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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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Jason E. Nachamkin
,
Yi Jin
,
Lewis D. Grasso
, and
Kim Richardson

Abstract

Cloud-top verification is inherently difficult because of large uncertainties in the estimates of observed cloud-top height. Misplacement of cloud top associated with transmittance through optically thin cirrus is one of the most common problems. Forward radiative models permit a direct comparison of predicted and observed radiance, but uncertainties in the vertical position of clouds remain. In this work, synthetic brightness temperatures are compared with forecast cloud-top heights so as to investigate potential errors and develop filters to remove optically thin ice clouds. Results from a statistical analysis reveal that up to 50% of the clouds with brightness temperatures as high as 280 K are actually optically thin cirrus. The filters successfully removed most of the thin ice clouds, allowing for the diagnosis of very specific errors. The results indicate a strong negative bias in midtropospheric cloud cover in the model, as well as a lack of land-based convective cumuliform clouds. The model also predicted an area of persistent stratus over the North Atlantic Ocean that was not apparent in the observations. In contrast, high cloud tops associated with deep convection were well simulated, as were mesoscale areas of enhanced trade cumulus coverage in the Sargasso Sea.

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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) 1 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.

Open access
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 McAnelly and Cotton. 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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Chuyen Nguyen
,
Jason E. Nachamkin
,
David Sidoti
,
Jacob Gull
,
Adam Bienkowski
,
Rich Bankert
, and
Melinda Surratt

Abstract

Given the diversity of cloud-forcing mechanisms, it is difficult to classify and characterize all cloud types through the depth of a specific troposphere. Importantly, different cloud families often coexist even at the same atmospheric level. The Naval Research Laboratory (NRL) is developing machine learning–based cloud forecast models to fuse numerical weather prediction model and satellite data. These models were built for the dual purpose of understanding numerical weather prediction model error trends as well as improving the accuracy and sensitivity of the forecasts. The framework implements a UNet convolutional neural network (UNet-CNN) with features extracted from clouds observed by the Geostationary Operational Environmental Satellite-16 (GOES-16) as well as clouds predicted by the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS). The fundamental idea behind this novel framework is the application of UNet-CNN for separate variable sets extracted from GOES-16 and COAMPS to characterize and predict broad families of clouds that share similar physical characteristics. A quantitative assessment and evaluation based on an independent dataset for upper-tropospheric (high) clouds suggests that UNet-CNN models capture the complexity and error trends of combined data from GOES-16 and COAMPS, and also improve forecast accuracy and sensitivity for different lead time forecasts (3–12 h). This paper includes an overview of the machine learning frameworks as well as an illustrative example of their application and a comparative assessment of results for upper-tropospheric clouds.

Significance Statement

Clouds are difficult to forecast because they require, in addition to spatial location, accurate height, depth, and cloud type. Satellite imagery is useful for verifying geographical location but is limited by 2D technology. Multiple cloud types can coexist at various heights within the same pixel. In this situation, cloud/no cloud verification does not convey much information about why the forecast went wrong. Sorting clouds by physical attributes such as cloud-top height, atmospheric stability, and cloud thickness contributes to a better understanding since very different physical mechanisms produce various types of clouds. Using a fusion of numerical model outputs and GOES-16 observations, we derive variables related to atmospheric conditions that form and move the clouds for our machine learning–based cloud forecast. The resulting verification over the U.S. mid-Atlantic region revealed our machine learning–based cloud forecast corrects systematic errors associated with high atmospheric clouds and provides accurate and consistent cloud forecasts from 3 to 12 h lead times.

Open access
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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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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