Browse

You are looking at 71 - 80 of 9,647 items for :

  • Journal of Applied Meteorology and Climatology x
  • Refine by Access: All Content x
Clear All
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.

Restricted access
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.

Restricted access
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.

Restricted access
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.

Restricted access
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.

Restricted access
P. Philip and B. Yu

Abstract

Rainfall in the southwest of Western Australia (SWWA) has decreased significantly over recent decades. Previous studies documented this decrease in terms of the change in rainfall depth or decrease in the frequency of rainfall events for selected sites. Although rainfall volume is of vital importance to determine water resources availability for a given region, no study has yet been undertaken to examine the change in rainfall volume in SWWA. The aim of this study is to examine the spatiotemporal changes in rainfall volume and to attribute this change to the changes in wet area and rainfall depth. Gridded daily rainfall data at 0.05° resolution for the period from 1911 to 2018 were used for an area of 265 952 km2 in SWWA. For the whole region and most zones, rainfall volume decreased, which was mostly due to a decrease in the wet area, despite an increase in the mean rain depth. In the regions near the coast with mean annual rainfall ≥ 600 mm, 84% of the decrease in rainfall volume could be attributed to a decrease in the wet area, whereas the decrease in rainfall depth only played a minor role. The regions near the coast with a higher number of rain days showed a decreasing trend in wet area, and the regions farther inland with a lower number of rain days showed an increasing trend in wet area. On the coast, the rate of decrease in rainfall has been reduced, and heavy rainfall, in fact, has increased over the past 30 years, although there was no concurrent change in the southern annular mode (SAM). This suggests that the relationship between SAM and rainfall could have changed and that other climate drivers could also be responsible for the recent rainfall trend and variations in the coastal regions of SWWA.

Restricted access
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, although these methods rely heavily 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 2004–16 Atlantic Ocean 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 with 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, whereas 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 midlevel vorticity magnitudes could be useful predictors for RI.

Restricted access
Kyle A. Hilburn, Imme Ebert-Uphoff, and Steven D. Miller

Abstract

The objective of this research is to develop techniques for assimilating GOES-R series observations in precipitating scenes for the purpose of improving short-term convective-scale forecasts of high-impact weather hazards. Whereas one approach is radiance assimilation, the information content of GOES-R radiances from its Advanced Baseline Imager saturates in precipitating scenes, and radiance assimilation does not make use of lightning observations from the GOES Lightning Mapper. Here, a convolutional neural network (CNN) is developed to transform GOES-R radiances and lightning into synthetic radar reflectivity fields to make use of existing radar assimilation techniques. We find that the ability of CNNs to utilize spatial context is essential for this application and offers breakthrough improvement in skill compared to traditional pixel-by-pixel based approaches. To understand the improved performance, we use a novel analysis method that combines several techniques, each providing different insights into the network’s reasoning. Channel-withholding experiments and spatial information–withholding experiments are used to show that the CNN achieves skill at high reflectivity values from the information content in radiance gradients and the presence of lightning. The attribution method, layerwise relevance propagation, demonstrates that the CNN uses radiance and lightning information synergistically, where lightning helps the CNN focus on which neighboring locations are most important. Synthetic inputs are used to quantify the sensitivity to radiance gradients, showing that sharper gradients produce a stronger response in predicted reflectivity. Lightning observations are found to be uniquely valuable for their ability to pinpoint locations of strong radar echoes.

Open access
Jingjing Dou, Robert Bornstein, Shiguang Miao, Jianning Sun, and Yizhou Zhang

Abstract

The aim of this study was the analysis and simulation of the life cycle of a bifurcating thunderstorm that passed over Beijing, China, on 22 July 2015. Data from 150 surface weather sites and an S-band radar were used in conjunction with WRF simulations that used its multilevel Building Environment Parameterization (BEP) urbanization option. The Urban-case simulation used Beijing land-use information, and the NoUrban one replaced all urban areas by croplands. The Urban case correctly simulated both the observed weak 10-m winds over Beijing (<1.0 m s−1) and the weak 2-m urban heat island (<0.5°C). Observed radar and rain gauge data, as well as the Urban-case results, all showed precipitation bifurcation around Beijing, with maximum accumulations in convergent flow areas on either side of the city. The Urban case also reproduced the observed precipitation minima over the urban area and in a downwind rain shadow. The observations and Urban-case results both also showed bifurcated flow, even when the storm was still upwind of Beijing. The subsequent bifurcated precipitation areas thus each moved along a preexisting flow branch. Urban-case vertical sections showed downward motion in the divergence areas over the urban core and upward motions over the lateral convergence zones, both up to 6 km. Given that the NoUrban case showed none of these features, these differences demonstrate how the impact of cities can extend upward into deep local convection. Additional case-study simulations are needed to more fully understand urban storm bifurcation mechanisms in this and other storms for cities in a variety of climates.

Restricted access
Lea Hartl, Martin Stuefer, Tohru Saito, and Yoshitomi Okura

Abstract

We present the data records and station history of an automatic weather station (AWS) on Denali Pass (5715 m MSL), Alaska. The station was installed by a team of climbers from the Japanese Alpine Club after a fatal accident involving Japanese climbers in 1989 and was operational intermittently between 1990 and 2007, measuring primarily air temperature and wind speed. In later years, the AWS was operated by the International Arctic Research Center of the University of Alaska Fairbanks. Station history is reconstructed from available documentation as archived by the expedition teams. To extract and preserve data records, the original datalogger files were processed. We highlight numerous challenges and sources of uncertainty resulting from the location of the station and the circumstances of its operation. The data records exemplify the harsh meteorological conditions at the site: air temperatures down to approximately −60°C were recorded, and wind speeds reached values in excess of 60 m s−1. Measured temperatures correlate strongly with reanalysis data at the 500-hPa level. An approximation of critical wind speed thresholds and a reanalysis-based reconstruction of the meteorological conditions during the 1989 accident confirm that the climbers faced extremely hazardous wind speeds and very low temperatures. The data from the Denali Pass AWS represent a unique historical record that can, we hope, serve as a basis for further monitoring efforts in the summit region of Denali.

Open access