Search Results

You are looking at 1 - 10 of 13 items for :

  • Progress in Advancing Drought Monitoring and Prediction x
  • All content x
Clear All
Anthony M. DeAngelis, Hailan Wang, Randal D. Koster, Siegfried D. Schubert, Yehui Chang, and Jelena Marshak

directly from station data. Results using the CPC data are not explicitly shown but are discussed throughout the paper where appropriate. To evaluate soil moisture initialization accuracy in the SubX ensemble, we utilize data from phase 2 of the North American Land Data Assimilation System (NLDAS-2) ( Xia et al. 2012 ). NLDAS-2 is a collection of LSMs that were run offline and driven with common atmospheric forcing data to yield various surface fields over North America over the period from 1979 to

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

–south dipole structure over CONUS largely induced by the remote ENSO forcing (e.g., Huang et al. 2019 ; among many others). Fig . 1. Anomaly correlation coefficient maps of 3-month SPI (SPI3) for 1979–2010 at (a) 1-month lead (October), (b) 3-month lead (December), and (c) 5-month lead (February) in (left) the CFSR reforecasts and (center) the GLDAS reforecasts with October initial conditions (ICs), and (right) difference between GLDAS and CFSR reforecasts. Dashed curves in the left and center panels

Restricted access
Yizhou Zhuang, Amir Erfanian, and Rong Fu

2012 over much of the Great Plains. The delayed response of a regional climate to slowly varying oceanic forcing and land–atmosphere interaction provides the foundation for seasonal prediction over many regions around the world. State-of-the-art seasonal prediction models provide relatively skillful predictions of winter hydroclimate over the United States, but show virtually no skill in prediction of summer rainfall anomalies over much of the North American continent ( Quan et al. 2012 ). Seasonal

Restricted access
Richard Seager, Jennifer Nakamura, and Mingfang Ting

-forcing-data . To examine the large-scale context of the DO&Ts, we use geopotential heights and SSTs from the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) Reanalysis ( Kistler et al. 2001 ; obtained from ) and precipitation over land and sea from the Global Precipitation Climatology Project (GPCP) version 2.3 ( Huffman et al. 1997 ; obtained from https

Restricted access
Kingtse C. Mo and Dennis P Lettenmaier

soil moisture (SM), and/or runoff deficits, usually for 6 months or longer ( Svoboda et al. 2002 ). Flash droughts have much shorter durations—typically a few weeks. Furthermore, while conventional droughts develop slowly, a key feature of flash droughts is their rapid onset and intensification ( Pendergrass et al. 2020 ). Mo and Lettenmaier (2015 , 2016) studied flash droughts over the United States, and classified them into two categories based on their forcings: heat wave flash drought ( Mo

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

atmospheric data such as precipitation and relative humidity, or land surface data acquisition such as SM and ET. The latter can be indirectly assimilated into the land surface models to achieve more accurate and reliable predictions of hydrologic fluxes as well as for monitoring purposes ( Kumar et al. 2014 ; Pan and Wood 2006 ; Pipunic et al. 2008 ; Reichle et al. 2014 ; Sawada et al. 2015 ; Xu et al. 2020 ). SM prediction using land surface models driven by meteorological forcing carries

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

, we discuss development of Noah-MP model forcings in section 2 . In section 3 , we discuss our experimental design, and the role of the Noah-MP model. In section 4 , we interpret and discuss our results, and we summarize and conclude in section 5 . 2. Forcing description The model’s surface meteorological forcings are hourly precipitation P , near-surface temperature, near-surface wind, near-surface humidity, downward shortwave and longwave radiation, and surface pressure. Our preparation of

Restricted access
Christa D. Peters-Lidard, David M. Mocko, Lu Su, Dennis P. Lettenmaier, Pierre Gentine, and Michael Barlage

represents an ongoing interest of NOAA’s Drought Task Force (DTF; Wood et al. 2015 ; Hoerling et al. 2014 ; Schubert et al. 2016 ), which has extensively discussed and evaluated the merits of drought indicators, especially in the context of compound drought events with strong temperature and precipitation contributions and under emergent conditions such as climate change and increased human management of the water cycle ( Zhou et al. 2019a , b ). These discussions build on years of DTF science

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

also used in the National Water Model (NWM; Gochis et al. 2020 ), which forecasts real-time streamflow conditions. In this study, we used an offline 2D Noah-MP configuration based on the High-Resolution Land Data Assimilation System (HRLDAS) as implemented in WRF, version 3.8. Hourly atmospheric forcing data (precipitation, air temperature, humidity, surface pressure, wind speed, and surface radiation) were taken from the NLDAS from the National Aeronautics and Space Administration (NASA; Xia et

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

table, and a dynamic vegetation scheme. The dynamic vegetation scheme adds biomass terms as prognostic variables, and the LAI is diagnosed from these masses, instead of input from maps or tables. Ma et al. (2017) used the dynamic vegetation scheme in Noah-MP with NLDAS Phase 2 (NLDAS-2) forcing, and found that the LSM was generally able to reproduce drought when compared to GRACE anomalies. However, Ma et al. (2017) also noted that Noah-MP was not able to accurately capture some recent droughts

Restricted access