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Joseph Sedlar, Laura D. Riihimaki, Kathleen Lantz, and David D. Turner

Abstract

Various methods have been developed to characterize cloud type, otherwise referred to as cloud regime. These include manual sky observations, combining radiative and cloud vertical properties observed from satellite, surface-based remote sensing, and digital processing of sky imagers. While each method has inherent advantages and disadvantages, none of these cloud-typing methods actually includes measurements of surface shortwave or longwave radiative fluxes. Here, a method that relies upon detailed, surface-based radiation and cloud measurements and derived data products to train a random-forest machine-learning cloud classification model is introduced. Measurements from five years of data from the ARM Southern Great Plains site were compiled to train and independently evaluate the model classification performance. A cloud-type accuracy of approximately 80% using the random-forest classifier reveals that the model is well suited to predict climatological cloud properties. Furthermore, an analysis of the cloud-type misclassifications is performed. While physical cloud types may be misreported, the shortwave radiative signatures are similar between misclassified cloud types. From this, we assert that the cloud-regime model has the capacity to successfully differentiate clouds with comparable cloud–radiative interactions. Therefore, we conclude that the model can provide useful cloud-property information for fundamental cloud studies, inform renewable energy studies, and be a tool for numerical model evaluation and parameterization improvement, among many other applications.

Open access
Jennifer Nakamura, Upmanu Lall, Yochanan Kushnir, Patrick A. Harr, and Kyra McCreery

Abstract

We present a hurricane risk assessment model that simulates North Atlantic Ocean tropical cyclone (TC) tracks and intensity, conditioned on the early season large-scale climate state. The model, Cluster-Based Climate-Conditioned Hurricane Intensity and Track Simulator (C3-HITS), extends a previous version of HITS. HITS is a nonparametric, spatial semi-Markov, stochastic model that generates TC tracks by conditionally simulating segments of randomly varying lengths from the TC tracks contained in NOAA’s Best Track Data, version 2, dataset. The distance to neighboring tracks, track direction, TC wind speed, and age are used as conditioning variables. C3-HITS adds conditioning on two early season, large-scale climate covariates to condition the track simulation: the Niño-3.4 index, representing the eastern equatorial Pacific Ocean sea surface temperature (SST) departure from climatology, and main development region, representing tropical North Atlantic SST departure from climatology in the North Atlantic TC main development region. A track clustering procedure is used to identify track families, and a Poisson regression model is used to model the probabilistic number of storms formed in each cluster, conditional on the two climate covariates. The HITS algorithm is then applied to evolve these tracks forward in time. The output of this two-step, climate-conditioned simulator is compared with an unconditional HITS application to illustrate its prognostic efficacy in simulating tracks during the subsequent season. As in the HITS model, each track retains information on velocity and other attributes that can be used for predictive coastal risk modeling for the upcoming TC season.

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William A. Gough

Abstract

A newly developed precipitation phase metric is used to detect the impact of urbanization on the nature of precipitation at Toronto, Ontario, Canada, by contrasting the relative amounts of rain and snow. A total of 162 years of observed precipitation data were analyzed to classify the nature of winter-season precipitation for the city of Toronto. In addition, shorter records were examined for nearby climate stations in less-urbanized areas in and near Toronto. For Toronto, all winters from 1849 to 2010 as well as three climate normal periods (1961–90, 1971–2000, and 1981–2010) were thus categorized for the Toronto climate record. The results show that Toronto winters have become increasingly “rainy” across these time periods in a statistically significant fashion, consistent with a warming climate. Toronto was compared with the other less urban sites to tease out the impacts of the urban heat island from larger-scale warming. This yielded an estimate of 19%–27% of the Toronto shift in precipitation type (from snow to rain) that can be attributed to urbanization for coincident time periods. Other regions characterized by similar climates and urbanization with temperatures near the freezing point are likely to experience similar climatic changes expressed as a change in the phase of winter-season precipitation.

Open access
Dario Ruggiu, Francesco Viola, and Andreas Langousis

Abstract

We develop a nonparametric procedure to assess the accuracy of the normality assumption for annual rainfall totals (ART), based on the marginal statistics of daily rainfall. The procedure is addressed to practitioners and hydrologists that operate in data-poor regions. To do so we use 1) goodness-of-fit metrics to conclude on the approximate convergence of the empirical distribution of annual rainfall totals to a normal shape and classify 3007 daily rainfall time series from the NOAA/NCDC Global Historical Climatology Network database, with at least 30 years of recordings, into Gaussian (G) and non-Gaussian (NG) groups; 2) logistic regression analysis to identify the statistics of daily rainfall that are most descriptive of the G/NG classification; and 3) a random-search algorithm to conclude on a set of constraints that allows classification of ART samples on the basis of the marginal statistics of daily rain rates. The analysis shows that the Anderson–Darling (AD) test statistic is the most conservative one in determining approximate Gaussianity of ART samples (followed by Cramer–Von Mises and Lilliefors’s version of Kolmogorov–Smirnov) and that daily rainfall time series with fraction of wet days f wd < 0.1 and daily skewness coefficient of positive rain rates skwd > 5.92 deviate significantly from the normal shape. In addition, we find that continental climate (type D) exhibits the highest fraction of Gaussian distributed ART samples (i.e., 74.45%; AD test at α = 5% significance level), followed by warm temperate (type C; 72.80%), equatorial (type A; 68.83%), polar (type E; 62.96%), and arid (type B; 60.29%) climates.

Open access
Paul E. Ciesielski and Richard H. Johnson

Abstract

During the Dynamics of the MJO (DYNAMO) field campaign, radiosonde launches were regularly conducted from three small islands/atolls (Malé and Gan, Maldives, and Diego Garcia, British Indian Ocean Territory) as part of a large-scale sounding network. Comparison of island upsondes with nearby and near-contemporaneous dropsondes over the ocean provides evidence for the magnitude and scope of the islands’ influence on the surrounding atmosphere and on the island upsonde profiles. The island’s impact on the upsonde data is most prominent in the lowest 200 m. Noting that the vertical gradients of temperature, moisture, and winds over the ocean are generally constant in the lowest 0.5 km of dropsondes, a simple procedure was constructed to adjust the upsonde profiles in the lowest few hundred meters to resemble the atmospheric structures over the open ocean. This procedure was applied to the soundings from the three islands mentioned above for the October–December 2011 period of DYNAMO. As a result of this procedure, the adjusted diurnal cycle amplitude of surface temperature is reduced fivefold, resembling that over the ocean, and low-level wind speeds are increased in ~90% of the island soundings. Examination of the impact of these sounding adjustments shows that dynamical and budget fields are primarily affected by adjustments to the wind field, whereas convective parameters are sensitive to the adjustments in thermodynamic fields. Although the impact of the adjustments is generally small (on the order of a few percent), intraseasonal wind regime changes result in some systematic variations in divergence and vertical motion over the sounding arrays.

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Daniel D. Tripp, Elinor R. Martin, and Heather D. Reeves

Abstract

Temperature and humidity profiles in the lowest 3 km of the atmosphere provide crucial information in determining the precipitation type, which aids forecasters in relaying winter-weather risks. In response to the challenges associated with forecasting mixed-phase environments, this study employs uncrewed aerial vehicles (UAVs) to explore the efficacy of high-resolution temporal and vertical measurements in winter-weather environments. On 19 February 2019, boundary layer measurements of an Oklahoma winter storm were collected by a UAV and radiosondes. UAV observations show a pronounced surface-based subfreezing layer that corresponds to observed ice pellets at the surface. This is in contrast to the High-Resolution Rapid Refresh (HRRR) model analyses, which show a subfreezing layer near the surface that is 3°C warmer than both the UAV and radiosonde observations. Using a spectral-bin-microphysics algorithm designed to provide hydrometeor-phase diagnosis throughout the vertical column, it was found that UAV measurements can improve discrimination between hydrometer types in environments near 0°C. A numerical-modeling study of the same winter-weather event illustrates the potential benefit of vertically sampling a mixed-phase environment at multiple mesonet sites and highlights future scientific and operational questions to be addressed by the UAV community.

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Jacob Coburn

Abstract

Variations in wind resources affect the reliability and feasibility of wind energy. At longer time scales, modes within the climate system and externally forced variability become important as the decadelong lifetimes of wind installations and upfront investment costs are considered. Understanding the influence of teleconnections may yield important insights for skillful seasonal predictions. In this study, several modes of variability, including the Arctic Oscillation (AO), El Niño–Southern Oscillation (ENSO), and the global surface solar flux, are assessed for their influence on wind energy anomalies in the upper Midwest (40°–52°N, 87°–105°W). Monthly wind energy is calculated using extrapolated 80-m wind fields from reanalysis data for the period 1980–2018. A multiple linear regression analysis is conducted for the monthly turbine energy output anomalies (TEOA) against the effects of synoptic patterns and pressure gradients, as well as the teleconnection indices, for each grid cell and season, yielding information on the spatial and temporal variations in influence throughout the region. The regression model indicated that each of the factors had significant influences on wind energy, although the effects varied spatially and by season. Periods of extremely low production are often embedded in prolonged declines over several months that were the result of a combination of synoptic variability and significant phases of the teleconnections such as large El Niño events, negative AO episodes, and volcanically induced reductions in surface solar flux. Monthly TEOA are found to vary by up to 37%, amounting to ±130 MW h and tens of thousands of dollars per turbine.

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Thomas A. Guinn, Daniel J. Halperin, and Christopher G. Herbster

Abstract

General aviation (GA) accidents involving controlled flight into terrain often occur when pilots are unaware that their aircraft’s true altitude is lower than the altitude indicated by the pressure altimeter as a result of colder-than-standard temperatures. However, little guidance is available that quantifies the magnitude of these altimeter errors and their variation with season. In this study, the fifth-generation European Centre for Medium-Range Weather Forecasts atmospheric reanalysis of the global climate (ERA5) dataset is combined with the pressure–altitude equation to construct a 30-yr monthly climatology, covering much of the United States and Canada, of D value (i.e., true altitude minus pressure altitude) corrected for the standard-atmosphere height separation between the altimeter setting and standard mean sea level pressure. This “corrected” D value therefore provides a useful estimate of the error between true and altimeter-indicated altitude. During winter, the mean corrected D values reach values as low as −350 m (~−1200 ft) in northern, low-terrain regions for flights near a pressure altitude of 3600 m, meaning the aircraft would be nearly 350 m lower than the altimeter indicates. Furthermore, the minimum (i.e., maximum negative) corrected D values are nearly double their mean values for the same time period. In addition, the reanalysis-based corrected D values are compared with estimated values calculated using a simple rule of thumb that is based solely on the air temperature at altitude and the surface elevation. The rule of thumb tends to underpredict the magnitude of the estimated error, in some cases by 70 m (~200 ft), and therefore gives a lower margin of safety.

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Elisa M. Murillo and Cameron R. Homeyer
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Hanii Takahashi, Matthew Lebsock, Zhengzhao Johnny Luo, Hirohiko Masunaga, and Cindy Wang

Abstract

This paper is the first attempt to document a simple convection-tracking method based on the IMERG precipitation product to generate an IMERG-based Convection Tracking (IMERG-CT) dataset. Up to now, precipitation datasets have been Eulerian accumulations. Now with IMERG-CT, we can estimate total rainfall based on Lagrangian accumulations, which is a very important step in diagnosing cloud-precipitation process following the evolution of air masses. Convection-tracking algorithms have traditionally been developed on the basis of brightness temperature (Tb) from satellite infrared (IR) retrievals. However, vigorous rainfall can be produced by warm-topped systems in a moist environment; this situation cannot be captured by traditional IR-based tracking but is observed in IMERG-CT. Therefore, an advantage of IMERG-CT is its ability to include the previously missing information of shallow clouds that grow into convective storms, which provides us more-complete life cycle records of convective storms than traditional IR-based tracking does. This study also demonstrates the utility of IMERG-CT through investigating various properties of convective systems in terms of the evolution before and after peak precipitation rate and amount. For example, composite analysis reveals a link between evolution of precipitation and convective development: the signature of stratiform anvils remaining after the storm has produced the maximum rainfall, as average Tb stays almost constant for 5 h after the peak of precipitation. Our study highlights the importance of joint analysis of cloud and precipitation data in time sequence, which helps to elucidate the underlying dynamic processes producing tropical rainfall and its resultant effects on the atmospheric thermodynamics.

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