Forecast Skill Score Test

1978 ◽  
Author(s):  
William F. Johnson ◽  
Arthur C. Kyle ◽  
Paul B. Knutson
Keyword(s):  
Score Test ◽  
Skill Score ◽  
Forecast Skill ◽  
Forecast Skill Score

scholarly journals Forecast skill score assessment of a relocatable ocean prediction system, using a simplified objective analysis method

Ocean Science ◽  
2017 ◽  
Vol 13 (6) ◽  
pp. 925-945 ◽  
Author(s):  
Reiner Onken
Keyword(s):  
Observational Data ◽  
Skill Score ◽  
Forecast Skill ◽  
Objective Analysis ◽  
Prediction System ◽  
Regional Ocean Modeling System ◽  
Data Set ◽  
Vast Number ◽  
Ocean Prediction ◽  
Forecast Skill Score

Abstract. A relocatable ocean prediction system (ROPS) was employed to an observational data set which was collected in June 2014 in the waters to the west of Sardinia (western Mediterranean) in the framework of the REP14-MED experiment. The observational data, comprising more than 6000 temperature and salinity profiles from a fleet of underwater gliders and shipborne probes, were assimilated in the Regional Ocean Modeling System (ROMS), which is the heart of ROPS, and verified against independent observations from ScanFish tows by means of the forecast skill score as defined by Murphy(1993). A simplified objective analysis (OA) method was utilised for assimilation, taking account of only those profiles which were located within a predetermined time window W. As a result of a sensitivity study, the highest skill score was obtained for a correlation length scale C = 12.5 km, W = 24 h, and r = 1, where r is the ratio between the error of the observations and the background error, both for temperature and salinity. Additional ROPS runs showed that (i) the skill score of assimilation runs was mostly higher than the score of a control run without assimilation, (i) the skill score increased with increasing forecast range, and (iii) the skill score for temperature was higher than the score for salinity in the majority of cases. Further on, it is demonstrated that the vast number of observations can be managed by the applied OA method without data reduction, enabling timely operational forecasts even on a commercially available personal computer or a laptop.

scholarly journals Forecast skill score assessment of a relocatable ocean prediction system, using a simplified objective analysis method

2017 ◽  
Author(s):  
Reiner Onken
Keyword(s):  
Observational Data ◽  
Skill Score ◽  
Forecast Skill ◽  
Objective Analysis ◽  
Prediction System ◽  
Regional Ocean Modeling System ◽  
Data Set ◽  
Vast Number ◽  
Ocean Prediction ◽  
Forecast Skill Score

Abstract. A Relocatable Ocean Prediction System (ROPS) was employed to an observational data set which was collected in June 2014 in the waters to the west of Sardinia (Western Mediterranean) in the mainframe of the REP14-MED experiment. The observational data, comprising almost 5000 temperature and salinity profiles from a fleet of underwater gliders and shipborne probes, were assimilated in the Regional Ocean Modeling System (ROMS) which is the heart of ROPS, and verified against independent observations from ScanFish tows by means of the forecast skill score as defined by Murphy (1993). A simplified objective analysis (OA) method was utilised for assimilation, taking account of only those profiles which were located within a predetermined time window W. As a result of a sensitivity study, the highest skill score was obtained for a correlation length scale C = 12.5 km, W = 24 hours, and r = 1, where r is the ratio between the error of the observations and the background error, both for temperature and salinity. Additional ROPS runs showed that (i) the skill score of assimilation runs was mostly higher than the score of a control run without assimilation, (i) the skill score increased with increasing forecast range, and (iii) the skill score for temperature was higher than the score for salinity in the majority of cases. Further on, it is demonstrated that the vast number of observations can be managed by the applied OA method without data reduction, enabling timely operational forecasts even on a commercially available Personal Computer or a laptop.

scholarly journals Yearly evolution of Euro-Atlantic weather regimes and of their sub-seasonal predictability

2021 ◽  
Author(s):  
Nicola Cortesi ◽  
Verónica Torralba ◽  
Llorenó Lledó ◽  
Andrea Manrique-Suñén ◽  
Nube Gonzalez-Reviriego ◽  
...  
Keyword(s):  
Annual Cycle ◽  
Winter Season ◽  
Skill Score ◽  
Seasonal Forecast ◽  
Forecast Skill ◽  
Seasonal Forecasts ◽  
Sea Level Pressure ◽  
Weather Regimes ◽  
Start Dates ◽  
Weekly Frequency

AbstractIt is often assumed that weather regimes adequately characterize atmospheric circulation variability. However, regime classifications spanning many months and with a low number of regimes may not satisfy this assumption. The first aim of this study is to test such hypothesis for the Euro-Atlantic region. The second one is to extend the assessment of sub-seasonal forecast skill in predicting the frequencies of occurrence of the regimes beyond the winter season. Two regime classifications of four regimes each were obtained from sea level pressure anomalies clustered from October to March and from April to September respectively. Their spatial patterns were compared with those representing the annual cycle. Results highlight that the two regime classifications are able to reproduce most part of the patterns of the annual cycle, except during the transition weeks between the two periods, when patterns of the annual cycle resembling Atlantic Low regime are not also observed in any of the two classifications. Forecast skill of Atlantic Low was found to be similar to that of NAO+, the regime replacing Atlantic Low in the two classifications. Thus, although clustering yearly circulation data in two periods of 6 months each introduces a few deviations from the annual cycle of the regime patterns, it does not negatively affect sub-seasonal forecast skill. Beyond the winter season and the first ten forecast days, sub-seasonal forecasts of ECMWF are still able to achieve weekly frequency correlations of r = 0.5 for some regimes and start dates, including summer ones. ECMWF forecasts beat climatological forecasts in case of long-lasting regime events, and when measured by the fair continuous ranked probability skill score, but not when measured by the Brier skill score. Thus, more efforts have to be done yet in order to achieve minimum skill necessary to develop forecast products based on weather regimes outside winter season.

scholarly journals Forecast Skill of the Madden–Julian Oscillation in Two Canadian Atmospheric Models

2008 ◽  
Vol 136 (11) ◽  
pp. 4130-4149 ◽  
Author(s):  
Hai Lin ◽  
Gilbert Brunet ◽  
Jacques Derome
Keyword(s):  
General Circulation ◽  
Initial Conditions ◽  
Circulation Model ◽  
Skill Score ◽  
Forecast Skill ◽  
Second Phase ◽  
Equatorial Waves ◽  
Madden Julian Oscillation ◽  
Atmospheric Models ◽  
Convectively Coupled Equatorial Waves

Abstract The output of two global atmospheric models participating in the second phase of the Canadian Historical Forecasting Project (HFP2) is utilized to assess the forecast skill of the Madden–Julian oscillation (MJO). The two models are the third generation of the general circulation model (GCM3) of the Canadian Centre for Climate Modeling and Analysis (CCCma) and the Global Environmental Multiscale (GEM) model of Recherche en Prévision Numérique (RPN). Space–time spectral analysis of the daily precipitation in near-equilibrium integrations reveals that GEM has a better representation of the convectively coupled equatorial waves including the MJO, Kelvin, equatorial Rossby (ER), and mixed Rossby–gravity (MRG) waves. An objective of this study is to examine how the MJO forecast skill is influenced by the model’s ability in representing the convectively coupled equatorial waves. The observed MJO signal is measured by a bivariate index that is obtained by projecting the combined fields of the 15°S–15°N meridionally averaged precipitation rate and the zonal winds at 850 and 200 hPa onto the two leading empirical orthogonal function (EOF) structures as derived using the same meridionally averaged variables following a similar approach used recently by Wheeler and Hendon. The forecast MJO index, on the other hand, is calculated by projecting the forecast variables onto the same two EOFs. With the HFP2 hindcast output spanning 35 yr, for the first time the MJO forecast skill of dynamical models is assessed over such a long time period with a significant and robust result. The result shows that the GEM model produces a significantly better level of forecast skill for the MJO in the first 2 weeks. The difference is larger in Northern Hemisphere winter than in summer, when the correlation skill score drops below 0.50 at a lead time of 10 days for GEM whereas it is at 6 days for GCM3. At lead times longer than about 15 days, GCM3 performs slightly better. There are some features that are common for the two models. The forecast skill is better in winter than in summer. Forecasts initialized with a large amplitude for the MJO are found to be more skillful than those with a weak MJO signal in the initial conditions. The forecast skill is dependent on the phase of the MJO at the initial conditions. Forecasts initialized with an MJO that has an active convection in tropical Africa and the Indian Ocean sector have a better level of forecast skill than those initialized with a different phase of the MJO.

scholarly journals The GEFS-Based Daily Reference Evapotranspiration (ETo) Forecast and Its Implication for Water Management in the Southeastern United States

2014 ◽  
Vol 15 (3) ◽  
pp. 1152-1165 ◽  
Author(s):  
Di Tian ◽  
Christopher J. Martinez
Keyword(s):  
Water Management ◽  
United States ◽  
Reference Evapotranspiration ◽  
Southeastern United States ◽  
Demand Forecasting ◽  
Skill Score ◽  
Irrigation Scheduling ◽  
Forecast Skill ◽  
Ensemble Forecasts ◽  
Relative Operating Characteristic

Abstract NOAA’s second-generation retrospective forecast (reforecast) dataset was created using the currently operational Global Ensemble Forecast System (GEFS). It has the potential to accurately forecast daily reference evapotranspiration ETo and can be useful for water management. This study was conducted to evaluate daily ETo forecasts using the GEFS reforecasts in the southeastern United States (SEUS) and to incorporate the ETo forecasts into irrigation scheduling to explore the usefulness of the forecasts for water management. ETo was estimated using the Penman–Monteith equation, and ensemble forecasts were downscaled and bias corrected using a forecast analog approach. The overall forecast skill was evaluated using the linear error in probability space skill score, and the forecast in five categories (terciles and 10th and 90th percentiles) was evaluated using the Brier skill score, relative operating characteristic, and reliability diagrams. Irrigation scheduling was evaluated by water deficit WD forecasts, which were determined based on the agricultural reference index for drought (ARID) model driven by the GEFS-based ETo forecasts. All forecast skill was generally positive up to lead day 7 throughout the year, with higher skill in cooler months compared to warmer months. The GEFS reforecast improved ETo forecast skill for all lead days over the SEUS compared to the first-generation reforecast. The WD forecasts driven by the ETo forecasts showed higher accuracy and less uncertainty than the forecasts driven by climatology, indicating their usefulness for irrigation scheduling, hydrological forecasting, and water demand forecasting in the SEUS.

Towards onset: shades of ENSO skill

2020 ◽  
Author(s):  
Dougal Squire ◽  
James Risbey
Keyword(s):  
El Niño ◽  
Statistical Models ◽  
El Nino ◽  
Southern Oscillation ◽  
Skill Score ◽  
Forecast Skill ◽  
The North ◽  
Forecast Models ◽  
The Impact ◽  
Better Than

<p>Climate forecast skill for the El Nino-Southern Oscillation (ENSO) is better than chance, but has increased little in recent decades. Further, the relative skill of dynamical and statistical models varies in skill assessments, depending on choices made about how to evaluate the forecasts. Using a suite of models from the North American Multi-Model Ensemble (NMME) archive we outline the consequences for skill of how the bias corrections and forecast anomalies are formed. We show that the method for computing forecast anomalies is such a critical part of the provenance of a skill score that any score for forecast anomalies lacking clarity about the method is open to wide interpretation. Many assessments of hindcast skill are likely to be overestimates of attainable forecast skill because the hindcast anomalies are informed by observations over the period assessed that would not be available to a real forecast. The relative skill rankings of forecast models can change between hindcast and forecast systems because the impact of model bias on skill is sensitive to the ways in which forecast anomalies are formed. Dynamical models are found to be more skillful than simple statistical models for forecasting the onset of El Nino events.</p>

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.

Systematic Evaluation of the Impact of Assimilating a Network of Ground-Based Remote Sensing Profilers for Forecasts of Nocturnal Convection Initiation during PECAN

2020 ◽  
Vol 148 (12) ◽  
pp. 4703-4728
Author(s):  
Samuel K. Degelia ◽  
Xuguang Wang ◽  
David J. Stensrud ◽  
David D. Turner
Keyword(s):  
Remote Sensing ◽  
High Frequency ◽  
Skill Score ◽  
Forecast Skill ◽  
Systematic Evaluation ◽  
Large Variability ◽  
Synoptic Forcing ◽  
Convection Initiation ◽  
The Impact ◽  
Forecast Quality

AbstractNocturnal convection is often initiated by mechanisms that cannot be easily observed within the large gaps between rawinsondes or by conventional surface networks. To improve forecasts of such events, we evaluate the systematic impact of assimilating a collocated network of high-frequency, ground-based thermodynamic and kinematic profilers collected as part of the 2015 Plains Elevated Convection At Night (PECAN) experiment. For 13 nocturnal convection initiation (CI) events, we find small but consistent improvements when assimilating thermodynamic observations collected by Atmospheric Emitted Radiance Interferometers (AERIs). Through midlevel cooling and moistening, assimilating the AERIs increases the fractions skill score (FSS) for both nocturnal CI and precipitation forecasts. The AERIs also improve various contingency metrics for CI forecasts. Assimilating composite kinematic datasets collected by Doppler lidars and radar wind profilers (RWPs) results in slight degradations to the forecast quality, including decreases in the FSS and traditional contingency metrics. The impacts from assimilating thermodynamic and kinematic profilers often counteract each other, such that we find little impact on the detection of CI when both are assimilated. However, assimilating both datasets improves various properties of the CI events that are successfully detected (timing, distance, shape, etc.). We also find large variability in the impact of assimilating these remote sensing profilers, likely due to the number of observing sites and the strength of the synoptic forcing for each case. We hypothesize that the lack of flow-dependent methods to diagnose observation errors likely contributes to degradations in forecast skill for many cases, especially when assimilating kinematic profilers.

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