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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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Richard Seager, Jennifer Nakamura, and Mingfang Ting

national economy. Often not recognized, drought also has serious impacts on the mental health of farming families and people in agricultural communities with long-lasting effects [see U.S.-based review by Vins et al. (2015) ]. Improved understanding and forecasting of drought at least provides the possibility of improved anticipation of, and adaptation to, drought conditions with potential benefits for people and society. Understanding the physical causes of droughts in North America, and the relative

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Chul-Su Shin, Bohua Huang, Paul A. Dirmeyer, Subhadeep Halder, and Arun Kumar

temperature (e.g., Yoon et al. 2012 ; Yuan and Wood 2013 ; Dutra et al. 2014 ; Mo and Lyon 2015 ). Current seasonal coupled forecast systems can predict major oceanic and atmospheric anomalies at useful lead times (e.g., Jin et al. 2008 ; Kirtman et al. 2014 ; Huang et al. 2017a ; Shin et al. 2019 ). However, the current level of skill in forecasting drought onset, development, and demise is limited (e.g., Quan et al. 2012 ; Mo and Lyon 2015 ), possibly because other sources of predictability

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Yizhou Zhuang, Amir Erfanian, and Rong Fu

regression models. 2. Data and methodology a. Data We used the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERAI) ( Dee et al. 2011 ) for the moisture budget analysis described in section 2c . The ERAI reanalysis provides 6-hourly upper-air parameters from 1979 to near-real-time and its data are available online ( http://apps.ecmwf.int/datasets/data/interim-full-daily/levtype=sfc/ ). The atmospheric model has a hybrid sigma-pressure vertical coordinate system with 60

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Chul-Su Shin, Paul A. Dirmeyer, Bohua Huang, Subhadeep Halder, and Arun Kumar

1. Introduction Useful predictability of deterministic weather forecasts is usually no more than 2 weeks, limited by the sensitivity to the atmospheric initial state, while longer memory from ocean heat content plays a dominant role in the climate predictability on seasonal and longer time scales (e.g., Lorenz 1963 , 1975 ; Shukla 1985 ; Lorenz 1993 ). There is a gap between the two time scales of weather and climate predictions, where inertia in the land surface, such as soil moisture

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Keyhan Gavahi, Peyman Abbaszadeh, Hamid Moradkhani, Xiwu Zhan, and Christopher Hain

) techniques with these models has been highly recommended because it improves the accuracy of water and energy balance computations and increases the model’s predictive skills ( Reichle et al. 2014 ; Sawada et al. 2015 ; Seo et al. 2003 ). Earth system DA seeks to exploit real-time observations for more accurate hydrologic forecasts ( Kumar et al. 2014 ; Reichle et al. 2014 ). DA aims at merging current and past observations with a dynamical model, using the model’s prognostic equations to estimate

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Wen-Ying Wu, Zong-Liang Yang, and Michael Barlage

have demonstrated the role of groundwater in modulating land surface fluxes and vegetation during droughts ( Barlage et al. 2015 ; Maxwell and Kollet 2008 ; Fan et al. 2017 ) and in the partitioning of evapotranspiration ( Maxwell and Condon 2016 ). Vegetation phenology can affect the land surface energy budget, and accounting for vegetation growth and groundwater in coupled land surface and climate models could improve seasonal precipitation forecasts over the central United States in summer

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David M. Mocko, Sujay V. Kumar, Christa D. Peters-Lidard, and Shugong Wang

1. Introduction Estimates of terrestrial water budget components from land surface models (LSMs) are routinely used in drought monitoring and forecasting environments (e.g., Mo et al. 2011 ; Houborg et al. 2012 ; Hao et al. 2014 ; Sheffield et al. 2014 ). In particular, Land Data Assimilation Systems (LDASs), which employ LSMs forced with observed meteorology, are used for operational drought monitoring at continental and global scales. The North American Land Data Assimilation System

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Yaling Liu, Dongdong Chen, Soukayna Mouatadid, Xiaoliang Lu, Min Chen, Yu Cheng, Zhenghui Xie, Binghao Jia, Huan Wu, and Pierre Gentine

; Miralles et al. 2014 ), and they may also affect the intensity, frequency, and distribution of precipitation ( Findell et al. 2011 ; Guillod et al. 2015 ; Taylor et al. 2012 ). Further, a shortage or excess of SM could trigger the occurrence of droughts ( Wang et al. 2011 ) or floods ( Koster et al. 2010 ). As such, SM is crucial for weather prediction, climate forecasting and ecosystem dynamics assessment. Moreover, SM is vital for agricultural production since it is the only direct source for crop

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Lu Su, Qian Cao, Mu Xiao, David M. Mocko, Michael Barlage, Dongyue Li, Christa D. Peters-Lidard, and Dennis P. Lettenmaier

Forecasting (WRF) regional atmospheric model in the NWM. Noah-MP extends the capabilities of the Noah LSM ( Chen et al. 1996 ; Chen and Dudhia 2001 ) and incorporates multiple options for key land–atmosphere interaction processes, such as surface water infiltration, runoff, groundwater transfer, and options for representing snow albedo and vegetation growth ( Niu et al. 2007 ; Niu et al. 2011 ). Here we used only Noah-MP, with its forcings provided by observations as described above, rather than in the

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