Propagating reliable estimates of hydrological forecast uncertainty to many lead times

Abstract Mesoscale frontal waves have the potential to modify the hydrometeorological impacts of atmospheric rivers (ARs). The small scale and rapid growth of these waves pose significant forecast challenges. We examined a frontal wave that developed a secondary cyclone during the landfall of an extreme AR in Northern California. We document rapid changes in significant storm features including integrated vapor transport and precipitation and connect these to high forecast uncertainty at 1–4-days’ lead time. We also analyze the skill of the Global Ensemble Forecast System in predicting secondary cyclogenesis and relate secondary cyclogenesis prediction skill to forecasts of AR intensity, AR duration, and upslope water vapor flux in the orographic controlling layer. Leveraging a measure of reference accuracy designed for cyclogenesis, we found forecasts were only able to skillfully predict secondary cyclogenesis for lead times less than 36 h. Forecast skill in predicting the large-scale pressure pattern and integrated vapor transport was lost by 96-h lead time. For lead times longer than 36 h, the failure to predict secondary cyclogenesis led to significant uncertainty in forecast AR intensity and to long bias in AR forecast duration. Failure to forecast a warm front associated with the secondary cyclone at lead times less than 36 h caused large overprediction of upslope water vapor flux, an important indicator of orographic precipitation forcing. This study highlights the need to identify offshore mesoscale frontal waves in real time and to characterize the forecast uncertainty inherent in these events when creating hydrometeorological forecasts.

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On the sources of hydrological prediction uncertainty in the Amazon

Hydrology and Earth System Sciences ◽

10.5194/hess-16-3127-2012 ◽

2012 ◽

Vol 16 (9) ◽

pp. 3127-3137 ◽

Cited By ~ 33

Author(s):

R. C. D. Paiva ◽

W. Collischonn ◽

M. P. Bonnet ◽

L. G. G. de Gonçalves

Keyword(s):

Data Assimilation ◽

River Basin ◽

Hydrological Model ◽

Initial Conditions ◽

State Variables ◽

Streamflow Prediction ◽

Amazon River Basin ◽

Forecast Uncertainty ◽

Ensemble Streamflow Prediction ◽

Hydrological Forecast

Abstract. Recent extreme events in the Amazon River basin and the vulnerability of local population motivate the development of hydrological forecast systems using process based models for this region. In this direction, the knowledge of the source of errors in hydrological forecast systems may guide the choice on improving model structure, model forcings or developing data assimilation systems for estimation of initial model states. We evaluate the relative importance of hydrologic initial conditions and model meteorological forcings errors (precipitation) as sources of stream flow forecast uncertainty in the Amazon River basin. We used a hindcast approach that compares Ensemble Streamflow Prediction (ESP) and a reverse Ensemble Streamflow Prediction (reverse-ESP). Simulations were performed using the physically-based and distributed hydrological model MGB-IPH, comprising surface energy and water balance, soil water, river and floodplain hydrodynamics processes. The model was forced using TRMM 3B42 precipitation estimates. Results show that uncertainty on initial conditions plays an important role for discharge predictability, even for large lead times (∼1 to 3 months) on main Amazonian Rivers. Initial conditions of surface waters state variables are the major source of hydrological forecast uncertainty, mainly in rivers with low slope and large floodplains. Initial conditions of groundwater state variables are important, mostly during low flow period and in the southeast part of the Amazon where lithology and the strong rainfall seasonality with a marked dry season may be the explaining factors. Analyses indicate that hydrological forecasts based on a hydrological model forced with historical meteorological data and optimal initial conditions may be feasible. Also, development of data assimilation methods is encouraged for this region.

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Hydrometeorological Ensemble Forecast of a Highly Localized Convective Event in the Mediterranean

Water ◽

10.3390/w12061545 ◽

2020 ◽

Vol 12 (6) ◽

pp. 1545

Author(s):

Luca Furnari ◽

Giuseppe Mendicino ◽

Alfonso Senatore

Keyword(s):

Ensemble Prediction ◽

Lead Times ◽

Forecast Uncertainty ◽

Ensemble Prediction System ◽

Distributed Information ◽

Convective Event ◽

Spatially Distributed ◽

Accumulated Rainfall ◽

Clear Information

The uncertainties that affect hydrometeorological modelling chains can be addressed through ensemble approaches. In this paper, a convection-permitting ensemble system was assessed based on the downscaling of all members of the ECMWF ensemble prediction system through the coupled atmospheric-hydrological WRF-Hydro modelling system. An exemplary highly localized convective event that occurred in a morphologically complex area of the southern Italian coast was selected as a case study, evaluating the performance of the system for two consecutive lead times up to the hydrological forecast on a very small (11.4 km2) catchment. The proposed approach accurately downscales the signal provided by the global model, improving up to almost 200% the quantitative forecast of the accumulated rainfall peak in the area affected by the event and supplying clear information about the forecast uncertainty. Some members of the ensemble simulations provide accurate results up to the hydrological scale over the catchment, with unit peak discharge forecasts up to 3 m3∙s−1∙km−2. Overall, the study highlights that for highly localized convective events in coastal Mediterranean catchments, ensemble approaches should be preferred to a classic single-based simulation approach, because they improve the forecast skills and provide spatially distributed information about the forecast uncertainty, which can be particularly useful for operational purposes.

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Assessment of the Long-Term Hydrological Forecast Skill Evolution across Lead-Times within the Ensemble Streamflow Prediction Framework

Water Resources ◽

10.1134/s009780781805038x ◽

2018 ◽

Vol 45 (S1) ◽

pp. 122-127

Author(s):

V. M. Moreido

Keyword(s):

Forecast Skill ◽

Lead Times ◽

Streamflow Prediction ◽

Ensemble Streamflow Prediction ◽

Hydrological Forecast

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A Probabilistic Tropical Cyclone Track Forecast Scheme Based on the Selective Consensus of Ensemble Prediction Systems

Weather and Forecasting ◽

10.1175/waf-d-17-0071.1 ◽

2017 ◽

Vol 32 (6) ◽

pp. 2143-2157 ◽

Cited By ~ 7

Author(s):

Xiping Zhang ◽

Hui Yu

Keyword(s):

Tropical Cyclone ◽

Ensemble Prediction ◽

Lead Times ◽

Track Forecast ◽

Forecast Errors ◽

Forecast Uncertainty ◽

Ensemble Prediction System ◽

The Mean ◽

Environmental Prediction ◽

Uncertainty Information

Abstract Selective consensus and a grand ensemble based on an ensemble prediction system (EPS) have been found to be effective in improving deterministic tropical cyclone (TC) track forecasts, while little attention has been paid to quantitative applications of the forecast uncertainty information provided by EPSs. In this paper the forecast uncertainty information is evaluated for two operational EPSs and their grand ensemble. Then, a probabilistic TC track forecast scheme is proposed based on the selective consensus of the two EPSs; this scheme is composed of member picking, mean track shifting, and probability ellipses. The operational EPSs are from the European Centre for Medium-Range Weather Forecasts (ECMWF-EPS) and the National Centers for Environmental Prediction (NCEP-GEFS). Evaluation exhibits that the hit ratios of ECMWF-EPS are above 80% for the 70% probability ellipses at all lead times until 120 h and are used in the proposed scheme. The other components of the proposed scheme are about picking potentially good EPS members. A picking ratio of 1/2 is found to be the best choice, and the member-picking technique is used for the grand ensemble but only for lead times out to 48 h. For lead times longer than 48 h, all of the grand ensemble members are used in obtaining the mean track. The effectiveness of the proposed scheme shows a 10% improvement in the mean track forecast errors over the grand ensemble and a 4.5% improvement in the hit ratio of 70% probability ellipses over the ECMWF-EPS at 24 h, demonstrating its good potential to be applied in operations.

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The Effect of Lead-Time Weather Forecast Uncertainty on Outage Prediction Modeling

Forecasting ◽

10.3390/forecast3030031 ◽

2021 ◽

Vol 3 (3) ◽

pp. 501-516

Author(s):

Feifei Yang ◽

Diego Cerrai ◽

Emmanouil N. Anagnostou

Keyword(s):

Numerical Experiment ◽

Lead Time ◽

Environmental Parameters ◽

Weather Forecast ◽

Tree Canopy ◽

Percentage Error ◽

Lead Times ◽

Wind Gust ◽

Forecast Uncertainty ◽

Prediction Uncertainty

Weather-related power outages affect millions of utility customers every year. Predicting storm outages with lead times of up to five days could help utilities to allocate crews and resources and devise cost-effective restoration plans that meet the strict time and efficiency requirements imposed by regulatory authorities. In this study, we construct a numerical experiment to evaluate how weather parameter uncertainty, based on weather forecasts with one to five days of lead time, propagates into outage prediction error. We apply a machine-learning-based outage prediction model on storm-caused outage events that occurred between 2016 and 2019 in the northeastern United States. The model predictions, fed by weather analysis and other environmental parameters including land cover, tree canopy, vegetation characteristics, and utility infrastructure variables exhibited a mean absolute percentage error of 38%, Nash–Sutcliffe efficiency of 0.54, and normalized centered root mean square error of 68%. Our numerical experiment demonstrated that uncertainties of precipitation and wind-gust variables play a significant role in the outage prediction uncertainty while sustained wind and temperature parameters play a less important role. We showed that, while the overall weather forecast uncertainty increases gradually with lead time, the corresponding outage prediction uncertainty exhibited a lower dependence on lead times up to 3 days and a stepwise increase in the four- and five-day lead times.

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An analysis of uncertainties and skill in forecasts of winter storm losses

Natural Hazards and Earth System Science ◽

10.5194/nhess-16-2391-2016 ◽

2016 ◽

Vol 16 (11) ◽

pp. 2391-2402 ◽

Cited By ~ 6

Author(s):

Tobias Pardowitz ◽

Robert Osinski ◽

Tim Kruschke ◽

Uwe Ulbrich

Keyword(s):

Model Uncertainty ◽

Damage Model ◽

Calibration Method ◽

Ensemble Prediction ◽

Brier Score ◽

Lead Times ◽

Storm Damage ◽

Winter Storm ◽

Forecast Uncertainty ◽

Ensemble Prediction System

Abstract. This paper describes an approach to derive probabilistic predictions of local winter storm damage occurrences from a global medium-range ensemble prediction system (EPS). Predictions of storm damage occurrences are subject to large uncertainty due to meteorological forecast uncertainty (typically addressed by means of ensemble predictions) and uncertainties in modelling weather impacts. The latter uncertainty arises from the fact that local vulnerabilities are not known in sufficient detail to allow for a deterministic prediction of damages, even if the forecasted gust wind speed contains no uncertainty. Thus, to estimate the damage model uncertainty, a statistical model based on logistic regression analysis is employed, relating meteorological analyses to historical damage records. A quantification of the two individual contributions (meteorological and damage model uncertainty) to the total forecast uncertainty is achieved by neglecting individual uncertainty sources and analysing resulting predictions. Results show an increase in forecast skill measured by means of a reduced Brier score if both meteorological and damage model uncertainties are taken into account. It is demonstrated that skilful predictions on district level (dividing the area of Germany into 439 administrative districts) are possible on lead times of several days. Skill is increased through the application of a proper ensemble calibration method, extending the range of lead times for which skilful damage predictions can be made.

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Uncertainties in Forecasts of Winter Storm Losses

10.5194/nhess-2016-182 ◽

2016 ◽

Author(s):

Tobias Pardowitz ◽

Robert Osinski ◽

Tim Kruschke ◽

Uwe Ulbrich

Keyword(s):

Damage Model ◽

Calibration Method ◽

Ensemble Prediction ◽

Lead Times ◽

Storm Damage ◽

Winter Storm ◽

Forecast Uncertainty ◽

Ensemble Prediction System ◽

Uncertainty Sources ◽

Sufficient Detail

Abstract. This paper describes an approach to derive probabilistic predictions of local winter storm damage occurrences from a global medium-range ensemble prediction system (EPS). Predictions of storm damage occurrences are subject to large uncertainty due to meteorological forecast uncertainty (typically addressed by means of ensemble predictions) and uncertainties in modelling weather impacts. The latter uncertainty arises from the fact that local vulnerabilities are not known in sufficient detail to allow for a deterministic assessment of damages. Thus to estimate the damage model uncertainty, a statistical model based on logistic regression analysis is employed, relating meteorological analyses to historical damage records. A quantification of the two individual contributions to the total forecast uncertainty is achieved by neglecting individual uncertainty sources and analysing resulting predictions. Results show an increase in forecast skill if both meteorological and damage occurrence uncertainties are taken into account. It is demonstrated that skilful predictions on district level are possible on lead times of several days. Skill is increased through the application of a proper ensemble calibration method, extending the range of lead times for which skilful damage predictions can be made.

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On the sources of hydrological prediction uncertainty in the Amazon

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-9-3739-2012 ◽

2012 ◽

Vol 9 (3) ◽

pp. 3739-3760 ◽

Cited By ~ 2

Author(s):

R. C. D. Paiva ◽

W. Collischonn ◽

M. P. Bonnet ◽

L. G. G. Gonçalves

Keyword(s):

River Basin ◽

Hydrological Model ◽

Initial Conditions ◽

Amazon River ◽

State Variables ◽

Streamflow Prediction ◽

Amazon River Basin ◽

Forecast Uncertainty ◽

Ensemble Streamflow Prediction ◽

Hydrological Forecast

Abstract. Recent extreme events in the Amazon River basin and the vulnerability of local population motivate the development of hydrological forecast systems (HFSs) using process based models for this region. In this direction, the knowledge of the source of errors in HFSs may guide the choice on improving model structure, model forcings or developing data assimilation (DA) systems for estimation of initial model states. We evaluate the relative importance of hydrologic initial conditions (ICs) and model meteorological forcings (MFs) errors (precisely precipitation) as sources of stream flow forecast uncertainty in the Amazon River basin. We used a hindcast approach developed by Wood and Lettenmaier (2008) that contrasts Ensemble Streamflow Prediction (ESP) and a reverse Ensemble Streamflow Prediction (reverse-ESP). Simulations were performed using the physically-based and distributed hydrological model MGB-IPH, comprising surface energy and water balance, soil water, river and floodplain hydrodynamics processes. Model was forced using TRMM 3B42 precipitation estimates. Results show that uncertainty on initial conditions play an important role for discharge predictability even for large lead times (~1 to 3 months) on main Amazonian Rivers. ICs of surface waters state variables are the major source of hydrological forecast uncertainty, mainly in rivers with low slope and large floodplains. ICs of groundwater state variables are important mostly during low flow period and southeast part of the Amazon, where lithology and the strong rainfall seasonality with a marked dry season may be the explaining factors. Analyses indicate that hydrological forecasts based on a hydrological model forced with historical meteorological data and optimal initial conditions, may be feasible. Also, development of DA methods is encouraged for this region.

Download Full-text