Which precipitation forecasts to use? Deterministic versus coarser‐resolution ensemble <scp>NWP</scp> models

Zhao, Pengcheng; Wang, QJ; Wu, Wenyan; Yang, Qiang

doi:10.1002/qj.3952

Cited by 18 publications

(6 citation statements)

References 69 publications

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“…(6) MAE , which can be considered as the CRPS for deterministic forecasts (Hersbach, 2000), measures the absolute deviation of forecast ensemble means from observations (Willmott & Matsuura, 2005):

italicMAE goodbreak= \frac{1}{K} \sum_{k = 1}^{K} (||, {\overset{true¯}{f}}_{k} goodbreak- o_{k})

MAE has a perfect value of 0 when forecast ensemble means exactly match observations, which is similar to the interpretation of CRPS (Abdulelah Al‐Sudani et al, 2019; Schaake et al, 2007; Zhao et al, 2020a).…”

Section: Verification Of Ensemble Hydroclimatic Forecastsmentioning

confidence: 90%

“…(7) MAESS can be interpreted as CRPSS for deterministic forecasts (Hersbach, 2000):

italicMAESS (() %) goodbreak= \frac{{MAE}_{ref} - {MAE}_{ens}}{{italicMAE}_{ref}} goodbreak\times 100

MAESS evaluates the performance of ensemble means relative to reference forecasts in terms of MAE . Similar to CRPSS , it is positively oriented with a perfect value of 100% (Foster et al, 2018; Zeng et al, 2011; Zhao et al, 2020a).…”

Section: Verification Of Ensemble Hydroclimatic Forecastsmentioning

confidence: 99%

“…Compared to conventional deterministic forecasts, ensemble forecasts employ an ensemble of scenarios to explicitly characterize the forecast uncertainty of hydroclimatic variables (Frías et al, 2018; Kirtman et al, 2014; Zhao et al, 2020a). Previous investigations suggest that ensemble forecasts can overall exhibit higher forecast skills and can be more informative for decision‐making (Pappenberger et al, 2015a; Richardson, 2000; Siddique & Mejia, 2017; Yuan et al, 2011; Zhao et al, 2020a). In recent years, ensemble hydroclimatic forecasting is becoming increasingly popular and substantial efforts are devoted to developing operational forecasting systems (Arsenault et al, 2020; Bell et al, 2017; Bennett et al, 2017; Johnson et al, 2019; Korecha & Sorteberg, 2013; Meißner et al, 2017; Ye et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…Ensemble hydroclimatic forecasts generated by perturbations of initial conditions and multiple forecasting models are valuable for environmental modeling and management (Giuliani et al, 2020; Kirtman et al, 2014; Peng et al, 2018; Roundy et al, 2019; Towler et al, 2013; Turner et al, 2017; Yuan et al, 2015). Compared to conventional deterministic forecasts, ensemble forecasts employ an ensemble of scenarios to explicitly characterize the forecast uncertainty of hydroclimatic variables (Frías et al, 2018; Kirtman et al, 2014; Zhao et al, 2020a). Previous investigations suggest that ensemble forecasts can overall exhibit higher forecast skills and can be more informative for decision‐making (Pappenberger et al, 2015a; Richardson, 2000; Siddique & Mejia, 2017; Yuan et al, 2011; Zhao et al, 2020a).…”

Section: Introductionmentioning

confidence: 99%

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Predictive performance of ensemble hydroclimatic forecasts: Verification metrics, diagnostic plots and forecast attributes

Huang

Zhao

2022

WIREs Water

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Predictive performance is one of the most important issues for practical applications of ensemble hydroclimatic forecasts. While different forecasting studies tend to use different combinations of verification metrics and diagnostic plots, this paper presents a literature survey of popular verification metrics, diagnostic plots and their corresponding forecast attributes. Twenty metrics to quantify predictive performance of ensemble, deterministic and categorical forecasts are detailed; six types of diagnostic plots to visualize the relationship between ensemble forecasts and observations are presented; and the correspondence relationships of verification metrics and diagnostic plots with ten forecast attributes are illustrated. Numerical experiments are devised for raw ensemble precipitation forecasts generated by the Climate Forecast System version 2 (CFSv2) to illustrate the verification metrics, diagnostic plots and forecast attributes. The results suggest that verification metrics that pay attention to some similar attributes can be related to one another. Meanwhile, the metrics are not necessarily in full accordance as they tend to reflect different attributes. Time series plots visualize forecasts over a period of interest and graphically illustrate forecast bias, reliability and skill. Quantile range plots provide an overall diagnosis for the association of ensemble forecasts with observations. Overall, verification metrics can be illustrated by diagnostic plots for individual grid cells and also be screened by spatial plots at regional and global scales. The survey presented in this article can serve as a roadmap for the selection of verification metrics, diagnostic plots and forecast attributes for verifying ensemble hydroclimatic forecasts. This article is categorized under: Science of Water > Hydrological Processes

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italicMAE goodbreak= \frac{1}{K} \sum_{k = 1}^{K} (||, {\overset{true¯}{f}}_{k} goodbreak- o_{k})

Section: Verification Of Ensemble Hydroclimatic Forecastsmentioning

confidence: 90%

“…(7) MAESS can be interpreted as CRPSS for deterministic forecasts (Hersbach, 2000):

italicMAESS (() %) goodbreak= \frac{{MAE}_{ref} - {MAE}_{ens}}{{italicMAE}_{ref}} goodbreak\times 100

Section: Verification Of Ensemble Hydroclimatic Forecastsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Predictive performance of ensemble hydroclimatic forecasts: Verification metrics, diagnostic plots and forecast attributes

Huang

Zhao

2022

WIREs Water

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show abstract

“…3) The raw daily forecasts were individually calibrated by using the Seasonally Coherent Calibration (SCC) model (Wang et al 2019;Yang et al 2021;Zhao et al 2021) to improve their match with the AWAP data for our study field site, and to generate 100 ensemble forecast members for the future 9-day period. 4) The forecast ensemble members at different lead times were stochastically linked using the Schaake Shuffle technique (Clark et al 2004) to form forecast ensemble time series with appropriate serial correlations between days.…”

Section: Ensemble Short-term Rainfall Forecastsmentioning

confidence: 99%

An analysis framework to evaluate irrigation decisions using short-term ensemble weather forecasts

et al. 2022

Self Cite

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Irrigation water is an expensive and limited resource and optimal scheduling can boost water efficiency. Scheduling decisions often need to be made several days prior to an irrigation event, so a key aspect of irrigation scheduling is the accurate prediction of crop water use and soil water status ahead of time. This prediction relies on several key inputs including initial soil water status, crop conditions and weather. Since each input is subject to uncertainty, it is important to understand how these uncertainties impact soil water prediction and subsequent irrigation scheduling decisions. This study aims to develop an uncertainty-based analysis framework for evaluating irrigation scheduling decisions under uncertainty, with a focus on the uncertainty arising from short-term rainfall forecasts. To achieve this, a biophysical process-based crop model, APSIM (The Agricultural Production Systems sIMulator), was used to simulate root-zone soil water content for a study field in south-eastern Australia. Through the simulation, we evaluated different irrigation scheduling decisions using ensemble short-term rainfall forecasts. This modelling produced an ensemble of simulations of soil water content, as well as ensemble simulations of irrigation runoff and drainage. This enabled quantification of risks of over- and under-irrigation. These ensemble estimates were interpreted to inform the timing of the next irrigation event to minimize both the risks of stressing the crop and/or wasting water under uncertain future weather. With extension to include other sources of uncertainty (e.g., evapotranspiration forecasts, crop coefficient), we plan to build a comprehensive uncertainty framework to support on-farm irrigation decision-making.

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Spatial Mode-based Calibration (SMoC) of Forecast Precipitation Fields from Numerical Weather Prediction Models

Zhao

Wang

et al. 2022

Preprint

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Which precipitation forecasts to use? Deterministic versus coarser‐resolution ensemble NWP models

Cited by 18 publications

References 69 publications

Predictive performance of ensemble hydroclimatic forecasts: Verification metrics, diagnostic plots and forecast attributes

Predictive performance of ensemble hydroclimatic forecasts: Verification metrics, diagnostic plots and forecast attributes

An analysis framework to evaluate irrigation decisions using short-term ensemble weather forecasts

Spatial Mode-based Calibration (SMoC) of Forecast Precipitation Fields from Numerical Weather Prediction Models

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