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Richard P. James and Paul M. Markowski

Abstract

A three-dimensional cloud model was used to investigate the sensitivity of deep convective storms to dry air above the cloud base. In simulations of both quasi-linear convective systems and supercells, dry air aloft was found to reduce the intensity of the convection, as measured by updraft mass flux and total condensation and rainfall. In high-CAPE line-type simulations, the downdraft mass flux and cold pool strength were enhanced at the rear of the trailing stratiform region in a drier environment. However, the downdraft and cold pool strengths were unchanged in the convective region, and were also unchanged or reduced in simulations of supercells and of line-type systems at lower CAPE. This result contrasts with previous interpretations of the role of dry air aloft in the development of severe low-level outflow winds.

The buoyancy-sorting framework is used to interpret the influence of environmental humidity on the updraft entrainment process and the observed strong dependence on the environmental CAPE. The reduction in convective vigor caused by dry air is relatively inconsequential at very high CAPE, but low-CAPE convection requires a humid environment in order to grow by entrainment.

The simulated responses of the downdraft and cold pool intensities to dry air aloft reflected the changes in diabatic cooling rates within the downdraft formation regions. When dry air was present, the decline in hydrometeor mass exerted a negative tendency on the diabatic cooling rates and acted to offset the favorable effects of dry air for cooling by evaporation. Thus, with the exception of the rearward portions of the high-CAPE line-type simulations, dry air was unable to strengthen the downdrafts and cold pool.

A review of the literature demonstrates that observational evidence does not unambiguously support the concept that dry air aloft favors downdraft and outflow strength. It is also shown that the use of warm rain microphysics in previous modeling studies may have reinforced the tendency to overemphasize the role of dry air aloft.

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James E. Peak and Paul M. Tag

Abstract

A significant task in the automated interpretation of cloud features on satellite imagery is the segmentation of the image into separate cloud features to be identified. A new technique, hierarchical threshold segmentation (HTS), is presented. In HTS, region boundaries are defined over a range of gray-shade thresholds. The hierarchy of the spatial relationships between collocated regions from different thresholds is represented in tree form. This tree is pruned, using a neural network, such that the regions of appropriate sizes and shapes are isolated. These various regions from the pruned tree are then collected to form the final segmentation of the entire image.

In segmentation testing using Geostationary Operational Environmental Satellite data, HTS selected 94% of 101 dependent sample pruning points correctly, and 93% of 105 independent sample pruning points. Using Advanced Very High Resolution Radiometer data, HTS correctly selected 90% of both the 235-case dependent sample and the 253-case independent sample pruning points.

The strength of this approach is that artificial intelligence, that is, reasoning about the sizes and shapes of the emergent regions, is applied during the segmentation process. The neural network component can be trained to respond more favorably to shapes of interest to a particular analysis problem.

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Paul M. Tag and James E. Peak

Abstract

In recent years, the field of artificial intelligence has contributed significantly to the science of meteorology, most notably in the now familiar form of expert systems. Expert systems have focused on rules or heuristics by establishing, in computer code, the reasoning process of a weather forecaster predicting, for example, thunderstorms or fog. In addition to the years of effort that goes into developing such a knowledge base is the time-consuming task of extracting such knowledge and experience from experts. In this paper, the induction of rules directly from meteorological data is explored-a process called machine learning. A commercial machine learning program called C4.5, is applied to a meteorological problem, forecasting maritime fog, for which a reliable expert system has been previously developed. Two detasets are used: 1) weather ship observations originally used for testing and evaluating the expert system, and 2) buoy measurements taken off the coast of California. For both datasets, the rules produced by C4.5 are reasonable and make physical sense, thus demonstrating that an objective induction approach can reveal physical processes directly from data. For the ship database, the machine-generated rules are not as accurate as those from the expert system but are still significantly better than persistence forecasts. For the buoy data, the forecast accuracies are very high, but only slightly superior to persistence. The results indicate that the machine learning approach is a viable tool for developing meteorological expertise, but only when applied to reliable data with sufficient cases of known outcome. In those instances when such databases are available, the use of machine learning can provide useful insight that otherwise might take considerable human analysis to produce.

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James E. Peak and Paul M. Tag

The U.S. Navy has plans to develop an automated system to analyze satellite imagery aboard its ships at sea. Lack of time for training, in combination with frequent personnel rotations, precludes the building of extensive imagery interpretation expertise by shipboard personnel. A preliminary design starts from pixel data from which clouds are classified. An image segmentation is performed to assemble and isolate cloud groups, which are then identified (e.g., as a cold front) using neural networks. A combination of neural networks and expert systems is subsequently used to transform key information about the identified cloud patterns as inputs to an expert system that provides sensible weather information, the ultimate objective of the imagery analysis.

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James E. Peak and Paul M. Tag

Abstract

An Expert system for Shipboard Obscuration Prediction (AESOP), an artificial intelligence approach to forecasting maritime visibility obscurations, has been designed, developed, and tested. The problem-solving model for AESOP, running within an IBM-PC environment, is rule-based, uses backward chaining, and has meta-rules; a user, in a consultation session, answers questions about certain atmospheric parameters. The current version, AESOP 2.0, has 232 rules and has been designed in terms of nowcasts (0–1 h) and forecasts (1–6 h). An extensive explanation feature allows the user to understand the reasoning process behind a particular forecast. AESOP has been evaluated against 83 test cases, in which clear, hazy, or foggy conditions are predicted. The overall performance of AESOP is 75% correct. This value indicates considerable forecast skill when compared to 47% for persistence and 41% for random chance. When the distinction between clear and haze is ignored, the expert system correctly forecasts 84% of the “Fog/No fog” situations.

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Paul T. Beaudoin, David M. Legler, and James J. O'Brien

Abstract

This study examines ERS-1 3-day repeat orbit scatterometer wind data from January through March 1992. The study region encompasses the North Pacific from 30° to 50°N and 160°E to 130°W longitude. The data are separated by orbit trajectory and binned to 26 km. These data are examined by direct comparative analysis to surface European Centre for Medium-Range Weather Forecasts (ECMWF) model analyses on daily, monthly, and 3-month timescales. The scatterometer wind fields compare favorably, but distinct, nonisolated differences exist. These differences, exhibited in the scatterometer winds, include slightly stronger wind speeds, more distinct curvature, and detail on structures smaller than the ECMWF resolution. Systematic relative northward displacements of cyclonic centers, 1°–3° in latitude, in ECMWF surface winds are also indicated. The scatterometer wind retrieval algorithm (CMODFD/NSCAT MLE) demonstrates some difficulty in selecting the true wind vector. Problems are generally identifiable by inspection. Complex empirical orthogonal function (EOF) analysis on the ascending and descending scatterometer wind fields reveal frequency and amplitude information about the sampled variance. The first four EOFs, for which the results suggest physically motivated phenomena, account for 50%–60% of the total variance sampled in the data. The EOF results partition the sampled variance in the ascending and descending data and suggest the more significant EOFs depict spatiotemporal “bands” of 18–21, 8–10, and 6–8 days, reflecting the planetary wave cycle. large-scale general circulation systems, and smaller-scale storm structures, respectively. The partitioning of the variance demonstrates only limited filtering capability in identifying erroneous ERS-1 wind vectors.

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Paul B. Bogner, Gary M. Barnes, and James L. Franklin

Abstract

One hundred and thirty Omega dropwindsondes deployed within 500-km radius of the eye of six North Atlantic hurricanes are used to determine the magnitudes and trends in convective available potential energy, and 10–1500-m and 0–6-km shear of the horizontal wind as a function of radius, quadrant, and hurricane intensity.

The moist convective instability found at large radii (400–500 km) decreases to near neutral stability by 75 km from the eyewall. Vertical shears increase as radius decreases, but maximum shear values are only one-half of those found over land. Scatter for both the conditional instability and the shear is influenced chiefly by hurricane intensity, but proximity to reflectivity features does modulate the pattern. The ratio of the conditional instability to the shear (bulk Richardson number) indicates that supercell formation is favored within 250 km of the circulation center, but helicity values are below the threshold to support strong waterspouts.

The difference between these oceanic observations and those made over land by other researchers is evidence for significant modification of the vertical profile of the horizontal wind in a hurricane at landfall.

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Richard P. James, Paul M. Markowski, and J. Michael Fritsch

Abstract

Bow echo development within quasi-linear convective systems is investigated using a storm-scale numerical model. A strong sensitivity to the ambient water vapor mixing ratio is demonstrated. Relatively dry conditions at low and midlevels favor intense cold-air production and strong cold pool development, leading to upshear-tilted, “slab-like” convection for various magnitudes of convective available potential energy (CAPE) and low-level shear. High relative humidity in the environment tends to reduce the rate of production of cold air, leading to weak cold pools and downshear-tilted convective systems, with primarily cell-scale three-dimensionality in the convective region. At intermediate moisture contents, long-lived, coherent bowing segments are generated within the convective line. In general, the scale of the coherent three-dimensional structures increases with increasing cold pool strength.

The bowing lines are characterized in their developing and mature stages by segments of the convective line measuring 15–40 km in length over which the cold pool is much stronger than at other locations along the line. The growth of bow echo structures within a linear convective system appears to depend critically on the local strengthening of the cold pool to the extent that the convection becomes locally upshear tilted. A positive feedback process is thereby initiated, allowing the intensification of the bow echo. If the environment favors an excessively strong cold pool, however, the entire line becomes uniformly upshear tilted relatively quickly, and the along-line heterogeneity of the bowing line is lost.

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Paul J. Roebber, James M. Frederick, and Thomas P. DeFelice

Abstract

Persistent low overcast conditions, defined as continuous overcast conditions (100% cloud cover) with ceiling heights at or below 2 km for a minimum of 5 days, are found to occur in the cold season in the U.S. upper Midwest on average slightly more often than once every two years. These occurrences are associated with two primary large-scale circulation patterns. Most commonly, the midlatitude westerlies are split across North America, with downstream confluence of the northwesterly polar and the southwesterly subtropical jet streams. A second, less frequent, pattern features an amplified westerly jet across North America, with a correspondingly rapid progression of weakly developed cyclones through the region. In the case of the split flow pattern, composite surface high pressure is established, occasionally disrupted by the emergence from either stream of relatively weak cyclones. These systems act to moisten the affected region at low levels through horizontal transport of moisture and, to a lesser extent, moisture convergence. Subsidence inversions established following the passage of these systems act to slowly erode the depth of the surface-based moist layer but are insufficient in combination with the weak solar radiative input to dissipate the cloud. The properties of the event structure, from the large scale down to that of the cloud layer itself, are stable. Under such conditions, the mechanism that finally removes the cloud is the passage of a relatively well-developed baroclinic wave and its associated forcing (subsidence, dry air advection, moisture divergence). Correspondingly, the difficult act of forecasting the end of such periods requires an accurate assessment of the sufficiency of that forcing to remove the low-level cloud. It is suggested that a relatively simple one-dimensional boundary layer model employed for the time to be critically tested in conjunction with the standard forecast model guidance (forecast vertical motion, profiles of temperature and moisture, Model Output Statistics cloud cover and ceiling) would provide additional information regarding forecast uncertainty.

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Paul M. James, Bernhard K. Reichert, and Dirk Heizenreder

Abstract

NowCastMIX is the core nowcasting guidance system at the German Weather Service. It automatically monitors several systems to capture rapidly developing high-impact mesoscale convective events, including 3D radar volume scanning, radar-based cell tracking and extrapolation, lightning detection, calibrated precipitation extrapolations, NWP, and live surface station reports. Within the context of the larger warning decision support process AutoWARN, NowCastMIX integrates the input data into a high-resolution analysis, based on a fuzzy logic approach for thunderstorm categorization and extrapolation, to provide an optimized warning solution with a 5-min update cycle for lead times of up to 1 h. Feature tracking is undertaken to optimize the direction of warning polygons, allowing individual, tangentially moving cells or cell clusters to be tracked explicitly. An adaptive ensemble clustering is deployed to reduce the spatial complexity of the resulting warning fields and smooth noisy temporal variations to a manageable level for duty forecasters. Further specialized outputs for civil aviation and for a public mobile phone warning app are generated. Now in its eighth year of operation, a comprehensive and complete set of thunderstorm analyses and nowcasts over Germany has been created, which is of unique value for ongoing research and development efforts for improving the system, as well as for addressing climatological aspects of severe convection. Verification has shown that NowCastMIX has helped to significantly improve the quality of the official warnings for severe convective weather events when used within the AutoWARN process.

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