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Barry H. Lynn

Abstract

Total lightning probability forecasts for 26 mostly springtime days and 27 summertime days were analyzed for their usefulness and economic value. The mostly springtime forecast days had a relatively high number of severe weather reports compared with the summertime forecast days. The lightning forecasts were made with a dynamic lightning forecast scheme (DLS), and each forecast dataset used lightning assimilation to hasten convective initiation and, in most cases, to improve short-term forecasts. A spatial smoothing parameter σ of 48 km yielded more skillful, reliable, and economically valuable hourly forecasts than other values of σ. Mostly springtime forecasts were more skillful and had more hours of useful skill than summertime forecasts, but the latter still demonstrated useful skill during the first two forecast hours. The DLS forecasts were compared to those obtained with the “McCaul” diagnostic scheme, which diagnoses lightning flash data. The DLS had significantly higher fractions skill scores than the McCaul scheme for or at least one event/flash (10 min)−1. Bias values of the forecast lightning fields with both schemes were overall small. Yet, DLS forecasts started in the early summer evening with RAP data did have positive bias, which was attributed to initial conditions within the RAP. Correlating fractions skill scores for lightning and precipitation indicated that more accurate forecasts of lightning were associated with more accurate precipitation forecasts for convection with a high, but not lower, number of severe weather reports.

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Barry H. Lynn
and
Wei-Kuo Tao

Abstract

To improve the triggering of clouds over landscape heterogeneity, it is suggested that the forcing by mesoscale circulations generated by landscape patches be included. For this purpose, it is suggested that a relatively simple zero-order closure be used to obtain a triggering parcel’s mesoscale perturbation vertical velocity, potential temperature, and specific humidity. In combination with a turbulent fluctuation averaged over a parcel area, one can obtain a parcel’s (total) velocity, temperature, and moisture. The authors used similarity theory to parameterize the mesoscale perturbations, using a dataset generated by a three-dimensional, high-resolution cumulus ensemble model with west-to-east land surface patches.

Alternatively, the authors used one-dimensional budget equations that contain mesoscale and turbulent fluctuations (and source terms) to obtain the vertical profile of potential temperature and specific humidity within a triggering parcel. Here, it is suggested that first-order closure be used; these equations with first-order closure should provide more realistic profiles of temperature and moisture within a triggering parcel than with the zero-order scheme above. This is especially the case when moist (cloud) processes occur. An analysis of the model-produced dataset indicated that parameterizations for two terms needed to be developed to close the budget equations: the vertical flux of the mesoscale temperature and moisture. Similarity theory is used to parameterize these fluxes.

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Barry H. Lynn
,
Guy Kelman
, and
Gary Ellrod

Abstract

Dynamic lightning forecasts [total and cloud to ground (CG)] were produced on a convection-allowing forecast grid with 4-km grid spacing with lightning assimilation (ASML) and without lightning assimilation [control (CNTL)]. A dynamic scheme produces time- and space-dependent potential electrical energy, which then converts this energy into lightning (e.g., number per hour per grid element). The assimilation scheme uses observed, gridded total lightning to determine how much water vapor is added at constant temperature in the mixed-phase region, leading to a convective response. ASML and CNTL lightning forecasts were compared to observed total and CG lightning. Four case studies—each representing a different type of convective regime—demonstrate that the spatial distribution and intensity of forecast lightning were improved when lightning assimilation was used. Over 3 days in March 2012, eight 18-h lightning forecasts quantified the advantages in forecast accuracy. Equitable threat scores for forecast CG lightning associated with strong [25 (3 h)−1], very strong [50 (3 h)−1], and extreme [100 (3 h)−1] events were significantly more accurate for convective storms that developed in forecasts with lightning assimilation than without. Improvements in forecasts of very strong and extreme events occurred out to 9 h of forecast time, while the forecasts of strong events showed improvement out to 15 h. Spurious convection was removed with filtering in one case study, which led to a notable improvement in the timing and intensity of the squall line. Sensitivity tests examined the utility of this filtering approach, and the importance of reducing mass imbalances liable to occur when too much water vapor mass is introduced into the model.

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Barry H. Lynn
,
Frank Abramopoulos
, and
Roni Avissar

Abstract

Similarity theory was used to develop a parameterization of mesoscale heat fluxes induced by landscape discontinuities for large-scale atmospheric models (e.g., general circulation models). For this purpose, Buckingham Pi theory, a systematic method for performing dimensional analysis, was used to derive a set of dimensionless groups, which describes the large-scale atmospheric background conditions, the spatial variability of surface sensible heat flux, and the characteristic structure of the landscape. These dimensionless groups were used to calculate the coefficients of a fourth-order Chebyshev polynomial, which represents the vertical profiles of dimensionless mesoscale heat fluxes obtained for a broad range of large-scale atmospheric conditions and different landscapes. The numerous three-dimensional numerical experiments performed to evaluate this similarity relationship suggest that the parameterization is quite robust.

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Barry H. Lynn
,
David Rind
, and
Roni Avissar

Abstract

A mesoscale atmospheric model was used to evaluate the impact of subgrid-scale landscape discontinuities on the vertical profiles of resolved temperature, moisture, and moist static energy in the planetary boundary layer (PBL) of GCMs. These profiles were produced with a 3D version of the model (using a horizontal grid resolution of 7.5 km and 13 vertical layers in the PBL) by averaging horizontally the various atmospheric variables over a 180×180 km2 domain-about the size of the horizontal domain represented by a single grid element in a GCM. They were compared to corresponding vertical profiles produced with a 1 D version of the model, which simulates the PBL, as in a GCM, over a single horizontal grid element. Differences obtained between the horizontally averaged atmospheric variables produced with the 3D situations and the 1 D simulations emphasize the impact of subgrid-scale landscape discontinuities on GCM-resolved variables. Various types of landscape discontinuities, characterized by horizontal contrasts of surface wetness and size of land patches, were simulated under various background-wind conditions. Differences of temperature, specific humidity, and moist static energy as large as 4 K, 6 g kg−1, and 10 kJ kg−1 were obtained in some cases. These differences were not affected significantly by moderate winds but were sensitive to the spatial distribution of surface wetness. Thew results emphasize the need to parameterize mesoscale processes induced by landscape discontinuities in GCMs.

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Barry H. Lynn
,
Wei-Kuo Tao
, and
Peter J. Wetzel

Abstract

A two-dimensional version of a cloud-resolving model was used to study the generation of deep moist convection over heterogeneous landscapes. Alternating patches of dry and wet soil were simulated for various profiles of background wind. Results suggested a significant, systematic impact of patch length and background wind on moist convection. Rainfall occurred most intensely along sea-breeze-like fronts, which formed at patch boundaries. Total accumulated rainfall—as the average over simulations with the same patch size but with different background wind profiles—was largest for a patch length of 128 km. This patch length was similar in size to a local radius of deformation (r o = HN/ω). The deposition of rainfall generated a much different distribution of soil moisture after one day of model simulation. This new distribution, however, was far from equilibrium, as the landscape still consisted of a number of wet and dry soil patches. The cloud structure of moist convection was also examined using a cloud classification technique. The greatest percentage of rainfall that occurred from deep clouds (which had “roots” in the middle troposphere) was also obtained over patches with length similar to r o . The results suggest the need to account for the triggering of moist convection by land surface heterogeneity in regional- and global-scale atmospheric models. It is also necessary to include the impact of patch size on cloud type. Moreover, because the distribution of soil moisture patches evolves over time in response to background atmospheric conditions, further study is suggested to gain a more full understanding of local-scale feedbacks between moist convection and soil moisture.

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Barry H. Lynn
,
Wei-Kuo Tao
, and
Frank Abramopoulos

Abstract

To develop a parameterization for the triggering of moist convection by landscape-generated mesoscale circulations, a set of relatively high-resolution three-dimensional (3D) simulations was produced. These simulations modeled the development of landscape generated mesoscale circulations that triggered moist convection over west-to-east dry patches. No clear relationship existed between average patch size and average rainfall. Rather, rainfall averaged over the area of individual patches varied linearly with the size of these patches. Thus, cumulus parameterization schemes need to account for a population of clouds (over individual patches) within each domain of a large-scale atmospheric model (i.e., numerical weather prediction and global circulation models).

It is demonstrated that mesoscale perturbations in velocity, temperature, and moisture need to be included in triggering functions when evaluating whether moist convection will occur. Yet, the largest patches did not always produce the largest mesoscale perturbations. Instead, the size of the perturbations depended upon the ratio of the local radius of deformation to patch size, the gradient of soil moisture between patches, as well as large-scale environmental conditions such as wind, stability, and specific humidity. These perturbations can be used to improve the representation of triggering functions associated with moist convection over landscape patches. Appropriate dimensionless numbers that can be used in a parameterization for the mesoscale perturbations are identified.

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Barry H. Lynn
,
Richard Healy
, and
Leonard M. Druyan

Abstract

The study analyzes observational climate data for June–August 1977–2004 and simulations of current and future climate scenarios from a nested GCM/regional climate model system to assess the potential for extreme temperature change over the eastern United States. Observational evidence indicates that anomalously warm summers in the eastern United States coincide with anomalously cool eastern Pacific sea surface temperatures, conditions that are conducive to geopotential ridging over the east, less frequent precipitation, and lower accumulated rainfall. The study also found that days following nighttime rain are warmer on average than daytime rain events, emphasizing the importance of the timing of precipitation on the radiation balance. Precipitation frequency and eastern Pacific sea surface temperature anomalies together account for 57% of the 28-yr variance in maximum surface temperature anomalies. Simulation results show the sensitivity of maximum surface air temperature to the moist convection parameterization that is employed, since different schemes produce different diurnal cycles and frequencies of precipitation. The study suggests that, in order to accurately project scenarios of extreme temperature change, models need to realistically simulate changes in the surface energy balance caused by the interannual variation of these precipitation characteristics. The mesoscale model that was realistic in this respect predicted much warmer mean and maximum surface air temperatures for five future summers than the parallel GCM driving simulation.

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Barry H. Lynn
,
Alexander P. Khain
,
Jimy Dudhia
,
Daniel Rosenfeld
,
Andrei Pokrovsky
, and
Axel Seifert

Abstract

Considerable research investments have been made to improve the accuracy of forecasting precipitation systems in cloud-resolving, mesoscale atmospheric models. Yet, despite a significant improvement in model grid resolution and a decrease in initial condition uncertainty, the accurate prediction of precipitation amount and distribution still remains a difficult problem. Now, the development of a fast version of spectral (bin) microphysics (SBM Fast) offers significant potential for improving the description of precipitation-forming processes in mesoscale atmospheric models.

The SBM Fast is based on solving a system of equations for size distribution functions for water drops and three types of ice crystals (plates, columns, and dendrites), as well as snowflakes, graupel, and hail/frozen drops. Ice processes are represented by three size distributions, instead of six in the original SBM code. The SBM uses first principles to simulate microphysical processes such as diffusional growth and collision. A budget for aerosols is used to obtain the spectrum of condensation nuclei, which is used to obtain the initial drop spectrum. Hence, SBM allows one to take into account aerosol effects on precipitation, and corresponding cloud effects on the atmospheric aerosol concentration and distribution. SBM Fast has been coupled with the three-dimensional fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5), which allows SBM Fast to simulate microphysics within a realistic, time-varying mesoscale environment.

This paper describes the first three-dimensional SBM mesoscale model and presents results using 1-km resolution to simulate initial development of a cloud system over Florida on 27 July 1991. The focus is on initial cloud development along the west coast, just prior to sea-breeze formation. The results indicate that the aerosol concentration had a very important impact on cloud dynamics, microphysics, and rainfall.

Vertical cross sections of clouds obtained using SBM Fast are compared to those from a version of the “Reisner2” bulk-parameterization scheme that uses the Kessler autoconversion formula. The results show that this version of “Reisner2” produced vertically upright clouds that progressed very quickly from initial cloud formation to raindrop formation. In contrast, clouds obtained using SBM were relatively long lasting with greater production of stratiform clouds.

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Barry H. Lynn
,
Alexander P. Khain
,
Jimy Dudhia
,
Daniel Rosenfeld
,
Andrei Pokrovsky
, and
Axel Seifert

Abstract

Spectral (bin) microphysics (SBM) has been implemented into the three-dimensional fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5). The new model was used to simulate a squall line that developed over Florida on 27 July 1991. It is shown that SBM reproduces precipitation rate, rain amounts, and location, radar reflectivity, and cloud structure much better than bulk parameterizations currently implemented in MM5.

Sensitivity tests show the importance of (i) raindrop breakup, (ii) in-cloud turbulence, (iii) different aerosol concentrations, and (iv) inclusion of scavenging of aerosols. Breakup decreases average and maximum rainfall. In-cloud turbulence enhances particle drop collision rates and increases rain rates. A “continental” aerosol concentration produces a much larger maximum rainfall rate versus that obtained with “maritime” aerosol concentration. At the same time accumulated rain is larger with maritime aerosol concentration. The scavenging of aerosols by nucleating water droplets strongly affected the concentration of aerosols in the atmosphere.

The spectral (bin) microphysics mesoscale model can potentially be used for studies of specific phenomena such as severe storms, winter storms, tropical cyclones, etc. The more realistic reproduction of cloud structure than that obtained with bulk parameterization implies that the model will be more useful for remote sensing applications and in the development of advanced rain retrieval algorithms. The model can also simulate the effect of cloud seeding on rain production.

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