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Jerry Y. Harrington, Michael P. Meyers, Robert L. Walko, and William R. Cotton

Abstract

Observational data collected during the FIRE II experiment showing the existence of bimodal ice spectra along with experimental evidence of the size dependence of riming are utilized in the development of a bimodal ice spectrum parameterization for use in the RAMS model. Two ice classes are defined: pristine ice and snow, each described by a separate, complete gamma distribution function. Pristine ice is small ice consisting of particles with mean sizes less than 125 µm, while snow is the large class consisting of particles greater than 125 µm. Analytical equations are formulated for the conversion between the ice classes by vapor depositional growth (sublimation). During ice subsaturated conditions, a number concentration sink is parameterized for all ice species. The performance of the parameterizations in a simple parcel model is discussed and evaluated against an explicit Lagrangian parcel microphysical model.

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Temple R. Lee, Michael Buban, David D. Turner, Tilden P. Meyers, and C. Bruce Baker

Abstract

The High-Resolution Rapid Refresh (HRRR) model became operational at the National Centers for Environmental Prediction (NCEP) in 2014 but the HRRR’s performance over certain regions of the coterminous United States has not been well studied. In the present study, we evaluated how well version 2 of the HRRR, which became operational at NCEP in August 2016, simulates the near-surface meteorological fields and the surface energy balance at two locations in northern Alabama. We evaluated the 1-, 3-, 6-, 12-, and 18-h HRRR forecasts, as well as the HRRR’s initial conditions (i.e., the 0-h initial fields) using meteorological and flux observations obtained from two 10-m micrometeorological towers installed near Belle Mina and Cullman, Alabama. During the 8-month model evaluation period, from 1 September 2016 to 30 April 2017, we found that the HRRR accurately simulated the observations of near-surface air and dewpoint temperature (R 2 > 0.95). When comparing the HRRR output with the observed sensible, latent, and ground heat flux at both sites, we found that the agreement was weaker (R 2 ≈ 0.7), and the root-mean-square errors were much larger than those found for the near-surface meteorological variables. These findings help motivate the need for additional work to improve the representation of surface fluxes and their coupling to the atmosphere in future versions of the HRRR to be more physically realistic.

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X. Lin, K. G. Hubbard, E. A. Walter-Shea, J. R. Brandle, and G. E. Meyer

Abstract

Air temperature measurement has inherent biases associated with the particular radiation shield and sensor deployed. The replacement of the Cotton Region Shelter (CRS) with the Maximum–Minimum Temperature System (MMTS) and the introduction of Automated Surface Observing System (ASOS) air temperature observing systems during the NWS modernization introduced bias shifts in federal networks that required quantification. In rapidly developing nonfederal networks, the Gill shield temperature systems are widely used. All of these systems house an air temperature sensor in a radiation shield to prevent radiation loading on the sensors; a side effect is that the air temperature entering a shield is modified by interior solar radiation, infrared radiation, airspeed, and heat conduction to or from the sensor so that the shield forms its own interior microclimate. The objectives of this study are to develop an energy balance model to evaluate the microclimate inside the ASOS, MMTS, Gill, and CRS shields, including the interior solar radiation, infrared radiation, and airspeed effects on air (sensor) temperature under day and night conditions. For all radiation shields, the model air temperature for shield effects was in good agreement between shields while the uncorrected “normal operating” temperatures were more variable from shield to shield. The solar radiation loading ratio was dramatically increased with a corresponding increase in the solar elevation angle for all shields except the ASOS shield, and are ranked as Gill > MMTS ≈ CRS > ASOS. The daytime infrared radiation effects on air temperature were ranked as ASOS > Gill > MMTS > CRS, but the nighttime infrared radiation effects were not so large and were uniformly distributed among negative and positive effects on air temperatures. For the nonaspirated radiation shields (MMTS, Gill, and CRS), increasing ambient wind speed improved the accuracy of air temperatures, but it was impossible to reach the accuracy claimed by manufacturers when the in situ measurements were taken under lower ambient wind speed (<4 ∼ 5 m s−1).

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Matthew R. Kumjian, Yvette P. Richardson, Traeger Meyer, Karen A. Kosiba, and Joshua Wurman

Abstract

Two of the “Doppler on Wheels” facility radars (DOW6 and DOW7) have been upgraded to dual-polarization capabilities and operate at two closely spaced X-band frequencies. For particles with sizes that are large relative to the wavelength, resonance scattering effects may lead to differences in the backscattered radiation between these two frequencies. This study investigates the utility of dual-frequency, dual-polarization DOW radars for hail detection and sizing. T-matrix scattering calculations at the two X-band DOW7 frequencies reveal that dual-frequency differences in the radar reflectivity factors at horizontal polarization (Δλ Z H) and differential reflectivities (Δλ Z DR) exist for hailstones, whereas negligible differences exist for raindrops. These differences are enhanced for wet or melting hailstones. Further, these dual-frequency differences may be positive or negative, thereby defining four distinct quadrants in the Δλ Z H–Δλ Z DR parameter space that occur for narrow bands of hail sizes. DOW7 data from two hail-bearing storms are analyzed: one produced only small hail, and the other produced severe hail up to ~3.8 cm in diameter. The analysis reveals dual-frequency signals that are consistent with the scattering calculations for those sizes, including consistent changes in the signatures below the melting layer in the first storm as hailstones acquire more liquid meltwater and a shift in the Δλ Z H–Δλ Z DR parameter space over time as the second storm grew upscale and hail sizes decreased. Implications for further applications and suggestions about closely spaced dual-frequency observations at other wavelengths are discussed.

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N. Voisin, M. Kintner-Meyer, D. Wu, R. Skaggs, T. Fu, T. Zhou, T. Nguyen, and I. Kraucunas

Abstract

The increasing interconnectedness of energy and water systems makes it important to understand how interannual variations in water availability—and climate oscillations—could potentially impact the electric grid operations. The authors assess the vulnerability of the current western U.S. grid to historical climate variability using multiple energy and water system models. A 55-yr-long natural water availability benchmark is combined with the 2010 level of water demand from an integrated assessment model to drive a large-scale water management model over the western United States. The regulated flow at hydropower and thermoelectric power plants is then translated into boundary conditions for electricity generation in a production cost model. This analysis focuses on assessing regional interdependencies and the impact of interannual changes in water availability on power system operations, including reliability, cost, and carbon emissions. Results for August grid operations—when stress on the grid is often highest—show a range of sensitivity in production cost (–8% to +11%) and carbon emissions (–7% to +11%), as well as a 1-in-10 chance that electricity demand will exceed estimated supply. The authors also show that operating costs are lower under neutral El Niño–Southern Oscillation (ENSO) conditions than under other ENSO phases; carbon emissions are highest under La Niña conditions, especially in California; and the risk of brownouts may be higher under neutral and negative ENSO conditions. These results help characterize the grid’s performance under historical climate variations, are useful for seasonal and multiyear planning of joint water–electricity management, and can be used to support impact, adaptation, and vulnerability analyses.

Open access
Caroline C. Ummenhofer, Alexander Sen Gupta, Peter R. Briggs, Matthew H. England, Peter C. McIntosh, Gary A. Meyers, Michael J. Pook, Michael R. Raupach, and James S. Risbey

Abstract

The relative influences of Indian and Pacific Ocean modes of variability on Australian rainfall and soil moisture are investigated for seasonal, interannual, and decadal time scales. For the period 1900–2006, observations, reanalysis products, and hindcasts of soil moisture during the cool season (June–October) are used to assess the impacts of El Niño–Southern Oscillation (ENSO) and the Indian Ocean dipole (IOD) on southeastern Australia and the Murray–Darling Basin, two regions that have recently suffered severe droughts. A distinct asymmetry is found in the impacts of the opposite phases of both ENSO and IOD on Australian rainfall and soil moisture. There are significant differences between the dominant drivers of drought at interannual and decadal time scales. On interannual time scales, both ENSO and the IOD modify southeastern Australian soil moisture, with the driest (wettest) conditions over the southeast and more broadly over large parts of Australia occurring during years when an El Niño and a positive IOD event (La Niña and a negative IOD event) co-occur. The atmospheric circulation associated with these responses is discussed. Lower-frequency variability over southeastern Australia, however, including multiyear drought periods, seems to be more robustly related to Indian Ocean temperatures than Pacific conditions. The frequencies of both positive and negative IOD events are significantly different during periods of prolonged drought compared to extended periods of “normal” rainfall. In contrast, the frequency of ENSO events remains largely unchanged during prolonged dry and wet periods. For the Murray–Darling Basin, there appears to be a significant influence by La Niña and both positive and negative IOD events. In particular, La Niña plays a much more prominent role than for more southern regions, especially on interannual time scales and during prolonged wet periods. For prolonged dry (wet) periods, positive IOD events also occur in unusually high (low) numbers.

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Jordan R. Bell, Esayas Gebremichael, Andrew L. Molthan, Lori A. Schultz, Franz J. Meyer, Christopher R. Hain, Suravi Shrestha, and K. Cole Payne

Abstract

The normalized difference vegetation index (NDVI) has been frequently used to map hail damage to vegetation, especially in agricultural areas, but observations can be blocked by cloud cover during the growing season. Here, the European Space Agency’s Sentinel-1A/1B C-band synthetic aperture radar (SAR) imagery in co- and cross polarization is used to identify changes in backscatter of corn and soybeans damaged by hail during intense thunderstorm events in the early and late growing season. Following a June event, hail-damaged areas produced a lower mean backscatter when compared with surrounding, unaffected pixels [vertical–vertical (VV): −1.1 dB; vertical–horizontal (VH): −1.5 dB]. Later, another event in August produced an increase in co- and cross-polarized backscatter (VV: 0.7 dB; VH: 1.7 dB) that is hypothesized to result from the combined effects of crop growth, change in structure of damaged crops, and soil moisture conditions. Hail damage regions inferred from changes in backscatter were further assessed through coherence change detections to support changes in the structure of crops damaged within the hail swath. While studies using NDVI have routinely concluded a decrease in NDVI is associated with damage, the cause of change with respect to the damaged areas in SAR backscatter values is more complex. Influences of environmental variables, such as vegetation structure, vegetation maturity, and soil moisture conditions, need to be considered when interpreting SAR backscatter and will vary throughout the growing season.

Free access
Caroline C. Ummenhofer, Alexander Sen Gupta, Peter R. Briggs, Matthew H. England, Peter C. McIntosh, Gary A. Meyers, Michael J. Pook, Michael R. Raupach, and James S. Risbey
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Howard J. Diamond, Thomas R. Karl, Michael A. Palecki, C. Bruce Baker, Jesse E. Bell, Ronald D. Leeper, David R. Easterling, Jay H. Lawrimore, Tilden P. Meyers, Michael R. Helfert, Grant Goodge, and Peter W. Thorne

The year 2012 marks a decade of observations undertaken by the U.S. Climate Reference Network (USCRN) under the auspices of NOAA's National Climatic Data Center and Atmospheric Turbulence and Diffusion Division. The network consists of 114 sites across the conterminous 48 states, with additional sites in Alaska and Hawaii. Stations are installed in open (where possible), rural sites very likely to have stable land-cover/use conditions for several decades to come. At each site a suite of meteorological parameters are monitored, including triple redundancy for the primary air temperature and precipitation variables and for soil moisture/temperature. Instrumentation is regularly calibrated to National Institute for Standards and Technology (NIST) standards and maintained by a staff of expert engineers. This attention to detail in USCRN is intended to ensure the creation of an unimpeachable record of changes in surface climate over the United States for decades to come. Data are made available without restriction for all public, private, and government use. This article describes the rationale for the USCRN, its implementation, and some of the highlights of the first decade of operations. One critical use of these observations is as an independent data source to verify the existing U.S. temperature record derived from networks corrected for nonhomogenous histories. Future directions for the network are also discussed, including the applicability of USCRN approaches for networks monitoring climate at scales from regional to global. Constructive feedback from end users will allow for continued improvement of USCRN in the future and ensure that it continues to meet stakeholder requirements for precise climate measurements.

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Volker Wulfmeyer, David D. Turner, B. Baker, R. Banta, A. Behrendt, T. Bonin, W. A. Brewer, M. Buban, A. Choukulkar, E. Dumas, R. M. Hardesty, T. Heus, J. Ingwersen, D. Lange, T. R. Lee, S. Metzendorf, S. K. Muppa, T. Meyers, R. Newsom, M. Osman, S. Raasch, J. Santanello, C. Senff, F. Späth, T. Wagner, and T. Weckwerth

Abstract

Forecast errors with respect to wind, temperature, moisture, clouds, and precipitation largely correspond to the limited capability of current Earth system models to capture and simulate land–atmosphere feedback. To facilitate its realistic simulation in next-generation models, an improved process understanding of the related complex interactions is essential. To this end, accurate 3D observations of key variables in the land–atmosphere (L–A) system with high vertical and temporal resolution from the surface to the free troposphere are indispensable.

Recently, we developed a synergy of innovative ground-based, scanning active remote sensing systems for 2D to 3D measurements of wind, temperature, and water vapor from the surface to the lower troposphere that is able to provide comprehensive datasets for characterizing L–A feedback independently of any model input. Several new applications are introduced, such as the mapping of surface momentum, sensible heat, and latent heat fluxes in heterogeneous terrain; the testing of Monin–Obukhov similarity theory and turbulence parameterizations; the direct measurement of entrainment fluxes; and the development of new flux-gradient relationships. An experimental design taking advantage of the sensors’ synergy and advanced capabilities was realized for the first time during the Land Atmosphere Feedback Experiment (LAFE), conducted at the Atmospheric Radiation Measurement Program Southern Great Plains site in August 2017. The scientific goals and the strategy of achieving them with the LAFE dataset are introduced. We envision the initiation of innovative L–A feedback studies in different climate regions to improve weather forecast, climate, and Earth system models worldwide.

Open access