Many Task Computing for Real-Time Uncertainty Prediction and Data Assimilation in the Ocean

Abstract. Opportunities offered by high performance computing provide a significant degree of promise in the enhancement of the performance of real-time flood forecasting systems. In this paper, a real-time framework for probabilistic flood forecasting through data assimilation is presented. The distributed rainfall-runoff real-time interactive basin simulator (RIBS) model is selected to simulate the hydrological process in the basin. Although the RIBS model is deterministic, it is run in a probabilistic way through the results of calibration developed in a previous work performed by the authors that identifies the probability distribution functions that best characterise the most relevant model parameters. Adaptive techniques improve the result of flood forecasts because the model can be adapted to observations in real time as new information is available. The new adaptive forecast model based on genetic programming as a data assimilation technique is compared with the previously developed flood forecast model based on the calibration results. Both models are probabilistic as they generate an ensemble of hydrographs, taking the different uncertainties inherent in any forecast process into account. The Manzanares River basin was selected as a case study, with the process being computationally intensive as it requires simulation of many replicas of the ensemble in real time.

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Hydrological data assimilation with the Ensemble Square-Root-Filter: Use of streamflow observations to update model states for real-time flash flood forecasting

Advances in Water Resources ◽

10.1016/j.advwatres.2013.06.010 ◽

2013 ◽

Vol 59 ◽

pp. 209-220 ◽

Cited By ~ 56

Author(s):

He Chen ◽

Dawen Yang ◽

Yang Hong ◽

Jonathan J. Gourley ◽

Yu Zhang

Keyword(s):

Data Assimilation ◽

Real Time ◽

Flash Flood ◽

Flood Forecasting ◽

Square Root ◽

Hydrological Data ◽

Square Root Filter ◽

Flash Flood Forecasting

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Advancing from Convection-Allowing NWP to Warn-on-Forecast: Evidence of Progress

Weather and Forecasting ◽

10.1175/waf-d-17-0145.1 ◽

2018 ◽

Vol 33 (2) ◽

pp. 599-607 ◽

Cited By ~ 20

Author(s):

John R. Lawson ◽

John S. Kain ◽

Nusrat Yussouf ◽

David C. Dowell ◽

Dustan M. Wheatley ◽

...

Keyword(s):

Data Assimilation ◽

Real Time ◽

Initial Conditions ◽

Spatial Scales ◽

Weather Prediction ◽

Radar Data ◽

Model Systems ◽

Deterministic Systems ◽

Forecast Lead Time ◽

The Impact

Abstract The Warn-on-Forecast (WoF) program, driven by advanced data assimilation and ensemble design of numerical weather prediction (NWP) systems, seeks to advance 0–3-h NWP to aid National Weather Service warnings for thunderstorm-induced hazards. An early prototype of the WoF prediction system is the National Severe Storms Laboratory (NSSL) Experimental WoF System for ensembles (NEWSe), which comprises 36 ensemble members with varied initial conditions and parameterization suites. In the present study, real-time 3-h quantitative precipitation forecasts (QPFs) during spring 2016 from NEWSe members are compared against those from two real-time deterministic systems: the operational High Resolution Rapid Refresh (HRRR, version 1) and an upgraded, experimental configuration of the HRRR. All three model systems were run at 3-km horizontal grid spacing and differ in initialization, particularly in the radar data assimilation methods. It is the impact of this difference that is evaluated herein using both traditional and scale-aware verification schemes. NEWSe, evaluated deterministically for each member, shows marked improvement over the two HRRR versions for 0–3-h QPFs, especially at higher thresholds and smaller spatial scales. This improvement diminishes with forecast lead time. The experimental HRRR model, which became operational as HRRR version 2 in August 2016, also provides added skill over HRRR version 1.

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