Performance Evaluation of Weather@home2 Simulations over West African Region
Abstract
:1. Introduction
2. Materials and Methods
2.1. Description of Study Area
2.2. Datasets—Observation, Reanalysis, and Simulation Datasets
2.3. Methodology and Analysis Procedures
Attributes | Descriptive Statistics | Inference |
---|---|---|
Reliability | Climatology | To determine the monthly, seasonal, or annual cycle of a variable [64]. |
Bias (B) | A measure of over- (positive bias) or under-estimations (negative bias) of variables. Generally, bias gives marginal distributions of variables [11]. | |
Mean bias error (MBE) | A measure to estimate the average bias in the model. It is the average forecast or simulation error representing the systematic error of a model to under- or over-forecast [11]. | |
Scatter diagrams | Provides information on bias, outliers, error magnitude, linear association, peculiar behaviors in extremes, misses, and false alarms. Perfect simulation points in comparison to observation should be on the 45° diagonal line [17,65]. | |
Association | ** Correlation coefficient (r) | A statistical measure of the strength of a linear relationship between the paired variables, i.e., simulations and observation/reanalysis datasets. By design, it is constrained as −1 ≤ r ≤ 1. Positive values denote direct linear association; negative values denote inverse linear association; a value of 0 denotes no linear association; while the closer the value is to 1 or −1, the stronger the linear association. Perfect relationship is denoted by 1. It is not sensitive to the bias but sensitive to outliers that may be present in the simulations [10,15,16]. |
Coefficient of determination (CoD) | CoD is a measure of potential skill, i.e., the level of skill attainable when the biases are eliminated. It is also a measure of the fit of regression between forecast and observation. It is a non-negative parameter with a maximum value of 1. For a perfect regression, CoD = 1. CoD tends zero for a non-useful forecast [10,16]. | |
Skill | Ranked probability skill score (RPSS) | Measures the forecast accuracy with respect to a reference forecast (e.g., observed climatology). Positive values (maximum of 1) have skill while negative values (up to negative infinity) have no skill [10,14,17,18,19]. |
Accuracy | Mean absolute error (MAE) | A measure of how big of an error we can expect from the forecast on average, without considering their directions. MAE measures the accuracy of a continuous variable. Though, just like the root mean square error (RMSE), it also measures the average magnitude of the errors in a set of forecasts; however, while RMSE utilizes a quadratic scoring rule, MAE is a linear score—which means that all the individual differences are weighted equally in the average. MAE ranges from zero to infinity. Lower values are better [10,66,67]. |
Root mean square error (RMSE) | It measures the magnitudes of the error, weighted on the squares of the errors. Though, it does not indicate the direction of the error; however, it is good in penalizing large errors. It is sensitive to large values (e.g., in precipitation) and outliers. This is very useful when large errors are undesirable. Ranges from zero to infinity. Lower values are better [10,68,69]. | |
Synchronization (Syn) | Synchronization focuses on the predictive capabilities of a model. It shows how much a simulated value agrees with an observed value in the signs of their anomalies without taking magnitudes into consideration. Therefore, the evaluated synchronization, in a probabilistic sense, is similar to accuracy. The best synchronization is 100% [10,13,70,71]. | |
Precision | Standard deviation (Std) | Std helps to determine the spread of simulations and/or observations from their respective means, i.e., how far from the mean a group of numbers is. It has the same unit as the mean [10,21,72,73]. |
Coefficient of variation (CoV) | It is used for comparing the degree of variation from one data series to another (in this case between forecast or simulation and observation where the means are significantly different from one another). A lower CoV implies a low degree of variation while a higher CoV implies a higher variation. Therefore, the higher the CoV, the greater the level of spreading around the mean [10,21]. | |
Normalized standard deviation (NSD) | This makes it possible to access the statistics of different fields (observations and simulations) on the same scale. Here, Taylor diagrams are used to depict the normalized standard deviation in line with correlation coefficients. The diagrams are able to measure how well observations and simulations match each other in terms of 1. similarity as measured by correlation coefficients, and 2. deviation factors as measured by normalized standard deviations. Taylor diagrams are able to provide a summarizing evaluation of model performance in simulating atmospheric parameters [10,21]. |
3. Results
3.1. Seasonality (and Reliability)
3.1.1. Precipitation
3.1.2. Temperature
3.2. Association
3.3. Skill
3.4. Accuracy
3.5. Precision
4. Discussion
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Lawal, K.A.; Akintomide, O.M.; Olaniyan, E.; Bowery, A.; Sparrow, S.N.; Wehner, M.F.; Stone, D.A. Performance Evaluation of Weather@home2 Simulations over West African Region. Atmosphere 2025, 16, 392. https://doi.org/10.3390/atmos16040392
Lawal KA, Akintomide OM, Olaniyan E, Bowery A, Sparrow SN, Wehner MF, Stone DA. Performance Evaluation of Weather@home2 Simulations over West African Region. Atmosphere. 2025; 16(4):392. https://doi.org/10.3390/atmos16040392
Chicago/Turabian StyleLawal, Kamoru Abiodun, Oluwatosin Motunrayo Akintomide, Eniola Olaniyan, Andrew Bowery, Sarah N. Sparrow, Michael F. Wehner, and Dáithí A. Stone. 2025. "Performance Evaluation of Weather@home2 Simulations over West African Region" Atmosphere 16, no. 4: 392. https://doi.org/10.3390/atmos16040392
APA StyleLawal, K. A., Akintomide, O. M., Olaniyan, E., Bowery, A., Sparrow, S. N., Wehner, M. F., & Stone, D. A. (2025). Performance Evaluation of Weather@home2 Simulations over West African Region. Atmosphere, 16(4), 392. https://doi.org/10.3390/atmos16040392