Efficient treatment of climate data uncertainty in ensemble Kalman filter (EnKF) based on an existing historical climate ensemble dataset

Liu, Hongli; Thiboult, Antoine; Tolson, Bryan A.; Anctil, François; Mai, Juliane

doi:10.1016/j.jhydrol.2018.11.047

Cited by 16 publications

(12 citation statements)

References 68 publications

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“…Additionally, historical estimates of precipitation and temperature with reliable uncertainty bounds may be used in many impact applications relevant across short‐term and seasonal forecasting, through to climate projections. Uncertainty estimates of precipitation and temperature are fundamental to ensemble data assimilation in hydrologic forecasting applications, and ensemble data generated with this methodology have been shown to be useful in both short‐term streamflow and seasonal volume forecasts (Huang et al, 2017; Liu et al, 2019). Gridded observation uncertainty is also useful when verifying weather and climate forecasts to assign confidence in the observation‐model differences (Liu et al, 2017; Prein & Gobiet, 2017) and could be incorporated into various statistical downscaling methodologies (e.g., Gangopadhyay et al, 2005).…”

Section: Discussionmentioning

confidence: 99%

Probabilistic Spatial Meteorological Estimates for Alaska and the Yukon

et al. 2020

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It is challenging to develop observationally based spatial estimates of meteorology in Alaska and the Yukon. Complex topography, frozen precipitation undercatch, and extremely sparse in situ observations all limit our capability to produce accurate spatial estimates of meteorological conditions. In this Arctic environment, it is necessary to develop probabilistic estimates of precipitation and temperature that explicitly incorporate spatiotemporally varying uncertainty and bias corrections. In this paper we exploit the recently developed ensemble Climatologically Aided Interpolation (eCAI) system to produce daily historical estimates of precipitation and temperature across Alaska and the Yukon Territory at a 2 km grid spacing for the time period 1980-2013. We extend the previous eCAI method to address precipitation gauge undercatch and wetting loss, which is of high importance for this high-latitude region where much of the precipitation falls as snow. Leave-one-out cross-validation shows our ensemble has little bias in daily precipitation and mean temperature at the station locations, with an overestimate in the daily standard deviation of precipitation. The ensemble is statistically reliable compared to climatology and can discriminate precipitation events across different precipitation thresholds. Long-term mean loss adjusted precipitation is up to 36% greater than the unadjusted estimate in windy areas that receive a large fraction of frozen precipitation, primarily due to wind induced undercatch. Comparing the ensemble mean climatology of precipitation and temperature to PRISM and Daymet v3 shows large interproduct differences, particularly in precipitation across the complex terrain of southeast and northern Alaska. Plain Language Summary It is challenging to create spatial maps of precipitation and temperature in Alaska and the Yukon. The rugged terrain and extreme conditions, particularly snow and wind, along with the extremely sparse station networks, limit our ability to estimate historical conditions. Thus, it is critical to understand the uncertainty in our spatial estimates of meteorological conditions. Here we develop a new estimate of uncertainty for historical meteorology and include corrections to precipitation measurements for errors due to snowfall and wind. We show that there are large differences between several independent mapping efforts. We then demonstrate that our ensemble product skillfully predicts the observed probability of occurrence of multiple precipitation events sizes (the ensemble is statistically reliable). Finally, our precipitation corrections increase precipitation estimates by more than 30% in some portions of Alaska.

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Section: Discussionmentioning

confidence: 99%

Probabilistic Spatial Meteorological Estimates for Alaska and the Yukon

et al. 2020

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“…MCRPS measures the overall prediction skill by comparing the simulation distribution with the observation distribution using the continuous ranked probability score (CRPS). When the measurement is deterministic, the CRPS at a single time step is calculated as (Gneiting & Raftery, 2007; Liu et al, 2019):

CRPS ((),, F_{t} ((), \overset{falsê}{Q}), Q_{t}) goodbreak= \int_{- \infty}^{+ \infty} {(F_{t} (\overset{Q}{true}) - H \{\overset{falsê}{Q} \geq {\overset{Q}{true}}_{t}\})}^{2} d \overset{Q}{true}

where

F_{t} ((), \overset{falsê}{Q})

is the cumulative distribution function of the ensemble flow simulation at time

t

F_{t} ((), \overset{falsê}{Q})

can be approximated using the ensemble simulation outputs.…”

Section: Experimental Designmentioning

confidence: 99%

“… Note : Q and P are the observed flow rate and precipitation, respectively (from Table 1 of Liu et al (2019)). …”

Section: Experimental Designmentioning

confidence: 99%

See 1 more Smart Citation

Leveraging ensemble meteorological forcing data to improve parameter estimation of hydrologic models

Liu

Tolson

Newman

et al. 2021

Hydrological Processes

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As continental to global scale high‐resolution meteorological datasets continue to be developed, there are sufficient meteorological datasets available now for modellers to construct a historical forcing ensemble. The forcing ensemble can be a collection of multiple deterministic meteorological datasets or come from an ensemble meteorological dataset. In hydrological model calibration, the forcing ensemble can be used to represent forcing data uncertainty. This study examines the potential of using the forcing ensemble to identify more robust parameters through model calibration. Specifically, we compare an ensemble forcing‐based calibration with two deterministic forcing‐based calibrations and investigate their flow simulation and parameter estimation properties and the ability to resist poor‐quality forcings. The comparison experiment is conducted with a six‐parameter hydrological model for 30 synthetic studies and 20 real data studies to provide a better assessment of the average performance of the deterministic and ensemble forcing‐based calibrations. Results show that the ensemble forcing‐based calibration generates parameter estimates that are less biased and have higher frequency of covering the true parameter values than the deterministic forcing‐based calibration does. Using a forcing ensemble in model calibration reduces the risk of inaccurate flow simulation caused by poor‐quality meteorological inputs, and improves the reliability and overall simulation skill of ensemble simulation results. The poor‐quality meteorological inputs can be effectively filtered out via our ensemble forcing‐based calibration methodology and thus discarded in any post‐calibration model applications. The proposed ensemble forcing‐based calibration method can be considered as a more generalized framework to include parameter and forcing uncertainties in model calibration.

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“…Goovaerts [26] performed a conditional geostatistical approach to match perturbations with the sample statistics of the observed rainfall fields. Hongli et al [29] applied a hyper-parameter tuning procedure to fine-tune the parameters of the statistical model (used for generating errors to perturb input climate data) in such a way to improve the representativeness of forcing data uncertainty. Another popular approach is to conditionally generate precipitation time-series, which has been widely applied in various hydrometeorological studies (see e.g.…”

Section: A C C E P T E D Mmentioning

confidence: 99%