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Rory G. J. Fitzpatrick, Caroline L. Bain, Peter Knippertz, John H. Marsham, and Douglas J. Parker

). The AODM is triggered every year across most locations in West Africa except toward northern West Africa (14°–16°N). Interannual variability in the AODM is high over much of continental West Africa with local standard deviations of more than two weeks being common ( Fig. 1b ). Conversely, in the longitude bound 10°–20°W, local standard deviation of the AODM is generally lower than elsewhere within our studied region. The high variability of the AODM found over much of West Africa suggests that

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Shun-ichi I. Watanabe, Hiroshi Niino, and Wataru Yanase

and Niino 2014 ). Fig . 2. Geographical distribution of the locations of individual PMCs observed in satellite images over the Sea of Japan and its vicinity during the winter of 1983/84 (adapted from A88 ). The large, medium, and small dots correspond to the PMCs with horizontal scale 150–300, 70–150, and 20–70 km, respectively. Several mechanisms have been proposed for the development of PMCs. Asai and Miura (1981) and Nagata (1993) suggested that small PMCs are related to the horizontal

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Thomas W. Collow, Wanqiu Wang, Arun Kumar, and Jinlun Zhang

December averaged for 2009–13 between 170° and 200°E. The NASA Team and NASA Bootstrap data were essentially the same with a strong seasonal cycle ( Figs. 5e,f ). Taking the location of 15%–30% concentration to measure the sea ice evolution, the observed sea ice retreated northward from near 65°N in June to 76°N in August and September and then expanded southward reaching 62°N in December. CFSv2CFSR produced weak seasonal cycle with 15%–30% concentration reaching slightly to the north of 70°N in

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Stephanie N. Stevenson, Kristen L. Corbosiero, and Sergio F. Abarca

lightning (cloud-to-ground and intracloud) activity over the open oceans. The OTD instrument observed cloud-top lightning illumination with a 46%–69% detection efficiency and 20–40-km location accuracy during 1995–2000 ( Boccippio et al. 2000 ). The LIS instrument, a follow-on to the OTD sensor, collected data during 1998–2015 with a detection efficiency of 62%–97% depending on the time of day ( Boccippio et al. 2002 ). Cecil and Zipser (1999) analyzed the relationship between satellite

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Mateusz Taszarek, Bartosz Czernecki, and Aneta Kozioł

intensity of thunderstorms ( Rakov and Uman 2003 ). Storms with either one lightning strike or thousands of flashes are reported as one thunderstorm. This however can be examined using lightning detection that allows one to count the number of flashes on particular days and in particular locations. CG lightning flash climatologies based on data from ground-based lightning detection networks have been developed for some European countries. a. Central Europe Based on data recorded between 1992 and 2001

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Ronald L. Holle, Kenneth L. Cummins, and William A. Brooks

stroke location accuracy (LA) have been validated over Florida ( Mallick et al. 2014 ). The validation showed a GLD360 CG flash DE (relative to the NLDN in Florida) of 67%, a CG stroke DE of 37%, and a CG stroke median LA of 2.0 km. The performance of GLD360 over North America is estimated to be a CG flash DE of 70% and a median CG stroke LA of 2–5 km. GLD360 stroke densities in the second portion of this study are also in 20 km by 20 km grid squares within geographical boundaries extending beyond

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Salvatore Pascale and Simona Bordoni

Abstract

In this study ERA-Interim data are used to study the influence of Gulf of California (GoC) moisture surges on the North American monsoon (NAM) precipitation over Arizona and western New Mexico (AZWNM), as well as the connection with larger-scale tropical and extratropical variability. To identify GoC surges, an improved index based on principal component analyses of the near-surface GoC winds is introduced. It is found that GoC surges explain up to 70% of the summertime rainfall over AZWNM. The number of surges that lead to enhanced rainfall in this region varies from 4 to 18 per year and is positively correlated with annual summertime precipitation. Regression analyses are performed to explore the relationship between GoC surges, AZWNM precipitation, and tropical and extratropical atmospheric variability at the synoptic (2–8 days), quasi-biweekly (10–20 days), and subseasonal (25–90 days) time scales. It is found that tropical and extratropical waves, responsible for intrusions of moist tropical air into midlatitudes, interact on all three time scales, with direct impacts on the development of GoC surges and positive precipitation anomalies over AZWNM. Strong precipitation events in this region are, however, found to be associated with time scales longer than synoptic, with the quasi-biweekly and subseasonal modes playing a dominant role in the occurrence of these more extreme events.

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Matthew A. Janiga and Chris D. Thorncroft

-scale environments and convective properties. The variability of large-scale thermodynamic and dynamic fields as a function of AEW phase and geography is also examined and used to explain the contrasts in convective properties. Section 4 summarizes the results and offers some conclusions. 2. Data and methodology As in the companion paper JT14 , this study uses data from TRMM and the ERA-Interim reanalysis from the period June–September (JAS) 1998–2012. TRMM PR–based precipitation features (PFs), defined as

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H. Reed Ogrosky and Samuel N. Stechmann

average is removed from each dynamical field prior to the regression in order to remove the effects of low-frequency variability due to, for example, ENSO. A separate regression equation is solved for each variable at each longitude, latitude, pressure level, and time lag. The resulting linear regression coefficients are then used to produce a composite picture of the evolution of each wave type. In these composites, the winds are plotted only at locations where they are deemed to be significant at

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Roderick van der Linden, Andreas H. Fink, Tan Phan-Van, and Long Trinh-Tuan

a resting period during the dry season. Besides impacts on the cultivation of coffee and other agricultural products, heavy rainfall bears a risk of flooding and landslides. Fig . 1. Topographic map of the study area. Map of Vietnam and adjacent countries and zoom in on the Central Highlands region. Numbers in the zoomed map correspond with the locations of stations that are used for the analysis of rainfall events ( Figs. 2 , 6 , 9 , and 12 ). The stations are listed in Table 1 . The dashed

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