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Jared W. Marquis, Mayra I. Oyola, James R. Campbell, Benjamin C. Ruston, Carmen Córdoba-Jabonero, Emilio Cuevas, Jasper R. Lewis, Travis D. Toth, and Jianglong Zhang

Abstract

Numerical weather prediction systems depend on Hyperspectral Infrared Sounder (HIS) data, yet the impacts of dust-contaminated HIS radiances on weather forecasts has not been quantified. To determine the impact of dust aerosol on HIS radiance assimilation, we use a modified radiance assimilation system employing a one-dimensional variational assimilation system (1DVAR) developed under the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Numerical Weather Prediction–Satellite Application Facility (NWP-SAF) project, which uses the Radiative Transfer for TOVS (RTTOV). Dust aerosol impacts on analyzed temperature and moisture fields are quantified using synthetic HIS observations from rawinsonde, Micropulse Lidar Network (MPLNET), and Aerosol Robotic Network (AERONET). Specifically, a unit dust aerosol optical depth (AOD) contamination at 550 nm can introduce larger than 2.4 and 8.6 K peak biases in analyzed temperature and dewpoint, respectively, over our test domain. We hypothesize that aerosol observations, or even possibly forecasts from aerosol predication models, may be used operationally to mitigate dust induced temperature and moisture analysis biases through forward radiative transfer modeling.

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Matthew R. Kumjian, Rachel Gutierrez, Joshua S. Soderholm, Stephen W. Nesbitt, Paula Maldonado, Lorena Medina Luna, James Marquis, Kevin A. Bowley, Milagros Alvarez Imaz, and Paola Salio

Abstract

On 8 February 2018, a supercell storm produced gargantuan (>15 cm or >6 in. in maximum dimension) hail as it moved over the heavily populated city of Villa Carlos Paz in Córdoba Province, Argentina. Observations of gargantuan hail are quite rare, but the large population density here yielded numerous witnesses and social media pictures and videos from this event that document multiple large hailstones. The storm was also sampled by the newly installed operational polarimetric C-band radar in Córdoba. During the RELAMPAGO campaign, the authors interviewed local residents about their accounts of the storm and uncovered additional social media video and photographs revealing extremely large hail at multiple locations in town. This article documents the case, including the meteorological conditions supporting the storm (with the aid of a high-resolution WRF simulation), the storm’s observed radar signatures, and three noteworthy hailstones observed by residents. These hailstones include a freezer-preserved 4.48-in. (11.38 cm) maximum dimension stone that was scanned with a 3D infrared laser scanner, a 7.1-in. (18 cm) maximum dimension stone, and a hailstone photogrammetrically estimated to be between 7.4 and 9.3 in. (18.8–23.7 cm) in maximum dimension, which is close to or exceeds the world record for maximum dimension. Such a well-observed case is an important step forward in understanding environments and storms that produce gargantuan hail, and ultimately how to anticipate and detect such extreme events.

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Irina V. Djalalova, Joseph Olson, Jacob R. Carley, Laura Bianco, James M. Wilczak, Yelena Pichugina, Robert Banta, Melinda Marquis, and Joel Cline

Abstract

During the summer of 2004 a network of 11 wind profiling radars (WPRs) was deployed in New England as part of the New England Air Quality Study (NEAQS). Observations from this dataset are used to determine their impact on numerical weather prediction (NWP) model skill at simulating coastal and offshore winds through data-denial experiments. This study is a part of the Position of Offshore Wind Energy Resources (POWER) experiment, a Department of Energy (DOE) sponsored project that uses National Oceanic and Atmospheric Administration (NOAA) models for two 1-week periods to measure the impact of the assimilation of observations from 11 inland WPRs. Model simulations with and without assimilation of the WPR data are compared at the locations of the inland WPRs, as well as against observations from an additional WPR and a high-resolution Doppler lidar (HRDL) located on board the Research Vessel Ronald H. Brown (RHB), which cruised the Gulf of Maine during the NEAQS experiment. Model evaluation in the lowest 2 km above the ground shows a positive impact of the WPR data assimilation from the initialization time through the next five to six forecast hours at the WPR locations for 12 of 15 days analyzed, when offshore winds prevailed. A smaller positive impact at the RHB ship track was also confirmed. For the remaining three days, during which time there was a cyclone event with strong onshore wind flow, the assimilation of additional observations had a negative impact on model skill. Explanations for the negative impact are offered.

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Simone Lolli, James R. Campbell, Jasper R. Lewis, Yu Gu, Jared W. Marquis, Boon Ning Chew, Soo-Chin Liew, Santo V. Salinas, and Ellsworth J. Welton

Abstract

Daytime top-of-the-atmosphere (TOA) cirrus cloud radiative forcing (CRF) is estimated for cirrus clouds observed in ground-based lidar observations at Singapore in 2010 and 2011. Estimates are derived both over land and water to simulate conditions over the broader Maritime Continent archipelago of Southeast Asia. Based on bookend constraints of the lidar extinction-to-backscatter ratio (20 and 30 sr), used to solve extinction and initialize corresponding radiative transfer model simulations, relative daytime TOA CRF is estimated at 2.858–3.370 W m−2 in 2010 (both 20 and 30 sr, respectively) and 3.078–3.329 W m−2 in 2011 and over water between −0.094 and 0.541 W m−2 in 2010 and −0.598 and 0.433 W m−2 in 2011 (both 30 and 20 sr, respectively). After normalizing these estimates for an approximately 80% local satellite-estimated cirrus cloud occurrence rate, they reduce in absolute daytime terms to 2.198–2.592 W m−2 in 2010 and 2.368–2.561 W m−2 in 2011 over land and −0.072–0.416 W m−2 in 2010 and −0.460–0.333 W m−2 in 2011 over water. These annual estimates are mostly consistent despite a tendency toward lower relative cloud-top heights in 2011. Uncertainties are described. Estimates support the open hypothesis of a meridional hemispheric gradient in cirrus cloud daytime TOA CRF globally, varying from positive near the equator to presumably negative approaching the non-ice-covered poles. They help expand upon the paradigm, however, by conceptualizing differences zonally between overland and overwater forcing that differ significantly. More global oceans are likely subject to negative daytime TOA CRF than previously implied.

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James R. Campbell, Erica K. Dolinar, Simone Lolli, Gilberto J. Fochesatto, Yu Gu, Jasper R. Lewis, Jared W. Marquis, Theodore M. McHardy, David R. Ryglicki, and Ellsworth J. Welton

Abstract

Cirrus cloud daytime top-of-the-atmosphere radiative forcing (TOA CRF) is estimated for a 2-yr NASA Micro-Pulse Lidar Network (532 nm; MPLNET) dataset collected at Fairbanks, Alaska. Two-year-averaged daytime TOA CRF is estimated to be between −1.08 and 0.78 W·m−2 (from −0.49 to 1.10 W·m−2 in 2017, and from −1.67 to 0.47 W·m−2 in 2018). This subarctic study completes a now trilogy of MPLNET ground-based cloud forcing investigations, following midlatitude and tropical studies by Campbell et al. at Greenbelt, Maryland, and Lolli et al. at Singapore. Campbell et al. hypothesize a global meridional daytime TOA CRF gradient that begins as positive at the equator (2.20–2.59 W·m−2 over land and from −0.46 to 0.42 W·m−2 over ocean at Singapore), becomes neutral in the midlatitudes (0.03–0.27 W·m−2 over land in Maryland), and turns negative moving poleward. This study does not completely confirm Campbell et al., as values are not found as exclusively negative. Evidence in historical reanalysis data suggests that daytime cirrus forcing in and around the subarctic likely once was exclusively negative. Increasing tropopause heights, inducing higher and colder cirrus, have likely increased regional forcing over the last 40 years. We hypothesize that subarctic interannual cloud variability is likely a considerable influence on global cirrus cloud forcing sensitivity, given the irregularity of polar versus midlatitude synoptic weather intrusions. This study and hypothesis lay the basis for an extrapolation of these MPLNET experiments to satellite-based lidar cirrus cloud datasets.

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Robert J. Trapp, Karen A. Kosiba, James N. Marquis, Matthew R. Kumjian, Stephen W. Nesbitt, Joshua Wurman, Paola Salio, Maxwell A. Grover, Paul Robinson, and Deanna A. Hence

Abstract

On 10 November 2018, during the RELAMPAGO field campaign in Argentina, South America, a thunderstorm with supercell characteristics was observed by an array of mobile observing instruments, including three Doppler on Wheels radars. In contrast to the archetypal supercell described in the Glossary of Meteorology, the updraft rotation in this storm was rather short lived (~25 min), causing some initial doubt as to whether this indeed was a supercell. However, retrieved 3D winds from dual-Doppler radar scans were used to document a high spatial correspondence between midlevel vertical velocity and vertical vorticity in this storm, thus providing evidence to support the supercell categorization. Additional data collected within the RELAMPAGO domain revealed other storms with this behavior, which appears to be attributable in part to effects of the local terrain. Specifically, the IOP4 supercell and other short-duration supercell cases presented had storm motions that were nearly perpendicular to the long axis of the Sierras de Córdoba Mountains; a long-duration supercell case, on the other hand, had a storm motion nearly parallel to these mountains. Sounding observations as well as model simulations indicate that a mountain-perpendicular storm motion results in a relatively short storm residence time within the narrow zone of terrain-enhanced vertical wind shear. Such a motion and short residence time would limit the upward tilting, by the left-moving supercell updraft, of the storm-relative, antistreamwise horizontal vorticity associated with anabatic flow near complex terrain.

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Yelena L. Pichugina, Robert M. Banta, Joseph B. Olson, Jacob R. Carley, Melinda C. Marquis, W. Alan Brewer, James M. Wilczak, Irina Djalalova, Laura Bianco, Eric P. James, Stanley G. Benjamin, and Joel Cline

Abstract

Evaluation of model skill in predicting winds over the ocean was performed by comparing retrospective runs of numerical weather prediction (NWP) forecast models to shipborne Doppler lidar measurements in the Gulf of Maine, a potential region for U.S. coastal wind farm development. Deployed on board the NOAA R/V Ronald H. Brown during a 2004 field campaign, the high-resolution Doppler lidar (HRDL) provided accurate motion-compensated wind measurements from the water surface up through several hundred meters of the marine atmospheric boundary layer (MABL). The quality and resolution of the HRDL data allow detailed analysis of wind flow at heights within the rotor layer of modern wind turbines and data on other critical variables to be obtained, such as wind speed and direction shear, turbulence, low-level jet properties, ramp events, and many other wind-energy-relevant aspects of the flow. This study will focus on the quantitative validation of NWP models’ wind forecasts within the lower MABL by comparison with HRDL measurements. Validation of two modeling systems rerun in special configurations for these 2004 cases—the hourly updated Rapid Refresh (RAP) system and a special hourly updated version of the North American Mesoscale Forecast System [NAM Rapid Refresh (NAMRR)]—are presented. These models were run at both normal-resolution (RAP, 13 km; NAMRR, 12 km) and high-resolution versions: the NAMRR-CONUS-nest (4 km) and the High-Resolution Rapid Refresh (HRRR, 3 km). Each model was run twice: with (experimental runs) and without (control runs) assimilation of data from 11 wind profiling radars located along the U.S. East Coast. The impact of the additional assimilation of the 11 profilers was estimated by comparing HRDL data to modeled winds from both runs. The results obtained demonstrate the importance of high-resolution lidar measurements to validate NWP models and to better understand what atmospheric conditions may impact the accuracy of wind forecasts in the marine atmospheric boundary layer. Results of this research will also provide a first guess as to the uncertainties of wind resource assessment using NWP models in one of the U.S. offshore areas projected for wind plant development.

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Robert M. Banta, Yelena L. Pichugina, W. Alan Brewer, Eric P. James, Joseph B. Olson, Stanley G. Benjamin, Jacob R. Carley, Laura Bianco, Irina V. Djalalova, James M. Wilczak, R. Michael Hardesty, Joel Cline, and Melinda C. Marquis

Abstract

To advance the understanding of meteorological processes in offshore coastal regions, the spatial variability of wind profiles must be characterized and uncertainties (errors) in NWP model wind forecasts quantified. These gaps are especially critical for the new offshore wind energy industry, where wind profile measurements in the marine atmospheric layer spanned by wind turbine rotor blades, generally 50–200 m above mean sea level (MSL), have been largely unavailable. Here, high-quality wind profile measurements were available every 15 min from the National Oceanic and Atmospheric Administration/Earth System Research Laboratory (NOAA/ESRL)’s high-resolution Doppler lidar (HRDL) during a monthlong research cruise in the Gulf of Maine for the 2004 New England Air Quality Study. These measurements were compared with retrospective NWP model wind forecasts over the area using two NOAA forecast-modeling systems [North American Mesoscale Forecast System (NAM) and Rapid Refresh (RAP)]. HRDL profile measurements quantified model errors, including their dependence on height above sea level, diurnal cycle, and forecast lead time. Typical model wind speed errors were ∼2.5 m s−1, and vector-wind errors were ∼4 m s−1. Short-term forecast errors were larger near the surface—30% larger below 100 m than above and largest for several hours after local midnight (biased low). Longer-term, 12-h forecasts had the largest errors after local sunset (biased high). At more than 3-h lead times, predictions from finer-resolution models exhibited larger errors. Horizontal variability of winds, measured as the ship traversed the Gulf of Maine, was significant and raised questions about whether modeled fields, which appeared smooth in comparison, were capturing this variability. If not, horizontal arrays of high-quality, vertical-profiling devices will be required for wind energy resource assessment offshore. Such measurement arrays are also needed to improve NWP models.

Open access
James R. Campbell, David A. Peterson, Jared W. Marquis, Gilberto J. Fochesatto, Mark A. Vaughan, Sebastian A. Stewart, Jason L. Tackett, Simone Lolli, Jasper R. Lewis, Mayra I. Oyola, and Ellsworth J. Welton
Open access
James Wilczak, Cathy Finley, Jeff Freedman, Joel Cline, Laura Bianco, Joseph Olson, Irina Djalalova, Lindsay Sheridan, Mark Ahlstrom, John Manobianco, John Zack, Jacob R. Carley, Stan Benjamin, Richard Coulter, Larry K. Berg, Jeffrey Mirocha, Kirk Clawson, Edward Natenberg, and Melinda Marquis

Abstract

The Wind Forecast Improvement Project (WFIP) is a public–private research program, the goal of which is to improve the accuracy of short-term (0–6 h) wind power forecasts for the wind energy industry. WFIP was sponsored by the U.S. Department of Energy (DOE), with partners that included the National Oceanic and Atmospheric Administration (NOAA), private forecasting companies (WindLogics and AWS Truepower), DOE national laboratories, grid operators, and universities. WFIP employed two avenues for improving wind power forecasts: first, through the collection of special observations to be assimilated into forecast models and, second, by upgrading NWP forecast models and ensembles. The new observations were collected during concurrent year-long field campaigns in two high wind energy resource areas of the United States (the upper Great Plains and Texas) and included 12 wind profiling radars, 12 sodars, several lidars and surface flux stations, 184 instrumented tall towers, and over 400 nacelle anemometers. Results demonstrate that a substantial reduction (12%–5% for forecast hours 1–12) in power RMSE was achieved from the combination of improved numerical weather prediction models and assimilation of new observations, equivalent to the previous decade’s worth of improvements found for low-level winds in NOAA/National Weather Service (NWS) operational weather forecast models. Data-denial experiments run over select periods of time demonstrate that up to a 6% improvement came from the new observations. Ensemble forecasts developed by the private sector partners also produced significant improvements in power production and ramp prediction. Based on the success of WFIP, DOE is planning follow-on field programs.

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