Estuary News, April 2001

San Francisco Estuary Partnership (SFEP) | April 15th, 2001

Estuary News, December 2017

San Francisco Estuary Partnership (SFEP) | December 15th, 2017

Estuary News, December 2020

San Francisco Estuary Partnership (SFEP) | December 15th, 2020

Estuary News, February 1997

San Francisco Estuary Partnership (SFEP) | February 15th, 1997

Estuary News, March 2015

San Francisco Estuary Partnership (SFEP) | March 15th, 2015

Estuary News, March 2017

San Francisco Estuary Partnership (SFEP) | March 15th, 2017

Estuary News, September 2017

San Francisco Estuary Partnership (SFEP) | September 15th, 2017

Evaluating GFS and ECMWF Ensemble Forecasts of Integrated Water Vapor Transport along the U.S. West Coast

American Meteorological Society (AMS) | October 28th, 2022

Atmospheric rivers (ARs) are long and narrow regions in the atmosphere of enhanced integrated water vapor transport (IVT) and can produce extreme precipitation and high s

Atmospheric rivers (ARs) are long and narrow regions in the atmosphere of enhanced integrated water vapor transport (IVT) and can produce extreme precipitation and high societal impacts. Reliable and skillful forecasts of landfalling ARs in the western United States are critical to hazard preparation and aid in decision support activities, such as Forecast-Informed Reservoir Operations (FIRO). The purpose of this study is to compare the cool-season water year skill of the NCEP Global Ensemble Forecast System (GEFS) and ECMWF Ensemble Prediction System (EPS) forecasts of IVT along the U.S. West Coast for 2017–20. The skill is analyzed using probability-over-threshold forecasts of IVT magnitudes ≥ 250 kg m−1 s−1 (P250) using contingency table skill metrics in coastal Northern California and along the west coast of North America. Analysis of P250 with lead time (dProg/dt) found that the EPS provided ∼1 day of additional lead time for situational awareness over the GEFS at lead times of 6–10 days. Forecast skill analysis highlights that the EPS leads over the GEFS with success ratios 0.10–0.15 higher at lead times > 6 days for P250 thresholds of ≥25% and ≥50%, while event-based skill analysis using the probability of detection (POD) found that both models were largely similar with minor latitudinal variations favoring higher POD for each model in different locations along the coast. The relative skill of the EPS over the GEFS is largely attributed to overforecasting by the GEFS at longer lead times and an increase in the false alarm ratio.

Evaluation of Non-hydrostatic Simulations of Northeast Pacific Atmospheric Rivers and Comparison to In-situ Observations

American Meteorological Society (AMS) | August 31st, 2015

Atmospheric rivers are long, narrow bands of concentrated atmospheric water vapor transport that provide an important atmospheric linkage between the subtropics and the m

Atmospheric rivers are long, narrow bands of concentrated atmospheric water vapor transport that provide an important atmospheric linkage between the subtropics and the midlatitudes, facilitating over 90% of meridional water vapor flux and often resulting in extreme precipitation events in regions of enhanced coastal orography. In this investigation, the authors conduct continuous (3 month), large-domain (3600 km × 3200 km), high-resolution (4 km), nonhydrostatic simulations using the Weather Research and Forecasting (WRF) Model and compare the observations to previously reported dropsonde observations from the California Land-Falling Jets Experiment (CALJET) and the Pacific Land-Falling Jets Experiment (PACJET) in order to address an existing gap in knowledge regarding the ability of atmospheric models to simulate the finescale vertical and horizontal structure of atmospheric rivers. The WRF simulations reproduce key structural and thermodynamic characteristics of atmospheric rivers—including well-defined corridors of strong water vapor transport, moist-neutral stability in the lower troposphere, and strong low-level jet/water vapor transport maxima near ~1 km MSL. While WRF does generally capture the extreme values of instantaneous vertically integrated water transport—a defining feature of real-world atmospheric rivers—constituent variables exhibit biases relative to observations, including −11.2% for integrated vapor transport, +5.9% for integrated water vapor, and −17.7% for 1 km MSL wind speed. Findings suggest that high-resolution nonhydrostatic atmospheric simulations are an appropriate tool for investigating atmospheric rivers in contexts where finescale spatial structure and realistic water vapor transport maxima are important.

Evaluation of the Subseasonal Forecast Skill of Floods Associated with Atmospheric Rivers in Coastal Western U.S. Watersheds

American Meteorological Society (AMS) | May 27th, 2021

Atmospheric rivers (ARs) are responsible for up to 90% of major flood events along the U.S. West Coast. The time scale of subseasonal forecasting (from 2 weeks to 1 month

Atmospheric rivers (ARs) are responsible for up to 90% of major flood events along the U.S. West Coast. The time scale of subseasonal forecasting (from 2 weeks to 1 month) is a critical lead time for proactive mitigation of flood disasters. The NOAA Climate Testbed Subseasonal Experiment (SubX) is a research-to-operations project with almost immediate availability of forecasts. It has produced a reforecast database that facilitates evaluation of flood forecasts at these subseasonal lead times. Here, we examine the SubX-driven forecast skill of AR-related flooding out to 4-week lead using the Distributed Hydrology Soil Vegetation Model (DHSVM), with particular attention to the role of antecedent soil moisture (ASM), which modulates the relationship between meteorological and hydrological forecast skill. We study three watersheds along a transect of the U.S. West Coast: the Chehalis River basin in Washington, the Russian River basin in Northern California, and the Santa Margarita River basin in Southern California. We find that the SubX-driven flood forecast skill drops quickly after week 1, during which there is relatively high deterministic forecast skill. We find some probabilistic forecast skill relative to climatology as well as ensemble streamflow prediction (ESP) in week 2, but minimal skill in weeks 3–4, especially for annual maximum floods, notwithstanding some probabilistic skill for smaller floods in week 3. Using ESP and reverse-ESP experiments to consider the relative influence of ASM and SubX reforecast skill, we find that ASM dominates probabilistic forecast skill only for small flood events at week 1, while SubX reforecast skill dominates for large flood events at all lead times.