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Alberto Ortolani, Francesca Caparrini, Samantha Melani, Luca Baldini, and Filippo Giannetti

operational limitations of the standard KF, namely, (i) the nonlinearity of many dynamical systems and (ii) the high computational effort required for the storage and forward integration of the forecast error covariance in large systems. In the EnKF, the covariance is estimated from the generation of an ensemble (statistical sample) of state replications. The EnKF has proved to be a robust estimator even in presence of deviation from Gaussianity assumption ( Katzfuss et al. 2016 ). Many successful

Open access
Yingzhao Ma, V. Chandrasekar, Haonan Chen, and Robert Cifelli

the contribution of lateral terrestrial water flow on regionally hydrological cycle. Coupled with the height above nearest drainage (HAND) technique, the National Water Model (NWM) system with its core component as WRF-Hydro offers an operational framework for real-time and forecast flood guidance across the contiguous United States ( Johnson et al. 2019 ). As noted above, the WRF-Hydro system has been implemented for a wide range of research and operational prediction problems over the world

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Zhe Li, Daniel B. Wright, Sara Q. Zhang, Dalia B. Kirschbaum, and Samantha H. Hartke

produced by the model’s dynamical equations and parameterizations, which are constrained through the assimilation of satellite radiances ( Benjamin et al. 2019 ). Hence, we refer to this as the “physics-based” approach. A number of datasets, particularly reanalyses such as the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2; Gelaro et al. 2017 ) from NASA and ERA5 ( Hersbach et al. 2018 ) from the European Centre for Medium-Range Weather Forecasts assimilate PMW TBs

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Phu Nguyen, Mohammed Ombadi, Vesta Afzali Gorooh, Eric J. Shearer, Mojtaba Sadeghi, Soroosh Sorooshian, Kuolin Hsu, David Bolvin, and Martin F. Ralph

the daily and subdaily scales are more important from the standpoint of operational watershed hydrology and water resources management for applications such as flood forecasting. Furthermore, since PDIR-Now is an IR-based precipitation dataset, it is intended to be particularly advantageous in providing timely and adequate precipitation estimates when other datasets based on PMW and multisensor fusion are not available. With these considerations in mind, analysis of PDIR-Now at the daily and

Open access
Samantha H. Hartke, Daniel B. Wright, Dalia B. Kirschbaum, Thomas A. Stanley, and Zhe Li

model to generate landslide hazard forecasts. We anticipate that other environmental prediction models such as the Global Flood Monitoring System ( Wu et al. 2012 ) and the Global Land Data Assimilation System (GLDAS; Fang et al. 2009 ) could likely benefit from deeper consideration of SMPP uncertainty and error—both systematic and random. In this study, we found it necessary to modify (albeit modestly) the operational LHASA framework to accommodate IMERG uncertainty estimates, as opposed to

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Akhil Sanjay Potdar, Pierre-Emmanuel Kirstetter, Devon Woods, and Manabendra Saharia

options to adjust hydrologic model parameters to reproduce the observed behavior of a given watershed (problem of equifinality; Beven 2001 ), while regional modeling involves capturing different hydrologic behaviors that vary with watershed characteristics such as geomorphology, climatology, geology, pedology, etc. Plus, at the flash flood scale process dynamics are driven by the spatial and temporal distribution of rainfall. Existing operational methods of regional flash flood monitoring focus on

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F. Joseph Turk, Sarah E. Ringerud, Yalei You, Andrea Camplani, Daniele Casella, Giulia Panegrossi, Paolo Sanò, Ardeshir Ebtehaj, Clement Guilloteau, Nobuyuki Utsumi, Catherine Prigent, and Christa Peters-Lidard

search of the a priori dataset with the 2-m air temperature (T2m) and TPW conditions, interpolated from (for near-real-time products) an operational global weather forecast model (later reprocessing of the GPROF data utilize these same quantities interpolated from the ERA model reanalysis). These three terms (surface classification index, T2m and TPW) are used to stratify the large a priori dataset for each passive MW radiometer in the constellation. Since these three terms can also be obtained at

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Shruti A. Upadhyaya, Pierre-Emmanuel Kirstetter, Jonathan J. Gourley, and Robert J. Kuligowski

resolutions are critical for near-real-time applications such as rapid monitoring and forecasting of high-impact societal events like flash floods, debris flows, and shallow landslides. Such resolution can be obtained primarily from satellite sensors on board geostationary Earth orbit (GEO) platforms. NOAA’s Advanced Baseline Imager (ABI) sensor on board the latest generation of Geostationary Operational Environmental Satellites (GOES-R Series) provides 3 times more spectral channels, 4 times the

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Clement Guilloteau, Efi Foufoula-Georgiou, Pierre Kirstetter, Jackson Tan, and George J. Huffman

Conference , B. Webb et al., Eds., British Hydrological Society, 120–127. Habib , E. , W. F. Krajewski , and A. Kruger , 2001 : Sampling errors of tipping-bucket rain gauge measurements . J. Hydrol. Eng. , 6 , 159 – 166 , . 10.1061/(ASCE)1084-0699(2001)6:2(159) Harris , D. , E. Foufoula-Georgiou , K. K. Droegemeier , and J. J. Levit , 2001 : Multiscale statistical properties of a high-resolution precipitation forecast . J

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Chandra Rupa Rajulapati, Simon Michael Papalexiou, Martyn P. Clark, Saman Razavi, Guoqiang Tang, and John W. Pomeroy

Forecast System Reanalysis (CFSR) v2, and 5) Water and Global Change (WATCH) Forcing Data–ERA-Interim (WFDEI) version 14 August 2018. The PERSN-CDR, MSWEP, and WFDEI datasets combine information from observations, satellites, and reanalysis. The CPC uses only observations and the CFSR is purely a reanalysis product. PERSN-CDR is derived from the satellite data (Gridsat-B1), adjusted using the precipitation data from Global Precipitation Climatology Project ( Ashouri et al. 2015 ; Nguyen et al. 2018

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