techniques incorporating NWP data produce substantially fewer false alarms than when only GOES data are used. The NWP information identifies environmental conditions (as favorable or unfavorable) for the development of convective storms and improves the skill of the CI nowcasts that operate on GOES-based cloud objects, as compared with when the satellite IR fields are used alone. The LR procedure performs slightly better overall when 14 skill measures are used to quantify the results and notably better on independent case study days.

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Operational forecasters have a variety of new research products and tools to interrogate precipitation systems for different environments and precipitation regimes. One such product is satellite-derived, columntotal precipitable water retrieved in discrete layers as an experimental product developed by the Cooperative Institute for Research in the Atmosphere (CIRA), and transitioned by the National Aeronautics and Space Administration's Short-term Prediction, Research, and Transition Center to numerous weather forecast offices (WFOs) to address specific forecast issues. In 2013 the CIRA layered precipitable water (LPW) product was formally assessed by National Weather Service WFOs in Alaska, the West Coast of the United States, and San Juan, Puerto Rico. Forecasters used LPW to address forecast challenges associated with atmospheric rivers, convective storms, and other types of precipitation events across diverse forecasting domains ranging from marine zones to complex topography. This paper describes the use of LPW by operational forecasters at their WFOs and shows the impact LPW had on precipitation forecasting, as determined by assessment results. During 72 formal user feedback submissions and multiple assessment periods, 62.5% of forecasters had high confidence in LPW. Fifty percent stated that LPW had a "large" impact on their decision process, and another 22.2% said LPW had "some" impact. For 76.4% of the events surveyed, forecasters stated that LPW had "large" to "very large" value over traditional total precipitable water products. Individual case examples will provide a context for forecasters' evaluation of the product in their county warning area.

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