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Rajib Maity and S. S. Kashid

1. Introduction The management of land and water resources involves designing and operating water resources systems to cope with variability in rainfall and streamflow with time and space. Such variability in streamflow imposes many challenges in management of risks and opportunities associated with water resources systems. Reliable forecasts of streamflow a few weeks in advance can reasonably improve the management of water resources systems in rural as well as urban environments ( Chiew et al

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Silvio Davolio, Francesco Silvestro, and Piero Malguzzi

is in charge of issuing forecasts and alerts for civil protection purposes. Finally, two modeling studies ( Fiori et al. 2014 ; Buzzi et al. 2014 ) were devoted to the identification of the key numerical aspects impacting the ability to perform quantitative precipitation forecasting (QPF). In particular, the latter study further investigated the main dynamical mechanisms responsible for the triggering, development, and propagation of the precipitating systems, highlighting the combined effect of

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Li Fang, Xiwu Zhan, Christopher R. Hain, Jifu Yin, and Jicheng Liu

integration of a satellite-based GVF dataset into the LSM has made a significant improvement to the NCEP forecasts in the past decades ( Gutman and Ignatov 1998 ; Zeng et al. 2003 ; Jiang et al. 2010 ; James et al. 2009 ; Miller et al. 2006 ; Ruhge and Barlage 2011 ; Yin et al. 2016 ). For instance, the climatological GVF datasets used in the operational NWP models at NCEP are derived from NOAA’s Advanced Very High Resolution Radiometer (AVHRR) top-of-atmosphere normalized difference vegetation

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Paul A. Dirmeyer and Subhadeep Halder

land–atmosphere signatures in an operational forecast model in the context of sensitivity, variability, and memory has not been undertaken. In this paper, we examine the operational forecast model of NOAA/NCEP (CFSv2) to determine its characteristics in each of the three elements of land–atmosphere coupling. This model has been investigated in the context of its existing Climate Forecast System Reanalysis (CFSR) and Reforecast (CFSRR; Saha et al. 2010 ) dataset for land–atmosphere feedbacks

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Jonathan L. Case, William L. Crosson, Sujay V. Kumar, William M. Lapenta, and Christa D. Peters-Lidard

surface temperature (SST) are necessary to better understand earth–atmosphere interactions and to improve numerical predictions of weather and climate phenomena. The hypothesis for this paper is that improved land surface states obtained through an offline land surface model spin-up at higher resolution and with better atmospheric forcing will lead to more accurate short-term forecasts of regional sensible weather elements (e.g., 2-m temperatures and dewpoints) in a mesoscale NWP model. Many previous

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Kingtse C. Mo and Dennis P Lettenmaier

drought often involves P deficits and high T air which start before or coincide with a rapid soil moisture decline in subseasonal time scales. This suggests that T air and P are the main forcings of flash drought, and accurate forecasts of these two variables will be the key to forecast flash droughts. ET plays an important role because it serves as a bridge which provides feedbacks between land and atmosphere, and also controls the rate of change of SM. Notwithstanding a rapidly evolving body

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Ingo Schlüter and Gerd Schädler

the trajectories and position of the synoptic patterns relative to orography and land use. It can be expected that synoptic situations with strong gradients in pressure or airmass properties are especially sensitive to position and trajectory deviations when interacting with complex terrain. The interaction between orography and stratiform precipitation has been studied by Kunz and Kottmeier (2006a , 2006b) within the priority program “Quantitative Precipitation Forecast” [Special Priority

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Kingtse C. Mo and Dennis P. Lettenmaier

seasonally dependent. For the CONUS, the predictability is higher during winter and lower in summer because the teleconnections that link the tropics to the CONUS as well as Rossby wave dynamics are stronger in winter. In contrast, these linkages are weaker during summer, and it is difficult to forecast dominantly convective dynamics, and thus land–atmosphere interactions, during this period. Yuan et al. (2013) evaluated precipitation and runoff forecasts from the National Centers for Environmental

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Rhonalyn V. Macalalad, Roy A. Badilla, Olivia C. Cabrera, and Gerry Bagtasa

, respectively. TY Nona brought heavier rainfall as compared to TY Lando but TY Nona’s response time was relatively longer. This difference is attributed to the associated earlier precipitation recorded upstream. Based on the PRB Flood Forecasting and Warning Center (PRBFFWC) postflood reports ( PRBFFWC 2015 ), TY Lando’s effect on the PRB started with continuous heavy to intense rainfall for almost 24 h that was also observed in an upstream station at Gabaldon (~72 km northeast of Arayat Station). In

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Haksu Lee, Yu Zhang, Dong-Jun Seo, Robert J. Kuligowski, David Kitzmiller, and Robert Corby

1. Introduction Precipitation is a crucial hydrometeorological input for hydrologic models to produce skilful flood prediction. At most U.S. River Forecast Centers (RFCs), the radar–gauge multisensor quantitative precipitation estimate (QPE), or MQPE hereinafter, serves as input data to the Sacramento Soil Moisture Accounting model (SAC-SMA) ( Burnash et al. 1973 ) to support flood forecasting on a daily basis. MQPEs are produced by merging radar and gauge data via the multisensor precipitation

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