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Jason Giovannettone

Abstract

Because many locations throughout the United States have recently experienced periods of extreme wet and dry conditions, an attempt is made to better understand the relationships between long-term total precipitation and climate variability. Correlations between total precipitation at over 1200 U.S. sites and low-frequency oscillations of the mean activity of 30 hydroclimate indices (HCIs) are analyzed using correlation analysis and sliding window sizes on the order of years to reduce the effects of high-frequency variability in the time series. The strength and significance of each relationship are assessed using the Pearson’s correlation coefficient r, leave-one-out cross validation, and a Monte Carlo approach. The sliding window size, lag time, and beginning month were varied to produce the optimal correlation at each site; a 60-month sliding window and lag times of 12 and 48 months resulted in the strongest correlations. Correlations with 7 and 8 HCIs at each lag time, respectively, were regionally delineated. The Madden–Julian oscillation represents the dominant HCI at the 12-month lag time throughout most of the western half of the United States, whereas El Niño–Southern Oscillation revealed strong links to annual and longer-term total precipitation in the eastern and western United States, respectively. Other HCIs, such as the North Atlantic Oscillation and the Pacific decadal oscillation, demonstrated dominance over much smaller and more well-defined regions within the Southwest and the South, respectively. The final results of this study allow a greater understanding of potential links between climate variability and long-term precipitation in the United States, leading to potentially improved predictions of the onset and persistence of future extreme meteorological events at longer lead times.

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Dereka Carroll-Smith, Robert J. Trapp, and James M. Done

Abstract

The overarching purpose of this study is to investigate the impacts of anthropogenic climate change both on the rainfall and tornadoes associated with tropical cyclones (TCs) making landfall in the U.S. Atlantic basin. The “pseudo–global warming” (PGW) approach is applied to Hurricane Ivan (2004), a historically prolific tropical cyclone tornado (TCT)-producing storm. Hurricane Ivan is simulated under its current climate forcings using the Weather Research and Forecasting Model. This control simulation (CTRL) is then compared with PGW simulations in which the current forcings are modified by climate-change differences obtained from the Community Climate System Model, version 4 (NCAR); Model for Interdisciplinary Research on Climate, version 5 (MIROC); and Geophysical Fluid Dynamics Laboratory Climate Model, version 3 (GFDL). Changes in TC intensity, TC rainfall, and TCT production, identified for the PGW-modified Ivan, are documented and analyzed. Relative to CTRL, all three PGW simulations show an increase in TC intensity and generate substantially more accumulated rainfall over the course of Ivan’s progression over land. However, only one of the TCs under PGW (MIROC) produced more TCTs than CTRL. Evidence is provided that, in addition to favorable environmental conditions, TCT production is related to the TC track length and to the strength of the interaction between the TC and an environmental midlevel trough. Enhanced TCT generation at landfall for MIROC and GFDL is attributed to increased values of convective available potential energy, low-level shear, and storm-relative environmental helicity.

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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 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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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. The present 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-polarised, dual-Doppler radar retrievals. Notable improvements in hailfall forecasts were observed through the use of HailTrack, and initialising the model with hail differential reflectivity (HDR) radar retrievals was found to produce the most accurate hailfall estimates. The analysis of a case study in Brisbane, Australia demonstrated that trajectory modelling 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 minute) 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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Andrew E. Mercer, Alexandria D. Grimes, and Kimberly M. Wood

Abstract

Tropical cyclone (TC) track forecasts have improved in recent decades while intensity forecasts, particularly predictions of rapid intensification (RI), continue to show low skill. Many statistical methods have shown promise in predicting RI using environmental fields, though these methods heavily rely upon supervised learning techniques such as classification. Advances in unsupervised learning techniques, particularly those that integrate nonlinearity into the class separation problem, can improve discrimination ability for difficult tasks such as RI prediction. This study quantifies separability between RI and non-RI environments for 2004 – 2016 Atlantic TCs using an unsupervised learning method that blends principal component analysis with k-means cluster analysis. Input fields consisted of TC-centered 1° Global Forecast System analysis (GFSA) grids (170 different variables and isobaric levels) for 3605 TC samples and five domain sizes. Results are directly compared against separability offered by operational RI forecast predictors for eight RI definitions. The unsupervised learning procedure produced improved separability over operational predictors for all eight RI definitions, five of which showed statistically significant improvement. Composites from these best-separating GFSA fields highlighted the importance of mid- and upper-level relative humidity in identifying the onset of short-term RI, while long-term, higher-magnitude RI was generally associated with weaker absolute vorticity. Other useful predictors included optimal thermodynamic RI ingredients along the mean trajectory of the TC. The results suggest that the orientation of a more favorable thermodynamic environment relative to the TC and mid-level vorticity magnitudes could be useful predictors for RI.

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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 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 (D ml) and the liquid equivalent normalized intercept parameter (N wl). Evaluations against a test dataset showed statistically significant improved ice water content (IWC) retrievals compared 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 D ml, N wl and IWC, respectively. An evaluation on three case-studies with co-located radar observations and in-situ microphysical data show that the NN retrieval has MPE of –13%, +120% and +10% for D ml, N wl and IWC, respectively. The NN retrieval applied directly to GPM-DPR data provides improved snowfall retrievals compared 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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Thomas A. Guinn, Daniel J. Halperin, and Christopher G. Herbster

Abstract

General-aviation (GA) controlled flight into terrain accidents often occur when a pilot is unaware their aircraft’s true altitude is lower than the altitude indicated by the pressure altimeter due to 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 (ECMWF) atmospheric reanalysis of the global climate (ERA5) data set is combined with the pressure-altitude equation to construct a 30-year monthly climatology covering much of the U.S. 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 (~ −1,200 feet) in northern, low-terrain regions for flights near a pressure altitude of 3,600 m, meaning the aircraft would be nearly 350 m lower than the altimeter indicates. Furthermore, the minimum (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 to estimated values calculated using a simple rule-of-thumb based solely on the air temperature at altitude and the surface elevation. The rule-of-thumb tends to under-predict the magnitude of the estimated error, in some cases by 70 m (~200 feet), and therefore gives a lower margin of safety.

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Juan Sulca, Mathias Vuille, Oliver Elison Timm, Bo Dong, and Ricardo Zubieta

Abstract

Precipitation is one of the most difficult variables to estimate using large-scale predictors. Over South America (SA), this task is even more challenging, given the complex topography of the Andes. Empirical–statistical downscaling (ESD) models can be used for this purpose, but such models, applicable for all of SA, have not yet been developed. To address this issue, we construct an ESD model using multiple-linear-regression techniques for the period 1982–2016 that is based on large-scale circulation indices representing tropical Pacific Ocean, Atlantic Ocean, and South American climate variability, to estimate austral summer [December–February (DJF)] precipitation over SA. Statistical analyses show that the ESD model can reproduce observed precipitation anomalies over the tropical Andes (Ecuador, Colombia, Peru, and Bolivia), the eastern equatorial Amazon basin, and the central part of the western Argentinian Andes. On a smaller scale, the ESD model also shows good results over the Western Cordillera of the Peruvian Andes. The ESD model reproduces anomalously dry conditions over the eastern equatorial Amazon and the wet conditions over southeastern South America (SESA) during the three extreme El Niños: 1982/83, 1997/98, and 2015/16. However, it overestimates the observed intensities over SESA. For the central Peruvian Andes as a case study, results further show that the ESD model can correctly reproduce DJF precipitation anomalies over the entire Mantaro basin during the three extreme El Niño episodes. Moreover, multiple experiments with varying predictor combinations of the ESD model corroborate the hypothesis that the interaction between the South Atlantic convergence zone and the equatorial Atlantic Ocean provoked the Amazon drought in 2015/16.

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James R. Campbell, Erica K. Dolinar, Simone Lolli, Gilberto J. Fochesatto, Yu Gu, Jasper R. Lewis, Jared W. Marquis, Theodore M. McHardy, David R. Ryglicki, and Ellsworth J. Welton

Abstract

Cirrus cloud daytime top-of-the-atmosphere radiative forcing (TOA CRF) is estimated for a 2-yr NASA Micro-Pulse Lidar Network (532 nm; MPLNET) dataset collected at Fairbanks, Alaska. Two-year-averaged daytime TOA CRF is estimated to be between −1.08 and 0.78 W·m−2 (from −0.49 to 1.10 W·m−2 in 2017, and from −1.67 to 0.47 W·m−2 in 2018). This subarctic study completes a now trilogy of MPLNET ground-based cloud forcing investigations, following midlatitude and tropical studies by Campbell et al. at Greenbelt, Maryland, and Lolli et al. at Singapore. Campbell et al. hypothesize a global meridional daytime TOA CRF gradient that begins as positive at the equator (2.20–2.59 W·m−2 over land and from −0.46 to 0.42 W·m−2 over ocean at Singapore), becomes neutral in the midlatitudes (0.03–0.27 W·m−2 over land in Maryland), and turns negative moving poleward. This study does not completely confirm Campbell et al., as values are not found as exclusively negative. Evidence in historical reanalysis data suggests that daytime cirrus forcing in and around the subarctic likely once was exclusively negative. Increasing tropopause heights, inducing higher and colder cirrus, have likely increased regional forcing over the last 40 years. We hypothesize that subarctic interannual cloud variability is likely a considerable influence on global cirrus cloud forcing sensitivity, given the irregularity of polar versus midlatitude synoptic weather intrusions. This study and hypothesis lay the basis for an extrapolation of these MPLNET experiments to satellite-based lidar cirrus cloud datasets.

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Wenxin Fan, Yi Liu, Adrian Chappell, Li Dong, Rongrong Xu, Marie Ekström, Tzung-May Fu, and Zhenzhong Zeng

Abstract

Global reanalysis products are important tools across disciplines to study past meteorological changes and are especially useful for wind energy resource evaluations. Studies of observed wind speed show that land surface wind speed declined globally since the 1960s (known as global terrestrial stilling) but reversed with a turning point around 2010. Whether the declining trend and the turning point have been captured by reanalysis products remains unknown so far. To fill this research gap, a systematic assessment of climatological winds and trends in five reanalysis products (ERA5, ERA-Interim, MERRA-2, JRA-55, and CFSv2) was conducted by comparing gridcell time series of 10-m wind speed with observational data from 1439 in situ meteorological stations for the period 1989–2018. Overall, ERA5 is the closest to the observations according to the evaluation of climatological winds. However, substantial discrepancies were found between observations and simulated wind speeds. No reanalysis product showed similar change to that of the global observations, although some showed regional agreement. This discrepancy between observed and reanalysis land surface wind speed indicates the need for prudence when using reanalysis products for the evaluation and prediction of winds. The possible reasons for the inconsistent wind speed trends between reanalysis products and observations are analyzed. The results show that wind energy production should select different products for different regions to minimize the discrepancy with observations.

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