Evaluation of GFS water vapor forecast errors during the 2009-2010 West Coast cool season using the MET/MODE object analyses package

Extreme events of any type are notoriously difficult to forecast, and extreme precipitation events due to atmospheric rivers (ARs) that impact the United States' West Coast are no exception. Today forecasters can typically provide 48-hour guidance that such an AR event is imminent. However, this guidance may spatially misplace the precipitation by a river basin and/or miss the time and duration of the event by 6 to 24 hours. In critical geographic locations the precipitation forecasts for these events are often underestimated or overestimated by inches.

Research over the last decade and a half confirms that the vast majority of West Coast cool-season extreme precipitation events are due to the landfall of intense wind-driven streams of concentrated water vapor associated with extratropical cyclones called atmospheric rivers (ARs). But accurate prediction of the effects of ARs as they come ashore depends on the accurate numeric modeling of the fields of wind and integrated water vapor (IWV) over the Northeast Pacific (NEP). Measuring and quantifying the uncertainty in these forecast fields constitutes an important step in understanding the causes of uncertainty in West Coast extreme event forecasts and is the focus of this presentation.

To this end GFS (Global Forecast System) model output obtained in real time of the relevant fields needed to calculate IWV and the vector components of the vapor flux, or integrated vapor transport (IVT), were archived and analyzed. GFS was used because it is well known, it covers our area of interest, and the output is readily available to the community. To estimate forecast uncertainties we used an object-based method that allows quantitative comparisons at a given time of object location, size, shape, and intensity of relevant features within the model output and observational fields. In particular, we used MODE, the Method for Object-based Diagnostic Evaluation. MODE is an object-based verification tool from the MET (Model Evaluation Tools) package developed and supported by the Developmental Testbed Center (DTC). This package of verification tools is readily available and intended to provide the community with a common software package incorporating the latest advances in forecast verification.

In the presentation we will describe results from two studies conducted as part of the Hydrometeorology Testbed (HMT)—DTC collaboration project. Both studies are based upon Northeast Pacific data collected during the 2009-2010 cool season.

In the first study, we focus on verifying GFS-analysis IWV with satellite-observed IWV. Specifically, IWV GFS analysis objects are compared with 12-hour composite, satellite-derived Special Sensor Microwave/Imager (SSM/I) observational objects. Then we incorporate MODE object attributes related to object location, size, shape and intensity into metrics that quantify the degree of agreement between the analyses and the observations.

In the second study we look not only at IWV but also the field crucial to making quality forecasts of AR associated precipitation events, the integrated vapor transport field (IVT). Since observed wind fields needed for calculating IVT were not readily available, we worked entirely with model data, letting the GFS analysis fields serve as the observations against which 24, 48, 72, and 96 hour lead-time GFS forecasts are compared. As above, certain MODE object attributes are used to create metrics, but this time the metrics are used, for both IWV and IVT, to quantify the degree of agreement between analysis and forecast objects with respect to location, size, shape and intensity. Although the entire season has not yet been verified, results from a quick look at IWV object location error for five days in January 2010 found median centroid displacements of about 100 km for 24 hour forecasts and 200 km, or twice as large, for 96 hour forecasts, supporting the importance of location error's role in reducing forecast accuracy.