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Anders A. Jensen
,
James O. Pinto
,
Sean C. C. Bailey
,
Ryan A. Sobash
,
Glen Romine
,
Gijs de Boer
,
Adam L. Houston
,
Suzanne W. Smith
,
Dale A. Lawrence
,
Cory Dixon
,
Julie K. Lundquist
,
Jamey D. Jacob
,
Jack Elston
,
Sean Waugh
,
David Brus
, and
Matthias Steiner

Abstract

Uncrewed aircraft system (UAS) observations from the Lower Atmospheric Profiling Studies at Elevation–A Remotely-Piloted Aircraft Team Experiment (LAPSE-RATE) field campaign were assimilated into a high-resolution configuration of the Weather Research and Forecasting (WRF) Model. The impact of assimilating targeted UAS observations in addition to surface observations was compared to that obtained when assimilating surface observations alone using observing system experiments (OSEs) for a terrain-driven flow case and a convection initiation (CI) case observed within Colorado’s San Luis Valley (SLV). The assimilation of UAS observations in addition to surface observations results in a clear increase in skill for both flow regimes over that obtained when assimilating surface observations alone. For the terrain-driven flow case, the UAS observations improved the representation of thermal stratification across the northern SLV, which produced stronger upvalley flow over the eastern half of the SLV that better matched the observations. For the CI case, the UAS observations improved the representation of the pre-convective environment by reducing dry biases across the SLV and over the surrounding terrain. This led to earlier CI and more organized convection over the foothills that spilled outflows into the SLV, ultimately helping to increase low-level convergence and CI there. In addition, the importance of UAS capturing an outflow that originated over the Sangre de Cristo Mountains and triggered CI is discussed. These outflows and subsequent CI were not well captured in the simulation that assimilated surface observations alone. Observations obtained with a fleet of UAS are shown to notably improve high-resolution analyses and short-term predictions of two very different mesogamma-scale weather events.

Free access
J. C. Dietrich
,
J. J. Westerink
,
A. B. Kennedy
,
J. M. Smith
,
R. E. Jensen
,
M. Zijlema
,
L. H. Holthuijsen
,
C. Dawson
,
R. A. Luettich Jr.
,
M. D. Powell
,
V. J. Cardone
,
A. T. Cox
,
G. W. Stone
,
H. Pourtaheri
,
M. E. Hope
,
S. Tanaka
,
L. G. Westerink
,
H. J. Westerink
, and
Z. Cobell

Abstract

Hurricane Gustav (2008) made landfall in southern Louisiana on 1 September 2008 with its eye never closer than 75 km to New Orleans, but its waves and storm surge threatened to flood the city. Easterly tropical-storm-strength winds impacted the region east of the Mississippi River for 12–15 h, allowing for early surge to develop up to 3.5 m there and enter the river and the city’s navigation canals. During landfall, winds shifted from easterly to southerly, resulting in late surge development and propagation over more than 70 km of marshes on the river’s west bank, over more than 40 km of Caernarvon marsh on the east bank, and into Lake Pontchartrain to the north. Wind waves with estimated significant heights of 15 m developed in the deep Gulf of Mexico but were reduced in size once they reached the continental shelf. The barrier islands further dissipated the waves, and locally generated seas existed behind these effective breaking zones.

The hardening and innovative deployment of gauges since Hurricane Katrina (2005) resulted in a wealth of measured data for Gustav. A total of 39 wind wave time histories, 362 water level time histories, and 82 high water marks were available to describe the event. Computational models—including a structured-mesh deepwater wave model (WAM) and a nearshore steady-state wave (STWAVE) model, as well as an unstructured-mesh “simulating waves nearshore” (SWAN) wave model and an advanced circulation (ADCIRC) model—resolve the region with unprecedented levels of detail, with an unstructured mesh spacing of 100–200 m in the wave-breaking zones and 20–50 m in the small-scale channels. Data-assimilated winds were applied using NOAA’s Hurricane Research Division Wind Analysis System (H*Wind) and Interactive Objective Kinematic Analysis (IOKA) procedures. Wave and surge computations from these models are validated comprehensively at the measurement locations ranging from the deep Gulf of Mexico and along the coast to the rivers and floodplains of southern Louisiana and are described and quantified within the context of the evolution of the storm.

Full access
Anders A. Jensen
,
James O. Pinto
,
Sean C. C. Bailey
,
Ryan A. Sobash
,
Gijs de Boer
,
Adam L. Houston
,
Phillip B. Chilson
,
Tyler Bell
,
Glen Romine
,
Suzanne W. Smith
,
Dale A. Lawrence
,
Cory Dixon
,
Julie K. Lundquist
,
Jamey D. Jacob
,
Jack Elston
,
Sean Waugh
, and
Matthias Steiner

Abstract

Uncrewed aircraft system (UAS) observations collected during the 2018 Lower Atmospheric Process Studies at Elevation—a Remotely Piloted Aircraft Team Experiment (LAPSE-RATE) field campaign were assimilated into a high-resolution configuration of the Weather Research and Forecasting Model using an ensemble Kalman filter. The benefit of UAS observations was assessed for a terrain-driven (drainage and upvalley) flow event that occurred within Colorado’s San Luis Valley (SLV) using independent observations. The analysis and prediction of the strength, depth, and horizontal extent of drainage flow from the Saguache Canyon and the subsequent transition to upvalley and up-canyon flow were improved relative to that obtained both without data assimilation (benchmark) and when only surface observations were assimilated. Assimilation of UAS observations greatly improved the analyses of vertical variations in temperature, relative humidity, and winds at multiple locations in the northern portion of the SLV, with reductions in both bias and the root-mean-square error of roughly 40% for each variable relative to the benchmark run. Despite these noted improvements, some biases remain that were tied to measurement error and/or the impact of the boundary layer parameterization on vertically spreading the observations, both of which require further exploration. The results presented here highlight how observations obtained with a fleet of profiling UAS improve limited-area, high-resolution analyses and short-term forecasts in complex terrain.

Full access