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Ming Liu, Jason E. Nachamkin, and Douglas L. Westphal

Abstract

Fu–Liou’s delta-four-stream (with a two-stream option) radiative transfer model has been implemented in the U.S. Navy’s Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) to calculate solar and thermal infrared fluxes in 6 shortwave and 12 longwave bands. The model performance is evaluated at high resolution for clear-sky and overcast conditions against the observations from the Southern Great Plains of the Atmospheric Radiation Measurement Program. In both cases, use of the Fu–Liou model provides significant improvement over the operational implementation of the standard Harshvardhan radiation parameterization in both shortwave and longwave fluxes. A sensitivity study of radiative flux on clouds reveals that the choices of cloud effective radius schemes for ice and liquid water are critical to the flux calculation due to the effects on cloud optical properties. The sensitivity study guides the selection of optimal cloud optical properties for use in the Fu–Liou parameterization as implemented in COAMPS. The new model is then used to produce 3-day forecasts over the continental United States for a winter and a summer month. The verifications of parallel runs using the standard and new parameterizations show that Fu–Liou dramatically reduces the model’s systematic warm bias in the upper troposphere in both winter and summer. The resultant cooling modifies the atmospheric stability and moisture transport, resulting in a significant reduction in the upper-tropospheric wet bias. Overall ice and liquid water paths are also reduced. At the surface, Fu–Liou reduces the negative temperature and sea level pressure biases by providing more accurate radiative heating rates to the land surface model. The error reductions increase with forecast length as the impact of improved radiative fluxes accumulates over time. A combination of the two- and four-stream options results in major computational efficiency gains with minimal loss in accuracy.

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Kevin J. Dougherty, John D. Horel, and Jason E. Nachamkin

Abstract

Precipitation forecasts from the High-Resolution Rapid Refresh model (HRRR) of the National Centers for Environmental Prediction (NCEP) and the Navy’s Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) are examined during heavy precipitation periods in California. Precipitation forecast discrepancies between the two models are examined during a recent heavy winter precipitation episode in California from 6 to 8 December 2019. The skill of initial 12-h precipitation forecasts is examined objectively from 1 December 2018 to 28 February 2019 from the HRRR, COAMPS, and NCEP’s North American Mesoscale Forecast System (NAM-3km). The HRRR exhibited lower seasonal biases and higher skill based on several metrics applied to a sample of 48 12-h periods during California’s second wettest winter season during the past 20 years. Overall, the NAM-3km and COAMPS exhibited a large wet bias over the interior mountain regions while the HRRR model indicated a dry bias along the northern coastal region. All models tended to underestimate precipitation along the coastal mountains of Northern California. To highlight the regional and localized nature of forecast skill, the fraction skill score (FSS) metric is applied across ranges of spatial scales and precipitation values. For the domain as a whole, the HRRR had higher precipitation forecast skill compared to the other two models, particularly within radial distances of 20–30 km and moderate (10–50 mm) precipitation totals. FSS computed locally highlights the HRRR’s overall higher skill as well as enhanced skill in the southern half of the state.

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Ray L. McAnelly, Jason E. Nachamkin, William R. Cotton, and Melville E. Nicholls

Abstract

The development of two small mesoscale convective systems (MCSs) in northeastern Colorado is investigated via dual-Doppler radar analysis. The first system developed from several initially isolated cumulonimbi, which gradually coalesced into a minimal MCS with relatively little stratiform precipitation. The second system developed more rapidly along an axis of convection and generated a more extensive and persistent stratiform echo and MCS cloud shield. In both systems, the volumetric precipitation rate exhibited an early meso-β-scale convective cycle (a maximum and subsequent minimum), followed by reintensification into a modest mature stage. This sequence is similar to that noted previously in the developing stage of larger MCSs by . They speculated that the early meso-β convective cycle is a characteristic feature of development in many MCSs that is dynamically linked to a rather abrupt transition toward mature stage structure. This study presents kinematic evidence in support of this hypothesis for these cases, as derived from dual-Doppler radar analyses over several-hour periods. Mature stage MCS characteristics such as deepened low- to midlevel convergence and mesoscale descent developed fairly rapidly, about 1 h after the early meso-β convective maximum.

The dynamic linkage between the meso-β convective cycle and evolution toward mature structure is examined with a simple analytical model of the linearized atmospheric response to prescribed heating. Heating functions that approximate the temporal and spatial characteristics of the meso-β convective cycle are prescribed. The solutions show that the cycle forces a response within and near the thermally forced region that is consistent with the observed kinematic evolution in the MCSs. The initial response to an intensifying convective ensemble is a self-suppressing mechanism that partially explains the weakening after a meso-β convective maximum. A lagged response then favors reintensification and areal growth of the weakened ensemble. A conceptual model of MCS development is proposed whereby the early meso-β convective cycle and the response to it are hypothesized to act as a generalized forcing–feedback mechanism that helps explain the upscale growth of a convective ensemble into an organized MCS.

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Jason E. Nachamkin, John Cook, Mike Frost, Daniel Martinez, and Gary Sprung

Abstract

Lagrangian parcel models are often used to predict the fate of airborne hazardous material releases. The atmospheric input for these integrations is typically supplied by surrounding surface and upper-air observations. However, situations may arise in which observations are unavailable and numerical model forecasts may be the only source of atmospheric data. In this study, the quality of the atmospheric forecasts for use in dispersion applications is investigated as a function of the horizontal grid spacing of the atmospheric model. The Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) was used to generate atmospheric forecasts for 14 separate Dipole Pride 26 trials. The simulations consisted of four telescoping one-way nested grids with horizontal spacings of 27, 9, 3, and 1 km, respectively. The 27- and 1-km forecasts were then used as input for dispersion forecasts using the Hazard Prediction Assessment Capability (HPAC) modeling system. The resulting atmospheric and dispersion forecasts were then compared with meteorological and gas-dosage observations collected during Dipole Pride 26. Although the 1-km COAMPS forecasts displayed considerably more detail than those on the 27-km grid, the RMS and bias statistics associated with the atmospheric observations were similar. However, statistics from the HPAC forecasts showed the 1-km atmospheric forcing produced more accurate trajectories than the 27-km output when compared with the dosage measurements.

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Craig H. Bishop, Teddy R. Holt, Jason Nachamkin, Sue Chen, Justin G. McLay, James D. Doyle, and William T. Thompson

Abstract

A computationally inexpensive ensemble transform (ET) method for generating high-resolution initial perturbations for regional ensemble forecasts is introduced. The method provides initial perturbations that (i) have an initial variance consistent with the best available estimates of initial condition error variance, (ii) are dynamically conditioned by a process similar to that used in the breeding technique, (iii) add to zero at the initial time, (iv) are quasi-orthogonal and equally likely, and (v) partially respect mesoscale balance constraints by ensuring that each initial perturbation is a linear sum of forecast perturbations from the preceding forecast. The technique is tested using estimates of analysis error variance from the Naval Research Laboratory (NRL) Atmospheric Variational Data Assimilation System (NAVDAS) and the Navy’s regional Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) over a 3-week period during the summer of 2005. Lateral boundary conditions are provided by a global ET ensemble. The tests show that the ET regional ensemble has a skillful mean and a useful spread–skill relationship in mass, momentum, and precipitation variables. Diagnostics indicate that ensemble variance was close to, but probably a little less than, the forecast error variance for wind and temperature variables, while precipitation ensemble variance was significantly smaller than precipitation forecast error variance.

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Jason E. Nachamkin, Adam Bienkowski, Rich Bankert, Krishna Pattipati, David Sidoti, Melinda Surratt, Jacob Gull, and Chuyen Nguyen

Abstract

A physics-based cloud identification scheme, originally developed for a machine-learning forecast system, was applied to verify cloud location and coverage bias errors from two years of 6-h forecasts. The routine identifies stable and unstable environments by assessing the potential for buoyant versus stable cloud formation. The efficacy of the scheme is documented by investigating its ability to identify cloud patterns and systematic forecast errors. Results showed that stable cloud forecasts contained widespread, persistent negative cloud cover biases most likely associated with turbulent, radiative, and microphysical feedback processes. In contrast, unstable clouds were better predicted despite being poorly resolved. This suggests that scale aliasing, while energetically problematic, results in less-severe short-term cloud cover errors. This study also evaluated Geostationary Operational Environmental Satellite (GOES) cloud-base retrievals for their effectiveness at identifying regions of lower-tropospheric cloud cover. Retrieved cloud-base heights were sometimes too high with respect to their actual values in regions of deep-layered clouds, resulting in underestimates of the extent of low cloud cover in these areas. Sensitivity experiments indicate that the most accurate cloud-base estimates existed in regions with cloud tops at or below 8 km.

Significance Statement

Cloud forecasts are difficult to verify because the height, depth, and type of the clouds are just as important as the spatial location. Satellite imagery and retrievals are good for verifying location, but these measurements are sometimes uncertain because of obscuration from above. Despite these uncertainties, we can learn a lot about specific forecast errors by tracking general areas of clouds based on their physical forcing mechanisms. We chose to sort by atmospheric stability because buoyant and stable processes are physically very distinct. Studies of this nature exist, but they typically assess mean cloud frequencies without considering spatial and temporal displacements. Here, we address displacement error by assessing the direct overlap between the observed and predicted clouds.

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Yi Jin, Shouping Wang, Jason Nachamkin, James D. Doyle, Gregory Thompson, Lewis Grasso, Teddy Holt, Jon Moskaitis, Hao Jin, Richard M. Hodur, Qingyun Zhao, Ming Liu, and Mark DeMaria

Abstract

The impact of ice phase cloud microphysical processes on prediction of tropical cyclone environment is examined for two microphysical parameterizations using the Coupled Ocean–Atmosphere Mesoscale Prediction System–Tropical Cyclone (COAMPS-TC) model. An older version of microphysical parameterization is a relatively typical single-moment scheme with five hydrometeor species: cloud water and ice, rain, snow, and graupel. An alternative newer method uses a hybrid approach of double moment in cloud ice and rain and single moment in the other three species. Basin-scale synoptic flow simulations point to important differences between these two schemes. The upper-level cloud ice concentrations produced by the older scheme are up to two orders of magnitude greater than the newer scheme, primarily due to differing assumptions concerning the ice nucleation parameterization. Significant (1°–2°C) warm biases near the 300-hPa level in the control experiments are not present using the newer scheme. The warm bias in the control simulations is associated with the longwave radiative heating near the base of the cloud ice layer. The two schemes produced different track and intensity forecasts for 15 Atlantic storms. Rightward cross-track bias and positive intensity bias in the control forecasts are significantly reduced using the newer scheme. Synthetic satellite imagery of Hurricane Igor (2010) shows more realistic brightness temperatures from the simulations using the newer scheme, in which the inner core structure is clearly discernible. Applying the synthetic satellite imagery in both quantitative and qualitative analyses helped to pinpoint the issue of excessive upper-level cloud ice in the older scheme.

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David D. Flagg, James D. Doyle, Teddy R. Holt, Daniel P. Tyndall, Clark M. Amerault, Daniel Geiszler, Tracy Haack, Jonathan R. Moskaitis, Jason Nachamkin, and Daniel P. Eleuterio

Abstract

The Trident Warrior observational field campaign conducted off the U.S. mid-Atlantic coast in July 2013 included the deployment of an unmanned aerial system (UAS) with several payloads on board for atmospheric and oceanic observation. These UAS observations, spanning seven flights over 5 days in the lowest 1550 m above mean sea level, were assimilated into a three-dimensional variational data assimilation (DA) system [the Naval Research Laboratory Atmospheric Variational Data Assimilation System (NAVDAS)] used to generate analyses for a numerical weather prediction model [the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS)] with a coupled ocean model [the Naval Research Laboratory Navy Coastal Ocean Model (NCOM)]. The impact of the assimilated UAS observations on short-term atmospheric prediction performance is evaluated and quantified. Observations collected from 50 radiosonde launches during the campaign adjacent to the UAS flight paths serve as model forecast verification. Experiments reveal a substantial reduction of model bias in forecast temperature and moisture profiles consistently throughout the campaign period due to the assimilation of UAS observations. The model error reduction is most substantial in the vicinity of the inversion at the top of the model-estimated boundary layer. Investigations reveal a consistent improvement to prediction of the vertical position, strength, and depth of the boundary layer inversion. The relative impact of UAS observations is explored further with experiments of systematic denial of data streams from the NAVDAS DA system and removal of individual measurement sources on the UAS platform.

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Jerome M. Schmidt, Piotr J. Flatau, Paul R. Harasti, Robert. D. Yates, David J. Delene, Nicholas J. Gapp, William J. Kohri, Jerome R. Vetter, Jason E. Nachamkin, Mark G. Parent, Joshua D. Hoover, Mark J. Anderson, Seth Green, and James E. Bennett

Abstract

Descriptions of the experimental design and research highlights obtained from a series of four multiagency field projects held near Cape Canaveral, Florida, are presented. The experiments featured a 3 MW, dual-polarization, C-band Doppler radar that serves in a dual capacity as both a precipitation and cloud radar. This duality stems from a combination of the radar’s high sensitivity and extremely small-resolution volumes produced by the narrow 0.22° beamwidth and the 0.543 m along-range resolution. Experimental highlights focus on the radar’s real-time aircraft tracking capability as well as the finescale reflectivity and eddy structure of a thin nonprecipitating stratus layer. Examples of precipitating storm systems focus on the analysis of the distinctive and nearly linear radar reflectivity signatures (referred to as “streaks”) that are caused as individual hydrometeors traverse the narrow radar beam. Each streak leaves a unique radar reflectivity signature that is analyzed with regard to estimating the underlying particle properties such as size, fall speed, and oscillation characteristics. The observed along-streak reflectivity oscillations are complex and discussed in terms of diameter-dependent drop dynamics (oscillation frequency and viscous damping time scales) as well as radar-dependent factors governing the near-field Fresnel radiation pattern and inferred drop–drop interference.

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Masashi Nagata, Lance Leslie, Yoshio Kurihara, Russell L. Elsberry, Masanori Yamasaki, Hirotaka Kamahori, Robert Abbey Jr., Kotaro Bessho, Javier Calvo, Johnny C. L. Chan, Peter Clark, Michel Desgagne, Song-You Hong, Detlev Majewski, Piero Malguzzi, John McGregor, Hiroshi Mino, Akihiko Murata, Jason Nachamkin, Michel Roch, and Clive Wilson

The Third Comparison of Mesoscale Prediction and Research Experiment (COMPARE) workshop was held in Tokyo, Japan, on 13–15 December 1999, cosponsored by the Japan Meteorological Agency (JMA), Japan Science and Technology Agency, and the World Meteorological Organization. The third case of COMPARE focuses on an event of explosive tropical cyclone [Typhoon Flo (9019)] development that occurred during the cooperative three field experiments, the Tropical Cyclone Motion experiment 1990, Special Experiment Concerning Recurvature and Unusual Motion, and TYPHOON-90, conducted in the western North Pacific in August and September 1990. Fourteen models from nine countries have participated in at least a part of a set of experiments using a combination of four initial conditions provided and three horizontal resolutions. The resultant forecasts were collected, processed, and verified with analyses and observational data at JMA. Archived datasets have been prepared to be distributed to participating members for use in further evaluation studies.

In the workshop, preliminary conclusions from the evaluation study were presented and discussed in the light of initiatives of the experiment and from the viewpoints of tropical cyclone experts. Initial conditions, depending on both large-scale analyses and vortex bogusing, have a large impact on tropical cyclone intensity predictions. Some models succeeded in predicting the explosive deepening of the target typhoon at least qualitatively in terms of the time evolution of central pressure. Horizontal grid spacing has a very large impact on tropical cyclone intensity prediction, while the impact of vertical resolution is less clear, with some models being very sensitive and others less so. The structure of and processes in the eyewall clouds with subsidence inside as well as boundary layer and moist physical processes are considered important in the explosive development of tropical cyclones. Follow-up research activities in this case were proposed to examine possible working hypotheses related to the explosive development.

New strategies for selection of future COMPARE cases were worked out, including seven suitability requirements to be met by candidate cases. The VORTEX95 case was withdrawn as a candidate, and two other possible cases were presented and discussed.

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