The Efficiency of Data Assimilation

Nearing, Grey; Yatheendradas, Soni; Crow, Wade T.; Zhan, Xiwu; Liu, Jicheng; Chen, Fan

doi:10.1029/2017wr020991

Cited by 33 publications

(32 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Using OpenForecast runoff forecasts as a first-guess forecast, CAHEM introduces the parsimonious data assimilation technique which uses recently observed runoff data for a further forecast updating procedure. This data assimilation technique significantly improves runoff prediction efficiency, and additionally underlines and confirms the importance of observational data assimilation for getting reliable hydrological modeling results [50,52].…”

Section: Discussionsupporting

confidence: 65%

OpenForecast: The First Open-Source Operational Runoff Forecasting System in Russia

Ayzel

Varentsova

Erina

et al. 2019

Water

View full text Add to dashboard Cite

The development and deployment of new operational runoff forecasting systems are a strong focus of the scientific community due to the crucial importance of reliable and timely runoff predictions for early warnings of floods and flashfloods for local businesses and communities. OpenForecast, the first operational runoff forecasting system in Russia, open for public use, is presented in this study. We developed OpenForecast based only on open-source software and data—GR4J hydrological model, ERA-Interim meteorological reanalysis, and ICON deterministic short-range meteorological forecasts. Daily forecasts were generated for two basins in the European part of Russia. Simulation results showed a limited efficiency in reproducing the spring flood of 2019. Although the simulations managed to capture the timing of flood peaks, they failed in estimating flood volume. However, further implementation of the parsimonious data assimilation technique significantly alleviates simulation errors. The revealed limitations of the proposed operational runoff forecasting system provided a foundation to outline its further development and improvement.

show abstract

Section: Discussionsupporting

confidence: 65%

OpenForecast: The First Open-Source Operational Runoff Forecasting System in Russia

Ayzel

Varentsova

Erina

et al. 2019

Water

View full text Add to dashboard Cite

show abstract

“…The pink shaded areas show the upstream sub-basins of the eight USGS streamflow sites evaluated in this study, with basin numbers labeled on the plot (see Table 1 for basin numbers and corresponding sites). (O'Neill et al, 2016) from 31 March 2015 to 31 December 2017 were used in this study. A few SMAP pixels with obvious quality flaws (i.e., near-constant retrieval values) were manually masked out.…”

Section: Smap Satellite Sm Datamentioning

confidence: 99%

Dual state/rainfall correction via soil moisture assimilation for improved streamflow simulation: evaluation of a large-scale implementation with Soil Moisture Active Passive (SMAP) satellite data

Mao

Crow

Nijssen

2020

Hydrol. Earth Syst. Sci.

Self Cite

View full text Add to dashboard Cite

Abstract. Soil moisture (SM) measurements contain information about both pre-storm hydrologic states and within-storm rainfall estimates, both of which are required inputs for event-based streamflow simulations. In this study, an existing dual state/rainfall correction system is extended and implemented in the 605 000 km2 Arkansas–Red River basin with a semi-distributed land surface model. The Soil Moisture Active Passive (SMAP) satellite surface SM retrievals are assimilated to simultaneously correct antecedent SM states in the model and rainfall estimates from the Global Precipitation Measurement (GPM) mission. While the GPM rainfall is corrected slightly to moderately, especially for larger events, the correction is smaller than that reported in past studies due primarily to the improved baseline quality of the new GPM satellite product. In addition, rainfall correction is poorer in regions with dense biomass due to lower SMAP quality. Nevertheless, SMAP-based dual state/rainfall correction is shown to generally improve streamflow estimates, as shown by comparisons with streamflow observations across eight Arkansas–Red River sub-basins. However, more substantial streamflow correction is limited by significant systematic errors present in model-based streamflow estimates that are uncorrectable via standard data assimilation techniques aimed solely at zero-mean random errors. These findings suggest that more substantial streamflow correction will likely require better quality SM observations as well as future research efforts aimed at reducing systematic errors in hydrologic systems.

show abstract

“…Information theoretic cost metrics have been previously used as likelihood functions for PFs in sensor network control, target tracking, and autonomous robotics (Charrow et al., 2014; Hoffmann & Tomlin, 2010; Zhang et al., 2018), however they have not been applied in hydrology. Some studies have used MI as a diagnostic metric for performance evaluation and observation impact (e.g., Fowler & Van Leeuwen, 2012, 2013; A. M. Fowler et al., 2018; Nearing et al., 2018), but it has never been used within a sequential data assimilation framework as a cost or likelihood function to rank model ensembles. Conversely, MI has been used rather ubiquitously in remote sensing; specifically in intensity based image matching and registration studies (Chen et al., 2003; Hirschmüller, 2008; Horkaew & Puttinaovarat, 2017; Liu et al., 2018; Suri & Reinartz, 2010).…”

Section: Introductionmentioning

confidence: 99%

“…Some studies have used MI as a diagnostic metric for performance evaluation and observation impact (e.g., Fowler & Van Leeuwen, 2012, 2013A. M. Fowler et al, 2018;Nearing et al, 2018), but it has never been used within a sequential data assimilation framework as a cost or likelihood function to rank model ensembles. Conversely, MI has been used rather ubiquitously in remote sensing; specifically in intensity based image matching and registration studies (Chen et al, 2003;Hirschmüller, 2008;Horkaew & Puttinaovarat, 2017;Liu et al, 2018;Suri & Reinartz, 2010).…”

mentioning

confidence: 99%

A Mutual Information‐Based Likelihood Function for Particle Filter Flood Extent Assimilation

Dasgupta

Hostache

Ramsankaran

et al. 2021

Water Resources Research

View full text Add to dashboard Cite

Accurate flood inundation forecasts have the potential to minimize socioeconomic losses, but uncertainties in inflows propagated from the precipitation forecasts result in large prediction errors. Recent studies suggest that by assimilating independent flood observations, inherent uncertainty in hydraulic flood inundation modeling can be mitigated. Satellite observations from Synthetic Aperture Radar (SAR) sensors, with demonstrated flood monitoring capability, can thus be used to reduce flood forecast uncertainties through assimilation. However, researchers have struggled to develop an appropriate cost function to determine the innovation to be applied at each assimilation time step. Thus, a novel likelihood function based on mutual information (MI) is proposed here, for use with a particle filter‐based (PF) flood extent assimilation framework. Using identical twin experiments, synthetic SAR‐based probabilistic flood extents were assimilated into the hydraulic model LISFLOOD‐FP using the proposed PF‐MI algorithm. The 2011 flood event in the Clarence Catchment, Australia was used for this study. The impact of assimilating flood extents was evaluated in terms of subsequent flood extent evolution, floodplain water depths, flow velocities and channel water levels (WLs). Water depth and flow velocity simulations improved by ∼60% over the open loop on an average and persisted for up to 7 days, following the sequential assimilation of two post‐peak flood extent observations. Flood extents and channel WLs also showed mean improvements of ∼10% and ∼80% in accuracy, respectively, indicating that the proposed MI likelihood function can improve flood extent assimilation.

show abstract

The Efficiency of Data Assimilation

Cited by 33 publications

References 43 publications

OpenForecast: The First Open-Source Operational Runoff Forecasting System in Russia

OpenForecast: The First Open-Source Operational Runoff Forecasting System in Russia

Dual state/rainfall correction via soil moisture assimilation for improved streamflow simulation: evaluation of a large-scale implementation with Soil Moisture Active Passive (SMAP) satellite data

A Mutual Information‐Based Likelihood Function for Particle Filter Flood Extent Assimilation

Contact Info

Product

Resources

About