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David A. Lavers, N. Bruce Ingleby, Aneesh C. Subramanian, David S. Richardson, F. Martin Ralph, James D. Doyle, Carolyn A. Reynolds, Ryan D. Torn, Mark J. Rodwell, Vijay Tallapragada, and Florian Pappenberger

.g., Uttal et al. 2002 ), and cloud processes ( Flamant et al. 2018 ). In January and February 2018, there was an observational campaign called Atmospheric River Reconnaissance (AR Recon) in which research aircraft released dropsondes into atmospheric rivers (ARs; Ralph et al. 2018 ) and other dynamically active regions across the eastern North Pacific Ocean, along with radiosondes from sites in California. ARs are important because they are responsible for much of the water vapor flux across the

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N. L. Uhlenbrock, K. M. Bedka, W. F. Feltz, and S. A. Ackerman

. 1992 ; Salomonson et al. 2002 ) instruments aboard the National Aeronautics and Space Administration (NASA) Earth Observing System (EOS) Aqua and Terra satellites. MODIS observations include measurements in the water vapor absorption channel at a 1-km nadir spatial resolution. These measurements provide a new opportunity to examine the spatial characteristics of mountain wave phenomena, particularly in clear-air conditions. The paper investigates mountain wave signatures in water vapor imagery

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Haiyan Jiang, Jeffrey B. Halverson, Joanne Simpson, and Edward J. Zipser

), vertical wind shear, upper-tropospheric eddy-relative angular momentum flux convergence (ERFC), and tropospheric water vapor flux ( Frank 1977 ). Many studies have shown that the sea surface temperatures (SSTs) warmer than 26°C and vertical wind shear of less than 10 m s −1 are necessary conditions for initiating and maintaining inner-core convective bursts and outer-core convective rings ( Emanuel 1986 ; Merrill 1988 ; Mundell 1991 ; Rodgers et al. 1994 , 2000 ; Rodgers and Pierce 1995 ; Frank

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Shibo Gao, Juanzhen Sun, Jinzhong Min, Ying Zhang, and Zhuming Ying

data assimilation (3DVar) system had a noticeable impact on the short-term precipitation prediction of summer convective events. Although cloud analysis has proven useful for convective-scale data assimilation and precipitation forecasting, problems remain. One is that most cloud analysis schemes mainly seek to enhance humidity (e.g., by assuming saturation within cloud) or to add latent heat where radar reflectivity indicates convection. However, adding water vapor in the observed convective area

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Wenyu Qiu, Liguang Wu, and Fumin Ren

north of the TCs in the RI cases, closer to the WNPSH. Fig . 6. As in Fig. 5 , but for the 600-hPa synoptic-scale wind field (vectors) and unfiltered relative humidity field (shaded). Forecasters in China have long recognized the importance of the moist air transport associated with the strengthening southwesterly monsoon flow on the southern flank of the monsoon trough, which is often called the monsoon surge. Figure 7 shows the 850-hPa low-pass-filtered wind field and the water vapor flux. For

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Abdoulaye Deme, Alain Viltard, and Pierre de Félice

that the order of the predictors appearing in the equations was unchanged. In the 1-day lead-time stepwise forward regression equation, the first index chosen was the lifting condensation level for 1000 hPa (index 1) taken at 17.5°N, 10°W, the second index chosen was the total water vapor zonal flux (index 61), and the third index chosen was the vertical velocity at 500 hPa (index 35). In the 3-day lead-time equation, the first index chosen was again index 1, but at 15°N, 5°E, the second index

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Paul A. Sisson and John R. Gyakum

the Burlington, Vermont, station. The composites of sea level pressure (SLP) and 1000–500-hPa thickness for three classifications of precipitation amounts are examined in section 4 . Surface cyclone tracks are studied in section 5 . Water vapor transports and precipitable water composites are discussed in section 6 . A concluding discussion is given in section 7 . 2. Data and methodology For the purpose of this study, we define the cold season as the period between November and March

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Robert M. Rabin, Lynn A. McMurdie, Christopher M. Hayden, and Gary S. Wade

M. HAYDEN, AND GARY S. WADENOAA /National Environmental Satellite Data and Information Service, Systems Design and Applications Branch, Madison, Wisconsin(Manuscript received 26 July 1990, in final form 1 February 1991)ABSTRACT Spatial and temporal changes of atmospheric water vapor and surface wind speeds are investigated for aperiod following an intrusion of cold continental air over the Gulf of Mexico, during the Gulf of MexicoExperiment (GUFMEX) in March 1988. Microwave and infrared

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Dongliang Wang, Xudong Liang, Yihong Duan, and Johnny C. L. Chan

1. Introduction The scarcity of observations, both near the storm center and in the surrounding environment, is a key factor in limiting the accuracy of tropical cyclone (TC) forecasts. With geostationary satellite imageries, some winds over the data-void regions could be derived by tracking cloud and water vapor features, which are generally referred to as atmospheric motion vectors (AMVs). This ability makes AMVs particularly useful for studying TCs ( Velden et al. 1998 ). Although AMVs have

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Philippe Arbogast, Karine Maynard, and Catherine Piriou

of PV modification, inspired by information derived from satellite imagery ( Rosting et al. 1996 ; Mansfield 1997 ; Browning 1997 ; Demirtas and Thorpe 1999 ). Georgiev and Martin (2001) and Santurette and Georgiev (2005) have shown that model PV fields near the tropopause can be checked against satellite images of water vapor (WV). The idea here is that when an error is detected, one should be able to modify the PV of a model field based on WV observations and subsequently let a numerical

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