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Patricia A. Jones and James E. Jiusto

Abstract

From historical weather records, a preliminary assessment was made of local climate changes in four major urban areas of New York State. Particular emphasis was placed on cold season precipitation and possible relationships to man's activities. Total snowfall was found to have increased significantly from about 1940, the start of a period of sharp increases in urbanization and industrialization. The relationship was merely coincidental, with the underlying cause of snowfall increases due to natural causes, apparently in part to a corresponding decline in ambient temperature. A few climate trends appeared linked to anthropogenic causes, particularly in New York City.

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G. A. Jones and S. K. Avery

Abstract

The effects of the zonal mean circulation and planetary-wave winds on the distribution of nitric oxide in the 55–120 km height region is investigated. A time-dependent numerical model is used to investigate the interaction between planetary waves and the zonal mean circulation, and the effect of the circulation on the nitric oxide distribution is determined. The initial nitric oxide (NO) distribution is obtained by using a simple source/sink chemistry, vertical eddy diffusion, and advective transport by the zonal mean circulation. Changes in the initial NO distribution which result from the addition of planetary-wave winds are described. Planetary waves are found to induce a wave-like structure in the nitric oxide distribution which resembles that derived from observational data. Planetary waves can affect the nitric oxide concentration in two ways: first,through the wave-induced changes in the mean meridional circulation, and second, through the nitric oxide perturbation induced by wave winds themselves. The changes in total nitric oxide are due primarily to the zonal asymmetries in nitric oxide induced by the planetary waves. Implications of this result for explaining the winter anomaly are discussed.

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Thomas A. Jones and David J. Stensrud

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One satellite data product that has received great interest in the numerical weather prediction community is the temperature and mixing ratio profiles derived from the Atmospheric Infrared Sounder (AIRS) instrument on board the Aqua satellite. This research assesses the impact of assimilating AIRS profiles on high-resolution ensemble forecasts of southern plains severe weather events occurring on 26 May 2009 and 10 May 2010 by comparing two ensemble forecasts. In one ensemble, the 1830 and 2000 UTC level 2 AIRS temperature and dewpoint profiles are assimilated with all other routine observations into a 36-member, 15-km Weather and Research Forecast Model (WRF) ensemble using a Kalman filter approach. The other ensemble is identical, except that only routine observations are assimilated. In addition, 3-km one-way nested-grid ensemble forecasts are produced during the periods of convection. Results indicate that over the contiguous United States, the AIRS profiles do not measurably improve the ensemble mean forecasts of midtropospheric temperature and dewpoint. However, the ensemble mean dewpoint profiles in the region of severe convective development are improved by the AIRS assimilation. Comparisons of the forecast ensemble radar reflectivity probabilities between the 1- and 4-h forecast times with nearby Weather Surveillance Radar-1988 Doppler (WSR-88D) observations show that AIRS-enhanced ensembles consistently generate more skillful forecasts of the convective features at these times.

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Thomas A. Jones and David J. Stensrud

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The sensitivity of assimilating satellite retrievals of cloud water path (CWP) to the microphysics scheme used by a convection-allowing numerical model is explored. All experiments use the Advanced Research core of the Weather Research and Forecasting Model (WRF-ARW), with observations assimilated using the Data Assimilation Research Testbed ensemble adjustment Kalman filter and a 40-member ensemble. Three-dimensional idealized supercell simulations are generated from a deterministic WRF nature run started from a homogeneous set of initial conditions. Four cloud microphysics schemes are tested: Lin–Farley–Orville (LFO), Thompson (THOMP), Morrison double-moment (MOR), and Milbrandt–Yau (MY).

For the idealized experiments, assimilating CWP generates a mature supercell after approximately 1 h for all microphysics schemes. Vertical profiles of ensemble covariances show large differences in the relationship between CWP and various hydrometeor mixing ratios. While the differences in overall CWP are small, the experiments generate very different reflectivity analyses of the simulated storm, with MOR and MY underestimating reflectivity by a large margin. Vertical profiles of hydrometeor mixing ratios from each experiment are generally consistent with scheme design, such that the Thompson scheme characterizes the storm top as mostly snow whereas the Milbrandt–Yau scheme characterizes the storm top as mostly ice. The impacts of these differences on 30-min forecasts show that MOR and MY are unable to maintain convection within the model while THOMP and LFO perform somewhat better, though all fail to capture the divergent movement of the storm split in the nature run.

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A. H. Woodcock and Richard H. Jones

Abstract

A recent study in Queensland, Australia, associates long-term downward trends in rain amount with the productivity of the sugarcane industry. The relationship is attributed to an increasing colloidal stability in the clouds caused by additional cloud condensation nuclei shown to be present in the smoke coming from local cane-harvesting fires.

As an additional test of the hypothesis, the rainfall records of several sugar-producing areas in Hawaii are examined where burning prior to harvesting is also practiced. Two physically similar leeward coastal areas were selected for comparison, one because it is downwind from a major cane-growing region and the other because it is not. The data suggest a downward trend in rainfall over periods of 30–60 years at both areas, but the trends are not statistically significant. However, the records for areas along the windward coastal regions of the two northwesternmost islands indicate an upward trend. It is concluded that factors other than cane-fire smoke are probably involved in any rainfall trends which may exist.

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Arthur L. Sims and Douglas M. A. Jones

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Two-minute rainfall rates have been measured along lines of recording rain gages in Florida and Illinois. Knowledge of the frequencies of occurrence of short-duration rainfall rates is needed for estimating attenuation of radio communications and radars. Rainfall rate frequencies are also useful in estimating the erosion of high-speed devices by rain. Results are presented for one summer of data taken at each location. The Florida lines were 9.6 and 21.5 km in length and the Illinois lines 23.9 and 62.2 km. These line frequencies are compared with single gage frequencies at each location. The frequencies by which various rates are exceeded are shown for those that occur more than 0.001% of the time. Rain at rates greater than 0.1 mm h−1 occurred less than 6% of the time at either location and for the longest line lengths. For similar line lengths, most rainfall rates have higher frequencies of occurrence in Florida than in Illinois. The rainfall rate frequencies were not significantly different for differing line orientations.

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Thomas A. Jones, Daniel Cecil, and Mark DeMaria

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The formulation and testing of an enhanced Statistical Hurricane Intensity Prediction Scheme (SHIPS) using new predictors derived from passive microwave imagery is presented. Passive microwave imagery is acquired for tropical cyclones in the Atlantic and eastern North Pacific basins between 1995 and 2003. Predictors relating to the inner-core (within 100 km of center) precipitation and convective characteristics of tropical cyclones are derived. These predictors are combined with the climatological and environmental predictors used by SHIPS in a simple linear regression model with change in tropical cyclone intensity as the predictand. Separate linear regression models are produced for forecast intervals of 12, 24, 36, 48, 60, and 72 h from the time of a microwave sensor overpass. Analysis of the resulting models indicates that microwave predictors, which provide an intensification signal to the model when above-average precipitation and convective signatures are present, have comparable importance to vertical wind shear and SST-related predictors. The addition of the microwave predictors produces a 2%–8% improvement in performance for the Atlantic and eastern North Pacific tropical cyclone intensity forecasts out to 72 h when compared with an environmental-only model trained from the same sample. Improvement is also observed when compared against the current version of SHIPS. The improvement in both basins is greatest for substantially intensifying or weakening tropical cyclones. Improvement statistics are based on calculating the forecast error for each tropical cyclone while it is held out of the training sample to approximate the use of independent data.

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Guillermo A. Baigorria and James W. Jones

Abstract

Weather generators are tools that create synthetic daily weather data over long periods of time. These tools have also been used for downscaling monthly to seasonal climate forecasts, from global and regional circulation models to daily values for use as inputs for crop and other environmental models. One main limitation of most weather generators is that they do not take into account the spatial structure of weather. Spatial correlation of daily rainfall is important when one aggregates, for example, simulated crop yields or hydrology in a watershed or region. A method was developed to generate realizations of daily rainfall for multiple sites in an area while preserving the spatial and temporal correlations among sites. A two-step method generates rainfall events at multiple sites followed by rainfall amounts at sites where generated rainfall events occur. The generation of rainfall events was based on a new orthogonal Markov chain for discrete distributions. For generating rainfall amounts, a vector of random numbers (from a uniform distribution), of order equal to the number of locations with rainfall events that were generated to occur in a day, was matrix-multiplied by the corresponding factorized correlation matrix to create spatially correlated random numbers. Elements from the resulting vector were transformed to a gamma distribution using cumulative probability functions for each location and rescaled to rainfall amounts. One study area was located in north-central Florida, where correlated rainfall data were generated for seven weather stations to evaluate its performance versus a widely used single-site weather generator. A second area was in North Carolina, where rainfall was generated for 25 weather stations to evaluate the effects of a larger number of stations in other regions. One thousand yearlong replications of daily rainfall data were generated for each area. Monthly spatial correlations of generated daily rainfall events and amounts among all pairs of weather stations closely matched their observed counterparts. For daily rainfall amounts the correlation coefficients between the observed pairwise correlation coefficients and the ones estimated from synthetic data among weather stations were 0.977 for Florida and 0.964 for North Carolina. The performance of the geospatial–temporal (GiST) weather generator was also analyzed by comparing the distributions of lengths of dry and wet spells, joint probabilities, Markov transitional probabilities, distance decay of correlation functions, and regionwide days without rainfall at any station. Multiannual mean and standard deviation of the number of rainy days per month and mean monthly rainfall were also calculated. All comparisons between observed and generated rainfall events and amounts using the GiST weather generator were highly correlated. The root-mean-square errors of pairwise correlation values ranged from 0.05 to 0.11 for rainfall events and from 0.03 to 0.06 for amounts.

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Alexandra L. Jones and Larry Di Girolamo

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The Intercomparison of 3D Radiation Codes (I3RC) community Monte Carlo model has been extended to include a source of photon emission from the surface and atmosphere, thereby making it capable of simulating scalar radiative transfer in a 3D scattering, absorbing, and emitting domain with both internal and external sources. The theoretical basis, computational implementation, verification of the internal emission, and computational performance of the resulting model, the “IMC+emission,” is presented. Thorough verification includes fundamental tests of reciprocity and energy conservation, comparison to analytical solutions, and comparison with another 3D model, the Spherical Harmonics Discrete Ordinate Method (SHDOM). All comparisons to fundamental tests and analytical solutions are accurate to within the precision of the simulations—typically better than 0.05%. Comparison cases to SHDOM were typically within a few percent, except for flux divergence near cloud edges, where the effects of grid definition between the two models manifest themselves. Finally, the model is applied to the established I3RC case 4 cumulus cloud field to provide a benchmark result, and computational performance and strong and weak scaling metrics are presented. The outcome is a thoroughly vetted, publicly available, open-source benchmark tool to study 3D radiative transfer from either internal or external sources of radiation at wavelengths for which scattering, emission, and absorption are important.

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Roland A. Madden and Richard H. Jones
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