scholarly journals Seasonal streamflow forecasts for Europe – I. Hindcast verification with pseudo- and real observations

2016 ◽  
Author(s):  
Wouter Greuell ◽  
Wietse H. P. Franssen ◽  
Hester Biemans ◽  
Ronald W. A. Hutjes
Keyword(s):  
Correlation Coefficient ◽  
Lead Time ◽  
Irrigation Management ◽  
Skill Score ◽  
Skill Assessment ◽  
General Tendency ◽  
Infiltration Capacity ◽  
Streamflow Forecasts ◽  
Meteorological Observations ◽  
North Germany

Abstract. Seasonal predictions can be exploited among others to optimize hydropower energy generation, navigability of rivers and irrigation management to decrease crop yield losses. This paper is the first of two papers dealing with a model-based system built to produce seasonal hydrological forecasts (WUSHP: Wageningen University Seamless Hydrological Prediction system), applied here to Europe. The present paper presents the development and the skill evaluation of the system. In WUSHP hydrology is simulated by running the Variable Infiltration Capacity (VIC) hydrological model with forcing from bias-corrected output of ECMWF's Seasonal Forecasting System 4. The system is probabilistic. For the assessment of skill, we performed hindcast simulations (1981–2010) and a reference simulation, in which VIC was forced by gridded meteorological observations, to generate initial hydrological conditions for the hindcasts and discharge output for skill assessment (pseudo-observations). Skill is analysed with monthly temporal resolution for the entire annual cycle. Using the pseudo-observations and taking the correlation coefficient as metric, hot spots of significant skill in runoff were identified in Fennoscandia (from January to October), the southern part of the Mediterranean (from June to August), Poland, North Germany, Romania and Bulgaria (mainly from November to January) and West France (from December to May). The spatial pattern of skill is fading with increasing lead time but some skill is left at the end of the hindcasts (7 months). On average across the domain, skill in discharge is slightly higher than skill in runoff. This can be explained by the delay between runoff and discharge and the general tendency of decreasing skill with lead time. Theoretical skill as determined with the pseudo-observations was compared to actual skill as determined with real discharge observations from 747 stations. Actual skill is mostly and often substantially less than theoretical skill, which is consistent with a conceptual analysis of the two types of verification. Qualitatively, results are hardly sensitive to the different skill metrics considered in this study (correlation coefficient, ROC area and Ranked Probability Skill Score) but ROC areas tend to be slightly larger for the Below Normal than for the Above Normal tercile.

scholarly journals Seasonal streamflow forecasts for Europe – Part I: Hindcast verification with pseudo- and real observations

2018 ◽  
Vol 22 (6) ◽  
pp. 3453-3472 ◽  
Author(s):  
Wouter Greuell ◽  
Wietse H. P. Franssen ◽  
Hester Biemans ◽  
Ronald W. A. Hutjes
Keyword(s):  
Correlation Coefficient ◽  
Lead Time ◽  
River Flow ◽  
Irrigation Management ◽  
Skill Score ◽  
Operating Characteristics ◽  
Infiltration Capacity ◽  
Run Off ◽  
Spatio Temporal ◽  
Roc Area

Abstract. Seasonal predictions of river flow can be exploited among others to optimise hydropower energy generation, navigability of rivers and irrigation management to decrease crop yield losses. This paper is the first of two papers dealing with a physical model-based system built to produce probabilistic seasonal hydrological forecasts, applied here to Europe. This paper presents the development of the system and the evaluation of its skill. The variable infiltration capacity (VIC) hydrological model is forced with bias-corrected output of ECMWF's seasonal forecast system 4. For the assessment of skill, we analysed hindcasts (1981–2010) against a reference run, in which VIC was forced by gridded meteorological observations. The reference run was also used to generate initial hydrological conditions for the hindcasts. The skill in run-off and discharge hindcasts is analysed with monthly temporal resolution, up to 7 months of lead time, for the entire annual cycle. Using the reference run output as pseudo-observations and taking the correlation coefficient as metric, hot spots of significant theoretical skill in discharge and run-off were identified in Fennoscandia (from January to October), the southern part of the Mediterranean (from June to August), Poland, northern Germany, Romania and Bulgaria (mainly from November to January), western France (from December to May) and the eastern side of Great Britain (January to April). Generally, the skill decreases with increasing lead time, except in spring in regions with snow-rich winters. In some areas some skill persists even at the longest lead times (7 months). Theoretical skill was compared to actual skill as determined with real discharge observations from 747 stations. Actual skill is generally substantially less than theoretical skill. This effect is stronger for small basins than for large basins. Qualitatively, the use of different skill metrics (correlation coefficient; relative operating characteristics, ROC, area; and ranked probability skill score, RPSS) leads to broadly similar spatio-temporal patterns of skill, but the level of skill decreases, and the area of skill shrinks, in the following order: correlation coefficient; ROC area below-normal (BN) tercile; ROC area above-normal (AN) tercile; ranked probability skill score; and, finally, ROC near-normal (NN) tercile.

Incorporating Misclassification Error in Skill Assessment

2005 ◽  
Vol 133 (11) ◽  
pp. 3382-3392 ◽  
Author(s):  
William Briggs ◽  
Matt Pocernich ◽  
David Ruppert
Keyword(s):  
Gold Standard ◽  
Score Test ◽  
Skill Score ◽  
Statistical Test ◽  
Skill Assessment ◽  
Misclassification Error ◽  
Model Parameters ◽  
Standard Data ◽  
Test Of Significance ◽  
Meteorological Observations

Abstract It is desirable to account for misclassification error of meteorological observations so that the true skill of the forecast can be assessed. Errors in observations can occur, among other places, in pilot reports of icing and in tornado spotting. Not accounting for misclassification error gives a misleading picture of the forecast’s true performance. An extension to the climate skill score test developed in Briggs and Ruppert is presented to account for possible misclassification error of the meteorological observation. This extension supposes a statistical misclassification-error model where “gold standard” data, or expert opinion, is available to characterize the misclassification-error characteristics of the observation. These model parameters are then inserted into the Briggs and Ruppert skill score for which a statistical test of significance can be performed.

scholarly journals Skilful seasonal forecasts of streamflow over Europe?

2017 ◽  
Author(s):  
Louise Arnal ◽  
Hannah L. Cloke ◽  
Elisabeth Stephens ◽  
Fredrik Wetterhall ◽  
Christel Prudhomme ◽  
...  
Keyword(s):  
Lead Time ◽  
Climate Model ◽  
Streamflow Forecasting ◽  
Streamflow Prediction ◽  
Seasonal Forecasts ◽  
Seasonal Climate ◽  
Climate Forecasts ◽  
Streamflow Forecasts ◽  
Meteorological Observations ◽  
Seasonal Streamflow

Abstract. This paper presents a Europe-wide analysis of the skill of the newly operational EFAS (European Flood Awareness System) seasonal streamflow forecasts, benchmarked against the Ensemble Streamflow Prediction (ESP) forecasting approach. The results suggest that, on average, the System 4 seasonal climate forecasts improve the streamflow predictability over historical meteorological observations for the first month of lead time only. However, the predictability varies in space and time and is greater in winter and autumn. Parts of Europe additionally exhibit a longer predictability, up to seven months of lead time, for certain months within a season. The results also highlight the potential usefulness of the EFAS seasonal streamflow forecasts for decision-making. Although the ESP is the most potentially useful forecasting approach in Europe, the EFAS seasonal streamflow forecasts appear more potentially useful than the ESP in some regions and for certain seasons, especially in winter for most of Europe. Patterns in the EFAS seasonal streamflow hindcasts skill are however not mirrored in the System 4 seasonal climate hindcasts, hinting the need for a better understanding of the link between hydrological and meteorological variables on seasonal timescales, with the aim to improve climate-model based seasonal streamflow forecasting.

scholarly journals Seasonal streamflow forecasts for Europe – II. Explanation of the skill

2016 ◽  
Author(s):  
Wouter Greuell ◽  
Wietse H. P. Franssen ◽  
Ronald W. A. Hutjes
Keyword(s):  
Climate Change ◽  
Soil Moisture ◽  
Initial Conditions ◽  
Irrigation Management ◽  
Streamflow Prediction ◽  
Seasonal Forecasts ◽  
Infiltration Capacity ◽  
Meteorological Forcing ◽  
Streamflow Forecasts ◽  
Hydrological Prediction

Abstract. Seasonal predictions can be exploited among others to optimize hydropower energy generation, navigability of rivers and irrigation management to decrease crop yield losses. This paper is the second of two papers dealing with a model-based system built to produce seasonal hydrological forecasts (WUSHP: Wageningen University Seamless Hydrological Prediction system), applied here to Europe. Whereas the first paper presents the development and the skill evaluation of the system, this paper provides explanations for the skill. In WUSHP hydrology is simulated by running the Variable Infiltration Capacity (VIC) hydrological model with meteorological forcing from bias-corrected output of ECMWF's Seasonal Forecasting System 4 (S4). WUSHP is probabilistic. For the assessment of skill, hindcast simulations (1981–2010) were carried out. To explain skill, we first looked at the forcing and found considerable skill in the precipitation forecasts of the first lead month but hardly any significant skill for later lead months. Seasonal forecasts for temperature have more skill. Skill in summer temperature is related to climate change and more or less independent of lead time. Skill in February and March is unrelated to climate change. Sources of skill in runoff were isolated with Ensemble Streamflow Prediction (ESP) experiments. These revealed that beyond the second lead month simulations with forcing that is identical for all years (ESPall) produce more skill in runoff than the simulations forced with S4 output (Full Hindcasts). This occurs because interannual variability of the S4 forcing has insufficient skill while it adds noise. Other ESP-experiments show that in Europe initial conditions of soil moisture form the dominant source of skill in runoff. From April to July, at the end of the melt season, initial conditions of snow contribute significantly to the skill, also when forecasts start much earlier. Some remarkable skill features are due to indirect effects, i.e. skill due to forcing or initial conditions of snow and soil moisture at an earlier stage is stored in the hydrological state (snow and/or soil moisture) of a later stage, which then contributes to persistence of skill. Finally, predictability of evapotranspiration was analysed in some detail, leading among others to the conclusion that it is due to all potential sources of skill but mostly to forcing.

scholarly journals Skilful seasonal forecasts of streamflow over Europe?

2018 ◽  
Vol 22 (4) ◽  
pp. 2057-2072 ◽  
Author(s):  
Louise Arnal ◽  
Hannah L. Cloke ◽  
Elisabeth Stephens ◽  
Fredrik Wetterhall ◽  
Christel Prudhomme ◽  
...  
Keyword(s):  
Lead Time ◽  
Added Value ◽  
Streamflow Forecasting ◽  
Seasonal Forecasts ◽  
Seasonal Climate ◽  
Climate Forecasts ◽  
Streamflow Forecasts ◽  
Meteorological Observations ◽  
Seasonal Streamflow ◽  
Seasonal Climate Forecasts

Abstract. This paper considers whether there is any added value in using seasonal climate forecasts instead of historical meteorological observations for forecasting streamflow on seasonal timescales over Europe. A Europe-wide analysis of the skill of the newly operational EFAS (European Flood Awareness System) seasonal streamflow forecasts (produced by forcing the Lisflood model with the ECMWF System 4 seasonal climate forecasts), benchmarked against the ensemble streamflow prediction (ESP) forecasting approach (produced by forcing the Lisflood model with historical meteorological observations), is undertaken. The results suggest that, on average, the System 4 seasonal climate forecasts improve the streamflow predictability over historical meteorological observations for the first month of lead time only (in terms of hindcast accuracy, sharpness and overall performance). However, the predictability varies in space and time and is greater in winter and autumn. Parts of Europe additionally exhibit a longer predictability, up to 7 months of lead time, for certain months within a season. In terms of hindcast reliability, the EFAS seasonal streamflow hindcasts are on average less skilful than the ESP for all lead times. The results also highlight the potential usefulness of the EFAS seasonal streamflow forecasts for decision-making (measured in terms of the hindcast discrimination for the lower and upper terciles of the simulated streamflow). Although the ESP is the most potentially useful forecasting approach in Europe, the EFAS seasonal streamflow forecasts appear more potentially useful than the ESP in some regions and for certain seasons, especially in winter for almost 40 % of Europe. Patterns in the EFAS seasonal streamflow hindcast skill are however not mirrored in the System 4 seasonal climate hindcasts, hinting at the need for a better understanding of the link between hydrological and meteorological variables on seasonal timescales, with the aim of improving climate-model-based seasonal streamflow forecasting.

scholarly journals Lagged Ensembles, Forecast Configuration, and Seasonal Predictions

2013 ◽  
Vol 141 (10) ◽  
pp. 3477-3497 ◽  
Author(s):  
Mingyue Chen ◽  
Wanqiu Wang ◽  
Arun Kumar
Keyword(s):  
Lead Time ◽  
Geographical Location ◽  
Seasonal Prediction ◽  
Skill Score ◽  
Negative Influence ◽  
Positive Influence ◽  
Prediction Skill ◽  
Ensemble Size ◽  
Seasonal Forecasts ◽  
Ensemble Forecasts

Abstract An analysis of lagged ensemble seasonal forecasts from the National Centers for Environmental Prediction (NCEP) Climate Forecast System, version 2 (CFSv2), is presented. The focus of the analysis is on the construction of lagged ensemble forecasts with increasing lead time (thus allowing use of larger ensemble sizes) and its influence on seasonal prediction skill. Predictions of seasonal means of sea surface temperature (SST), 200-hPa height (z200), precipitation, and 2-m air temperature (T2m) over land are analyzed. Measures of prediction skill include deterministic (anomaly correlation and mean square error) and probabilistic [rank probability skill score (RPSS)]. The results show that for a fixed lead time, and as one would expect, the skill of seasonal forecast improves as the ensemble size increases, while for a fixed ensemble size the forecast skill decreases as the lead time becomes longer. However, when a forecast is based on a lagged ensemble, there exists an optimal lagged ensemble time (OLET) when positive influence of increasing ensemble size and negative influence due to an increasing lead time result in a maximum in seasonal prediction skill. The OLET is shown to depend on the geographical location and variable. For precipitation and T2m, OLET is relatively longer and skill gain is larger than that for SST and tropical z200. OLET is also dependent on the skill measure with RPSS having the longest OLET. Results of this analysis will be useful in providing guidelines on the design and understanding relative merits for different configuration of seasonal prediction systems.

scholarly journals Prediction of local geomagnetic activity on the example of data of “Lviv” Magnetic Observatory

2021 ◽  
Vol 27 (1) ◽  
pp. 78-84
Author(s):  
D.I. Vlasov ◽  
◽  
A.S. Parnowski ◽  
Keyword(s):  
Predictive Models ◽  
Lead Time ◽  
Measurement Data ◽  
Correlation Coefficients ◽  
Skill Score ◽  
Magnetic Observatory ◽  
Using Data ◽  
First Time ◽  
Prediction Efficiency ◽  
Main Center

For the first time in world practice, predictive models were constructed for X, Y, Z geomagnetic elements. Based on these models, the prediction was made with 3 hours lead time using data of the “Lviv” magnetic observatory. The properties of models are as follows: observatory — LVV, рredicted values — XYZ; lead time — 3 hours; correlation coefficients’ averaged measurement data — 0.824 (X), 0.811 (Y), 0.804 (Z); prediction efficiency — 0.816 (X), 0.803 (Y), 0.801 (Z); skill score — 0.115 (X), 0.095 (Y), 0.099 (Z). The developed models were tested in the Main Center of Special Monitoring, and they were found to meet the Basic Requirements for operational predictive models.

scholarly journals The skill assessment of ENSO prediction issued by JMA ensemble prediction system and CFSv2

2021 ◽  
Vol 893 (1) ◽  
pp. 012047
Author(s):  
R Rahmat ◽  
A M Setiawan ◽  
Supari
Keyword(s):  
Southern Oscillation ◽  
Skill Score ◽  
Skill Assessment ◽  
Japan Meteorological Agency ◽  
Ensemble Prediction ◽  
In Situ Observation ◽  
Prediction System ◽  
Ensemble Prediction System ◽  
Enso Prediction ◽  
The Government

Abstract Indonesian climate is strongly affected by El Niño-Southern Oscillation (ENSO) as one of climate-driven factor. ENSO prediction during the upcoming months or year is crucial for the government in order to design the further strategic policy. Besides producing its own ENSO prediction, BMKG also regularly releases the status and ENSO prediction collected from other climate centers, such as Japan Meteorological Agency (JMA) and National Oceanic and Atmospheric Administration (NOAA). However, the skill of these products is not well known yet. The aim of this study is to conduct a simple assessment on the skill of JMA Ensemble Prediction System (EPS) and NOAA Climate Forecast System version 2 (CFSv2) ENSO prediction using World Meteorological Organization (WMO) Standard Verification System for Long Range Forecast (SVS-LRF) method. Both ENSO prediction results also compared each other using Student's t-test. The ENSO predictions data were obtained from the ENSO JMA and ENSO NCEP forecast archive files, while observed Nino 3.4 were calculated from Centennial in situ Observation-Based Estimates (COBE) Sea Surface Temperature Anomaly (SSTA). Both ENSO prediction issued by JMA and NCEP has a good skill on 1 to 3 months lead time, indicated by high correlation coefficient and positive value of Mean Square Skill Score (MSSS). However, the skill of both skills significantly reduced for May-August target month. Further careful interpretation is needed for ENSO prediction issued on this mentioned period.

scholarly journals Simulation of the Occurrence of Water Runoff in Soils Under Cultivation of Industrial Tomato Irrigated by Center Pivot

2019 ◽  
Vol 11 (7) ◽  
pp. 48
Author(s):  
Diogo. H. M. Moraes ◽  
José Alves Júnior ◽  
Marcio Mesquita ◽  
Adão. W. P. Evangelista ◽  
Derblai Casaroli ◽  
...  
Keyword(s):  
Surface Runoff ◽  
Irrigation System ◽  
Numerical Technique ◽  
Irrigation Management ◽  
Water Runoff ◽  
Clayey Soils ◽  
Infiltration Capacity ◽  
Tomato Crop ◽  
Center Pivot ◽  
Different Types

The tomato crop is almost totally irrigated. Among the irrigation methods utilized, mechanized sprinkling by center pivot stands out in tomato cultivation. A cultural treatment used in the tomato is the synchronization of the irrigations with the applications of the pesticides since with the leaf wetting the plants become unprotected and susceptible to diseases. In an attempt to reduce pesticide applications, growers seek to increase the time between irrigations, however, there are limitations, inherent to the soil and the irrigation system itself. The objective of this work was to simulate the soil water runoff tendency for irrigation management in the tomato crop, simulating three different types of soils (sandy, medium and clayey), three declines (0, 5 and 10%), and two types of deflectors (I-Wob and Spray). For this, four pivot sizes (25, 50, 75 and 100 ha) were defined and the methodology of maximum allowable precipitation estimated by the Newton-Raphson numerical technique was used to verify the different runoff conditions. The results showed that clayey soils are more susceptible when compared to medium and sandy soils, to surface runoff. Pivots of 100, 75 and 50 ha present greater susceptibility to runoff, with 25 ha being the best suitability for infiltration capacity in both soils. There is a percentage reduction of the maximum allowable rainfall of 40.74 % (±1.54) when the terrain is plan and pass to have 5% inclination and 22.99% (±1.47) between 5 and 10 %. I-Wob type deflectors have a better distribution of application, a consequently better relation with the maximum allowable precipitation intensity and less possibility of the surface runoff.

scholarly journals Inter-comparison between retrospective ensemble streamflow forecasts using meteorological inputs from ECMWF and NOAA/ESRL in the Hudson River sub-basins during Hurricane Irene (2011)

2018 ◽  
Vol 50 (1) ◽  
pp. 166-186 ◽  
Author(s):  
F. Saleh ◽  
V. Ramaswamy ◽  
N. Georgas ◽  
A. F. Blumberg ◽  
J. Pullen
Keyword(s):  
Lead Time ◽  
Hudson River ◽  
Model Systems ◽  
Model Ensemble ◽  
Single Model ◽  
Ensemble Forecasts ◽  
Resampling Methods ◽  
Hurricane Irene ◽  
Resampling Method ◽  
Streamflow Forecasts

Abstract The objective of this work was to evaluate the benefits of using multi-model meteorological ensembles in representing the uncertainty of hydrologic forecasts. An inter-comparison experiment was performed using meteorological inputs from different models corresponding to Hurricane Irene (2011), over three sub-basins of the Hudson River basin. The ensemble-based precipitation inputs were used as forcing in a hydrological model to retrospectively forecast hourly streamflow, with a 96-hour lead time. The inputs consisted of 73 ensemble members, namely one high-resolution ECMWF deterministic member, 51 ECMWF members and 21 NOAA/ESRL (GEFS Reforecasts v2) members. The precipitation inputs were resampled to a common grid using the bilinear resampling method that was selected upon analysing different resampling methods. The results show the advantages of forcing hydrologic forecasting systems with multi-model ensemble forecasts over using deterministic and single model ensemble forecasts. The work showed that using the median of all 73 ensemble streamflow forecasts relatively improved the Nash–Sutcliffe Efficiency and lowered the biases across the examined sub-basins, compared with using the ensemble median from an individual model. This research contributes to the growing literature that demonstrates the promising capabilities of multi-model systems to better describe the uncertainty in streamflow predictions.

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