A Single-Station Approach to Model Output Statistics Temperature Forecast Error Assessment

Forecasts of certain weather elements are improved by linearly relating observed elements to past observations, climatological information, and numerical weather‐prediction‐model output. Model Output Statistics (MOS) is a statistical post‐processing of model output that is capable of forecasting some subgrid scale, synoptically‐forced events and of correcting some systematic, but state dependent, model bias. This tendency of accounting for model error is exploited by feeding MOS forecasts back into the state estimation problem. MOS forecasts and their associated uncertainty are treated as ‘observations’ of the future system state, and a four‐dimensional variational assimilation procedure is employed to improve the original analysis and resulting model forecast. In a simple‐model scenario, it is found that this approach has a small negative impact on the magnitude of forecast errors relative to MOS with a decrease in the number of very good forecasts, but a large positive impact on the variance about the forecast errors: forecast busts are reduced. As a further step, a second round of MOS is performed on the new model forecasts in a manner identical to the original MOS approach. This second application of MOS results in a significant reduction in both the forecast errors and the variance about those errors relative to the first application of MOS, and includes an increase in very good forecasts. Copyright © 2003 Royal Meteorological Society

The golden rule of creating a machine learning algorithm is to handle as much data as possible under consistent conditions. In the field of air quality, these models can be used for short-term air quality forecasting, helping predict high pollution peaks and alerting residents in cases where pollutant concentrations exceed legal or World Health Organization (WHO) limits.For daily air quality predictions and short-term forecasts, the Slovenian Environment Agency uses the CAMx dispersion model, which is frequently upgraded with new input data. Based on the model results and expert opinions, we inform the public about air quality levels and issue alerts when concentrations reach the limit values. The Copernicus Atmosphere Monitoring Service (CAMS) has developed Model Output Statistics (MOS), which downscale air quality forecasts produced by regional ensemble models and incorporate measurement values from observation sites across Europe.In our study, we evaluated and compared CAMS MOS forecasts with forecasts from the Slovenian operational CAMx model and raw CAMS ensemble forecasts at Slovenian observational sites. The evaluation was conducted using time series plots, scatter plots, and Taylor diagrams, alongside statistical metrics such as the fraction of forecasts within a factor of two (FAC2), mean bias (MB), mean gross error (MGE), normalized mean bias (NMB), normalized gross error (NMGE), root mean square error (RMSE), correlation coefficient (r), coefficient of efficiency (COE), and index of agreement (IOA).Our findings indicate that CAMS MOS forecasts are almost always more accurate than raw CAMS or CAMx model predictions, particularly for the MOS day-1 forecast. However, for PM10 and PM2.5, longer-term MOS forecasts (day-2, day-3, and day-4) were less accurate than RAW and/or CAMx predictions based on certain statistical measures. When analysing individual stations, we observed occasional instances where MOS forecasts were less accurate for specific pollutants.CAMS MOS is a useful tool for improving the national alert system, helping to warn residents about poor air quality conditions and enabling timely protective measures. Consequently, our results suggest that the MOS day-1 forecast can serve as a valuable additional tool for issuing daily PM10 forecasts and, for some stations, even O3 daily maxima predictions. However, all MOS forecasts should be used with an awareness of their limitations—for example, the time lag in predicting high PM10 episodes and the systematic underestimation of O3 daily maxima at many Slovenian stations.

The Oklahoma Dispersion Model (ODM) represents a current innovative application of the classic Gaussian plume model in an operational setting. Utilizing a statewide mesoscale automated weather station network (the Oklahoma Mesonet) for current weather conditions and 60-h gridded Nested Grid Model (NGM) model output statistics (MOS) forecasts for future conditions, the ODM is an Internet-based management tool that can be used to qualitatively assess current and future atmospheric dispersion conditions across Oklahoma for near-surface releases of gases and small particulates. The ODM is designed to qualitatively assess concentrations at ground level near the plume centerline at downwind distances of up to 4000 m. The Gaussian plume model is used in conjunction with rural Briggs sigma-y and sigma-z coefficients to estimate horizontal and vertical dispersion. Pasquill stability class is calculated in two ways: for current conditions, Oklahoma Mesonet weather data are used in conjunction with algorithms recommended by the Environmental Protection Agency; for forecast conditions, the Turner method is used. A method is employed that breaks the atmosphere into six dispersion categories, ranging from excellent to very poor. The ODM generates both graphical and text output. Statewide colored maps showing current conditions for dispersion (dilution of plume) and transport (direction of plume movement) are generated every 15 and 5 min, respectively. Similar maps for future conditions are generated every 12 h using gridded 60-h NGM MOS forecasts. In addition to graphical output, tabular output for future conditions at specific MOS locations is available. The ODM has been used as a management tool in the agriculture and natural resources arenas in conjunction with prescribed burning (smoke), pesticide application, and odors associated with animal agriculture.

Simulated evolution is used to generate consensus forecasts of next-day minimum temperature for a site in Ohio. The evolved forecast algorithm logic is interpretable in terms of physics that might be accounted for by experienced forecasters, but the logic of the individual algorithms that form the consensus is unique. As a result, evolved program consensus forecasts produce substantial increases in forecast accuracy relative to forecast benchmarks such as model output statistics (MOS) and those from the National Weather Service (NWS). The best consensus produces a mean absolute error (MAE) of 2.98°F on an independent test dataset, representing a 27% improvement relative to MOS. These results translate to potential annual cost savings for electricity production in the state of Ohio of the order of $2 million relative to the NWS forecasts. Perfect forecasts provide nearly $6 million in additional annual electricity production cost savings relative to the evolved program consensus. The frequency of outlier events (forecast busts) falls from 24% using NWS to 16% using the evolved program consensus. Information on when busts are most likely can be provided through a logistic regression equation with two variables: forecast wind speed and the deviation of the NWS minimum temperature forecast from persistence. A forecast of a bust is 4 times more likely to be correct than wrong, suggesting some utility in anticipating the most egregious forecast errors. Discussion concerning the probabilistic applications of evolved programs, the application of this technique to other forecast problems, and the relevance of these findings to the future role of human forecasting is provided.

Skillful global temperature forecasting is crucial for mitigating the escalating impacts of rising temperature on human society and natural ecosystems. While global climate models generate invaluable dynamical temperature forecasts, the crucial role of model output statistics (MOS) in enhancing forecast skill has not been thoroughly investigated. This paper aims to unravel the potential of MOS methods for improving global temperature forecasts. It is achieved by developing a MOS toolkit to iteratively incorporate the attributes of bias, spread, trend, and association into forecast post‐processing, resulting in a series of methodical one‐factor‐at‐a‐time experiments. A case study is devised for monthly forecasts of July 2‐m air temperature (T2m) over land and sea surface temperature (SST) generated by the National Center for Environmental Prediction's Climate Forecast System version 2. The results expose the detrimental impacts of biases and unreliable ensemble spreads within raw temperature forecasts. At the lead time of 0 months, the continuous ranked probability skill score (CRPSS) is −128.51 ± 252.46% for T2m over land and 7.72 ± 76.66% for SST over ocean, indicating considerable underperformance of raw forecasts against reference climatological forecasts across numerous grid cells. The incremental considerations of bias, spread, trend, and association of the MOS methods result in substantial skill enhancements across global land and marine grid cells. Notably, the CRPSS of T2m is improved to 21.00 ± 23.63% and the SST forecast skill is improved to 42.26 ± 22.43%. Despite the anticipated degradation of skill with lead time, the results underscore MOS's efficacy in exploiting the information of raw forecasts to generate skillful temperature forecasts.

Accurate forecasting of photovoltaic (PV) power generation is essential for mitigating weather-induced variability and maintaining power-system stability. This study aims to improve PV power forecasting accuracy by enhancing the quality of numerical weather prediction (NWP) inputs rather than modifying forecasting model structures. Specifically, systematic errors in temperature, wind speed, and solar radiation data produced by the Unified Model–Local Data Assimilation and Prediction System (UM-LDAPS) are corrected using a Model Output Statistics (MOS) approach. A case study was conducted for a 20 kW rooftop PV system in Buan, South Korea, comparing forecasting performance before and after MOS application using a random forest-based PV forecasting model. The results show that MOS significantly improves meteorological input accuracy, reducing the root mean square error (RMSE) of temperature, wind speed, and solar radiation by 38.1–62.3%. Consequently, PV power forecasting errors were reduced by 70.0–78.7% across lead times of 1–6 h, 7–12 h, and 19–24 h. After MOS correction, the normalized mean absolute percentage error (nMAPE) remained consistently low at approximately 7–8%, indicating improved forecasting robustness across the evaluated lead-time ranges. In addition, an economic evaluation based on the Korean renewable energy forecast-settlement mechanism estimated an annual benefit of approximately 854 USD for the analyzed 20 kW PV system. A complementary valuation using an NREL-based framework yielded an annual benefit of approximately 296 USD. These results demonstrate that improving meteorological data quality through MOS enhances PV forecasting performance and provide measurable economic value.

Rainfall is the height of rainwater that falls on a flat area, assuming it doesn't evaporate, doesn't seep, and doesn't flow. Rain levels are measured in mm (millimeters). The target of the research being conducted is in Badung Regency, Bali because Bali is a tourist area that is often visited by tourists and from Indonesian itself, so predictions of meteorology, such as rainfall will greatly impact tourism. In this test, predictions use the Long Short Term Memory (LSTM) method, using daily weather data from the BMKG from 2010 to 2020 as training data and daily weather data for 2021 as prediction data. Based on the test results above, the results show that the two LSTM tests with LSTM Model 128.64 and LSTM Model 64.32 have low MAE and MAPE error values. From First Scenario, the Mean Absolute Error (MAE) value is 8.97246598930908 and Mean Absolute Percentage Error (MAPE) value is 1.7657206683278308%. From Second Scenario, the Mean Absolute Error is 9.706669940783014 and Mean Absolute Percentage Error is 1.9028466692362323%. From the MAE and MAPE values obtained in these two scenarios, it can be proven that from the evaluation results of Rainfall predictions in Badung Regency, Bali, the predictions can be said to be very accurate because they have an error value of less than 10.

The Meteorological Development Laboratory (MDL) has developed and implemented an aviation weather prediction system that runs each hour and produces forecast guidance for each hour into the future out to 25 h covering the major forecast period of the National Weather Service (NWS) Terminal Aerodrome Forecast. The Localized Aviation Model Output Statistics (MOS) Program (LAMP) consists of analyses of observations, simple advective models, and a statistical component that updates the longer-range MOS forecasts from the Global Forecast System (GFS) model. LAMP, being an update to GFS MOS, is shown to be an improvement over it, as well as improving over persistence. LAMP produces probabilistic forecasts for the aviation weather elements of ceiling height, sky cover, visibility, obstruction to vision, precipitation occurrence and type, and thunderstorms. Best-category forecasts are derived from these probabilities and their associated thresholds. The LAMP guidance of sensible weather is available for 1591 stations in the contiguous United States, Alaska, Hawaii, Puerto Rico, and the Virgin Islands. Probabilistic guidance of thunderstorms is also available on a grid. The LAMP guidance is available to the entire weather enterprise via NWS communication networks and the World Wide Web. In the future, all station guidance will be gridded and be made available in a form compatible with the NWS’s National Digital Forecast Database.

<strong class="journal-contentHeaderColor">Abstract.</strong> Air quality (AQ) forecasting systems are usually built upon physics-based numerical models that are affected by a number of uncertainty sources. In order to reduce forecast errors, first and foremost the bias, they are often coupled with model output statistics (MOS) modules. MOS methods are statistical techniques used to correct raw forecasts at surface monitoring station locations, where AQ observations are available. In this study, we investigate the extent to which AQ forecasts can be improved using a variety of MOS methods, including moving average, quantile mapping, Kalman filter, analogs and gradient boosting machine methods, and consider as well the persistence method as a reference. We apply our analysis to the Copernicus Atmospheric Monitoring Service (CAMS) regional ensemble median O<span class="inline-formula">₃</span> forecasts over the Iberian Peninsula during 2018–2019. A key aspect of our study is the evaluation, which is performed using a comprehensive set of continuous and categorical metrics at various timescales, along different lead times and using different meteorological input datasets. Our results show that O<span class="inline-formula">₃</span> forecasts can be substantially improved using such MOS corrections and that improvements go well beyond the correction of the systematic bias. Depending on the timescale and lead time, root mean square errors decreased from 20 %–40 % to 10 %–30 %, while Pearson correlation coefficients increased from 0.7–0.8 to 0.8–0.9. Although the improvement typically affects all lead times, some MOS methods appear more adversely impacted by the lead time. The MOS methods relying on meteorological data were found to provide relatively similar performance with two different meteorological inputs. Importantly, our results also clearly show the trade-offs between continuous and categorical skills and their dependencies on the MOS method. The most sophisticated MOS methods better reproduce O<span class="inline-formula">₃</span> mixing ratios overall, with the lowest errors and highest correlations. However, they are not necessarily the best in predicting the peak O<span class="inline-formula">₃</span> episodes, for which simpler MOS methods can achieve better results. Although the complex impact of MOS methods on the distribution of and variability in raw forecasts can only be comprehended through an extended set of complementary statistical metrics, our study shows that optimally implementing MOS in AQ forecast systems crucially requires selecting the appropriate skill score to be optimized for the forecast application of interest.

Dental age estimation in Malaysian adults based on volumetric analysis of pulp/tooth ratio using CBCT data.

Improving forecasts, particularly the accuracy, efficiency, and precision of time-series forecasts, is becoming critical for authorities to predict, monitor, and prevent the spread of the Coronavirus disease. However, the results obtained from the predictive models are imprecise and inefficient because the dataset contains linear and non-linear patterns, respectively. Linear models such as autoregressive integrated moving average cannot be used effectively to predict complex time series, so nonlinear approaches are better suited for such a purpose. Therefore, to achieve a more accurate and efficient predictive value of COVID-19 that is closer to the true value of COVID-19, a hybrid approach was implemented. Therefore, the objectives of this study are twofold. The first objective is to propose intelligence-based prediction methods to achieve better prediction results called autoregressive integrated moving average-least-squares support vector machine. The second objective is to investigate the performance of these proposed models by comparing them with the autoregressive integrated moving average, support vector machine, least-squares support vector machine, and autoregressive integrated moving average-support vector machine. Our investigation is based on three COVID-19 real datasets, i.e., daily new cases data, daily new death cases data, and daily new recovered cases data. Then, statistical measures such as mean square error, root mean square error, mean absolute error, and mean absolute percentage error were performed to verify that the proposed models are better than the autoregressive integrated moving average, support vector machine model, least-squares support vector machine, and autoregressive integrated moving average-support vector machine. Empirical results using three recent datasets of known the Coronavirus Disease-19 cases in Malaysia show that the proposed model generates the smallest mean square error, root mean square error, mean absolute error, and mean absolute percentage error values for training and testing datasets compared to the autoregressive integrated moving average, support vector machine, least-squares support vector machine, and autoregressive integrated moving average-support vector machine models. This means that the predicted value of the proposed model is closer to the true value. These results demonstrate that the proposed model can generate estimates more accurately and efficiently. Compared to the autoregressive integrated moving average, support vector machine, least-squares support vector machine, and autoregressive integrated moving average-support vector machine models, our proposed models perform much better in terms of percent error reduction for both training and testing all datasets. Therefore, the proposed model is possibly the most efficient and effective way to improve prediction for future pandemic performance with a higher level of accuracy and efficiency.

Drought is a complex and frequently occurring natural hazard in many parts of the world. Therefore, accurate drought forecasting is essential to mitigate its adverse impacts. This research has inferred the implication and the appropriateness of the extreme learning machine (ELM) algorithm for drought forecasting. For numerical evaluation, time series data of the Standardized Precipitating Temperature Index (SPTI) are used for nine meteorological stations located in various climatological zones of Pakistan. To assess the performance of ELM, this research includes parallel inferences of multilayer perceptron (MLP) and autoregressive integrated moving average (ARIMA) models. The performance of each model is assessed using root mean square error (RMSE), mean absolute error (MAE), mean absolute percent error (MAPE), Kling‐Gupta efficiency (KGE), Willmott index (WI), and Karl Pearson’s correlation coefficient. Generally, graphical results illustrated an excellent performance of the ELM algorithm over MLP and ARIMA models. For training data of SPTI‐1, ELM’s best performance has observed at Chitral station (RMSE = 0.374, KGE = 0.838, WI = 0.960, MAE = 0.272, MAPE = 259.59, R = 0.93). For SPTI‐1 at Astore station, the numerical results are (RMSE = 0.688, KGE = 0.988, WI = 0.997, MAE = 0.798, MAPE = 247.35). The overall results indicate that the ELM outperformed by producing the smallest RMSE, MAE, and MAPE values and maximum values for KGE, WI, and correlation coefficient values at almost all the selected meteorological stations for (1, 3, 6, 9, and 12) month time scales. In summary, this research endorses the use of ELM for accurate drought forecasting.

Road inconsistency may cause dangerous driving experience that leads to carelessness while handling the vehicle. To determine the road inconsistency, development of models which are based on the 85th percentile of speed is adopted. The models which are taken into consideration are other geometric road characteristics such as superelevation, radius of curve, width of curve, tangent length, and road gradient that can help to minimize the risk of accidents at curve. To further analyze the effectiveness of these developed models, validation of the models using new set of data and comparing it with other established models is crucial. The validation process is conducted by calculating the mean squared error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). MSE value will indicate the differences between the empirical results with the predicted results, while MAE calculates the absolute difference between the empirical results and predicted results. MAPE evaluates the percentage errors between the empirical results with the predicted results. Small values of MSE, MAE, and MAPE is a proof that the models can predict good results.

Forecasts on transportation meteorology, such as pavement temperature, are becoming increasingly important in the face of global warming and frequent disruptions from extreme weather and climate events. In this study, we propose a pavement temperature forecast model based on stepwise regression—model output statistics (SRMOS) at the short-term timescale, using highways in Jiangsu, China, as examples. Experiments demonstrate that the SRMOS model effectively calibrates against the benchmark of the linear regression model based on surface air temperature (LRT). The SRMOS model shows a reduction in mean absolute errors by 0.7–1.6 °C, with larger magnitudes observed for larger biases in the LRT forecasts. Both forecasts exhibit higher accuracy in predicting minimum nighttime temperatures compared to maximum daytime temperatures. Additionally, it overall shows increasing biases from the north to the south, and the SRMOS superiority is greater over the south with larger initial LRT biases. Predictor importance analysis indicates that temperature, moisture, and larger-scale background are basically the key predictors in the SRMOS model for pavement temperature forecasts, of which the air temperature is the most crucial factor in the model’s construction. Although larger-scale circulation backgrounds are generally characterized by relatively low importance, their significance increases with longer lead times. The presented results demonstrate the considerable skill of the SRMOS model in predicting pavement temperatures, highlighting its potential in disaster prevention for extreme transportation meteorology events.

Air pollution poses a critical risk to both human health and the environment, particularly in the Arctic and Northern Europe, where pollutants are primarily transported from mid-latitudes by atmospheric circulation. Also, local sources further contribute to pollution levels in Arctic communities. Accurate short-term forecasts of atmospheric pollutant concentrations are vital for enabling adaptive measures and protecting public health during pollution episodes.The Copernicus Atmospheric Monitoring Service (CAMS) provides 96-hour forecasts for key pollutants across Europe using 11 state-of-the-art models and an ensemble approach. However, these forecasts exhibit significant errors in Northern Europe and the Arctic. To address this, we investigate the applicability of deep learning (Transformer-based) models for 48-hour PM10 concentration forecasting at monitoring stations in Northern Europe. Our approach integrates in situ PM10 observations with CAMS model outputs and forecasted meteorological parameters as input features. We evaluated four time-series specialized models—Informer, Autoformer, FEDformer, and Crossformer—to identify the most effective architecture for this task. The Crossformer model demonstrated superior performance, outperforming CAMS by 30% in Mean Squared Error (MSE) and 23% in Mean Absolute Error (MAE). It also surpassed the newly introduced CAMS Model Output Statistics (MOS), reducing MSE by 12% and MAE by 14%.With its low computational complexity, fast execution time, and minimal resource requirements, the Crossformer presents a viable alternative to traditional numerical models for local-scale predictions. Future work will extend the forecasting window to 72 hours and incorporate additional pollutants, such as PM2.5, NO2, and O3, to enhance predictive capabilities for Arctic and Northern European communities.