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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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Randy J. Chase, Stephen W. Nesbitt, and Greg M. McFarquhar

Abstract

With the launch of the Global Precipitation Measurement Dual-Frequency Precipitation Radar (GPM-DPR) in 2014, renewed interest in retrievals of snowfall in the atmospheric column has occurred. The current operational GPM-DPR retrieval largely underestimates surface snowfall accumulation. Here, a neural network (NN) trained on data that are synthetically derived from state-of-the-art ice particle scattering models and measured in situ particle size distributions (PSDs) is used to retrieve two parameters of the PSD: liquid equivalent mass-weighted mean diameter Dml and the liquid equivalent normalized intercept parameter Nwl. Evaluations against a test dataset showed statistically significantly improved ice water content (IWC) retrievals relative to a standard power-law approach and an estimate of the current GPM-DPR algorithm. Furthermore, estimated median percent errors (MPE) on the test dataset were −0.7%, +2.6%, and +1% for Dml, Nwl, and IWC, respectively. An evaluation on three case studies with collocated radar observations and in situ microphysical data shows that the NN retrieval has MPE of −13%, +120%, and +10% for Dml, Nwl, and IWC, respectively. The NN retrieval applied directly to GPM-DPR data provides improved snowfall retrievals relative to the default algorithm, removing the default algorithm’s ray-to-ray instabilities and recreating the high-resolution radar retrieval results to within 15% MPE. Future work should aim to improve the retrieval by including PSD data collected in more diverse conditions and rimed particles. Furthermore, different desired outputs such as the PSD shape parameter and snowfall rate could be included in future iterations.

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Jordan P. Brook, Alain Protat, Joshua Soderholm, Jacob T. Carlin, Hamish McGowan, and Robert A. Warren

Abstract

A spatial mismatch between radar-based hail swaths and surface hail reports is commonly noted in meteorological literature. The discrepancy is partly due to hailstone advection and melting between detection aloft and observation at the ground. This study aims to mitigate this problem by introducing a model named HailTrack, which estimates hailfall at the surface using radar observations. The model operates by detecting, tracking, and collating hailstone trajectories using dual-polarized, dual-Doppler radar retrievals. Notable improvements in hailfall forecasts were observed through the use of HailTrack, and initializing the model with radar retrievals of hail differential reflectivity H DR was found to produce the most accurate hailfall estimates. The analysis of a case study in Brisbane, Australia, demonstrated that trajectory modeling significantly improved the correlation between hail swaths and hail-related insurance losses, increasing Heidke skill scores from 0.48 to 0.58. The accumulated kinetic energy of hailstone impacts also showed some skill in identifying areas that were exposed to particularly severe hailfall. Other unique impact estimates are presented, such as hailstone advection information and hailstone impact angle statistics. The potential to run the model in real time and produce short-term (10–15 min) nowcasts is also introduced. Model applications include improving radar-based hail climatologies, validating hail detection techniques and insurance claims data, and providing real-time hail impact maps to improve public awareness of hail risk.

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Duong Hoang Trinh, Hoang Duc Cuong, Duong Van Kham, and Chanh Kieu

Abstract

This study examines the teleconnection between sea surface temperature (SST) in different ocean regions and tropical cyclone (TC) activity affecting Vietnam’s coastal region. Using spatial correlation and principal component analyses, it is found that the variability of TCs affecting Vietnam during 1982–2018 is remotely connected with SST in the Indian Ocean, the southwestern Pacific Ocean, and the northern Philippine Sea. Among the three regions, SST in the northern Philippine Sea displays the most significant inverse relationship with TC activity in the South China Sea (SCS), with lower June–November TC accumulated energy (ACE) for warmer northern Philippine Sea SST. Further analyses of large-scale atmospheric circulations show that this teleconnection between the northern Philippine Sea SST and TC activity in the SCS is linked to the East Asian subtropical jet (EASJ). Principal component analyses of the 200-hPa zonal wind associated with EASJ capture indeed a strong relationship between the second principal component, which characterizes the EASJ intensity, and ACE. Specifically, higher EASJ intensity corresponding to colder northern Philippine Sea SST would enhance large-scale ascending motion and low-level cyclonic anomalies in the SCS, which are favorable for TC formation and result in an overall increased ACE. Examination of the correlation between this second principal component and the northern Philippine Sea SST confirms that this correlation is statistically significant at a 95% confidence level. In this regard, these results support the Pacific–Japan teleconnection between the northern Philippine Sea SST and TC activity in the SCS.

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Pham Thi Thanh Nga, Pham Thanh Ha, and Vu Thanh Hang

Abstract

This study presents the application of k-means clustering to satellite-based solar irradiation in different regions of Vietnam. The solar irradiation products derived from the Himawari-8 satellite, named AMATERASS by the solar radiation consortium under the Japan Science and Technology Agency (JST), are validated with observations recorded at five stations in the period from October 2017 to September 2018 before their use for clustering. High correlations among them enable the use of satellite-based daily global horizontal irradiation for spatial variability analysis and regionalization. With respect to the climate regime in Vietnam, the defined 6-cluster groups demonstrate better agreement with the conventionally classified seven climatic zones rather than the four climatic zones of the Köppen classification. The spatial distribution and seasonal variation in the regionalized solar irradiation reflect interchangeable influences of large-scale atmospheric circulation in terms of the East Asian winter monsoon and the South Asian summer monsoon as well as the effect of local topography. Higher daily averaged solar radiation and its weaker seasonal variation were found in two clusters in the southern region where the South Asian summer monsoon dominates in the rainy season. Pronounced seasonal variability in solar irradiation in four clusters in the northern region is associated with the influence of the East Asian monsoon, resulting in its clear reduction during the winter months.

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